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circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var zr,wa=0;function Sa(){return typeof mermaid=="undefined"||mermaid instanceof Element?ht("https://unpkg.com/mermaid@9.4.3/dist/mermaid.min.js"):j(void 0)}function On(e){return e.classList.remove("mermaid"),zr||(zr=Sa().pipe(w(()=>mermaid.initialize({startOnLoad:!1,themeCSS:Tn,sequence:{actorFontSize:"16px",messageFontSize:"16px",noteFontSize:"16px"}})),m(()=>{}),J(1))),zr.subscribe(()=>{e.classList.add("mermaid");let t=`__mermaid_${wa++}`,r=T("div",{class:"mermaid"}),o=e.textContent;mermaid.mermaidAPI.render(t,o,(n,i)=>{let s=r.attachShadow({mode:"closed"});s.innerHTML=n,e.replaceWith(r),i==null||i(s)})}),zr.pipe(m(()=>({ref:e})))}var Mn=T("table");function Ln(e){return e.replaceWith(Mn),Mn.replaceWith(dn(e)),j({ref:e})}function Ta(e){let t=q(":scope > input",e),r=t.find(o=>o.checked)||t[0];return _(...t.map(o=>h(o,"change").pipe(m(()=>W(`label[for="${o.id}"]`))))).pipe(V(W(`label[for="${r.id}"]`)),m(o=>({active:o})))}function _n(e,{viewport$:t}){let r=Nr("prev");e.append(r);let o=Nr("next");e.append(o);let n=W(".tabbed-labels",e);return H(()=>{let i=new x,s=i.pipe(Z(),re(!0));return B([i,ye(e)]).pipe(Ce(1,Oe),Y(s)).subscribe({next([{active:a},c]){let p=Je(a),{width:l}=he(a);e.style.setProperty("--md-indicator-x",`${p.x}px`),e.style.setProperty("--md-indicator-width",`${l}px`);let f=er(n);(p.xf.x+c.width)&&n.scrollTo({left:Math.max(0,p.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),B([dt(n),ye(n)]).pipe(Y(s)).subscribe(([a,c])=>{let p=bt(n);r.hidden=a.x<16,o.hidden=a.x>p.width-c.width-16}),_(h(r,"click").pipe(m(()=>-1)),h(o,"click").pipe(m(()=>1))).pipe(Y(s)).subscribe(a=>{let{width:c}=he(n);n.scrollBy({left:c*a,behavior:"smooth"})}),te("content.tabs.link")&&i.pipe(je(1),ne(t)).subscribe(([{active:a},{offset:c}])=>{let p=a.innerText.trim();if(a.hasAttribute("data-md-switching"))a.removeAttribute("data-md-switching");else{let l=e.offsetTop-c.y;for(let u of q("[data-tabs]"))for(let d of q(":scope > input",u)){let v=W(`label[for="${d.id}"]`);if(v!==a&&v.innerText.trim()===p){v.setAttribute("data-md-switching",""),d.click();break}}window.scrollTo({top:e.offsetTop-l});let f=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([p,...f])])}}),i.pipe(Y(s)).subscribe(()=>{for(let a of q("audio, video",e))a.pause()}),Ta(e).pipe(w(a=>i.next(a)),A(()=>i.complete()),m(a=>R({ref:e},a)))}).pipe(rt(ae))}function An(e,{viewport$:t,target$:r,print$:o}){return _(...q(".annotate:not(.highlight)",e).map(n=>gn(n,{target$:r,print$:o})),...q("pre:not(.mermaid) > code",e).map(n=>wn(n,{target$:r,print$:o})),...q("pre.mermaid",e).map(n=>On(n)),...q("table:not([class])",e).map(n=>Ln(n)),...q("details",e).map(n=>Sn(n,{target$:r,print$:o})),...q("[data-tabs]",e).map(n=>_n(n,{viewport$:t})))}function Oa(e,{alert$:t}){return t.pipe(E(r=>_(j(!0),j(!1).pipe(ze(2e3))).pipe(m(o=>({message:r,active:o})))))}function Cn(e,t){let r=W(".md-typeset",e);return H(()=>{let o=new x;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),Oa(e,t).pipe(w(n=>o.next(n)),A(()=>o.complete()),m(n=>R({ref:e},n)))})}function Ma({viewport$:e}){if(!te("header.autohide"))return j(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Le(2,1),m(([n,i])=>[nMath.abs(i-n.y)>100),m(([,[n]])=>n),X()),o=We("search");return B([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),X(),E(n=>n?r:j(!1)),V(!1))}function kn(e,t){return H(()=>B([ye(e),Ma(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),X((r,o)=>r.height===o.height&&r.hidden===o.hidden),J(1))}function Hn(e,{header$:t,main$:r}){return H(()=>{let o=new x,n=o.pipe(Z(),re(!0));return o.pipe(ee("active"),Ge(t)).subscribe(([{active:i},{hidden:s}])=>{e.classList.toggle("md-header--shadow",i&&!s),e.hidden=s}),r.subscribe(o),t.pipe(Y(n),m(i=>R({ref:e},i)))})}function La(e,{viewport$:t,header$:r}){return ar(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=he(e);return{active:o>=n}}),ee("active"))}function $n(e,t){return H(()=>{let r=new x;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=ce(".md-content h1");return typeof o=="undefined"?L:La(o,t).pipe(w(n=>r.next(n)),A(()=>r.complete()),m(n=>R({ref:e},n)))})}function Rn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),X()),n=o.pipe(E(()=>ye(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),ee("bottom"))));return B([o,n,t]).pipe(m(([i,{top:s,bottom:a},{offset:{y:c},size:{height:p}}])=>(p=Math.max(0,p-Math.max(0,s-c,i)-Math.max(0,p+c-a)),{offset:s-i,height:p,active:s-i<=c})),X((i,s)=>i.offset===s.offset&&i.height===s.height&&i.active===s.active))}function _a(e){let t=__md_get("__palette")||{index:e.findIndex(r=>matchMedia(r.getAttribute("data-md-color-media")).matches)};return j(...e).pipe(se(r=>h(r,"change").pipe(m(()=>r))),V(e[Math.max(0,t.index)]),m(r=>({index:e.indexOf(r),color:{scheme:r.getAttribute("data-md-color-scheme"),primary:r.getAttribute("data-md-color-primary"),accent:r.getAttribute("data-md-color-accent")}})),J(1))}function Pn(e){let t=T("meta",{name:"theme-color"});document.head.appendChild(t);let r=T("meta",{name:"color-scheme"});return document.head.appendChild(r),H(()=>{let o=new x;o.subscribe(i=>{document.body.setAttribute("data-md-color-switching","");for(let[s,a]of Object.entries(i.color))document.body.setAttribute(`data-md-color-${s}`,a);for(let s=0;s{let i=Ee("header"),s=window.getComputedStyle(i);return r.content=s.colorScheme,s.backgroundColor.match(/\d+/g).map(a=>(+a).toString(16).padStart(2,"0")).join("")})).subscribe(i=>t.content=`#${i}`),o.pipe(Se(ae)).subscribe(()=>{document.body.removeAttribute("data-md-color-switching")});let n=q("input",e);return _a(n).pipe(w(i=>o.next(i)),A(()=>o.complete()),m(i=>R({ref:e},i)))})}function In(e,{progress$:t}){return H(()=>{let r=new x;return r.subscribe(({value:o})=>{e.style.setProperty("--md-progress-value",`${o}`)}),t.pipe(w(o=>r.next({value:o})),A(()=>r.complete()),m(o=>({ref:e,value:o})))})}var qr=Ht(Vr());function Aa(e){e.setAttribute("data-md-copying","");let t=e.closest("[data-copy]"),r=t?t.getAttribute("data-copy"):e.innerText;return e.removeAttribute("data-md-copying"),r}function Fn({alert$:e}){qr.default.isSupported()&&new P(t=>{new qr.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||Aa(W(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(w(t=>{t.trigger.focus()}),m(()=>be("clipboard.copied"))).subscribe(e)}function Ca(e){if(e.length<2)return[""];let[t,r]=[...e].sort((n,i)=>n.length-i.length).map(n=>n.replace(/[^/]+$/,"")),o=0;if(t===r)o=t.length;else for(;t.charCodeAt(o)===r.charCodeAt(o);)o++;return e.map(n=>n.replace(t.slice(0,o),""))}function cr(e){let t=__md_get("__sitemap",sessionStorage,e);if(t)return j(t);{let r=me();return Zo(new URL("sitemap.xml",e||r.base)).pipe(m(o=>Ca(q("loc",o).map(n=>n.textContent))),de(()=>L),He([]),w(o=>__md_set("__sitemap",o,sessionStorage,e)))}}function jn(e){let t=W("[rel=canonical]",e);t.href=t.href.replace("//localhost:","//127.0.0.1");let r=new Map;for(let o of q(":scope > *",e)){let n=o.outerHTML;for(let i of["href","src"]){let s=o.getAttribute(i);if(s===null)continue;let a=new URL(s,t.href),c=o.cloneNode();c.setAttribute(i,`${a}`),n=c.outerHTML;break}r.set(n,o)}return r}function Wn({location$:e,viewport$:t,progress$:r}){let o=me();if(location.protocol==="file:")return L;let n=cr().pipe(m(l=>l.map(f=>`${new URL(f,o.base)}`))),i=h(document.body,"click").pipe(ne(n),E(([l,f])=>{if(!(l.target instanceof Element))return L;let u=l.target.closest("a");if(u===null)return L;if(u.target||l.metaKey||l.ctrlKey)return L;let d=new URL(u.href);return d.search=d.hash="",f.includes(`${d}`)?(l.preventDefault(),j(new URL(u.href))):L}),le());i.pipe(xe(1)).subscribe(()=>{let l=ce("link[rel=icon]");typeof l!="undefined"&&(l.href=l.href)}),h(window,"beforeunload").subscribe(()=>{history.scrollRestoration="auto"}),i.pipe(ne(t)).subscribe(([l,{offset:f}])=>{history.scrollRestoration="manual",history.replaceState(f,""),history.pushState(null,"",l)}),i.subscribe(e);let s=e.pipe(V(pe()),ee("pathname"),je(1),E(l=>ir(l,{progress$:r}).pipe(de(()=>(ot(l,!0),L))))),a=new DOMParser,c=s.pipe(E(l=>l.text()),E(l=>{let f=a.parseFromString(l,"text/html");for(let b of["[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...te("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let z=ce(b),K=ce(b,f);typeof z!="undefined"&&typeof K!="undefined"&&z.replaceWith(K)}let u=jn(document.head),d=jn(f.head);for(let[b,z]of d)z.getAttribute("rel")==="stylesheet"||z.hasAttribute("src")||(u.has(b)?u.delete(b):document.head.appendChild(z));for(let b of u.values())b.getAttribute("rel")==="stylesheet"||b.hasAttribute("src")||b.remove();let v=Ee("container");return Fe(q("script",v)).pipe(E(b=>{let z=f.createElement("script");if(b.src){for(let K of b.getAttributeNames())z.setAttribute(K,b.getAttribute(K));return b.replaceWith(z),new P(K=>{z.onload=()=>K.complete()})}else return z.textContent=b.textContent,b.replaceWith(z),L}),Z(),re(f))}),le());return h(window,"popstate").pipe(m(pe)).subscribe(e),e.pipe(V(pe()),Le(2,1),M(([l,f])=>l.pathname===f.pathname&&l.hash!==f.hash),m(([,l])=>l)).subscribe(l=>{var f,u;history.state!==null||!l.hash?window.scrollTo(0,(u=(f=history.state)==null?void 0:f.y)!=null?u:0):(history.scrollRestoration="auto",nr(l.hash),history.scrollRestoration="manual")}),e.pipe(Cr(i),V(pe()),Le(2,1),M(([l,f])=>l.pathname===f.pathname&&l.hash===f.hash),m(([,l])=>l)).subscribe(l=>{history.scrollRestoration="auto",nr(l.hash),history.scrollRestoration="manual",history.back()}),c.pipe(ne(e)).subscribe(([,l])=>{var f,u;history.state!==null||!l.hash?window.scrollTo(0,(u=(f=history.state)==null?void 0:f.y)!=null?u:0):nr(l.hash)}),t.pipe(ee("offset"),ke(100)).subscribe(({offset:l})=>{history.replaceState(l,"")}),c}var Dn=Ht(Nn());function Vn(e){let t=e.separator.split("|").map(n=>n.replace(/(\(\?[!=<][^)]+\))/g,"").length===0?"\uFFFD":n).join("|"),r=new RegExp(t,"img"),o=(n,i,s)=>`${i}${s}`;return n=>{n=n.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator}|)(${n.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return s=>(0,Dn.default)(s).replace(i,o).replace(/<\/mark>(\s+)]*>/img,"$1")}}function Mt(e){return e.type===1}function pr(e){return e.type===3}function zn(e,t){let r=an(e);return _(j(location.protocol!=="file:"),We("search")).pipe($e(o=>o),E(()=>t)).subscribe(({config:o,docs:n})=>r.next({type:0,data:{config:o,docs:n,options:{suggest:te("search.suggest")}}})),r}function qn({document$:e}){let t=me(),r=Ue(new URL("../versions.json",t.base)).pipe(de(()=>L)),o=r.pipe(m(n=>{let[,i]=t.base.match(/([^/]+)\/?$/);return n.find(({version:s,aliases:a})=>s===i||a.includes(i))||n[0]}));r.pipe(m(n=>new Map(n.map(i=>[`${new URL(`../${i.version}/`,t.base)}`,i]))),E(n=>h(document.body,"click").pipe(M(i=>!i.metaKey&&!i.ctrlKey),ne(o),E(([i,s])=>{if(i.target instanceof Element){let a=i.target.closest("a");if(a&&!a.target&&n.has(a.href)){let c=a.href;return!i.target.closest(".md-version")&&n.get(c)===s?L:(i.preventDefault(),j(c))}}return L}),E(i=>{let{version:s}=n.get(i);return cr(new URL(i)).pipe(m(a=>{let p=pe().href.replace(t.base,"");return a.includes(p.split("#")[0])?new URL(`../${s}/${p}`,t.base):new URL(i)}))})))).subscribe(n=>ot(n,!0)),B([r,o]).subscribe(([n,i])=>{W(".md-header__topic").appendChild(hn(n,i))}),e.pipe(E(()=>o)).subscribe(n=>{var s;let i=__md_get("__outdated",sessionStorage);if(i===null){i=!0;let a=((s=t.version)==null?void 0:s.default)||"latest";Array.isArray(a)||(a=[a]);e:for(let c of a)for(let p of n.aliases)if(new RegExp(c,"i").test(p)){i=!1;break e}__md_set("__outdated",i,sessionStorage)}if(i)for(let a of oe("outdated"))a.hidden=!1})}function Pa(e,{worker$:t}){let{searchParams:r}=pe();r.has("q")&&(Ke("search",!0),e.value=r.get("q"),e.focus(),We("search").pipe($e(i=>!i)).subscribe(()=>{let i=pe();i.searchParams.delete("q"),history.replaceState({},"",`${i}`)}));let o=Zt(e),n=_(t.pipe($e(Mt)),h(e,"keyup"),o).pipe(m(()=>e.value),X());return B([n,o]).pipe(m(([i,s])=>({value:i,focus:s})),J(1))}function Kn(e,{worker$:t}){let r=new x,o=r.pipe(Z(),re(!0));B([t.pipe($e(Mt)),r],(i,s)=>s).pipe(ee("value")).subscribe(({value:i})=>t.next({type:2,data:i})),r.pipe(ee("focus")).subscribe(({focus:i})=>{i&&Ke("search",i)}),h(e.form,"reset").pipe(Y(o)).subscribe(()=>e.focus());let n=W("header [for=__search]");return h(n,"click").subscribe(()=>e.focus()),Pa(e,{worker$:t}).pipe(w(i=>r.next(i)),A(()=>r.complete()),m(i=>R({ref:e},i)),J(1))}function Qn(e,{worker$:t,query$:r}){let o=new x,n=Ko(e.parentElement).pipe(M(Boolean)),i=e.parentElement,s=W(":scope > :first-child",e),a=W(":scope > :last-child",e);We("search").subscribe(l=>a.setAttribute("role",l?"list":"presentation")),o.pipe(ne(r),$r(t.pipe($e(Mt)))).subscribe(([{items:l},{value:f}])=>{switch(l.length){case 0:s.textContent=f.length?be("search.result.none"):be("search.result.placeholder");break;case 1:s.textContent=be("search.result.one");break;default:let u=tr(l.length);s.textContent=be("search.result.other",u)}});let c=o.pipe(w(()=>a.innerHTML=""),E(({items:l})=>_(j(...l.slice(0,10)),j(...l.slice(10)).pipe(Le(4),Ir(n),E(([f])=>f)))),m(fn),le());return c.subscribe(l=>a.appendChild(l)),c.pipe(se(l=>{let f=ce("details",l);return typeof f=="undefined"?L:h(f,"toggle").pipe(Y(o),m(()=>f))})).subscribe(l=>{l.open===!1&&l.offsetTop<=i.scrollTop&&i.scrollTo({top:l.offsetTop})}),t.pipe(M(pr),m(({data:l})=>l)).pipe(w(l=>o.next(l)),A(()=>o.complete()),m(l=>R({ref:e},l)))}function Ia(e,{query$:t}){return t.pipe(m(({value:r})=>{let o=pe();return o.hash="",r=r.replace(/\s+/g,"+").replace(/&/g,"%26").replace(/=/g,"%3D"),o.search=`q=${r}`,{url:o}}))}function Yn(e,t){let r=new x,o=r.pipe(Z(),re(!0));return r.subscribe(({url:n})=>{e.setAttribute("data-clipboard-text",e.href),e.href=`${n}`}),h(e,"click").pipe(Y(o)).subscribe(n=>n.preventDefault()),Ia(e,t).pipe(w(n=>r.next(n)),A(()=>r.complete()),m(n=>R({ref:e},n)))}function Bn(e,{worker$:t,keyboard$:r}){let o=new x,n=Ee("search-query"),i=_(h(n,"keydown"),h(n,"focus")).pipe(Se(ae),m(()=>n.value),X());return o.pipe(Ge(i),m(([{suggest:a},c])=>{let p=c.split(/([\s-]+)/);if(a!=null&&a.length&&p[p.length-1]){let l=a[a.length-1];l.startsWith(p[p.length-1])&&(p[p.length-1]=l)}else p.length=0;return p})).subscribe(a=>e.innerHTML=a.join("").replace(/\s/g," ")),r.pipe(M(({mode:a})=>a==="search")).subscribe(a=>{switch(a.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(M(pr),m(({data:a})=>a)).pipe(w(a=>o.next(a)),A(()=>o.complete()),m(()=>({ref:e})))}function Gn(e,{index$:t,keyboard$:r}){let o=me();try{let n=zn(o.search,t),i=Ee("search-query",e),s=Ee("search-result",e);h(e,"click").pipe(M(({target:c})=>c instanceof Element&&!!c.closest("a"))).subscribe(()=>Ke("search",!1)),r.pipe(M(({mode:c})=>c==="search")).subscribe(c=>{let p=Re();switch(c.type){case"Enter":if(p===i){let l=new Map;for(let f of q(":first-child [href]",s)){let 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top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n extendStatics = Object.setPrototypeOf ||\r\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n if (typeof b !== \"function\" && b !== null)\r\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n extendStatics(d, b);\r\n function __() { this.constructor = d; }\r\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n __assign = Object.assign || function __assign(t) {\r\n for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n s = arguments[i];\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n }\r\n return t;\r\n }\r\n return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n var t = {};\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n t[p] = s[p];\r\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n t[p[i]] = s[p[i]];\r\n }\r\n return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n return new (P || (P = Promise))(function (resolve, reject) {\r\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n function verb(n) { return function (v) { return step([n, v]); }; }\r\n function step(op) {\r\n if (f) throw new TypeError(\"Generator is already executing.\");\r\n while (_) try {\r\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n if (y = 0, t) op = [op[0] & 2, t.value];\r\n switch (op[0]) {\r\n case 0: case 1: t = op; break;\r\n case 4: _.label++; return { value: op[1], done: false };\r\n case 5: _.label++; y = op[1]; op = [0]; continue;\r\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n default:\r\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n if (t[2]) _.ops.pop();\r\n _.trys.pop(); continue;\r\n }\r\n op = body.call(thisArg, _);\r\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n if (m) return m.call(o);\r\n if (o && typeof o.length === \"number\") return {\r\n next: function () {\r\n if (o && i >= o.length) o = void 0;\r\n return { value: o && o[i++], done: !o };\r\n }\r\n };\r\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n if (!m) return o;\r\n var i = m.call(o), r, ar = [], e;\r\n try {\r\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n }\r\n catch (error) { e = { error: error }; }\r\n finally {\r\n try {\r\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n }\r\n finally { if (e) throw e.error; }\r\n }\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n for (var ar = [], i = 0; i < arguments.length; i++)\r\n ar = ar.concat(__read(arguments[i]));\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n r[k] = a[j];\r\n return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n if (ar || !(i in from)) {\r\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n ar[i] = from[i];\r\n }\r\n }\r\n return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n function fulfill(value) { resume(\"next\", value); }\r\n function reject(value) { resume(\"throw\", value); }\r\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n var i, p;\r\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var m = o[Symbol.asyncIterator], i;\r\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n if (mod && mod.__esModule) return mod;\r\n var result = {};\r\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n __setModuleDefault(result, mod);\r\n return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n /** @nocollapse */\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n * @return {void}\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @nocollapse\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param {T} [value] The `next` value.\n * @return {void}\n */\n next(value?: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param {any} [err] The `error` exception.\n * @return {void}\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n * @return {void}\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @constructor\n * @param {Function} subscribe the function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @owner Observable\n * @method create\n * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n * @return {Observable} a new observable\n * @nocollapse\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @method lift\n * @param operator the operator defining the operation to take on the observable\n * @return a new observable with the Operator applied\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n * Observable.\n * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n * @return {Subscription} a subscription reference to the registered handlers\n * @method subscribe\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next a handler for each value emitted by the observable\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @method Symbol.observable\n * @return {Observable} this instance of the observable\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n * @method pipe\n * @return {Observable} the Observable result of all of the operators having\n * been called in the order they were passed in.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @method toPromise\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @nocollapse\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return {Observable} Observable that the Subject casts to\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\n/**\n * @class AnonymousSubject\n */\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param bufferSize The size of the buffer to replay on subscription\n * @param windowTime The amount of time the buffered items will stay buffered\n * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler.\n * @return {void}\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n * @return {any}\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return {number} A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param {function(state: ?T): ?Subscription} work A function representing a\n * task, or some unit of work to be executed by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler itself.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @return {Subscription} A subscription in order to be able to unsubscribe\n * the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @type {boolean}\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @type {any}\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n const flushId = this._scheduled;\n this._scheduled = undefined;\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an

  • May 2021: Iter8 at KubeCon + CloudNativeCon Europe

  • Mar 2021: Iter8 at Knative meetup
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/contributing/index.html b/1.1/contributing/index.html new file mode 100644 index 00000000..440d6a3d --- /dev/null +++ b/1.1/contributing/index.html @@ -0,0 +1,19 @@ + Contributing - Iter8

Overview

Welcome! We are delighted that you want to contribute to Iter8! 💖

As you get started, you are in the best position to give us feedback on key areas including:

  • Problems found during setup of Iter8
  • Gaps in our getting started tutorial and other documentation
  • Bugs in our test and automation scripts

If anything doesn't make sense, or doesn't work when you run it, please open a bug report and let us know!

Ways to contribute

We welcome many types of contributions including:

Ask for help

The best ways to reach us with a question is to ask...

Find or file an issue

Iter8 issues are tracked here.

Pull request lifecycle

  • Your PR is associated with one (and infrequently, with more than one) GitHub issue. You can start the submission of your PR as soon as this issue has been created.
  • Follow the standard GitHub fork and pull request process when creating and submitting your PR.
  • The associated GitHub issue might need to go through design discussions and may not be ready for development. Your PR might require new tests; these new or existing tests may not yet be running successfully. At this stage, keep your PR as a draft, to signal that it is not yet ready for review.
  • Once design discussions are complete and tests pass, convert the draft PR into a regular PR to signal that it is ready for review. Additionally, post a message in the #development Slack channel of the Iter8 Slack workspace with a link to your PR. This will expedite the review.
  • You can expect an initial review within 1-2 days of submitting a PR, and follow up reviews (if any) to happen over 2-5 days.
  • Use the #development Slack channel of Iter8 Slack workspace to ping/bump when the pull request is ready for further review or if it appears stalled.
  • Iter8 releases happen frequently. Once your PR is merged, you can expect your contribution to show up live in a short amount of time at https://iter8.tools.

Sign your commits

Licensing is important to open source projects. It provides some assurances that the software will continue to be available based under the terms that the author(s) desired. We require that contributors sign off on commits submitted to our project's repositories. The Developer Certificate of Origin (DCO) is a way to certify that you wrote and have the right to contribute the code you are submitting to the project.

Read GitHub's documentation on signing your commits.

You sign-off by adding the following to your commit messages. Your sign-off must match the Git user and email associated with the commit.

This is my commit message
+
+Signed-off-by: Your Name <your.name@example.com>
+

Git has a -s command line option to do this automatically:

git commit -s -m 'This is my commit message'
+

If you forgot to do this and have not yet pushed your changes to the remote repository, you can amend your commit with the sign-off by running:

git commit --amend -s
+

Development environment setup

The Iter8 project consists of the following repos.

  1. iter8-tools/iter8: source for the Iter8 CLI, performance test, and controller charts
  2. iter8-tools/docs: source for Iter8 docs

iter8-tools/iter8

This is the source repo for Iter8 CLI.

Clone iter8

git clone https://github.com/iter8-tools/iter8.git
+

Run unit tests and see coverage information

make tests
+make coverage
+make htmlcov
+

Lint Iter8

make lint
+

Build and push Iter8 image

Define a name for your Docker image

IMG=[Docker image name]
+

Build and push Iter8 image to Docker

docker build -f docker/Dockerfile -t $IMG . 
+docker push $IMG
+

Install controller

To install a development build of the controller, set the image property to the name of the Docker image $IMG:

helm upgrade --install --repo https://iter8-tools.github.io/iter8 --version 1.1 contoller \ --set image=$IMG

iter8-tools/docs

This is the source repo for Iter8 documentation.

Clone docs

git clone https://github.com/iter8-tools/docs.git
+

Locally serve docs

From the root of this repo:

python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+mkdocs serve -s
+

You can now see your local docs at http://localhost:8000. You will also see live updates to http://localhost:8000 as you update the contents of the docs folder.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/advantages/index.html b/1.1/getting-started/advantages/index.html new file mode 100644 index 00000000..da7770ea --- /dev/null +++ b/1.1/getting-started/advantages/index.html @@ -0,0 +1 @@ + Advantages - Iter8

Advantages

Use any resources

Iter8 allows an application to be composed of resources of any type, including custom resources defined by custom resource definitions (CRDs). In Iter8, the set of resources that make up an application version is declarative making it easy to extend for new resource types. The same extension mechanism also allows Iter8 to be used with any service mesh or ingress.

A/B/n testing of backend components

Using the Iter8 SDK enables frontend application components to reliably associate business metrics with the contributing versions of the backend. This addresses a key challenge of A/B/n testing of backend application components/ML models.

Simplified performance testing

Iter8 makes it easy to start running performance tests. Tests are composed with easily configurable tasks. Furthermore, there is no need to setup and configure an external metrics database -- Iter8 captures the metrics data and provides a REST API to access it, allowing it to be visualized and evaluated in Grafana.

Comparison to other tools

Flagger and Argo Rollouts share similarities with Iter8. Both provide support for advanced application rollout on Kubernetes with blue-green and canary analysis. They work with many service meshes and ingress products to provide this support. Users specify the desired rollout using a Kubernetes custom resource.

Iter8 is inspired by both projects. However, Iter8 differs in several regards. For example, with Iter8:

  • Applications can be composed of any resource type. For example, it works with machine learning applications built using KServe InferenceService resources out of the box. To do so, Iter8 allows the user to specify the resources being deployed as part of the specification of the rollout instead of assuming a particular pattern.

  • Users can A/B/n test application backend components. Beyond providing HTTP header and cookie-based routing, Iter8 provides a client SDK with a simple API that allows users to write frontend components designed to focus A/B/n testing on the backend components.

  • No custom resource is required to specify rollouts. Both Flagger and Argo Rollouts, requires the user to install and use a custom resource type to define rollouts. In Iter8, users specify rollouts using Helm configuration files.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/concepts/index.html b/1.1/getting-started/concepts/index.html new file mode 100644 index 00000000..c13820f1 --- /dev/null +++ b/1.1/getting-started/concepts/index.html @@ -0,0 +1 @@ + Concepts - Iter8

Iter8

Iter8 is the Kubernetes release optimizer built for DevOps, MLOps, SRE and data science teams. Iter8 automates traffic control for new versions of apps/ML models in the cluster and visualizes their performance metrics.

Use cases

Iter8 simplifies a variety of traffic engineering and metrics-driven validation use cases. To support such use cases, Iter8 provides support for the key challenges enabling simpler implementation and quicker adoption of testing in the CD process.

Progressive release with automated traffic management Iter8 supports blue-green, canary and mirrored releases of new applications and ML models. When new models are deployed, Iter8 automatically reconfigures the routing to desired traffic pattern. Deployment of new versions and their promotion is done by describing the desired state.

A/B/n testing with client SDK and business metrics Iter8 addresses the challenge of doing A/B/n testing of backend application components/ML models. It provides a simple client SDK allowing a user-facing component to easily and reliably associate business metrics with the backend components that were used. This SDK provides sticky lookup based on user request headers.

Performance testing for HTTP and gRPC endpoints To enable rapid testing, Iter8 provides synthetic load generation and notification support. A set of reusable tasks can be used to implement the desired test and notification behavior.

Design Principles

Support all applications Iter8 does not limit what types of resources an application is composed of. It supports applications that are composed of any Kubernetes resources including those defined by custom resource definitions (CRDs). Adding support for a new resource type is both straightforward and declarative.

Support any routing technology Progress release use cases are supported using an service mesh or ingress. Iter8 natively supports the Kubernetes Gateway API allowing easy adoption of many of these technologies. However, native interfaces can also be supported declaratively.

Simplify user interaction Iter8 leverages Helm to allow users to declaratively specify deployment patterns and to describe test scenarios. The Helm charts provided by Iter8 minimize the barrier to entry by providing common examples. Extension is often possible just be modifying the input to the charts. However, more complicated use cases can also be supported by (user) modification of the Helm charts as well.

Minimize Access Progressive release and A/B/n use cases require the user to install a Kubernetes controller. However, Iter8 allows for users with only namespace level access to install and use Iter8. Iter8 can also be installed and run with cluster level access.

Implementation Choices

Iter8 controller

Iter8 is a controller based architecture. It monitors the Kubernetes objects that make up versions of an application/ML model and automatically reconfigures the routing resources based on their state. In this way, a user just deploys and evaluates versions; she does not con

The following picture illustrates a blue-green rollout scenario that is orchestrated by this controller.

Blue-green

As part of the dynamic reconfiguration of route resources, the Iter8 controller also checks for readiness (for e.g., in KServe ModelMesh), availability (for e.g., in Kubernetes deployments) and other relevant status conditions before configuring traffic splits to candidate versions. Similarly, before candidate versions are deleted, the Iter8 controller uses finalizers to first ensure that all traffic flows to the primary version of the ML model. This makes for a very high-degree of reliability and zero-downtime/loss-less rollouts of new app/ML model versions. Users do not get this level of reliability out-of-the-box with a vanilla service mesh.

With Iter8, the barrier to entry for end-users is significantly reduced. In particular, by just providing names of their ML serving resources, and (optional) traffic weights/labels, end users can get started with their release optimization use cases rapidly. Further, Iter8 does not limit the capabilities of the underlying service mesh in any way. This means more advanced teams still get to use all the power of the service-mesh alongside the reliability and ease-of-use that Iter8 brings.

Client SDK

Iter8 provides a client-side SDK to facilitate routing as well as a metrics collection task associated with distributed (i.e., client-server architecture-based) A/B/n testing in Kubernetes.

The following picture illustrates the use of the SDK for A/B testing.

A/B testing

Iter8's SDK is designed to handle user stickiness, collection of business metrics, and decoupling of front-end and back-end releases processes during A/B/n testing.

In addition, Iter8 provides a simple metrics store, eliminating the need for an external database.

Performance testing tasks

Iter8 introduces a set of tasks which can be composed in order to conduct a variety of performance tests.

The following picture illustrates a performance test for an HTTP application, and this test consists of two tasks. One verifies the application is ready and the second generates synthetic HTTP load against the application and captures the resulting performance metrics.

Iter8 performance test

In addition to load testing HTTP and gRPC services, Iter8 tasks can perform other actions such as sending notifications to Slack or GitHub.

Built using open source

Iter8 is written in go and builds on a few awesome open source projects including:

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/first-abn/index.html b/1.1/getting-started/first-abn/index.html new file mode 100644 index 00000000..f050ed50 --- /dev/null +++ b/1.1/getting-started/first-abn/index.html @@ -0,0 +1,50 @@ + Your first A/B test - Iter8

A/B Testing with the Iter8 SDK

This tutorial describes how to do A/B testing of a backend component using the Iter8 SDK.

A/B/n testing

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy the sample application

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components of this application as described in each tab:

Install the frontend component using an implementation in the language of your choice:

kubectl create deployment frontend --image=iter8/abn-sample-frontend-node:0.17.3
+kubectl expose deployment frontend --name=frontend --port=8090
+
kubectl create deployment frontend --image=iter8/abn-sample-frontend-go:0.17.3
+kubectl expose deployment frontend --name=frontend --port=8090
+

The frontend component is implemented to call Lookup() before each call to the backend component. The frontend component uses the returned version number to route the request to the recommended version of the backend component.

Release an initial version of the backend named backend:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment
+application: 
+  port: 8091
+  versions:
+  - metadata:
+      name: backend
+    image: iter8/abn-sample-backend:0.17-v1
+EOF
+

Generate load

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090
+
In another shell, run a script to generate load from multiple users: 
curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --
+

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: {"Id":19,"Name":"sample","Source":"backend-74ff88c76d-nb87j"}
+

Deploy candidate

A candidate version of the backend component can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment
+application: 
+  port: 8091
+  versions:
+  - metadata:
+      name: backend
+    image: iter8/abn-sample-backend:0.17-v1
+  - metadata:
+      name: backend-candidate-1
+    image: iter8/abn-sample-backend:0.17-v2
+EOF
+

While the candidate version is deploying, Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: {"Id":19,"Name":"sample","Source":"backend-candidate-1-56cb7cd5cf-bkrjv"}
+

Compare versions using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source default/backend with the following parameters:

  • URL: http://iter8.default:8080/abnDashboard
  • Query string: namespace=default&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

A/B dashboard

Once you identify a winner, it can be promoted, and the candidate version deleted.

Promote candidate

To promote the candidate version (backend-candidate-1), re-release the application, updating the image of the primary (the first) version to use the image of the candidate version and remove the candidate version:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment
+application: 
+  port: 8091
+  versions:
+  - metadata:
+      name: backend
+    image: iter8/abn-sample-backend:0.17-v2
+EOF
+

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: {"Id":19,"Name":"sample","Source":"backend-74ff88c76d-nb87j"}
+

Cleanup

Delete the sample application:

kubectl delete svc/frontend deploy/frontend
+helm delete backend
+

Uninstall the Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+

Congratulations! 🎉 You completed your first A/B test with Iter8.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/first-performance/index.html b/1.1/getting-started/first-performance/index.html new file mode 100644 index 00000000..414963e6 --- /dev/null +++ b/1.1/getting-started/first-performance/index.html @@ -0,0 +1,40 @@ + Your first performance test - Iter8

Load test HTTP endpoint

Run your first performance test by load testing a Kubernetes HTTP service and visualizing the performance with an Iter8 Grafana dashboard.

Load test HTTP

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Deploy the sample HTTP service in the Kubernetes cluster. 
    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80
+kubectl expose deploy httpbin --port=80
+
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Launch performance test

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={ready,http}" \
+--set ready.deploy=httpbin \
+--set ready.service=httpbin \
+--set ready.timeout=60s \
+--set http.url=http://httpbin.default/get
+
helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={ready,http}" \
+--set ready.deploy=httpbin \
+--set ready.service=httpbin \
+--set ready.timeout=60s \
+--set http.url=http://httpbin.default/post \
+--set http.payloadStr=hello
+
About this performance test

This performance test consists of two tasks, namely, ready and http.

The ready task checks if the httpbin deployment exists and is available, and the httpbin service exists.

The http task sends requests to the cluster-local HTTP service using the specified url, and collects Iter8's built-in HTTP load test metrics. This tasks supports both GET and POST requests, and for POST requests, a payload can be provided by using either payloadStr or payloadURL.

View results using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source httpbin-test with the following parameters:

  • URL: http://iter8.default:8080/httpDashboard
  • Query string: namespace=default&test=httpbin-test

Create a new dashboard by import. Paste the contents of the http Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

http Iter8 dashboard

View logs

Logs are useful for debugging. To see the test logs:

kubectl logs -l iter8.tools/test=httpbin-test
+
Sample performance test logs 
  time=2023-09-01 19:31:40 level=info msg=task 2: ready: started
+  time=2023-09-01 19:31:40 level=info msg=task 2: ready: completed
+  time=2023-09-01 19:31:40 level=info msg=task 3: http: started
+  {"ts":1693596700.013507,"level":"info","file":"httprunner.go","line":100,"msg":"Starting http test","run":"0","url":"http://httpbin.default/get","threads":"4","qps":"8.0","warmup":"parallel","conn-reuse":""}
+  {"ts":1693596712.606946,"level":"info","file":"periodic.go","line":832,"msg":"T001 ended after 12.534760214s : 25 calls. qps=1.9944537887591696"}
+  {"ts":1693596712.616122,"level":"info","file":"periodic.go","line":832,"msg":"T002 ended after 12.544591006s : 25 calls. qps=1.9928907995519867"}
+  {"ts":1693596712.623089,"level":"info","file":"periodic.go","line":832,"msg":"T003 ended after 12.551572714s : 25 calls. qps=1.9917822706086104"}
+  {"ts":1693596712.628555,"level":"info","file":"periodic.go","line":832,"msg":"T000 ended after 12.557040548s : 25 calls. qps=1.9909149695293316"}
+  {"ts":1693596712.629567,"level":"info","file":"periodic.go","line":564,"msg":"Run ended","run":"0","elapsed":"12.557657464s","calls":"100","qps":"7.963268649959411"}
+  time=2023-09-01 19:31:52 level=info msg=task 3: http: completed
+

Cleanup

Remove the performance test and the sample app from the Kubernetes cluster. 

helm delete httpbin-test
+kubectl delete svc/httpbin
+kubectl delete deploy/httpbin
+

Uninstall the Iter8 controller

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+

Congratulations! 🎉 You completed your first performance test with Iter8.

Some variations and extensions of this performance test
  1. The http task can be configured with load related parameters such as the number of requests, queries per second, or number of parallel connections.
  2. The http task can be configured to send various types of content as payload.
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/first-release/index.html b/1.1/getting-started/first-release/index.html new file mode 100644 index 00000000..9861cd02 --- /dev/null +++ b/1.1/getting-started/first-release/index.html @@ -0,0 +1,73 @@ + Your first progressive release - Iter8

Your first progressive release

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

Blue-green release

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the application using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  strategy: blue-green
+EOF
+
What happens?

Because environment is set to deployment-istio, a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (of type ExternalName) named default/httpbin pointing at the Istio gateway, istio-ingressgateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created. Finally, to support the blue-green release, a ConfigMap (httpbin-0-weight-config) is created to be used to manage the proportion of traffic sent to this version.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all traffic to the only deployed version, httpbin-0.

Verify routing

You can verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice httpbin -o yaml
+

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. Send requests: 

    curl httpbin.default -s -D - | grep -e '^HTTP' -e app-version
+

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK
+app-version: httpbin-0
+
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80
+
(b) Send requests using the Host header: 
curl -H 'Host: httpbin.default' localhost:8080 -s -D - | grep -e '^HTTP' -e app-version
+

Deploy candidate

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: blue-green
+EOF
+
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing; the VirtualService is updated to distribute traffic between versions based on the weights.

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Requests will now be handled equally by both versions. Output will be something like:

HTTP/1.1 200 OK
+app-version: httpbin-0
+...
+HTTP/1.1 200 OK
+app-version: httpbin-1
+

Modify weights (optional)

To modify the request distribution between the versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+    weight: 30
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+    weight: 70
+  strategy: blue-green
+EOF
+

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version; the remaining 30 percent by the primary version.

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: blue-green
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Cleanup

Delete the application and its routing configuration:

helm delete httpbin
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Congratulations! 🎉 You completed your first blue-green release with Iter8.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/help/index.html b/1.1/getting-started/help/index.html new file mode 100644 index 00000000..96c61226 --- /dev/null +++ b/1.1/getting-started/help/index.html @@ -0,0 +1 @@ + Get help - Iter8
Iter8 is open-source and available under the Apache 2 License.
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", + "created": 1646078626605 + } + } +} \ No newline at end of file diff --git a/1.1/getting-started/install/index.html b/1.1/getting-started/install/index.html new file mode 100644 index 00000000..155645da --- /dev/null +++ b/1.1/getting-started/install/index.html @@ -0,0 +1,4 @@ + Installation - Iter8

Installation

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/logs/index.html b/1.1/getting-started/logs/index.html new file mode 100644 index 00000000..a3af42ce --- /dev/null +++ b/1.1/getting-started/logs/index.html @@ -0,0 +1,11 @@ + Logs - Iter8

Logs

Sample performance test logs 
  time=2023-09-01 19:31:40 level=info msg=task 2: ready: started
+  time=2023-09-01 19:31:40 level=info msg=task 2: ready: completed
+  time=2023-09-01 19:31:40 level=info msg=task 3: http: started
+  {"ts":1693596700.013507,"level":"info","file":"httprunner.go","line":100,"msg":"Starting http test","run":"0","url":"http://httpbin.default/get","threads":"4","qps":"8.0","warmup":"parallel","conn-reuse":""}
+  {"ts":1693596712.606946,"level":"info","file":"periodic.go","line":832,"msg":"T001 ended after 12.534760214s : 25 calls. qps=1.9944537887591696"}
+  {"ts":1693596712.616122,"level":"info","file":"periodic.go","line":832,"msg":"T002 ended after 12.544591006s : 25 calls. qps=1.9928907995519867"}
+  {"ts":1693596712.623089,"level":"info","file":"periodic.go","line":832,"msg":"T003 ended after 12.551572714s : 25 calls. qps=1.9917822706086104"}
+  {"ts":1693596712.628555,"level":"info","file":"periodic.go","line":832,"msg":"T000 ended after 12.557040548s : 25 calls. qps=1.9909149695293316"}
+  {"ts":1693596712.629567,"level":"info","file":"periodic.go","line":564,"msg":"Run ended","run":"0","elapsed":"12.557657464s","calls":"100","qps":"7.963268649959411"}
+  time=2023-09-01 19:31:52 level=info msg=task 3: http: completed
+
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/getting-started/uninstall/index.html b/1.1/getting-started/uninstall/index.html new file mode 100644 index 00000000..f4621f89 --- /dev/null +++ b/1.1/getting-started/uninstall/index.html @@ -0,0 +1,2 @@ + Uninstall - Iter8

Uninstall

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
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", + "created": 1646078626605 + } + } +} \ No newline at end of file diff --git a/1.1/images/underthecovers.png b/1.1/images/underthecovers.png new file mode 100644 index 00000000..a802f81f Binary files /dev/null and b/1.1/images/underthecovers.png differ diff --git a/1.1/index.html b/1.1/index.html new file mode 100644 index 00000000..217e4bf7 --- /dev/null +++ b/1.1/index.html @@ -0,0 +1,45 @@ + Iter8

Kubernetes Release Optimizer

Iter8 makes it easy to ensure that Kubernetes apps and ML models perform well and maximize business value.

Get started in seconds Star / Fork

Use-cases

  • Progressive release with automated traffic management
  • A/B/n testing with client SDK and business metrics
  • Performance testing for HTTP and gRPC endpoints

Use with any Kubernetes resource types including CRDs

Iter8 experiment

Open Source

Built with for DevOps, MLOps, SRE, and data science teams

\ No newline at end of file diff --git a/1.1/roadmap/index.html b/1.1/roadmap/index.html new file mode 100644 index 00000000..5348d95d --- /dev/null +++ b/1.1/roadmap/index.html @@ -0,0 +1 @@ + Roadmap - Iter8

Roadmap

  1. Support multi-cluster installs
  2. Open Data Hub tier 1 project
  3. Metrics & evaluation for foundation model/LLM-based apps
  4. Hyperparameter tuning for foundation model/LLM-based inference pipelines
  5. Data/concept drift detection for ML models
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/search/search_index.json b/1.1/search/search_index.json new file mode 100644 index 00000000..8bb74787 --- /dev/null +++ b/1.1/search/search_index.json @@ -0,0 +1 @@ +{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Iter8","text":""},{"location":"contributing/","title":"Overview","text":"

Welcome! We are delighted that you want to contribute to Iter8! \ud83d\udc96

As you get started, you are in the best position to give us feedback on key areas including:

  • Problems found during setup of Iter8
  • Gaps in our getting started tutorial and other documentation
  • Bugs in our test and automation scripts

If anything doesn't make sense, or doesn't work when you run it, please open a bug report and let us know!

"},{"location":"contributing/#ways-to-contribute","title":"Ways to contribute","text":"

We welcome many types of contributions including:

  • Iter8 controller and test charts
  • Docs
  • CI, builds, and tests
  • Reviewing pull requests
"},{"location":"contributing/#ask-for-help","title":"Ask for help","text":"

The best ways to reach us with a question is to ask...

  • On the original GitHub issue
  • In the #development channel in the Iter8 Slack workspace
"},{"location":"contributing/#find-or-file-an-issue","title":"Find or file an issue","text":"

Iter8 issues are tracked here.

"},{"location":"contributing/#pull-request-lifecycle","title":"Pull request lifecycle","text":"
  • Your PR is associated with one (and infrequently, with more than one) GitHub issue. You can start the submission of your PR as soon as this issue has been created.
  • Follow the standard GitHub fork and pull request process when creating and submitting your PR.
  • The associated GitHub issue might need to go through design discussions and may not be ready for development. Your PR might require new tests; these new or existing tests may not yet be running successfully. At this stage, keep your PR as a draft, to signal that it is not yet ready for review.
  • Once design discussions are complete and tests pass, convert the draft PR into a regular PR to signal that it is ready for review. Additionally, post a message in the #development Slack channel of the Iter8 Slack workspace with a link to your PR. This will expedite the review.
  • You can expect an initial review within 1-2 days of submitting a PR, and follow up reviews (if any) to happen over 2-5 days.
  • Use the #development Slack channel of Iter8 Slack workspace to ping/bump when the pull request is ready for further review or if it appears stalled.
  • Iter8 releases happen frequently. Once your PR is merged, you can expect your contribution to show up live in a short amount of time at https://iter8.tools.
"},{"location":"contributing/#sign-your-commits","title":"Sign your commits","text":"

Licensing is important to open source projects. It provides some assurances that the software will continue to be available based under the terms that the author(s) desired. We require that contributors sign off on commits submitted to our project's repositories. The Developer Certificate of Origin (DCO) is a way to certify that you wrote and have the right to contribute the code you are submitting to the project.

Read GitHub's documentation on signing your commits.

You sign-off by adding the following to your commit messages. Your sign-off must match the Git user and email associated with the commit.

This is my commit message\n\nSigned-off-by: Your Name <your.name@example.com>\n

Git has a -s command line option to do this automatically:

git commit -s -m 'This is my commit message'\n

If you forgot to do this and have not yet pushed your changes to the remote repository, you can amend your commit with the sign-off by running:

git commit --amend -s\n
"},{"location":"contributing/#development-environment-setup","title":"Development environment setup","text":"

The Iter8 project consists of the following repos.

  1. iter8-tools/iter8: source for the Iter8 CLI, performance test, and controller charts
  2. iter8-tools/docs: source for Iter8 docs
"},{"location":"contributing/#iter8-toolsiter8","title":"iter8-tools/iter8","text":"

This is the source repo for Iter8 CLI.

"},{"location":"contributing/#clone-iter8","title":"Clone iter8","text":"
git clone https://github.com/iter8-tools/iter8.git\n
"},{"location":"contributing/#run-unit-tests-and-see-coverage-information","title":"Run unit tests and see coverage information","text":"
make tests\nmake coverage\nmake htmlcov\n
"},{"location":"contributing/#lint-iter8","title":"Lint Iter8","text":"
make lint\n
"},{"location":"contributing/#build-and-push-iter8-image","title":"Build and push Iter8 image","text":"

Define a name for your Docker image

IMG=[Docker image name]\n

Build and push Iter8 image to Docker

docker build -f docker/Dockerfile -t $IMG . \ndocker push $IMG\n
"},{"location":"contributing/#install-controller","title":"Install controller","text":"

To install a development build of the controller, set the image property to the name of the Docker image $IMG:

helm upgrade --install --repo https://iter8-tools.github.io/iter8 --version 1.1 contoller \\ --set image=$IMG

"},{"location":"contributing/#iter8-toolsdocs","title":"iter8-tools/docs","text":"

This is the source repo for Iter8 documentation.

"},{"location":"contributing/#clone-docs","title":"Clone docs","text":"
git clone https://github.com/iter8-tools/docs.git\n
"},{"location":"contributing/#locally-serve-docs","title":"Locally serve docs","text":"

From the root of this repo:

python3 -m venv .venv\nsource .venv/bin/activate\npip install -r requirements.txt\nmkdocs serve -s\n

You can now see your local docs at http://localhost:8000. You will also see live updates to http://localhost:8000 as you update the contents of the docs folder.

"},{"location":"roadmap/","title":"Roadmap","text":"
  1. Support multi-cluster installs
  2. Open Data Hub tier 1 project
  3. Metrics & evaluation for foundation model/LLM-based apps
  4. Hyperparameter tuning for foundation model/LLM-based inference pipelines
  5. Data/concept drift detection for ML models
"},{"location":"community/community/","title":"Community","text":""},{"location":"community/community/#slack","title":"Slack","text":"

Join the Iter8 Slack for usage and development related discussions.

"},{"location":"community/community/#github-issues","title":"GitHub issues","text":"

GitHub issues for all Iter8 repositories are managed here.

"},{"location":"community/news/","title":"News and announcements","text":"

  • September 2023: Iter8 has been accepted as a tier 2 component for Open Data Hub.

  • June 2023: Our proposal to integrate with Open Data Hub has been accepted!

  • March 2023: New Stack blog article by Michael Kalantar. Iter8: Simple A/B/n Testing of Kubernetes Apps, ML Models

  • February 2023: DZone article by Alan Cha. Automated Performance Testing With ArgoCD and Iter8

  • December 2022: DZone article by Michael Kalantar. Simplifying A/B/n Testing of Backend Services

  • October 2022: Iter8 at KubeCon. Conference details

    • Presentation by Srinivasan Parthasarathy. Video coming soon
    • Lightning talk by Alan Cha. Video link here

  • August 2022: ITNEXT article by Alan Cha. Performance testing with Iter8, now with custom metrics!

  • August 2022: Knative blog article by Srinivasan Parthasarathy. Simple Performance Testing with SLOs

  • June 2022: Iter8 at Open Source Summit. Video coming soon. Conference details

  • May 2022: IBM Developer blog article by Srinivasan Parthasarathy. Dead simple benchmarking and SLO validation for Kubernetes services

  • May 2022: New Stack blog article by Srinivasan Parthasarathy. Iter8 Unifies Performance Validation for gRPC and HTTP

  • March 2022: New Stack blog article by Michael Kalantar. Simple Load Testing with GitHub Actions

  • Feb 2022: New Stack blog article on Simple HTTP Load Testing with SLOs

  • Nov 2021: Iter8 at ACM Symposium on Cloud Computing. Full paper here

Iter8 v0.7 and older
  • Oct 2021: New Stack blog article by Hai Huang: Progressive Delivery on OpenShift
  • Oct 2021: Iter8 at PREVAIL conference. Video coming soon. Conference details
  • Oct 2021: New Stack blog article by Srinivasan Parthasarathy: Validate Service-Level Objectives of REST APIs Using Iter8
  • Jul 2021: Blog article by Clive Cox: ML\u200c \u200cProgressive\u200c \u200cRollouts\u200c \u200cwith\u200c \u200cSeldon\u200c \u200cand\u200c \u200cIter8\u200c

  • Jul 2021: Iter8 at Knative meetup

  • May 2021: Iter8 at KubeCon + CloudNativeCon Europe

  • Mar 2021: Iter8 at Knative meetup

  • Mar 2021: Kubeflow blog article by Animesh Singh and Dan Sun: Operationalize, Scale and Infuse Trust in AI Models using KFServing
  • Oct 2020: Medium blog article by Michael Kalantar: Automated Canary Release of Microservices on Kubernetes using Tekton and iter8
  • Oct 2020: Medium blog article by Kusuma Chalasani: Better Performance with kruize and iter8 for your microservices application
  • Oct 2020: Medium blog article by Srinivasan Parthasarathy: Automated Canary Release of TensorFlow Models on Kubernetes
  • Oct 2020: Medium blog article by Sushma Ravichandran: Iter8: Take a look at the magic under the hood
  • Aug 2020: Medium blog article by Fabio Oliveira: Iter8: Achieving Agility with Control
"},{"location":"getting-started/advantages/","title":"Advantages","text":""},{"location":"getting-started/advantages/#use-any-resources","title":"Use any resources","text":"

Iter8 allows an application to be composed of resources of any type, including custom resources defined by custom resource definitions (CRDs). In Iter8, the set of resources that make up an application version is declarative making it easy to extend for new resource types. The same extension mechanism also allows Iter8 to be used with any service mesh or ingress.

"},{"location":"getting-started/advantages/#abn-testing-of-backend-components","title":"A/B/n testing of backend components","text":"

Using the Iter8 SDK enables frontend application components to reliably associate business metrics with the contributing versions of the backend. This addresses a key challenge of A/B/n testing of backend application components/ML models.

"},{"location":"getting-started/advantages/#simplified-performance-testing","title":"Simplified performance testing","text":"

Iter8 makes it easy to start running performance tests. Tests are composed with easily configurable tasks. Furthermore, there is no need to setup and configure an external metrics database -- Iter8 captures the metrics data and provides a REST API to access it, allowing it to be visualized and evaluated in Grafana.

"},{"location":"getting-started/advantages/#comparison-to-other-tools","title":"Comparison to other tools","text":"

Flagger and Argo Rollouts share similarities with Iter8. Both provide support for advanced application rollout on Kubernetes with blue-green and canary analysis. They work with many service meshes and ingress products to provide this support. Users specify the desired rollout using a Kubernetes custom resource.

Iter8 is inspired by both projects. However, Iter8 differs in several regards. For example, with Iter8:

  • Applications can be composed of any resource type. For example, it works with machine learning applications built using KServe InferenceService resources out of the box. To do so, Iter8 allows the user to specify the resources being deployed as part of the specification of the rollout instead of assuming a particular pattern.

  • Users can A/B/n test application backend components. Beyond providing HTTP header and cookie-based routing, Iter8 provides a client SDK with a simple API that allows users to write frontend components designed to focus A/B/n testing on the backend components.

  • No custom resource is required to specify rollouts. Both Flagger and Argo Rollouts, requires the user to install and use a custom resource type to define rollouts. In Iter8, users specify rollouts using Helm configuration files.

"},{"location":"getting-started/concepts/","title":"Iter8","text":"

Iter8 is the Kubernetes release optimizer built for DevOps, MLOps, SRE and data science teams. Iter8 automates traffic control for new versions of apps/ML models in the cluster and visualizes their performance metrics.

"},{"location":"getting-started/concepts/#use-cases","title":"Use cases","text":"

Iter8 simplifies a variety of traffic engineering and metrics-driven validation use cases. To support such use cases, Iter8 provides support for the key challenges enabling simpler implementation and quicker adoption of testing in the CD process.

Progressive release with automated traffic management Iter8 supports blue-green, canary and mirrored releases of new applications and ML models. When new models are deployed, Iter8 automatically reconfigures the routing to desired traffic pattern. Deployment of new versions and their promotion is done by describing the desired state.

A/B/n testing with client SDK and business metrics Iter8 addresses the challenge of doing A/B/n testing of backend application components/ML models. It provides a simple client SDK allowing a user-facing component to easily and reliably associate business metrics with the backend components that were used. This SDK provides sticky lookup based on user request headers.

Performance testing for HTTP and gRPC endpoints To enable rapid testing, Iter8 provides synthetic load generation and notification support. A set of reusable tasks can be used to implement the desired test and notification behavior.

"},{"location":"getting-started/concepts/#design-principles","title":"Design Principles","text":"

Support all applications Iter8 does not limit what types of resources an application is composed of. It supports applications that are composed of any Kubernetes resources including those defined by custom resource definitions (CRDs). Adding support for a new resource type is both straightforward and declarative.

Support any routing technology Progress release use cases are supported using an service mesh or ingress. Iter8 natively supports the Kubernetes Gateway API allowing easy adoption of many of these technologies. However, native interfaces can also be supported declaratively.

Simplify user interaction Iter8 leverages Helm to allow users to declaratively specify deployment patterns and to describe test scenarios. The Helm charts provided by Iter8 minimize the barrier to entry by providing common examples. Extension is often possible just be modifying the input to the charts. However, more complicated use cases can also be supported by (user) modification of the Helm charts as well.

Minimize Access Progressive release and A/B/n use cases require the user to install a Kubernetes controller. However, Iter8 allows for users with only namespace level access to install and use Iter8. Iter8 can also be installed and run with cluster level access.

"},{"location":"getting-started/concepts/#implementation-choices","title":"Implementation Choices","text":""},{"location":"getting-started/concepts/#iter8-controller","title":"Iter8 controller","text":"

Iter8 is a controller based architecture. It monitors the Kubernetes objects that make up versions of an application/ML model and automatically reconfigures the routing resources based on their state. In this way, a user just deploys and evaluates versions; she does not con

The following picture illustrates a blue-green rollout scenario that is orchestrated by this controller.

As part of the dynamic reconfiguration of route resources, the Iter8 controller also checks for readiness (for e.g., in KServe ModelMesh), availability (for e.g., in Kubernetes deployments) and other relevant status conditions before configuring traffic splits to candidate versions. Similarly, before candidate versions are deleted, the Iter8 controller uses finalizers to first ensure that all traffic flows to the primary version of the ML model. This makes for a very high-degree of reliability and zero-downtime/loss-less rollouts of new app/ML model versions. Users do not get this level of reliability out-of-the-box with a vanilla service mesh.

With Iter8, the barrier to entry for end-users is significantly reduced. In particular, by just providing names of their ML serving resources, and (optional) traffic weights/labels, end users can get started with their release optimization use cases rapidly. Further, Iter8 does not limit the capabilities of the underlying service mesh in any way. This means more advanced teams still get to use all the power of the service-mesh alongside the reliability and ease-of-use that Iter8 brings.

"},{"location":"getting-started/concepts/#client-sdk","title":"Client SDK","text":"

Iter8 provides a client-side SDK to facilitate routing as well as a metrics collection task associated with distributed (i.e., client-server architecture-based) A/B/n testing in Kubernetes.

The following picture illustrates the use of the SDK for A/B testing.

Iter8's SDK is designed to handle user stickiness, collection of business metrics, and decoupling of front-end and back-end releases processes during A/B/n testing.

In addition, Iter8 provides a simple metrics store, eliminating the need for an external database.

"},{"location":"getting-started/concepts/#performance-testing-tasks","title":"Performance testing tasks","text":"

Iter8 introduces a set of tasks which can be composed in order to conduct a variety of performance tests.

The following picture illustrates a performance test for an HTTP application, and this test consists of two tasks. One verifies the application is ready and the second generates synthetic HTTP load against the application and captures the resulting performance metrics.

In addition to load testing HTTP and gRPC services, Iter8 tasks can perform other actions such as sending notifications to Slack or GitHub.

"},{"location":"getting-started/concepts/#built-using-open-source","title":"Built using open source","text":"

Iter8 is written in go and builds on a few awesome open source projects including:

  • Helm
  • Istio
  • Kubernetes Gateway API
  • Fortio
  • ghz
  • Grafana
"},{"location":"getting-started/first-abn/","title":"A/B Testing with the Iter8 SDK","text":"

This tutorial describes how to do A/B testing of a backend component using the Iter8 SDK.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"getting-started/first-abn/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"getting-started/first-abn/#deploy-the-sample-application","title":"Deploy the sample application","text":"

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components of this application as described in each tab:

FrontendBackend

Install the frontend component using an implementation in the language of your choice:

NodeGo 
kubectl create deployment frontend --image=iter8/abn-sample-frontend-node:0.17.3\nkubectl expose deployment frontend --name=frontend --port=8090\n
kubectl create deployment frontend --image=iter8/abn-sample-frontend-go:0.17.3\nkubectl expose deployment frontend --name=frontend --port=8090\n

The frontend component is implemented to call Lookup() before each call to the backend component. The frontend component uses the returned version number to route the request to the recommended version of the backend component.

Release an initial version of the backend named backend:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment\napplication: \n  port: 8091\n  versions:\n  - metadata:\n      name: backend\n    image: iter8/abn-sample-backend:0.17-v1\nEOF\n
"},{"location":"getting-started/first-abn/#generate-load","title":"Generate load","text":"

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090\n
In another shell, run a script to generate load from multiple users: 
curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --\n

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: {\"Id\":19,\"Name\":\"sample\",\"Source\":\"backend-74ff88c76d-nb87j\"}\n
"},{"location":"getting-started/first-abn/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the backend component can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment\napplication: \n  port: 8091\n  versions:\n  - metadata:\n      name: backend\n    image: iter8/abn-sample-backend:0.17-v1\n  - metadata:\n      name: backend-candidate-1\n    image: iter8/abn-sample-backend:0.17-v2\nEOF\n

While the candidate version is deploying, Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: {\"Id\":19,\"Name\":\"sample\",\"Source\":\"backend-candidate-1-56cb7cd5cf-bkrjv\"}\n
"},{"location":"getting-started/first-abn/#compare-versions-using-grafana","title":"Compare versions using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source default/backend with the following parameters:

  • URL: http://iter8.default:8080/abnDashboard
  • Query string: namespace=default&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

Once you identify a winner, it can be promoted, and the candidate version deleted.

"},{"location":"getting-started/first-abn/#promote-candidate","title":"Promote candidate","text":"

To promote the candidate version (backend-candidate-1), re-release the application, updating the image of the primary (the first) version to use the image of the candidate version and remove the candidate version:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment\napplication: \n  port: 8091\n  versions:\n  - metadata:\n      name: backend\n    image: iter8/abn-sample-backend:0.17-v2\nEOF\n

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: {\"Id\":19,\"Name\":\"sample\",\"Source\":\"backend-74ff88c76d-nb87j\"}\n
"},{"location":"getting-started/first-abn/#cleanup","title":"Cleanup","text":"

Delete the sample application:

kubectl delete svc/frontend deploy/frontend\nhelm delete backend\n

Uninstall the Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n

Congratulations! You completed your first A/B test with Iter8.

"},{"location":"getting-started/first-performance/","title":"Load test HTTP endpoint","text":"

Run your first performance test by load testing a Kubernetes HTTP service and visualizing the performance with an Iter8 Grafana dashboard.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Deploy the sample HTTP service in the Kubernetes cluster. 
    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80\nkubectl expose deploy httpbin --port=80\n
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"getting-started/first-performance/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"getting-started/first-performance/#launch-performance-test","title":"Launch performance test","text":"GET examplePOST example 
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={ready,http}\" \\\n--set ready.deploy=httpbin \\\n--set ready.service=httpbin \\\n--set ready.timeout=60s \\\n--set http.url=http://httpbin.default/get\n
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={ready,http}\" \\\n--set ready.deploy=httpbin \\\n--set ready.service=httpbin \\\n--set ready.timeout=60s \\\n--set http.url=http://httpbin.default/post \\\n--set http.payloadStr=hello\n
About this performance test

This performance test consists of two tasks, namely, ready and http.

The ready task checks if the httpbin deployment exists and is available, and the httpbin service exists.

The http task sends requests to the cluster-local HTTP service using the specified url, and collects Iter8's built-in HTTP load test metrics. This tasks supports both GET and POST requests, and for POST requests, a payload can be provided by using either payloadStr or payloadURL.

"},{"location":"getting-started/first-performance/#view-results-using-grafana","title":"View results using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source httpbin-test with the following parameters:

  • URL: http://iter8.default:8080/httpDashboard
  • Query string: namespace=default&test=httpbin-test

Create a new dashboard by import. Paste the contents of the http Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

"},{"location":"getting-started/first-performance/#view-logs","title":"View logs","text":"

Logs are useful for debugging. To see the test logs:

kubectl logs -l iter8.tools/test=httpbin-test\n
Sample performance test logs 
  time=2023-09-01 19:31:40 level=info msg=task 2: ready: started\n  time=2023-09-01 19:31:40 level=info msg=task 2: ready: completed\n  time=2023-09-01 19:31:40 level=info msg=task 3: http: started\n  {\"ts\":1693596700.013507,\"level\":\"info\",\"file\":\"httprunner.go\",\"line\":100,\"msg\":\"Starting http test\",\"run\":\"0\",\"url\":\"http://httpbin.default/get\",\"threads\":\"4\",\"qps\":\"8.0\",\"warmup\":\"parallel\",\"conn-reuse\":\"\"}\n  {\"ts\":1693596712.606946,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T001 ended after 12.534760214s : 25 calls. qps=1.9944537887591696\"}\n  {\"ts\":1693596712.616122,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T002 ended after 12.544591006s : 25 calls. qps=1.9928907995519867\"}\n  {\"ts\":1693596712.623089,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T003 ended after 12.551572714s : 25 calls. qps=1.9917822706086104\"}\n  {\"ts\":1693596712.628555,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T000 ended after 12.557040548s : 25 calls. qps=1.9909149695293316\"}\n  {\"ts\":1693596712.629567,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":564,\"msg\":\"Run ended\",\"run\":\"0\",\"elapsed\":\"12.557657464s\",\"calls\":\"100\",\"qps\":\"7.963268649959411\"}\n  time=2023-09-01 19:31:52 level=info msg=task 3: http: completed\n
"},{"location":"getting-started/first-performance/#cleanup","title":"Cleanup","text":"

Remove the performance test and the sample app from the Kubernetes cluster. 

helm delete httpbin-test\nkubectl delete svc/httpbin\nkubectl delete deploy/httpbin\n

"},{"location":"getting-started/first-performance/#uninstall-the-iter8-controller","title":"Uninstall the Iter8 controller","text":"
helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n

Congratulations! You completed your first performance test with Iter8.

Some variations and extensions of this performance test
  1. The http task can be configured with load related parameters such as the number of requests, queries per second, or number of parallel connections.
  2. The http task can be configured to send various types of content as payload.
"},{"location":"getting-started/first-release/","title":"Your first progressive release","text":"

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y\n
"},{"location":"getting-started/first-release/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"getting-started/first-release/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the application using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  strategy: blue-green\nEOF\n
What happens?

Because environment is set to deployment-istio, a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (of type ExternalName) named default/httpbin pointing at the Istio gateway, istio-ingressgateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created. Finally, to support the blue-green release, a ConfigMap (httpbin-0-weight-config) is created to be used to manage the proportion of traffic sent to this version.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all traffic to the only deployed version, httpbin-0.

"},{"location":"getting-started/first-release/#verify-routing","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice httpbin -o yaml\n

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. Send requests: 

    curl httpbin.default -s -D - | grep -e '^HTTP' -e app-version\n

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80\n
(b) Send requests using the Host header: 
curl -H 'Host: httpbin.default' localhost:8080 -s -D - | grep -e '^HTTP' -e app-version\n

"},{"location":"getting-started/first-release/#deploy-candidate","title":"Deploy candidate","text":"

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: blue-green\nEOF\n
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing; the VirtualService is updated to distribute traffic between versions based on the weights.

"},{"location":"getting-started/first-release/#verify-routing_1","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Requests will now be handled equally by both versions. Output will be something like:

HTTP/1.1 200 OK\napp-version: httpbin-0\n...\nHTTP/1.1 200 OK\napp-version: httpbin-1\n
"},{"location":"getting-started/first-release/#modify-weights-optional","title":"Modify weights (optional)","text":"

To modify the request distribution between the versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n    weight: 30\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n    weight: 70\n  strategy: blue-green\nEOF\n

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

"},{"location":"getting-started/first-release/#verify-routing_2","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version; the remaining 30 percent by the primary version.

"},{"location":"getting-started/first-release/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: blue-green\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"getting-started/first-release/#verify-routing_3","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"getting-started/first-release/#cleanup","title":"Cleanup","text":"

Delete the application and its routing configuration:

helm delete httpbin\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

Congratulations! You completed your first blue-green release with Iter8.

"},{"location":"getting-started/help/","title":"Get Help","text":"
  1. Read Iter8 docs.
  2. Join the Iter8 Slack workspace.
  3. File an issue or start a discussion on the Iter8 GitHub repo.
"},{"location":"getting-started/install/","title":"Installation","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"getting-started/logs/","title":"Logs","text":"Sample performance test logs 
  time=2023-09-01 19:31:40 level=info msg=task 2: ready: started\n  time=2023-09-01 19:31:40 level=info msg=task 2: ready: completed\n  time=2023-09-01 19:31:40 level=info msg=task 3: http: started\n  {\"ts\":1693596700.013507,\"level\":\"info\",\"file\":\"httprunner.go\",\"line\":100,\"msg\":\"Starting http test\",\"run\":\"0\",\"url\":\"http://httpbin.default/get\",\"threads\":\"4\",\"qps\":\"8.0\",\"warmup\":\"parallel\",\"conn-reuse\":\"\"}\n  {\"ts\":1693596712.606946,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T001 ended after 12.534760214s : 25 calls. qps=1.9944537887591696\"}\n  {\"ts\":1693596712.616122,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T002 ended after 12.544591006s : 25 calls. qps=1.9928907995519867\"}\n  {\"ts\":1693596712.623089,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T003 ended after 12.551572714s : 25 calls. qps=1.9917822706086104\"}\n  {\"ts\":1693596712.628555,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":832,\"msg\":\"T000 ended after 12.557040548s : 25 calls. qps=1.9909149695293316\"}\n  {\"ts\":1693596712.629567,\"level\":\"info\",\"file\":\"periodic.go\",\"line\":564,\"msg\":\"Run ended\",\"run\":\"0\",\"elapsed\":\"12.557657464s\",\"calls\":\"100\",\"qps\":\"7.963268649959411\"}\n  time=2023-09-01 19:31:52 level=info msg=task 3: http: completed\n
"},{"location":"getting-started/uninstall/","title":"Uninstall","text":"
helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/abn/","title":"A/B Testing with the Iter8 SDK","text":"

Your first A/B/n test describes how to perform an A/B test of an backend component using the with the Iter8 SDK.

"},{"location":"tutorials/blue-green/","title":"Blue-green release","text":"

Your first automated release describes how to implement a progressive blue-green release of a Kubernetes application.

"},{"location":"tutorials/canary/","title":"Canary release","text":"

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a canary release strategy. In a canary release, requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y\n
"},{"location":"tutorials/canary/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/canary/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the application using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  strategy: canary\nEOF\n
What happens?

Because environment is set to deployment-istio, a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (of type ExternalName) named default/httpbin pointing at the Istio gateway, istio-ingressgateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all traffic to the only deployed version, httpbin-0.

"},{"location":"tutorials/canary/#verify-routing","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice httpbin -o yaml\n

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. Send requests: 

    curl httpbin.default -s -D - | grep -e '^HTTP' -e app-version\n

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80\n
(b) Send requests using the Host header: 
curl -H 'Host: httpbin.default' localhost:8080 -s -D - | grep -e '^HTTP' -e app-version\n

"},{"location":"tutorials/canary/#deploy-candidate","title":"Deploy candidate","text":"

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: canary\nEOF\n
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that requests with the header traffic set to test will be sent to the candidate model:

HTTP/1.1 200 OK\napp-version: httpbin-1\n

All other requests will be sent to the primary model (httpbin-0):

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/canary/#verify-routing_1","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate model (httpbin-1) while all others will be handled by the primary version.

"},{"location":"tutorials/canary/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: canary\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/canary/#verify-routing_2","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/canary/#cleanup","title":"Cleanup","text":"

Delete the application and its routing configuration:

helm delete httpbin\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/mirror/","title":"Mirrored release","text":"

This tutorial shows how Iter8 can be used to release a new version of an application using mirroring. When an application is mirrored, all requests are sent to the primary version of the application. A percentage of requests is replicated and sent to the candidate version of the model. This percentage can be changed over time. Only the responses from the primary version are returned to the user. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the mirrored release strategy.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y\n
"},{"location":"tutorials/mirror/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/mirror/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  strategy: mirror\nEOF\n
What happens?

Because environment is set to deployment-istio, a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (of type ExternalName) named default/httpbin pointing at the Istio gateway, istio-ingressgateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all traffic to the only deployed version, httpbin-0.

"},{"location":"tutorials/mirror/#verify-routing","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice httpbin -o yaml\n

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. Send requests: 

    curl httpbin.default -s -D - | grep -e '^HTTP' -e app-version\n

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80\n
(b) Send requests using the Host header: 
curl -H 'Host: httpbin.default' localhost:8080 -s -D - | grep -e '^HTTP' -e app-version\n

"},{"location":"tutorials/mirror/#deploy-candidate","title":"Deploy candidate","text":"

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n    weight: 100\n  strategy: mirror\nEOF\n
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

To support modifying the percentage of traffic sent to the candidate, a ConfigMap (httpbin-1-weight-config) is created to be used to manage the proportion of traffic sent to this version.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing; the VirtualService is updated to mirror a portion of the traffic to the candidate version.

"},{"location":"tutorials/mirror/#verify-routing_1","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService. With mirroring, only the response of the primary is returned to the user so output will continue to be:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/mirror/#modify-mirrored-weight-optional","title":"Modify mirrored weight (optional)","text":"

To modify the percentage of traffic mirrored, set the weight in the candidate version:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n    weight: 50\n  strategy: mirror\nEOF\n

Iter8 automatically reconfigures the routing to send the desired percentage of requests to the candidate version.

"},{"location":"tutorials/mirror/#verify-routing_2","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Again, user requests will always be responded to by the primary version.

"},{"location":"tutorials/mirror/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-istio\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: mirror\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/mirror/#verify-routing_3","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/mirror/#cleanup","title":"Cleanup","text":"

Delete the application and its routing configuration:

helm delete httpbin\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/integrations/ghactions/","title":"Trigger a GitHub Actions workflow during a performance test","text":"

Iter8 provides a github task that sends a repository_dispatch which can trigger the workflows in the default branch of a GitHub repository.

"},{"location":"tutorials/integrations/ghactions/#example","title":"Example","text":"

In this example, you will run the Your first performance test but at the end of the performance test, Iter8 will trigger a workflow on GitHub.

In this simple example, the workflow will simply print out a test summary that it will receive with the repository_dispatch. In a more sophisticated scenario, the workflow could, for example, read from the test summary and determine what to do next.

To summarize what will happen, you will create a new GitHub repository, add a workflow that will respond to the github task, set up and run a performance test, and check if the workflow was triggered.

The github task requires the name of a repository, the name of the owner, as well as an authentication token in order to send the repository_dispatch. To see a full list of the github task parameters, see here.

  1. Create a new repository on GitHub.
  2. Add the following workflow.
name: iter8 `github` task test\non:\n  repository_dispatch:\n    types: iter8\njobs:\n  my-job:\n    runs-on: ubuntu-latest\n    steps:\n      - run: 'echo \"payload: ${{ toJson(github.event.client_payload) }}\"'\n

Note that this workflow has one job that will print out the client_payload. The default github task payload is configured with client_payload set to .Report, a summary of the performance test.

Also note that the on.repository_dispatch.types is set to iter8. The default github task payload is configured with event_type set to iter8. This indicates that once the repository_dispatch has been sent, only workflows on the default branch with on.repository_dispatch.types set to iter8 will be triggered.

  1. Create a GitHub personal access token for the token parameter.
  2. Ensure that you have a Kubernetes cluster and the kubectl CLI. You can create a local Kubernetes cluster using tools like Kind or Minikube.

  3. Install the Iter8 controller

    Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

    Namespace-scopedCluster-scoped 
    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

    For additional install options, see Iter8 Installation.

  4. Deploy the sample HTTP service in the Kubernetes cluster. 

    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80\nkubectl expose deploy httpbin --port=80\n

  5. Launch the performance test using the github task with the appropriate values. 
    helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http,github}\" \\\n--set http.url=http://httpbin.default/get \\\n--set github.owner=<GitHub owner> \\\n--set github.repo=<GitHub repository> \\\n--set github.token=<GitHub token>\n
  6. Verify that the workflow has been triggered after the performance test has completed.
Some variations and extensions of the github task

The default github task payload sends a summary of the performance test.

In your workflow, you can read from the report and use that data for control flow or use snippets of that data in different actions. For example, you can check to see if there have been any task failures and take alternative actions.

You do not need to use the default github task payload. You can provide your own payload by overriding the default of the payloadTemplateURL.

"},{"location":"tutorials/integrations/overview/","title":"Integrations","text":"

The tutorials under the integrations section are maintained by members of the Iter8 community. They may become outdated.

If you find that something is not working, please lend a helping hand and fix it in a PR.

More integrations and examples are always welcome.

"},{"location":"tutorials/integrations/slack/","title":"Use Iter8 to send a message to a Slack channel","text":"

Iter8 provides a slack task that sends a message to a Slack channel using a webhook.

"},{"location":"tutorials/integrations/slack/#example","title":"Example","text":"

In this example, you will run the Your first performance test but at the end of the performance test, Iter8 will send a message on Slack.

The message will simply contain a summary of the performance test in text form. However, you can easily construct a more sophisticated message by providing your own payload template.

This task could provide important updates on a performance test over Slack, for example a summary at the end of the test.

To summarize what will happen, you will create a new channel on Slack and configure a webhook, set up and run a performance test, and check if a message was sent to the channel.

The slack task requires the URL of the Slack webhook. To see a full list of the github task parameters, see here.

  1. Create a new channel in your Slack organization.
  2. Create a Slack app, enable incoming webhooks, and create a new incoming webhook. See here.
  3. Ensure that you have a Kubernetes cluster and the kubectl CLI. You can create a local Kubernetes cluster using tools like Kind or Minikube.

  4. Install the Iter8 controller

    Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

    Namespace-scopedCluster-scoped 
    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

    For additional install options, see Iter8 Installation.

  5. Deploy the sample HTTP service in the Kubernetes cluster. 

    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80\nkubectl expose deploy httpbin --port=80\n

  6. Launch the performance test with the slack task with the appropriate values. 
    helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http,slack}\" \\\n--set http.url=http://httpbin.default/get \\\n--set slack.url=<Slack webhook> \\\n--set slack.method=POST\n
  7. Verify that the message has been sent after the performance test has completed.
Some variations and extensions of the slack task

The default slack task payload sends a summary of the performance test.

However, you do not need to use the default payload. You can provide your own payload by overriding the default of the payloadTemplateURL.

For example, you can also use Slack's Block Kit to create more sophisticated messages. You can use markdown, create different sections, or add interactivity, such as buttons.

"},{"location":"tutorials/integrations/kserve/abn-grpc/","title":"A/B Testing a backend ML model","text":"

This tutorial describes how to do A/B testing as part of the release of a backend ML model hosted on KServe using the Iter8 SDK. In this tutorial, communication with the model is via gRPC calls.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s \"https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh\" | bash\n
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"tutorials/integrations/kserve/abn-grpc/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve/abn-grpc/#deploy-the-sample-application","title":"Deploy the sample application","text":"

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components:

"},{"location":"tutorials/integrations/kserve/abn-grpc/#frontend","title":"Frontend","text":"

The frontend component uses the Iter8 SDK method Lookup() before each call to the backend (ML model). The frontend uses the returned version number to route the request to the recommended version of backend.

Deploy the frontend:

kubectl create deployment frontend --image=iter8/abn-sample-kserve-grpc-frontend-go:0.17.3\nkubectl expose deployment frontend --name=frontend --port=8090\n
"},{"location":"tutorials/integrations/kserve/abn-grpc/#backend","title":"Backend","text":"

The backend application component is an ML model. Release it using the Iter8 release chart:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/version: backend\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  protocolVersion: v2\n  ports:\n  - containerPort: 9000\n    name: h2c\n    protocol: TCP\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\nEOF\n

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/backend-0 --timeout=600s\n
"},{"location":"tutorials/integrations/kserve/abn-grpc/#generate-load","title":"Generate load","text":"

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090\n

In another shell, run a script to generate load from multiple users: 

curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --\n

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: backend-0\n
"},{"location":"tutorials/integrations/kserve/abn-grpc/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the model can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/version: backend\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  protocolVersion: v2\n  ports:\n  - containerPort: 9000\n    name: h2c\n    protocol: TCP\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\nEOF\n
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

Until the candidate version is ready, calls to Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: backend-1\n
"},{"location":"tutorials/integrations/kserve/abn-grpc/#compare-versions-using-grafana","title":"Compare versions using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source default/backend with the following parameters:

  • URL: http://iter8.default:8080/abnDashboard
  • Query string: namespace=default&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

Once you identify a winner, it can be promoted, and the candidate version deleted.

"},{"location":"tutorials/integrations/kserve/abn-grpc/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/version: backend\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  protocolVersion: v2\n  ports:\n  - containerPort: 9000\n    name: h2c\n    protocol: TCP\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend-0 (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: backend-0\n
"},{"location":"tutorials/integrations/kserve/abn-grpc/#cleanup","title":"Cleanup","text":"

Delete the backend:

helm delete backend\n

Delete the frontend:

kubectl delete deploy/frontend svc/frontend\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
"},{"location":"tutorials/integrations/kserve/abn-http/","title":"A/B Testing a backend ML model","text":"

This tutorial describes how to do A/B testing as part of the release of a backend ML model hosted on KServe when using the Iter8 SDK. In this tutorial, communication from the frontend to the backend model is via HTTP calls.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s \"https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh\" | bash\n
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"tutorials/integrations/kserve/abn-http/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve/abn-http/#deploy-the-sample-application","title":"Deploy the sample application","text":"

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components:

"},{"location":"tutorials/integrations/kserve/abn-http/#frontend","title":"Frontend","text":"

The frontend component uses the Iter8 SDK method Lookup() before each call to the backend (ML model). The frontend uses the returned version number to route the request to the recommended version of backend.

Deploy the frontend:

kubectl create deployment frontend --image=iter8/abn-sample-kserve-http-frontend-go:0.17.3\nkubectl expose deployment frontend --name=frontend --port=8090\n
"},{"location":"tutorials/integrations/kserve/abn-http/#backend","title":"Backend","text":"

The backend application component is an ML model. Release it using the Iter8 release chart:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/version: backend\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\nEOF\n

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/backend-0 --timeout=600s\n
"},{"location":"tutorials/integrations/kserve/abn-http/#generate-load","title":"Generate load","text":"

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090\n

In another shell, run a script to generate load from multiple users: 

curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --\n

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: backend-0\n
"},{"location":"tutorials/integrations/kserve/abn-http/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the model can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/version: backend\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\nEOF\n
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

Until the candidate version is ready, calls to Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: backend-1\n
"},{"location":"tutorials/integrations/kserve/abn-http/#compare-versions-using-grafana","title":"Compare versions using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source default/backend with the following parameters:

  • URL: http://iter8.default:8080/abnDashboard
  • Query string: namespace=default&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

Once you identify a winner, it can be promoted, and the candidate version deleted.

"},{"location":"tutorials/integrations/kserve/abn-http/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/version: backend\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend-0 (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: backend-0\n
"},{"location":"tutorials/integrations/kserve/abn-http/#cleanup","title":"Cleanup","text":"

Delete the backend:

helm delete backend\n

Delete the frontend:

kubectl delete deploy/frontend svc/frontend\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
"},{"location":"tutorials/integrations/kserve/blue-green/","title":"Blue-green release of a KServe ML model","text":"

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe environment using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s \"https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh\" | bash\n
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
"},{"location":"tutorials/integrations/kserve/blue-green/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve/blue-green/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  strategy: blue-green\nEOF\n

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s\n
What happens?
  • Because environment is set to kserve, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a Service (of type ExternalName) named default/wisdom pointing at the KNative gateway, knative-local-gateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created. Finally, to support the blue-green release, a ConfigMap (wisdom-0-weight-config) is created to be used to manage the proportion of traffic sent to this version.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

"},{"location":"tutorials/integrations/kserve/blue-green/#verify-routing","title":"Verify routing","text":"

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml\n

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. Send requests: 

    curl -H 'Content-Type: application/json' \\\nhttp://wisdom.default -d @input.json -s -D - \\\n| grep -e HTTP -e app-version\n

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK\napp-version: wisdom-0\n
To send requests from outside the cluster

To configure the release for traffic from outside the cluster:

(a) In a separate terminal, port-forward the Istio ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80\n
(b) Download the sample input: 
curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/kserve-serving/input.json\n
(c) Send inference requests using the Host header: 
curl -H 'Content-Type: application/json' \\\n-H 'Host: wisdom.default' \\\nlocalhost:8080 -d @input.json -s -D - \\\n| grep -e '^HTTP' -e app-version\n

"},{"location":"tutorials/integrations/kserve/blue-green/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  strategy: blue-green\nEOF\n
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate model is ready, Iter8 will automatically reconfigure the routing so that inference requests are sent to both versions.

"},{"location":"tutorials/integrations/kserve/blue-green/#verify-routing_1","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Requests will be handled equally by both versions. Output will be something like:

HTTP/1.1 200 OK\napp-version: wisdom-0\n...\nHTTP/1.1 200 OK\napp-version: wisdom-1\n
"},{"location":"tutorials/integrations/kserve/blue-green/#modify-weights-optional","title":"Modify weights (optional)","text":"

To modify the request distribution between versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n    weight: 30\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n    weight: 70\n  strategy: blue-green\nEOF\n

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

"},{"location":"tutorials/integrations/kserve/blue-green/#verify-routing_2","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version; the remaining 30 percent by the primary version.

"},{"location":"tutorials/integrations/kserve/blue-green/#promote-candidate","title":"Promote candidate","text":"

The candidate model can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  strategy: blue-green\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/integrations/kserve/blue-green/#verify-routing_3","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK\napp-version: wisdom-0\n
"},{"location":"tutorials/integrations/kserve/blue-green/#cleanup","title":"Cleanup","text":"

Delete the models are their routing:

helm delete wisdom\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/integrations/kserve/canary/","title":"Canary release of a KServe ML model","text":"

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe environment using a canary release strategy. In a canary release, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s \"https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh\" | bash\n
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
"},{"location":"tutorials/integrations/kserve/canary/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve/canary/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  strategy: canary\nEOF\n

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s\n
What happens?
  • Because environment is set to kserve, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a Service (of type ExternalName) named default/wisdom pointing at the KNative gateway, knative-local-gateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

"},{"location":"tutorials/integrations/kserve/canary/#verify-routing","title":"Verify routing","text":"

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml\n

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. To send requests without the header traffic: 

    curl -H 'Content-Type: application/json' \\\nhttp://wisdom.default -d @input.json -s -D - \\\n| grep -e HTTP -e app-version\n

  4. Requests can also be sent with the header traffic: test. When a candidate is deployed, requests with this header will be routed to the candidate. When no candidate is deployed, all requests will be routed to the primary version. 

    curl -H 'Content-Type: application/json' \\\n-H 'traffic: test' \\\nhttp://wisdom.default -d @input.json -s -D - \\\n| grep -e HTTP -e app-version\n

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK\napp-version: wisdom-0\n
To send requests from outside the cluster

To configure the release for traffic from outside the cluster:

(a) In a separate terminal, port-forward the Istio ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80\n
(b) Download the sample input: 
curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/kserve-serving/input.json\n
(c) To send requests without the header traffic: 
curl -H 'Content-Type: application/json' \\\n-H 'Host: wisdom.default' \\\nlocalhost:8080 -d @input.json -s -D - \\\n| grep -e '^HTTP' -e app-version\n
(d) To send requests with the header traffic: test: 
curl -H 'Content-Type: application/json' \\\n-H 'Host: wisdom.default' \\\n-H 'traffic: test' \\\nlocalhost:8080 -d @input.json -s -D - \\\n| grep -e HTTP -e app-version\n

"},{"location":"tutorials/integrations/kserve/canary/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  strategy: canary\nEOF\n
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that inference requests with the header traffic set to test will be sent to the candidate model:

HTTP/1.1 200 OK\napp-version: wisdom-1\n

All other requests will be sent to the primary model (wisdom-0):

HTTP/1.1 200 OK\napp-version: wisdom-0\n
"},{"location":"tutorials/integrations/kserve/canary/#verify-routing_1","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate model (wisdom-1) while all others will be handled by the primary version.

"},{"location":"tutorials/integrations/kserve/canary/#promote-candidate","title":"Promote candidate","text":"

The candidate model can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n  modelFormat: sklearn\n  runtime: kserve-mlserver\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n  strategy: canary\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/integrations/kserve/canary/#verify-routing_2","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK\napp-version: wisdom-0\n
"},{"location":"tutorials/integrations/kserve/canary/#cleanup","title":"Cleanup","text":"

Delete the models and their routing:

helm delete wisdom\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/integrations/kserve/grpc/","title":"Load test a KServe model (via gRPC)","text":"

This tutorial shows how easy it is to run a load test for KServe when using gRPC to make requests. We use a sklearn model to demonstrate. The same approach works for any model type.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s \"https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh\" | bash\n
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"tutorials/integrations/kserve/grpc/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve/grpc/#deploy-an-inferenceservice","title":"Deploy an InferenceService","text":"

Create an InferenceService which exposes a gRPC port. The following serves the sklearn irisv2 model:

cat <<EOF | kubectl create -f -\napiVersion: \"serving.kserve.io/v1beta1\"\nkind: \"InferenceService\"\nmetadata:\n  name: \"sklearn-irisv2\"\nspec:\n  predictor:\n    model:\n      modelFormat:\n        name: sklearn\n      runtime: kserve-mlserver\n      protocolVersion: v2\n      storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\n      ports:\n      - containerPort: 9000\n        name: h2c\n        protocol: TCP\nEOF\n
"},{"location":"tutorials/integrations/kserve/grpc/#launch-performance-test","title":"Launch performance test","text":"
GRPC_HOST=$(kubectl get isvc sklearn-irisv2 -o jsonpath='{.status.components.predictor.address.url}' | sed 's#.*//##')\nGRPC_PORT=80\n
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 model-test iter8 \\\n--set \"tasks={ready,grpc}\" \\\n--set ready.isvc=sklearn-irisv2 \\\n--set ready.timeout=180s \\\n--set grpc.protoURL=https://raw.githubusercontent.com/kserve/kserve/master/docs/predict-api/v2/grpc_predict_v2.proto \\\n--set grpc.host=${GRPC_HOST}:${GRPC_PORT} \\\n--set grpc.call=inference.GRPCInferenceService.ModelInfer \\\n--set grpc.dataURL=https://gist.githubusercontent.com/kalantar/6e9eaa03cad8f4e86b20eeb712efef45/raw/56496ed5fa9078b8c9cdad590d275ab93beaaee4/sklearn-irisv2-input-grpc.json\n
About this performance test

This performance test consists of two tasks, namely, ready and grpc.

The ready task checks if the sklearn-irisv2 InferenceService exists and is Ready.

The grpc task sends call requests to the inference.GRPCInferenceService.ModelInfer method of the cluster-local gRPC service with host address ${GRPC_HOST}:${GRPC_PORT}, and collects Iter8's built-in gRPC load test metrics.

"},{"location":"tutorials/integrations/kserve/grpc/#view-results-using-grafana","title":"View results using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source model-test with the following parameters:

  • URL: http://iter8.default:8080/grpcDashboard
  • Query string: namespace=default&test=model-test

Create a new dashboard by import. Paste the contents of the grpc Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

"},{"location":"tutorials/integrations/kserve/grpc/#cleanup","title":"Cleanup","text":"
helm delete model-test\nkubectl delete inferenceservice sklearn-irisv2\n
"},{"location":"tutorials/integrations/kserve/grpc/#uninstall-the-iter8-controller","title":"Uninstall the Iter8 controller","text":"
helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
Some variations and extensions of this performance test
  1. The grpc task can be configured with load related parameters such as the number of requests, requests per second, or number of concurrent connections.
"},{"location":"tutorials/integrations/kserve/http/","title":"Load test a KServe model (via HTTP)","text":"

This tutorial shows how easy it is to run a load test for KServe when using HTTP to make requests. We use a sklearn model to demonstrate. The same approach works for any model type.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s \"https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh\" | bash\n
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"tutorials/integrations/kserve/http/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve/http/#deploy-an-inferenceservice","title":"Deploy an InferenceService","text":"

Create an InferenceService which exposes an HTTP port. The following serves the sklearn irisv2 model:

cat <<EOF | kubectl apply -f -\napiVersion: \"serving.kserve.io/v1beta1\"\nkind: \"InferenceService\"\nmetadata:\n  name: \"sklearn-irisv2\"\nspec:\n  predictor:\n    model:\n      modelFormat:\n        name: sklearn\n      runtime: kserve-mlserver\n      storageUri: \"gs://seldon-models/sklearn/mms/lr_model\"\nEOF\n
"},{"location":"tutorials/integrations/kserve/http/#launch-performance-test","title":"Launch performance test","text":"
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 model-test iter8 \\\n--set \"tasks={ready,http}\" \\\n--set ready.isvc=sklearn-irisv2 \\\n--set ready.timeout=180s \\\n--set http.url=http://sklearn-irisv2.default.svc.cluster.local/v2/models/sklearn-irisv2/infer \\\n--set http.payloadURL=https://gist.githubusercontent.com/kalantar/d2dd03e8ebff2c57c3cfa992b44a54ad/raw/97a0480d0dfb1deef56af73a0dd31c80dc9b71f4/sklearn-irisv2-input.json \\\n--set http.contentType=\"application/json\"\n
About this performance test

This performance test consists of two tasks, namely, ready and http.

The ready task checks if the sklearn-irisv2 InferenceService exists and is Ready.

The http task sends requests to the cluster-local HTTP service whose URL exposed by the InferenceService, http://sklearn-irisv2.default.svc.cluster.local/v2/models/sklearn-irisv2/infer, and collects Iter8's built-in HTTP load test metrics.

"},{"location":"tutorials/integrations/kserve/http/#view-results-using-grafana","title":"View results using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source model-test with the following parameters:

  • URL: http://iter8.default:8080/httpDashboard
  • Query string: namespace=default&test=model-test

Create a new dashboard by import. Paste the contents of the http Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

"},{"location":"tutorials/integrations/kserve/http/#cleanup","title":"Cleanup","text":"
helm delete model-test\nkubectl delete inferenceservice sklearn-irisv2\n
"},{"location":"tutorials/integrations/kserve/http/#uninstall-the-iter8-controller","title":"Uninstall the Iter8 controller","text":"
helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
Some variations and extensions of this performance test
  1. The http task can be configured with load related parameters such as the number of requests, queries per second, or number of parallel connections.
  2. The http task can be configured to send various types of content as payload.
"},{"location":"tutorials/integrations/kserve-mm/abn/","title":"A/B Testing a backend ML model","text":"

This tutorial describes how to do A/B testing as part of the release of a backend ML model hosted on KServe ModelMesh using the Iter8 SDK.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe ModelMesh Serving. For example, you can create a modelmesh-serving Quickstart environment. If using the Quickstart environment, your default namespace will be changed to modelmesh-serving. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"tutorials/integrations/kserve-mm/abn/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve-mm/abn/#deploy-the-sample-application","title":"Deploy the sample application","text":"

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components:

"},{"location":"tutorials/integrations/kserve-mm/abn/#frontend","title":"Frontend","text":"

The frontend component uses the Iter8 SDK method Lookup() before each call to the backend (ML model). The frontend uses the returned version number to route the request to the recommended version of backend.

kubectl create deployment frontend --image=iter8/abn-sample-mm-frontend-go:0.17.3\nkubectl expose deployment frontend --name=frontend --port=8090\n
"},{"location":"tutorials/integrations/kserve-mm/abn/#backend","title":"Backend","text":"

The backend application component is an ML model. Release it using the Iter8 release chart:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: backend\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib\nEOF\n

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/backend-0 --timeout=600s\n
"},{"location":"tutorials/integrations/kserve-mm/abn/#generate-load","title":"Generate load","text":"

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090\n

In another shell, run a script to generate load from multiple users: 

curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --\n

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: backend-0__isvc-3642375d03\n
"},{"location":"tutorials/integrations/kserve-mm/abn/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the model can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: backend\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib\nEOF\n
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

Until the candidate version is ready, calls to Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: backend-1__isvc-3642375d03\n
"},{"location":"tutorials/integrations/kserve-mm/abn/#compare-versions-using-grafana","title":"Compare versions using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source modelmesh-serving/backend with the following parameters:

  • URL: http://iter8.modelmesh-serving:8080/abnDashboard
  • Query string: namespace=modelmesh-serving&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

Once you identify a winner, it can be promoted, and the candidate version deleted.

"},{"location":"tutorials/integrations/kserve-mm/abn/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: backend\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend-0 (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: backend-0__isvc-3642375d03\n
"},{"location":"tutorials/integrations/kserve-mm/abn/#cleanup","title":"Cleanup","text":"

Delete the backend:

helm delete backend\n

Delete the frontend:

kubectl delete deploy/frontend svc/frontend\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
"},{"location":"tutorials/integrations/kserve-mm/blue-green/","title":"Blue-green release of a ML model","text":"

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe ModelMesh environment using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe ModelMesh Serving. For example, you can create a modelmesh-serving Quickstart environment. If using the Quickstart environment, your default namespace will be changed to modelmesh-serving. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y\n
"},{"location":"tutorials/integrations/kserve-mm/blue-green/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve-mm/blue-green/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib\n  strategy: blue-green\nEOF\n

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s\n
What happens?
  • Because environment is set to kserve-modelmesh-istio, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a ServiceEntry named default/wisdom is deployed. Further, an Iter8 routemap is created.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

"},{"location":"tutorials/integrations/kserve-mm/blue-green/#verify-routing","title":"Verify routing","text":"

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml\n

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. Send requests: 

    cat grpc_input.json \\\n| grpcurl -vv -plaintext -proto kserve.proto -d @ \\\n  -authority wisdom.modelmesh-serving \\\n  modelmesh-serving.modelmesh-serving:8033 \\\n  inference.GRPCInferenceService.ModelInfer \\\n| grep -e app-version\n

The output includes the version of the application that responded (in the app-version response header). In this example:

app-version: wisdom-0\n
"},{"location":"tutorials/integrations/kserve-mm/blue-green/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"s3://modelmesh-example-models/sklearn/mnist-svm.joblib\"\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"s3://modelmesh-example-models/sklearn/mnist-svm.joblib\"\n  strategy: blue-green\nEOF\n
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that requests are sent to both versions.

"},{"location":"tutorials/integrations/kserve-mm/blue-green/#verify-routing_1","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Requests will be handled equally by both versions. Output will be something like:

app-version: wisdom-0\n...\napp-version: wisdom-1\n
"},{"location":"tutorials/integrations/kserve-mm/blue-green/#modify-weights-optional","title":"Modify weights (optional)","text":"

To modify the request distribution between versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"s3://modelmesh-example-models/sklearn/mnist-svm.joblib\"\n    weight: 30\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"s3://modelmesh-example-models/sklearn/mnist-svm.joblib\"\n    weight: 70\n  strategy: blue-green\nEOF\n

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

"},{"location":"tutorials/integrations/kserve-mm/blue-green/#verify-routing_2","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version (wisdom-1); the remaining 30 percent by the primary version (wisdom-0).

"},{"location":"tutorials/integrations/kserve-mm/blue-green/#promote-candidate","title":"Promote candidate","text":"
cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib\n  strategy: blue-green\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/integrations/kserve-mm/blue-green/#verify-routing_3","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

app-version: wisdom-0\n
"},{"location":"tutorials/integrations/kserve-mm/blue-green/#cleanup","title":"Cleanup","text":"

Delete the models are their routing:

helm delete wisdom\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/integrations/kserve-mm/canary/","title":"Canary release of a ML model","text":"

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe ModelMesh environment using a canary release strategy. In a canary release, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe ModelMesh Serving. For example, you can create a modelmesh-serving Quickstart environment. If using the Quickstart environment, your default namespace will be changed to modelmesh-serving. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y\n
"},{"location":"tutorials/integrations/kserve-mm/canary/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kserve-mm/canary/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib\n  strategy: canary\nEOF\n

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s\n
What happens?
  • Because environment is set to kserve-modelmesh-istio, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a ServiceEntry named default/wisdom is deployed. Further, an Iter8 routemap is created.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

"},{"location":"tutorials/integrations/kserve-mm/canary/#verify-routing","title":"Verify routing","text":"

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml\n

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. To send requests without the header traffic: 

    cat grpc_input.json \\\n| grpcurl -vv -plaintext -proto kserve.proto -d @ \\\n  -authority wisdom.modelmesh-serving \\\n  modelmesh-serving.modelmesh-serving:8033 \\\n  inference.GRPCInferenceService.ModelInfer \\\n| grep -e app-version\n

  4. Requests can also be sent with the header traffic: test. When a candidate is deployed, requests with this header will be routed to the candidate. When no candidate is deployed, all requests will be routed to the primary version. 
    cat grpc_input.json \\\n| grpcurl -vv -plaintext -proto kserve.proto -d @ \\\n  -H 'traffic: test' \\\n  -authority wisdom.modelmesh-serving \\\n  modelmesh-serving.modelmesh-serving:8033 \\\n  inference.GRPCInferenceService.ModelInfer \\\n| grep -e app-version\n

The output includes the version of the application that responded (in the app-version response header). In this example:

app-version: wisdom-0\n
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the Istio ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80\n
(b) Download the proto file and sample input: 
curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/kserve.proto\ncurl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/grpc_input.json\n
(c) To send requests without the header traffic: 
cat grpc_input.json | \\\ngrpcurl -vv -plaintext -proto kserve.proto -d @ \\\n-authority wisdom.modelmesh-serving \\\nlocalhost:8080 inference.GRPCInferenceService.ModelInfer \\\n| grep -e app-version\n
(d) To send requests with the header traffic: test: 
cat grpc_input.json | \\\ngrpcurl -vv -plaintext -proto kserve.proto -d @ \\\n-H 'traffic: test' \\\n-authority wisdom.modelmesh-serving \\\nlocalhost:8080 inference.GRPCInferenceService.ModelInfer \\\n| grep -e app-version\n

"},{"location":"tutorials/integrations/kserve-mm/canary/#deploy-candidate","title":"Deploy candidate","text":"

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    storageUri: \"s3://modelmesh-example-models/sklearn/mnist-svm.joblib\"\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"s3://modelmesh-example-models/sklearn/mnist-svm.joblib\"\n  strategy: canary\nEOF\n
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that inference requests with the header traffic set to test will be sent to the candidate model. All other requests will be sent to the primary model.

"},{"location":"tutorials/integrations/kserve-mm/canary/#verify-routing_1","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate model (wisdom-1):

app-version: wisdom-1\n

All others will be handled by the primary version (wisdom-0):

app-version: wisdom-0\n
"},{"location":"tutorials/integrations/kserve-mm/canary/#promote-candidate","title":"Promote candidate","text":"

Redefine the primary to use the candidate model remove the candidate:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: kserve-modelmesh-istio\napplication: \n  metadata:\n    labels:\n      app.kubernetes.io/name: wisdom\n    annotations:\n      serving.kserve.io/secretKey: localMinIO\n  modelFormat: sklearn\n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    storageUri: \"s3://modelmesh-example-models/sklearn/mnist-svm.joblib\"\n  strategy: canary\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/integrations/kserve-mm/canary/#verify-routing_2","title":"Verify Routing","text":"

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

app-version: wisdom-0\n
"},{"location":"tutorials/integrations/kserve-mm/canary/#cleanup","title":"Cleanup","text":"

Delete the models and their routing:

helm delete wisdom\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/integrations/kserve-mm/performance/","title":"Performance testing a ML model","text":"

You can load test an ML model deployed to KServe ModelMesh similar to performance testing other gRPC methods. See the Single gRPC method tutorial or the Load test a KServe model (via gRPC) tutorial for examples.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/","title":"Blue-green release","text":"

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

This tutorial uses the Kubernetes Gateway API to allow the use any service mesh that supports this API. In this case, we use demonstrate with Linkerd.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Linkerd.
"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the application (httpbin) using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used. Note that we deploy the application to the namespace test.

About creating a namespace for Linkerd deployments

When creating a namespace, it should be annotated so that all created pods are injected with the Linkerd proxy. This can be done, for example, by using the Linkerd CLI: 

kubectl create ns test --dry-run=client -o yaml | linkerd inject - | kubectl apply -f -\n

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-gtw\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  strategy: blue-green\nEOF\n
What happens?

The environment defines what kind of application is deployed. In this case (deployment-gtw), a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (httpbin) is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created. Finally, to support the blue-green release, a ConfigMap (httpbin-0-weight-config) is created to be used to manage the proportion of traffic sent to this version.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an HTTPRoute. It is configured to route all traffic to the only deployed version, httpbin-0.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#verify-routing","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the HTTPRoute:

kubectl -n test get httproute.gateway.networking.k8s.io httpbin -o yaml\n

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    export SERVICE_MESH=linkerd\ncurl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. Send requests: 

    curl httpbin.test -s -D - | grep -e '^HTTP' -e app-version\n

The output includes the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#deploy-candidate","title":"Deploy candidate","text":"

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-gtw\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: blue-green\nEOF\n
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing; the HTTPRoute is updated to distribute traffic between versions based on the weights.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#verify-routing_1","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. Requests will now be handled equally by both versions. Output will be something like:

HTTP/1.1 200 OK\napp-version: httpbin-0\n...\nHTTP/1.1 200 OK\napp-version: httpbin-1\n
"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#modify-weights-optional","title":"Modify weights (optional)","text":"

To modify the request distribution between the versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-gtw\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n    weight: 30\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n    weight: 70\n  strategy: blue-green\nEOF\n

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#verify-routing_2","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version; the remaining 30 percent by the primary version.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-gtw\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: blue-green\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#verify-routing_3","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/integrations/kubernetes-gateway-api/blue-green/#cleanup","title":"Cleanup","text":"

Delete the application and its routing configuration:

helm -n test delete httpbin\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/","title":"Canary release","text":"

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a canary release strategy. In a canary release, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

This tutorial uses the Kubernetes Gateway API to allow the use any service mesh that supports this API. In this case, we use demonstrate with Linkerd.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Linkerd.
"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#deploy-initial-version","title":"Deploy initial version","text":"

Deploy the initial version of the application (httpbin) using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used. Note that we deploy the application to the namespace test.

About creating a namespace for Linkerd deployments

When creating a namespace, it should be annotated so that all created pods are injected with the Linkerd proxy. This can be done, for example, by using the Linkerd CLI: 

kubectl create ns test --dry-run=client -o yaml | linkerd inject - | kubectl apply -f -\n

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-gtw\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  strategy: canary\nEOF\n
What happens?

The environment defines what kind of application is deployed. In this case (deployment-gtw), a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (httpbin) is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an HTTPRoute. It is configured to route all traffic to the only deployed version, httpbin-0.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#verify-routing","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the HTTPRoute:

kubectl -n test get httproute.gateway.networking.k8s.io httpbin -o yaml\n

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    export SERVICE_MESH=linkerd\ncurl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -\n

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty \"$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)\" -c sleep -- /bin/sh\n

  3. To send requests without the header traffic: 

    curl httpbin.test -s -D - | grep -e '^HTTP' -e app-version\n

  4. Requests can also be sent with the header traffic: test. When a candidate is deployed, requests with this header will be routed to the candidate. When no candidate is deployed, all requests will be routed to the primary version. 

    curl httpbin.test -H 'traffic: test' -s -D - | grep -e '^HTTP' -e app-version\n

The output includes the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#deploy-candidate","title":"Deploy candidate","text":"

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-gtw\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v0\n    image: kennethreitz/httpbin\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n    matches:\n    - headers:\n      - name: traffic\n        value: test\n  strategy: canary\nEOF\n
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing so that requests with the header traffic set to test will be sent to the candidate. All other requests will be sent to the primary version.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#verify-routing_1","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate version:

HTTP/1.1 200 OK\napp-version: httpbin-1\n

All others will be handled by the primary version:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#promote-candidate","title":"Promote candidate","text":"

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -\nenvironment: deployment-gtw\napplication: \n  versions:\n  - metadata:\n      labels:\n        app.kubernetes.io/version: v1\n    image: kennethreitz/httpbin\n  strategy: canary\nEOF\n
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#verify-routing_2","title":"Verify routing","text":"

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK\napp-version: httpbin-0\n
"},{"location":"tutorials/integrations/kubernetes-gateway-api/canary/#cleanup","title":"Cleanup","text":"

Delete the application and its routing configuration:

helm -n test delete httpbin\n

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep\n

Uninstall Iter8 controller:

helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

"},{"location":"tutorials/performance/load-test-grpc-multiple/","title":"Load test multiple gRPC methods","text":"

This tutorial expands on the tutorial Single gRPC method by describing how to load test multiple gRPC methods.

Before you begin
  1. Try Your first performance test. Understand the main concepts behind Iter8.
  2. Deploy the sample gRPC service in the Kubernetes cluster. 
    kubectl create deployment routeguide --image=golang --port=50051 \\\n-- bash -c \"git clone -b v1.52.0 --depth 1 https://github.com/grpc/grpc-go; cd grpc-go/examples/route_guide; sed -i \"''\" \"'\"s/localhost//\"'\" server/server.go; go run server/server.go\"\nkubectl expose deployment routeguide --port=50051\n
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"tutorials/performance/load-test-grpc-multiple/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/performance/load-test-grpc-multiple/#launch-performance-test","title":"Launch performance test","text":"
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={ready,grpc}\" \\\n--set ready.deploy=routeguide \\\n--set ready.service=routeguide \\\n--set ready.timeout=60s \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \\\n--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json\n
About this performance test

This performance test consists of two tasks, namely, ready, and grpc.

The ready task checks if the routeguide deployment exists and is available, and the routeguide service exists.

The grpc task sends call requests to two methods of the cluster-local gRPC service, and collects Iter8's built-in gRPC load test metrics. The two methods are routeguide.RouteGuide.GetFeature and routeguide.RouteGuide.ListFeatures. Note that each method also has its own dataURL for the request payload.

"},{"location":"tutorials/performance/load-test-grpc-multiple/#view-results-using-grafana","title":"View results using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source routeguide-test with the following parameters:

  • URL: http://iter8.default:8080/grpcDashboard
  • Query string: namespace=default&test=routeguide-test

Create a new dashboard by import. Paste the contents of the grpc Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

"},{"location":"tutorials/performance/load-test-grpc-multiple/#view-logs","title":"View logs","text":"

Logs are useful for debugging.

kubectl logs -l iter8.tools/test=routeguide-test\n
"},{"location":"tutorials/performance/load-test-grpc-multiple/#cleanup","title":"Cleanup","text":"

Remove the performance test and the sample app from the Kubernetes cluster.

helm delete routeguide-test\nkubectl delete svc/routeguide\nkubectl delete deploy/routeguide\n
"},{"location":"tutorials/performance/load-test-grpc-multiple/#uninstall-the-iter8-controller","title":"Uninstall the Iter8 controller","text":"
helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
Some variations and extensions of this performance test
  1. The grpc task can be configured with load related parameters such as the total number of requests, requests per second, or number of concurrent connections.
"},{"location":"tutorials/performance/load-test-grpc/","title":"Load test gRPC method","text":"

Load test a Kubernetes gRPC service and visualize the performance metrics with an Iter8 Grafana dashboard.

Before you begin
  1. Try Your first performance test. Understand the main concepts behind.

  2. Deploy the sample gRPC service in the Kubernetes cluster. 

    kubectl create deployment routeguide --image=golang --port=50051 \\\n-- bash -c \"git clone -b v1.52.0 --depth 1 https://github.com/grpc/grpc-go; cd grpc-go/examples/route_guide; sed -i \"''\" \"'\"s/localhost//\"'\" server/server.go; go run server/server.go\"\nkubectl expose deployment routeguide --port=50051\n

  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 

    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n

"},{"location":"tutorials/performance/load-test-grpc/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/performance/load-test-grpc/#launch-performance-test","title":"Launch performance test","text":"Unary exampleServer streaming exampleClient streaming exampleBidirectional example 
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={ready,grpc}\" \\\n--set ready.deploy=routeguide \\\n--set ready.service=routeguide \\\n--set ready.timeout=60s \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json\n
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={ready,grpc}\" \\\n--set ready.deploy=routeguide \\\n--set ready.service=routeguide \\\n--set ready.timeout=60s \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json\n
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={ready,grpc}\" \\\n--set ready.deploy=routeguide \\\n--set ready.service=routeguide \\\n--set ready.timeout=60s \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.call=routeguide.RouteGuide.RecordRoute \\\n--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json\n
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={ready,grpc}\" \\\n--set ready.deploy=routeguide \\\n--set ready.service=routeguide \\\n--set ready.timeout=60s \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.call=routeguide.RouteGuide.RouteChat \\\n--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/bidirectional.json\n
About this performance test

This performance test consists of two tasks, namely, ready, and grpc.

The ready task checks if the routeguide deployment exists and is available, and the routeguide service exists.

The grpc task sends call requests to the specified method of the cluster-local gRPC service with host address routeguide.default:50051 and collects Iter8's built-in gRPC load test metrics. This task supports all four gRPC service methods: unary, server streaming, client streaming, and bidirectional streaming, and will provide payload in the appropriate manner using dataURL.

"},{"location":"tutorials/performance/load-test-grpc/#view-results-using-grafana","title":"View results using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source routeguide-test with the following parameters:

  • URL: http://iter8.default:8080/grpcDashboard
  • Query string: namespace=default&test=routeguide-test

Create a new dashboard by import. Paste the contents of the grpc Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

"},{"location":"tutorials/performance/load-test-grpc/#view-logs","title":"View logs","text":"

Logs are useful for debugging.

kubectl logs -l iter8.tools/test=routeguide-test\n
"},{"location":"tutorials/performance/load-test-grpc/#cleanup","title":"Cleanup","text":"

Remove the performance test and the sample app from the Kubernetes cluster.

helm delete routeguide-test\nkubectl delete svc/routeguide\nkubectl delete deploy/routeguide\n
"},{"location":"tutorials/performance/load-test-grpc/#uninstall-the-iter8-controller","title":"Uninstall the Iter8 controller","text":"
helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
Some variations and extensions of this performance test
  1. The grpc task can be configured with load related parameters such as the total number of requests, requests per second, or number of concurrent connections.
"},{"location":"tutorials/performance/load-test-http-multiple/","title":"Load test multiple HTTP endpoints","text":"

Your first performance test describes how to load test a single HTTP service. This tutorial expands on the previous tutorial and describes how to load test multiple HTTP endpoints.

Before you begin
  1. Try Your first performance test. Understand the main concepts behind Iter8.
  2. Ensure that you have a Kubernetes cluster and the kubectl CLI. You can create a local Kubernetes cluster using tools like Kind or Minikube.
  3. Deploy the sample HTTP service in the Kubernetes cluster. 
    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80\nkubectl expose deploy httpbin --port=80\n
  4. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana\nkubectl expose deploy grafana --port=3000\n
"},{"location":"tutorials/performance/load-test-http-multiple/#install-the-iter8-controller","title":"Install the Iter8 controller","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

For additional install options, see Iter8 Installation.

"},{"location":"tutorials/performance/load-test-http-multiple/#launch-performance-test","title":"Launch performance test","text":"
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={ready,http}\" \\\n--set ready.deploy=httpbin \\\n--set ready.service=httpbin \\\n--set ready.timeout=60s \\\n--set http.endpoints.get.url=http://httpbin.default/get \\\n--set http.endpoints.getAnything.url=http://httpbin.default/anything \\\n--set http.endpoints.post.url=http://httpbin.default/post \\\n--set http.endpoints.post.payloadStr=hello\n
About this performance test

This performance test consists of two tasks, namely, ready and http.

The ready task checks if the httpbin deployment exists and is available, and the httpbin service exists.

The http task sends requests to three endpoints from the cluster-local HTTP service, and collects Iter8's built-in HTTP load test metrics. The three endpoints are http://httpbin.default/get, http://httpbin.default/anything, and http://httpbin.default/post. The last endpoint also has a payload string hello.

"},{"location":"tutorials/performance/load-test-http-multiple/#view-results-using-grafana","title":"View results using Grafana","text":"

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000\n

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source httpbin-test with the following parameters:

  • URL: http://iter8.default:8080/httpDashboard
  • Query string: namespace=default&test=httpbin-test

Create a new dashboard by import. Paste the contents of the http Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

"},{"location":"tutorials/performance/load-test-http-multiple/#view-logs","title":"View logs","text":"

Logs are useful for debugging.

kubectl logs -l iter8.tools/test=httpbin-test\n
"},{"location":"tutorials/performance/load-test-http-multiple/#cleanup","title":"Cleanup","text":"

Remove the performance test and the sample app from the Kubernetes cluster.

helm delete httpbin-test\nkubectl delete svc/httpbin\nkubectl delete deploy/httpbin\n
"},{"location":"tutorials/performance/load-test-http-multiple/#uninstall-the-iter8-controller","title":"Uninstall the Iter8 controller","text":"
helm delete iter8\n

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana\n
Some variations and extensions of this performance test
  1. The http task can be configured with load related parameters such as the number of requests, queries per second, or number of parallel connections.
  2. The http task can be configured to send various types of content as payload.
"},{"location":"tutorials/performance/load-test-http/","title":"Load test HTTP endpoint","text":"

Your first performance test describes how to load test a HTTP service.

"},{"location":"user-guide/install/","title":"Install options","text":"

By default, Iter8 uses BadgerDB to store metrics from A/B/n and performance tests. BadgerDB is not suitable for production use. To install for production, use Redis.

"},{"location":"user-guide/install/#install-with-helm","title":"Install with helm","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller\n
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \\\n--set clusterScoped=true\n

To install Iter8 in a non-default namespace, use the -n option.

"},{"location":"user-guide/install/#install-with-kustomize","title":"Install with kustomize","text":"

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped).

Namespace-scopedCluster-scoped 
kubectl apply -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/namespaceScoped?ref=v1.1.1'\n
kubectl apply -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/clusterScoped?ref=v1.1.1'\n

To install Iter8 in a non-default namespace, download the kustomize folder and modify the namespace field in the kustomization.yaml file.

"},{"location":"user-guide/install/#install-using-rancher-desktop","title":"Install using Rancher Desktop","text":"

Rancher does not support a standard storage class by default. Install the local path provisioner:

kubectl apply -f https://raw.githubusercontent.com/rancher/local-path-provisioner/v0.0.26/deploy/local-path-storage.yaml\n

And set metrics.badgerdb.storageClassName when starting the Iter8 controller:

--set metrics.badgerdb.storageClassName=local-path\n
"},{"location":"user-guide/install/#install-on-opendatahub","title":"Install on OpenDataHub","text":"

See https://github.com/opendatahub-io-contrib/odh-contrib-manifests/tree/main/iter8

"},{"location":"user-guide/metrics_store/","title":"Metrics store","text":"

Iter8 provides a metrics store for A/B/n and performance metrics. This simplifies the set up and execution of test scenarios. Iter8 currently supports the following databases:

  • BadgerDB
  • Redis

By default, Iter8 uses BadgerDB. Note, however, that BadgerDB is not suitable for production use and is only suitable for a single instance of Iter8. Support for other databases is in the works. See below for details on how you can contribute additional implementations.

"},{"location":"user-guide/metrics_store/#badgerdb-configuration-options","title":"BadgerDB configuration options","text":"

BadgerDB is a file system-based database. When installing Iter8, the following options can be configured:

  • bagderdb.storage - The amount of space allocated to BadgerDB (default 50Mi).
  • badgerdb.storageClassName - The Kubernetes storage class that should be used. The default (standard) may not work for all clusters.
"},{"location":"user-guide/metrics_store/#redis-configuration-options","title":"Redis configuration options","text":"

We assume that Redis is deployed. For example, for a basic deployment:

kubectl create deploy redis --image=redis/redis-stack:latest --port=6379\nkubectl expose deploy redis --port=6379\n

Run Iter8 with the metrics store implementation set to redis and specify its endpoint:

helm upgrade --install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller  \\\n--set clusterScoped=true \\\n--set metrics.implementation=redis \\\n--set metrics.redis.addresss=redis:6379\n
"},{"location":"user-guide/metrics_store/#contribute-a-new-metrics-store-implementation","title":"Contribute a new metrics store implementation","text":"

To contribute a new metrics store implementation:

  1. Create an issue for discussion.

  2. Submit a pull request on the Iter8 project with the following updates:

    • Create sub-folder in storage and provide an implementation of this interface including test cases.

    • Add a new case to metrics.GetClient()

    • Update go.mod and go.sum if needed.

    • Update the default Helm chart configuration values.yaml and bump the chart version in Chart.yaml.

    • Please also consider including your information in our list of adopters.

  3. Submit a second pull request on the Iter8 docs project updating the list of available implementations.

  4. Alert the project reviewers on Slack #development channel.

"},{"location":"user-guide/routemap/","title":"Routemaps","text":"

A routemap contains a description of each version of an application and may contain one or more routing templates. The description of versions is used by Iter8 to identify which versions of the application are available at any moment. Whenever versions become available or disappear, any defined routing templates will be applied. This results in the automatic reconfiguration of the routing.

"},{"location":"user-guide/routemap/#version-list","title":"Version list","text":"

A version is a list of resources that, when available and ready, indicate that the version is available. For example, the following describes a application with two versions. In this case, each version has a Service and a Deployment. In one version, they are named httpbin-0, and in the other, httpbin-1:

versions:\n- resources:\n  - gvrShort: svc\n    name: httpbin-0\n    namespace: default\n  - gvrShort: deploy\n    name: httpbin-0\n    namespace: default\n- resources:\n  - gvrShort: svc\n    name: httpbin-1\n    namespace: default\n  - gvrShort: deploy\n    name: httpbin-1\n    namespace: default\n

Note that the resources types are specified using a short name. In this example, svc and deploy. A short name is used to simplify the specification of the resources in a version. A mapping of short name to Kubernetes group, version, resource is captured in the configuration of the Iter8 controller. This set can be extended to include any types including custom resources; that is, those defined by a CRD.

A version may optionally specify an integer weight indicating the proportion of traffic that should be sent to it relative to other versions.

"},{"location":"user-guide/routemap/#routing-templates","title":"Routing templates","text":"

A routing template is a Go template that is applied each time a new version becomes available or an old one goes away. Multiple templates can be defined/applied.

THe application of the templates allows Iter8 to automatically reconfigure the routing when versions come and go. For example, the template created in the blue-green release tutorial contains an Istio VirtualService. Applying the template to the available versions yields the necessary VirtualSerivce definition. In this tutorial, the template definition is:

blue-green:\n  gvrShort: vs\n  template: |\n    apiVersion: networking.istio.io/v1beta1\n    kind: VirtualService\n    metadata:\n      name: httpbin\n    spec:\n      gateways:\n      - mm-gateway\n      - mesh\n      hosts:\n      - httpbin.default\n      - httpbin.default.svc\n      - httpbin.default.svc.cluster.local\n      http:\n      - route:\n        # primary version\n        - destination:\n            host: httpbin-0.default.svc.cluster.local\n          {{- if gt (index .Weights 1) 0 }}\n          weight: {{ index .Weights 0 }}\n          {{- end }}\n          headers:\n            response:\n              add:\n                app-version: httpbin-0\n        # other versions\n        {{- if gt (index .Weights 1) 0 }}\n        - destination:\n            host: httpbin-1.default.svc.cluster.local\n          weight: {{ index .Weights 1 }}\n          headers:\n            response:\n              add:\n                app-version: httpbin-1\n        {{- end }}\n
"},{"location":"user-guide/routemap/#implementation","title":"Implementation","text":"

A routemap is implemented as a ConfigMap with the following labels: - iter8.tools/kind with value routemap - iter8.tools/version with value corresponding the version of the controller being used

The list of versions and routing templates are stored as stringified YAML.

"},{"location":"user-guide/uninstall/","title":"Uninstall","text":""},{"location":"user-guide/uninstall/#uninstall-with-helm","title":"Uninstall with helm","text":"

If installed with helm, uninstall with:

helm delete iter8\n
"},{"location":"user-guide/uninstall/#uninstall-with-kustomize","title":"Uninstall with kustomize","text":"

If installed with kustomize, uninstall with one of the following:

Namespace-scopedCluster-scoped 
kubectl delete -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/namespaceScoped?ref=v1.1.1'\n
kubectl delete -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/clusterScoped?ref=v1.1.1'\n
"},{"location":"user-guide/abn/about/","title":"A/B/n Testing","text":"

A/B/n testing relies on business metrics typically computed by a frontend, user-facing, application component.

Metric values often depend on one or more interactions with backend (not user-facing) application components. To run an A/B/n test on a backend component, it is necessary to be able to associate a metric value (computed by the frontend component) to the version of the backend component that contributed to its computation. The challenge is that the frontend component often does not know which version of the backend component processed a given request. To address this challenge, Iter8 introduces an A/B/n SDK.

The Iter8 SDK uses a fixed set of versions numbers (0, 1, etc.) as a way to refer to the current set of versions of a Kubernetes application or ML model. The version of the application associated with a given version number changes over time as new versions are developed, deployed for testing, and either promoted or deleted. Since the set of version numbers is fixed, they can be used to configure routing to the application.

The Iter8 SDK provides two APIs to frontend application components:

a. Lookup() - Given an application and user session, returns a version number to be used as an index to a table of routes. So long as there are no changes in configuration, the version number (and hence the route) will be same for the same user session, guaranteeing session stickiness.

b. WriteMetric() - Given an application, a user session, a metric name its value, WriteMetric() associates the metric value with the appropriate version of the application.

"},{"location":"user-guide/abn/about/#configuring-the-iter8-controller","title":"Configuring the Iter8 controller","text":"

The Iter8 controller is implemented using gRPC. The service is configured to watch a given set of Kubernetes resource types. The default set of types Iter8 can watch are identified in the default values.yaml file. Other configuration options are described in the same file.

To configure the specific resources to watch for a given application, a Kubernetes ConfigMap is created. It identifies the specific resources that comprise each version. For example, consider the ConfigMap:

apiVersion: v1\nkind: ConfigMap\nmetadata:\n  name: backend\n  labels:\n    app.kubernetes.io/managed-by: iter8\n    iter8.tools/kind: routemap\n    iter8.tools/version: \"v1.1\"\nimmutable: true\ndata:\n  strSpec: |\n    versions:\n    - resources:\n      - gvrShort: svc\n        name: backend\n        namespace: default\n      - gvrShort: deploy\n        name: backend\n        namespace: default\n    - resources:\n      - gvrShort: svc\n        name: backend-candidate-1\n        namespace: default\n      - gvrShort: deploy\n        name: backend-candidate-1\n        namespace: default\n

This ConfigMap describes an application backend. It identifies two versions of the application. The first is comprised of a Kubernetes Deployment and a Service object both named backend in the default namespace. The second is comprised of the same resource types named backend-candidate-1 in the same namespace.

"},{"location":"user-guide/abn/about/#deployment-time-configuration-of-backend-components","title":"Deployment time configuration of backend components","text":"

As versions of a watched application are deployed or deleted, the Iter8 controller keeps track of which versions are available enabling it to respond appropriately to Lookup() and WriteMetric() requests.

"},{"location":"user-guide/abn/about/#developing-frontend-components-using-the-sdk","title":"Developing frontend components: Using the SDK","text":"

The basic steps to author a frontend application component using the Iter8 SDK are outlined below for Node.js and Go. Similar steps would be required for any gRPC supported language.

"},{"location":"user-guide/abn/about/#useimport-language-specific-libraries","title":"Use/import language specific libraries","text":"

The gRPC protocol buffer definition is used to generate language specific implementation. These files can be used directly or packaged and imported as a library. As examples, the Node.js sample uses manually generated files directly. On the other hand, the Go sample imports the library provided by the core Iter8 service implementation. In addition to the API specific methods, some general gRPC libraries are required.

Node.jsGo

The manually generated node files abn_pd.js and abn_grpc_pb.js used in the sample application can be copied and used without modification.

var grpc = require('@grpc/grpc-js');\n\nvar messages = require('./abn_pb.js');\nvar services = require('./abn_grpc_pb.js');\n
import (\n    \"google.golang.org/grpc\"\n    \"google.golang.org/grpc/credentials/insecure\"\n\n    pb \"github.com/iter8-tools/iter8/abn/grpc\"\n)\n
"},{"location":"user-guide/abn/about/#instantiate-a-grpc-client","title":"Instantiate a gRPC client","text":"

Instantiate a client to the Iter8 controller:

Node.jsGo 
var client = new services.ABNClient(abnEndpoint, grpc.credentials.createInsecure());\n
opts := []grpc.DialOption{grpc.WithTransportCredentials(insecure.NewCredentials())}\nconn, err := grpc.Dial(fmt.Sprintf(\"%s:%s\", getAbnService(), getAbnServicePort()), opts...)\nif err != nil {\n    panic(\"Cannot establish connection with abn service\")\n}\nc := pb.NewABNClient(conn)\nclient = &c\n
"},{"location":"user-guide/abn/about/#define-routing","title":"Define routing","text":"

Track identifiers are mapped to a static set of endpoints. One approach is to maintain a map from track identifier to endpoint:

Node.jsGo 
const versionNumberToRoute = [\n    \"http://backend.default.svc.cluster.local:8091\",\n    \"http://backend-candidate-1.default.svc.cluster.local:8091\",\n]\n
versionNumberToRoute = []string{\n    \"http://backend.default.svc.cluster.local:8091\",\n    \"http://backend-candidate-1.default.svc.cluster.local:8091\",\n}\n
"},{"location":"user-guide/abn/about/#using-lookup","title":"Using Lookup()","text":"

Given a user session identifier, Lookup() returns a version number that can be used to route requests. In code sample below, the user session identifier is assumed to be passed in the X-User header of user requests. The version number is used as an index to the versionNumberToRoute map defined above. A default is used if the call to Lookup() fails for any reason.

Node.jsGo 
var application = new messages.Application();\napplication.setName('default/backend');\napplication.setUser(req.header('X-User'));\nclient.lookup(application, function(err, versionRecommendation) {\n    if (err) {\n        // use default route (see above)\n        console.warn(\"error calling Lookup()\")\n    } else {\n        // use route determined by recommended track\n        console.info('lookup suggested track %d', versionRecommendation.getVersionnumber())\n        versionNumber = versionRecommendation.getVersionnumber()\n        if (versionNumber != NaN && 0 <= versionNumber && versionNumber < versionNumberToRoute.length) {\n            route = versionNumberToRoute[versionNumber]\n        }\n    }\n\n    // call backend service using route\n    ...\n});\n
route := versionNumberToRoute[0]\nuser := req.Header[\"X-User\"][0]\ns, err := (*client).Lookup(\n    ctx,\n    &pb.Application{\n        Name: \"default/backend\",\n        User: user,\n    },\n)\nif err == nil && s != nil {\n    versionNumber := int(s.GetVersionNumber())\n    if err == nil && 0 <= versionNumber && versionNumber < len(versionNumberToRoute) {\n        route = versionNumberToRoute[versionNumber]\n    } // else use default value for route\n}\n\n// call backend service using route\n...\n
"},{"location":"user-guide/abn/about/#using-writemetric","title":"Using WriteMetric()","text":"

As an example, a single metric named sample_metric is assigned a random value between 0 and 100 and written.

Node.jsGo 
var mv = new messages.MetricValue();\nmv.setName('sample_metric');\nmv.setValue(random({min: 0, max: 100, integer: true}).toString());\nmv.setApplication('default/backend');\nmv.setUser(user);\nclient.writeMetric(mv, function(err, session) {});\n
_, _ = (*client).WriteMetric(\n    ctx,\n    &pb.MetricValue{\n        Name:        \"sample_metric\",\n        Value:       fmt.Sprintf(\"%f\", rand.Float64()*100.0),\n        Application: \"default/backend\",\n        User:        user,\n    },\n)\n
"},{"location":"user-guide/abn/extension/","title":"Using new resource types","text":"

Like progressive release, A/B/n tests use the Iter8 release chart. Extending this chart for a new resource type is similar to extending it for progressive release. The key difference is in the definition of the routemap. We briefly describe how to extend the chart for an Knative application.

"},{"location":"user-guide/abn/extension/#example-knative-service","title":"Example (Knative Service)","text":"

For example, to extend the chart to support A/B/n testing for Knative services, the following files could be added:

  • _knative-istio.tpl - wrapper for all objects that should be deployed
  • _knative-istio.version.ksvc.tpl - the Knative service object that should be deployed for a version
  • _knative-istio.none.tpl - wrapper for all objects that should be deployed to support the no automated traffic pattern
  • _knative-istio.none.routemap.tpl - the routemap definition
  • _knative.helpers.tpl - supporting functions

An implementation of these is here.

Note that many of these additions are the same as in the example for progressive release. Of the above files, only _knative-istio.tpl is different.

Finally, update release.yaml to include knative-istio as a valid option:

{{- else if eq \"knative-istio\" .Values.environment }}\n  {{- include \"env.knative-istio\" . }}\n
"},{"location":"user-guide/abn/extension/#extend-the-controller","title":"Extend the controller","text":"

The Iter8 controller will need to be restarted with permission to watch Knative service objects. Re-install the controller using the following additional options:

--set resourceTypes.ksvc.Group=serving.knative.dev \\\n--set resourceTypes.ksvc.Version=v1 \\\n--set resourceTypes.ksvc.Resource=services \\\n--set \"resourceTypes.ksvc.conditions[0]=Ready\"\n
"},{"location":"user-guide/abn/extension/#using-the-modified-chart","title":"Using the modified chart","text":"

To use the modified chart to run A/B tests for Knative services, see the example for progressive release. Unset the strategy and weight fields.

"},{"location":"user-guide/abn/using-sdk/","title":"Using the SDK","text":"

The basic steps to author a frontend application component using the Iter8 SDK are outlined below for Node.js and Go. Similar steps would be required for any gRPC-supported language.

"},{"location":"user-guide/abn/using-sdk/#useimport-language-specific-libraries","title":"Use/import language specific libraries","text":"

The gRPC protocol buffer definition is used to generate a language-specific implementation. These files can be used directly or packaged and imported as a library. As examples, the Node.js sample uses manually generated files directly. On the other hand, the Go sample imports the library provided by the core Iter8 service implementation. In addition to the API specific methods, some general gRPC libraries are required.

Node.jsGo

The manually generated node files abn_pd.js and abn_grpc_pb.js used in the sample application can be copied and used without modification.

var grpc = require('@grpc/grpc-js');\n\nvar messages = require('./abn_pb.js');\nvar services = require('./abn_grpc_pb.js');\n
import (\n    \"google.golang.org/grpc\"\n    \"google.golang.org/grpc/credentials/insecure\"\n\n    pb \"github.com/iter8-tools/iter8/abn/grpc\"\n)\n
"},{"location":"user-guide/abn/using-sdk/#instantiate-a-grpc-client","title":"Instantiate a gRPC client","text":"

Instantiate a client to the Iter8 controller:

Node.jsGo 
var client = new services.ABNClient(abnEndpoint, grpc.credentials.createInsecure());\n
opts := []grpc.DialOption{grpc.WithTransportCredentials(insecure.NewCredentials())}\nconn, err := grpc.Dial(fmt.Sprintf(\"%s:%s\", getAbnService(), getAbnServicePort()), opts...)\nif err != nil {\n    panic(\"Cannot establish connection with abn service\")\n}\nc := pb.NewABNClient(conn)\nclient = &c\n
"},{"location":"user-guide/abn/using-sdk/#define-routing","title":"Define routing","text":"

Track identifiers are mapped to a static set of endpoints. One approach is to maintain a map from track identifier to endpoint:

Node.jsGo 
const versionNumberToRoute = [\n    \"http://backend.default.svc.cluster.local:8091\",\n    \"http://backend-candidate-1.default.svc.cluster.local:8091\",\n]\n
versionNumberToRoute = []string{\n    \"http://backend.default.svc.cluster.local:8091\",\n    \"http://backend-candidate-1.default.svc.cluster.local:8091\",\n}\n
"},{"location":"user-guide/abn/using-sdk/#using-lookup","title":"Using Lookup()","text":"

Given a user session identifier, Lookup() returns a version number that can be used to route requests. In code sample below, the user session identifier is assumed to be passed in the X-User header of user requests. The version number is used as an index to the versionNumberToRoute map defined above. A default is used if the call to Lookup() fails for any reason.

Node.jsGo 
var application = new messages.Application();\napplication.setName('default/backend');\napplication.setUser(req.header('X-User'));\nclient.lookup(application, function(err, versionRecommendation) {\n    if (err) {\n        // use default route (see above)\n        console.warn(\"error calling Lookup()\")\n    } else {\n        // use route determined by recommended track\n        console.info('lookup suggested track %d', versionRecommendation.getVersionnumber())\n        versionNumber = versionRecommendation.getVersionnumber()\n        if (versionNumber != NaN && 0 <= versionNumber && versionNumber < versionNumberToRoute.length) {\n            route = versionNumberToRoute[versionNumber]\n        }\n    }\n\n    // call backend service using route\n    ...\n});\n
route := versionNumberToRoute[0]\nuser := req.Header[\"X-User\"][0]\ns, err := (*client).Lookup(\n    ctx,\n    &pb.Application{\n        Name: \"default/backend\",\n        User: user,\n    },\n)\nif err == nil && s != nil {\n    versionNumber := int(s.GetVersionNumber())\n    if err == nil && 0 <= versionNumber && versionNumber < len(versionNumberToRoute) {\n        route = versionNumberToRoute[versionNumber]\n    } // else use default value for route\n}\n\n// call backend service using route\n...\n
"},{"location":"user-guide/abn/using-sdk/#using-writemetric","title":"Using WriteMetric()","text":"

As an example, a single metric named sample_metric is assigned a random value between 0 and 100 and written.

Node.jsGo 
var mv = new messages.MetricValue();\nmv.setName('sample_metric');\nmv.setValue(random({min: 0, max: 100, integer: true}).toString());\nmv.setApplication('default/backend');\nmv.setUser(user);\nclient.writeMetric(mv, function(err, session) {});\n
_, _ = (*client).WriteMetric(\n    ctx,\n    &pb.MetricValue{\n        Name:        \"sample_metric\",\n        Value:       fmt.Sprintf(\"%f\", rand.Float64()*100.0),\n        Application: \"default/backend\",\n        User:        user,\n    },\n)\n
"},{"location":"user-guide/performance/extension/","title":"Using new resource types","text":"

The Iter8 performance test task ready ensures that an object exists and is ready. To use this task with new resource types, including CRDs, add the new resource type to the list of known types defined in the default values.yaml file for the chart. Alternatively, the new type can be specified at run time with the --set option.

"},{"location":"user-guide/performance/extension/#example","title":"Example","text":"

To include a Knative service as part of a version definition, add the following to the map of resourceTypes in the values.yaml file used to configure the controller. The addition identifies the Kubernetes group, version, and resource (GVR) and the status condition that should be checked for readiness.

ksvc:\n    Group: serving.knative.dev\n    Version: v1\n    Resource: services\n    conditions:\n    - Ready\n

Alternatively, to set the values at run time:

--set resourceTypes.ksvc.Group=serving.knative.dev \\\n--set resourceTypes.ksvc.Version=v1 \\\n--set resourceTypes.Resource=services \\\n--set \"resourceTypes.conditions[0]=Ready\"\n
"},{"location":"user-guide/performance/parameters/","title":"Performance test parameters","text":"

Iter8 performance tests are run using Helm. Tests can be configured with parameters in the same manner as any other Helm command -- using chart values and the --set option.

The tasks parameter is used to identify the sequence of tasks that should be executed. Each task has its own parameters. Iter8 uses the convention that the parameters of a task are nested under the name of that task. In the following example, the url parameter of the http task is nested under the http object.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.url=https://httpbin.org/get\n

All the parameters of the performance test (including of all included tasks) are optional unless otherwise documented. The task-specific parameters are documented in the task documentation.

Currently available tasks are:

  • http - generate synthetic load and capture performance metrics for HTTP endpoints
  • grpc - generate synthetic load and capture performance metrics for gRPC methods
  • ready - check readiness of an object
  • github - send a GitHub notification
  • slack - send a Slack notification
"},{"location":"user-guide/performance/parameters/#global-parameters","title":"Global parameters","text":"

In addition to task-specific parameters, the following global parameters are available:

Name Type Description serviceAccountName string Optional name of a service account to use. If specified, it is assumed the service account has the necessary permissions to run a performance test. If not specified, Iter8 will create a service account. logLevel string Log level. Must be one of trace, debug, info (default), warning, or error."},{"location":"user-guide/performance/tasks/github/","title":"github","text":"

Trigger GitHub workflows via a repository_dispatch.

A repository_dispatch will trigger workflows in the default branch of the GitHub repository. By default, an summary of the performance test will also be sent.

"},{"location":"user-guide/performance/tasks/github/#usage-example","title":"Usage Example","text":"
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http,github}\" \\\n--set http.url=http://httpbin.default/get \\\n--set github.owner=<GitHub owner> \\\n--set github.repo=<GitHub repository> \\\n--set github.token=<GitHub token>\n

See here for a more in-depth tutorial.

"},{"location":"user-guide/performance/tasks/github/#parameters","title":"Parameters","text":"Name Type Required Default value Description owner string Yes N/A Owner of the GitHub repository repo string Yes N/A GitHub repository token string Yes N/A Authorization token payloadTemplateURL string No https://raw.githubusercontent.com/iter8-tools/iter8/v1.1.1/templates/notify/_payload-github.tpl URL to a payload template softFailure bool No true Indicates the performance test should not fail if the task cannot successfully send the request"},{"location":"user-guide/performance/tasks/github/#default-payload","title":"Default payload","text":"

A repository_dispatch requires a payload that contains the type of the event.

The default payload template will set the event_type to iter8. In addition, it will also provide a performance test summary in the client_payload, which means that this data will be accessible in the GitHub workflow via ${{ toJson(github.event.client_payload) }}.

However, if you would like to use a different payload template, simply set a payloadTemplateURL and Iter8 will not use the default.

"},{"location":"user-guide/performance/tasks/grpc/","title":"grpc","text":"

Generate requests for a gRPC service and and collect latency and error-related metrics.

"},{"location":"user-guide/performance/tasks/grpc/#usage-example","title":"Usage example","text":"

In this performance test, the grpc task generates call requests for a gRPC service hosted at hello.default:50051, defined in the protobuf file located at grpc.protoURL, with a gRPC method named helloworld.Greeter.SayHello. Metrics collected by this task are viewable with a Grafana dashboard.

Single method: 

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={grpc}\" \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json\n

Multiple methods: 

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={grpc}\" \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \\\n--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \\\n--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \\\n--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json \\\n--set grpc.endpoints.routeChat.call=routeguide.RouteGuide.RouteChat \\\n--set grpc.endpoints.routeChat.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/bidirectional.json\n

"},{"location":"user-guide/performance/tasks/grpc/#parameters","title":"Parameters","text":"

Any field in the Config struct of the ghz runner package can be used as a parameter in this task. The JSON tags of the struct fields directly correspond to the names of the parameters of this task. In the usage example, the parameters host and call correspond to the Host and Call fields respectively in the Config struct.

In addition, the following fields are defined by this task.

Name Type Description protoURL string (URL) URL where the protobuf file that defines the gRPC service is located. dataURL string (URL) URL where JSON data to be used in call requests is located. binaryDataURL string (URL) URL where binary data to be used in call requests is located. metadataURL string (URL) URL where the JSON metadata data to be used in call requests is located. warmupNumRequests int Number of requests to be sent in a warmup task (results are ignored). warmupDuration string Duration of warmup task (results are ignored). Specified in the Go duration string format (example, 5s). If both warmupDuration and warmupNumRequests are specified, then warmupDuration is ignored. endpoints map[string]EndPoint Used to specify multiple endpoints and their configuration. The string is the name of the endpoint and the EndPoint struct includes all the parameters described above as well as those from the Config struct. Load testing and metric collection will be conducted separately for each endpoint."},{"location":"user-guide/performance/tasks/grpc/#precedence","title":"Precedence","text":"

Some parameters have a default value, which can be overwritten. In addition, with the endpoints parameter, you can test multiple endpoints and configure parameters for each of those endpoint. In these cases, the priority order is the default value, the value set at the base level, and the value set at the endpoint value.

In the following example, all three endpoints will use the default timeout of 20s (from Config struct).

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={grpc}\" \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \\\n--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \\\n--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \\\n--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json\n

In the following example, the getFeature and listFeatures endpoints will use the default timeout of 20s and the recordRoute endpoint will use a timeout of 30s.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={grpc}\" \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \\\n--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \\\n--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \\\n--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json\n

In the following example, all three endpoints will use a qps of 40s.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={grpc}\" \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.timeout=40s \\\n--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \\\n--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \\\n--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \\\n--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json\n

In the following example, the getFeature and listFeatures endpoints will use a timeout of 40s and the listFeatures endpoint will use a timeout of 30s.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={grpc}\" \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.timeout=40s \\\n--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \\\n--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \\\n--set grpc.endpoints.listFeatures.timeout=30s \\\n--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \\\n--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json \\\n--set grpc.endpoints.recordRoute.timeout=30s\n

Further more, set parameters will trickle down to the endpoints.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \\\n--set \"tasks={grpc}\" \\\n--set grpc.host=routeguide.default:50051 \\\n--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \\\n--set grpc.skipFirst=5 \\\n--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \\\n--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \\\n--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \\\n--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \\\n--set grpc.endpoints.listFeatures.timeout=30s \\\n--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \\\n--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json\n

In this example, all three endpoints will have a skipFirst of 5.

"},{"location":"user-guide/performance/tasks/grpc/#grafana-dashboard","title":"Grafana Dashboard","text":"

The results of the grpc task is visualized using the grpc Iter8 Grafana dashboard. The dashboard can be found here.

Assuming the URL to the Grafana service is $GRAFANA_URL, you can install the dashboard as follows:

  1. Open Grafana in a browser.
  2. Add a new data JSON API data source with the following parameters
    • URL: $GRAFANA_URL/grpcDashboard
    • Query string: namespace=<namespace>&test=<test name>
  3. Import the grpc Iter8 Grafana dashboard
    • Copy and paste the contents of this link into the text box

You will see a visualization of the performance test like the following:

For multiple endpoints, the visualization will look like the following:

"},{"location":"user-guide/performance/tasks/http/","title":"http","text":"

Generate requests for an HTTP service and and collect latency and error-related metrics.

"},{"location":"user-guide/performance/tasks/http/#usage-example","title":"Usage example","text":"

In this performance test, the http task generates requests for https://httpbin.org/get, and collects latency and error-related metrics. Metrics collected by this task are viewable with a Grafana dashboard.

Single endpoint: 

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.url=https://httpbin.org/get\n

Multiple endpoints: 

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.endpoints.get.url=http://httpbin.default/get \\\n--set http.endpoints.getAnything.url=http://httpbin.default/anything \\\n--set http.endpoints.post.url=http://httpbin.default/post \\\n--set http.endpoints.post.payloadStr=hello\n

"},{"location":"user-guide/performance/tasks/http/#parameters","title":"Parameters","text":"Name Type Description url string (URL) URL where requests are sent. headers map[string]string HTTP headers to use in the requests. numRequests int Number of requests to be sent to the app. Default value is 100. duration string Duration of this task. Specified in the Go duration string format (example, 5s). If both duration and numRequests are specified, then duration is ignored. qps float qps stands for queries-per-second. Number of requests per second sent to the app. Default value is 8.0. connections int Number of parallel connections used to send requests. Default value is 4. payloadURL string (URL) URL from which to download the content that will be used as the request payload. If this field is specified, Iter8 will send HTTP POST requests to the app using this content as the payload. payloadStr string String data to be used as the request payload. If this field is specified, Iter8 will send HTTP POST requests to the app using this string as the payload. contentType string Content type of the payload. This is intended to be used in conjunction with one of the payload* fields. If this field is specified, Iter8 will send HTTP POST requests to the app using this as the Content-Type header value. warmupNumRequests int Number of requests to be sent in a warmup task (results are ignored). warmupDuration string Duration of warmup task (results are ignored). Specified in the Go duration string format (example, 5s). If both warmupDuration and warmupNumRequests are specified, then warmupDuration is ignored. endpoints map[string]EndPoint Used to specify multiple endpoints and their configuration. The string is the name of the endpoint and the EndPoint struct includes all the parameters described above. Load testing and metric collection will be conducted separately for each endpoint."},{"location":"user-guide/performance/tasks/http/#precedence","title":"Precedence","text":"

Some parameters have a default value, which can be overwritten. In addition, with the endpoints parameter, you can test multiple endpoints and configure parameters for each of those endpoint. In these cases, the priority order is the default value, the value set at the base level, and the value set at the endpoint value.

In the following example, all three endpoints will use the default qps (queries-per-second) of 8.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.endpoints.get.url=http://httpbin.default/get \\\n--set http.endpoints.getAnything.url=http://httpbin.default/anything \\\n--set http.endpoints.post.url=http://httpbin.default/post \\\n--set http.endpoints.post.payloadStr=hello\n

In the following example, the get and getAnything endpoints will use the default qps of 8 and the post endpoint will use a qps of 15.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.endpoints.get.url=http://httpbin.default/get \\\n--set http.endpoints.getAnything.url=http://httpbin.default/anything \\\n--set http.endpoints.post.url=http://httpbin.default/post \\\n--set http.endpoints.post.payloadStr=hello \\\n--set http.endpoints.post.qps=15\n

In the following example, all three endpoints will use a qps (queries-per-second) of 10.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.qps=10 \\\n--set http.endpoints.get.url=http://httpbin.default/get \\\n--set http.endpoints.getAnything.url=http://httpbin.default/anything \\\n--set http.endpoints.post.url=http://httpbin.default/post \\\n--set http.endpoints.post.payloadStr=hello\n

In the following example, the get and getAnything endpoints will use a qps of 10 and the post endpoint will use a qps of 15.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.qps=10 \\\n--set http.endpoints.get.url=http://httpbin.default/get \\\n--set http.endpoints.getAnything.url=http://httpbin.default/anything \\\n--set http.endpoints.post.url=http://httpbin.default/post \\\n--set http.endpoints.post.payloadStr=hello \\\n--set http.endpoints.post.qps=15\n

Further more, set parameters will trickle down to the endpoints.

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http}\" \\\n--set http.numRequests=50 \\\n--set http.endpoints.get.url=http://httpbin.default/get \\\n--set http.endpoints.getAnything.url=http://httpbin.default/anything \\\n--set http.endpoints.post.url=http://httpbin.default/post \\\n--set http.endpoints.post.payloadStr=hello\n

In this example, all three endpoints will have a numRequests of 50.

"},{"location":"user-guide/performance/tasks/http/#grafana-dashboard","title":"Grafana Dashboard","text":"

The results of the http task is visualized using the http Iter8 Grafana dashboard. The dashboard can be found here.

Assuming the URL to the Grafana service is $GRAFANA_URL, you can install the dashboard as follows:

  1. Open Grafana in a browser.
  2. Add a new data JSON API data source with the following parameters
    • URL: $GRAFANA_URL/httpDashboard
    • Query string: namespace=<namespace>&test=<test name>
  3. Import the http Iter8 Grafana dashboard
    • Copy and paste the contents of this link into the text box

You will see a visualization of the performance test like the following:

For multiple endpoints, the visualization will look like the following:

"},{"location":"user-guide/performance/tasks/ready/","title":"ready","text":"

Check if a Kubernetes object exists and is ready.

"},{"location":"user-guide/performance/tasks/ready/#usage-example","title":"Usage example","text":"

In the following example, the ready task checks if a deployment named httpbin-prod exists and its availability condition is set to true, and a service named httpbin exists. 

helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={ready,http}\" \\\n--set ready.deploy=httpbin-prod \\\n--set ready.service=httpbin \\\n--set http.url=http://httpbin.default/get\n

"},{"location":"user-guide/performance/tasks/ready/#parameters","title":"Parameters","text":"Name Type Description deploy string Name of a Kubernetes deployment. The task checks if the deployment exists and its Available condition is set to true. service string Name of a Kubernetes service. The task checks if the service exists. ksvc string Name of a Knative service. The task checks if the service exists and its Ready condition is set to true. timeout string Timeout for readiness check to succeed. Default value is 60s. namespace string The namespace under which to look for the Kubernetes objects."},{"location":"user-guide/performance/tasks/ready/#extensions","title":"Extensions","text":"

Iter8 can be easily extended to support readiness checks for any type of Kubernetes object (including objects with custom resource types). To do so, add the new resource type to the list of known types defined in the default values.yaml file for the chart.

"},{"location":"user-guide/performance/tasks/ready/#example","title":"Example","text":"

To include a Knative service as part of a version definition, add the following to the map of resourceTypes in the values.yaml file used to configure the controller. The addition identifies the Kubernetes group, version, and resource (GVR) and the status condition that should be checked for readiness.

ksvc:\n    Group: serving.knative.dev\n    Version: v1\n    Resource: services\n    conditions:\n    - Ready\n
"},{"location":"user-guide/performance/tasks/slack/","title":"slack","text":"

Send an performance test summary in a message to a Slack channel using a incoming webhook.

"},{"location":"user-guide/performance/tasks/slack/#usage-example","title":"Usage Example","text":"
helm upgrade --install \\\n--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \\\n--set \"tasks={http,slack}\" \\\n--set http.url=http://httpbin.default/get \\\n--set slack.url=<Slack webhook> \\\n--set slack.method=POST\n

See here for a more in-depth tutorial.

"},{"location":"user-guide/performance/tasks/slack/#parameters","title":"Parameters","text":"Name Type Required Default value Description url string Yes N/A URL to the Slack webhook payloadTemplateURL string No https://raw.githubusercontent.com/iter8-tools/iter8/v1.1.1/templates/notify/_payload-slack.tpl URL to a payload template softFailure bool No true Indicates the performance test should not fail if the task cannot successfully send the request"},{"location":"user-guide/performance/tasks/slack/#default-payload","title":"Default payload","text":"

The payload will determine what will be contained in the Slack message. The default payload template of the slack task is to send a performance test summary in text form.

However, if you would like to use a different payload template, simply set a payloadTemplateURL and Iter8 will not use the default.

"},{"location":"user-guide/progressive-release/about/","title":"About progressive release","text":"

Progressive release is an approach to application/ML model release that involves deploying a candidate version at the same time as the current, or primary, version. A portion of user traffic is directed to the candidate version allowing it to be evaluated for behavior and performance. Once approved, the candidate can be promoted -- becoming the new primary version.

There are different strategies for distributing traffic between the primary and candidate version:

  • blue-green - A percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The remaining requests go to the primary version.
  • canary - Requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary version.
  • mirrored - All requests are sent to the primary version. A percentage of requests is replicated and sent to a candidate version of the model. This percentage can be changed over time. Only responses from the primary version are returned to the user.

Progressive release uses the Iter8 release Helm chart. Options are described in the default values.yaml file. The progressive release tutorials show how it can be used to progressively release versions of an application or ML model.

The chart provided by Iter8 supports many common deployment scenarios including:

  • Applications composed of a Deployment and a Service object using Istio as a service mesh
  • Applications composed of a Deployment and a Service object using the Kubernetes Gateway API
  • ML models deployed to KServe (using KNative)
  • ML models deploy to KServe ModelMesh using Istio as a service mesh
"},{"location":"user-guide/progressive-release/about/#other-deployment-environments","title":"Other deployment environments","text":"

The release chart can be easily extended to include other deployment environments. Please consider contributing any extensions to Iter8.

"},{"location":"user-guide/progressive-release/extension/","title":"Extending progressive release to other deployment environments","text":"

The Iter8 release chart can be easily extended for applications/ML models composed of new types. We briefly describe how to extend the chart for an Knative application.

"},{"location":"user-guide/progressive-release/extension/#approach","title":"Approach","text":"

The release chart can be found in the charts/release sub-folder of the Iter8 GitHub repository. The file release.yaml is the starting point. For each valid environment, the chart contains a set of files defining the resources that should be created. These may include:

  • application object(s)
  • routemaps for different traffic patterns
  • configmaps used to specify request percentages
  • a service defining a common entry for requests (if needed)

Note that the file naming helps identify related template files.

"},{"location":"user-guide/progressive-release/extension/#example-knative-service","title":"Example (Knative Service)","text":"

For example, to implement a blue-green release for Knative services, the following files could be added.

  • _knative-istio.tpl - wrapper for all objects that should be deployed
  • _knative-istio.version.ksvc.tpl - the Knative service object that should be deployed for a version
  • _knative-istio.blue-green.tpl - wrapper for all objects that should be deployed to support the blue-green traffic pattern
  • _knative-istio.blue-green.routemap.tpl - the routemap definition
  • _knative-istio.service.tpl - a supporting external service
  • _knative.helpers.tpl - supporting functions

An implementation of these is here.

Note that this sample only implements the blue-green traffic pattern. A more complete implementation would include canary and mirroring traffic patterns.

Finally, update release.yaml to include knative-istio as a valid option:

{{- else if eq \"knative-istio\" .Values.environment }}\n  {{- include \"env.knative-istio\" . }}\n
"},{"location":"user-guide/progressive-release/extension/#extend-the-controller","title":"Extend the controller","text":"

The Iter8 controller will need to be restarted with permission to watch Knative service objects. Re-install the controller using the following additional options:

--set resourceTypes.ksvc.Group=serving.knative.dev \\\n--set resourceTypes.ksvc.Version=v1 \\\n--set resourceTypes.ksvc.Resource=services \\\n--set \"resourceTypes.ksvc.conditions[0]=Ready\"\n
"},{"location":"user-guide/progressive-release/extension/#using-the-modified-chart","title":"Using the modified chart","text":"

Reference the location of the local copy of the chart instead of using the --repo and --version options. For example assuming the location is $CHART, a deployment of two versions of the Knative hello service with a 30-70 traffic split would be:

cat <<EOF | helm upgrade --install hello $CHART -f -\nenvironment: knative-istio\napplication:\n  versions:\n  - ksvcSpecification:\n      spec:\n        template:\n          spec:\n            containers:\n            - image: ghcr.io/knative/helloworld-go:latest\n              ports:\n              - containerPort: 80\n              env:\n              - name: TARGET\n                value: \"v1\"\n    weight: 30\n  - ksvcSpecification:\n      spec:\n        template:\n          spec:\n            containers:\n            - image: ghcr.io/knative/helloworld-go:latest\n              ports:\n              - containerPort: 80\n              env:\n              - name: TARGET\n                value: \"v2\"\n    weight: 70\n  strategy: blue-green\nEOF\n
"},{"location":"user-guide/progressive-release/extension/#contribute-your-extensions","title":"Contribute your extensions","text":"

Please consider contributing any extensions to Iter8 by submitting a pull request.

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margin-right: 0.5rem; + color: inherit; +} +.mdx-hero .md-button:focus, .mdx-hero .md-button:hover { + color: #96034a; + background-color: #ffffff; + border-color: #ffffff; +} +.mdx-hero .md-button--reverse { + color: #96034a; + background-color: #ffffff; + border-color: #ffffff; +} +.mdx-hero .md-button--reverse:focus, .mdx-hero .md-button--reverse:hover { + color: inherit; + background-color: inherit; + border-color: inherit; +} +.mdx-hero .text-large { + font-size: 1rem; +} + +@keyframes heart { + 0%, 40%, 80%, 100% { + transform: scale(1); + } + 20%, 60% { + transform: scale(1.15); + } +} +.heart { + animation: heart 1000ms infinite; + color: #ff006f; +} + +.text-light { + color: var(--md-primary-bg-color); +} +.text-light a { + color: #ffffff; +} +.text-light a:focus, .text-light a:hover { + color: #fff07d; +} +.text-light .social a { + border: 0rem; + color: inherit; +} + +.md-typeset h3.social { + letter-spacing: 0.5rem; +} + +.text-center { + text-align: center; + padding: 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Iter8 is open-source and available under the Apache 2 License.
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Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/canary/index.html b/1.1/tutorials/canary/index.html new file mode 100644 index 00000000..9aa5b308 --- /dev/null +++ b/1.1/tutorials/canary/index.html @@ -0,0 +1,56 @@ + Canary - Iter8

Canary release

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a canary release strategy. In a canary release, requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

Canary release

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the application using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  strategy: canary
+EOF
+
What happens?

Because environment is set to deployment-istio, a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (of type ExternalName) named default/httpbin pointing at the Istio gateway, istio-ingressgateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all traffic to the only deployed version, httpbin-0.

Verify routing

You can verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice httpbin -o yaml
+

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. Send requests: 

    curl httpbin.default -s -D - | grep -e '^HTTP' -e app-version
+

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK
+app-version: httpbin-0
+
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80
+
(b) Send requests using the Host header: 
curl -H 'Host: httpbin.default' localhost:8080 -s -D - | grep -e '^HTTP' -e app-version
+

Deploy candidate

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: canary
+EOF
+
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that requests with the header traffic set to test will be sent to the candidate model:

HTTP/1.1 200 OK
+app-version: httpbin-1
+

All other requests will be sent to the primary model (httpbin-0):

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate model (httpbin-1) while all others will be handled by the primary version.

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: canary
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Cleanup

Delete the application and its routing configuration:

helm delete httpbin
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
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+ "appState": { + "gridSize": null, + "viewBackgroundColor": "#ffffff" + }, + "files": {} +} \ No newline at end of file diff --git a/1.1/tutorials/integrations/ghactions/index.html b/1.1/tutorials/integrations/ghactions/index.html new file mode 100644 index 00000000..a3d4d183 --- /dev/null +++ b/1.1/tutorials/integrations/ghactions/index.html @@ -0,0 +1,22 @@ + GitHub Actions - Iter8

Trigger a GitHub Actions workflow during a performance test

Iter8 provides a github task that sends a repository_dispatch which can trigger the workflows in the default branch of a GitHub repository.

Example

In this example, you will run the Your first performance test but at the end of the performance test, Iter8 will trigger a workflow on GitHub.

In this simple example, the workflow will simply print out a test summary that it will receive with the repository_dispatch. In a more sophisticated scenario, the workflow could, for example, read from the test summary and determine what to do next.

To summarize what will happen, you will create a new GitHub repository, add a workflow that will respond to the github task, set up and run a performance test, and check if the workflow was triggered.

The github task requires the name of a repository, the name of the owner, as well as an authentication token in order to send the repository_dispatch. To see a full list of the github task parameters, see here.

  1. Create a new repository on GitHub.
  2. Add the following workflow.
name: iter8 `github` task test
+on:
+  repository_dispatch:
+    types: iter8
+jobs:
+  my-job:
+    runs-on: ubuntu-latest
+    steps:
+      - run: 'echo "payload: ${{ toJson(github.event.client_payload) }}"'
+

Note that this workflow has one job that will print out the client_payload. The default github task payload is configured with client_payload set to .Report, a summary of the performance test.

Also note that the on.repository_dispatch.types is set to iter8. The default github task payload is configured with event_type set to iter8. This indicates that once the repository_dispatch has been sent, only workflows on the default branch with on.repository_dispatch.types set to iter8 will be triggered.

  1. Create a GitHub personal access token for the token parameter.
  2. Ensure that you have a Kubernetes cluster and the kubectl CLI. You can create a local Kubernetes cluster using tools like Kind or Minikube.

  3. Install the Iter8 controller

    Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

    For additional install options, see Iter8 Installation.

  4. Deploy the sample HTTP service in the Kubernetes cluster. 

    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80
+kubectl expose deploy httpbin --port=80
+

  5. Launch the performance test using the github task with the appropriate values. 
    helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http,github}" \
+--set http.url=http://httpbin.default/get \
+--set github.owner=<GitHub owner> \
+--set github.repo=<GitHub repository> \
+--set github.token=<GitHub token>
+
  6. Verify that the workflow has been triggered after the performance test has completed.
Some variations and extensions of the github task

The default github task payload sends a summary of the performance test.

In your workflow, you can read from the report and use that data for control flow or use snippets of that data in different actions. For example, you can check to see if there have been any task failures and take alternative actions.

You do not need to use the default github task payload. You can provide your own payload by overriding the default of the payloadTemplateURL.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve-mm/abn/index.html b/1.1/tutorials/integrations/kserve-mm/abn/index.html new file mode 100644 index 00000000..0b38b26e --- /dev/null +++ b/1.1/tutorials/integrations/kserve-mm/abn/index.html @@ -0,0 +1,68 @@ + A/B testing - Iter8

A/B Testing a backend ML model

This tutorial describes how to do A/B testing as part of the release of a backend ML model hosted on KServe ModelMesh using the Iter8 SDK.

A/B/n testing

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe ModelMesh Serving. For example, you can create a modelmesh-serving Quickstart environment. If using the Quickstart environment, your default namespace will be changed to modelmesh-serving. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy the sample application

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components:

Frontend

The frontend component uses the Iter8 SDK method Lookup() before each call to the backend (ML model). The frontend uses the returned version number to route the request to the recommended version of backend.

kubectl create deployment frontend --image=iter8/abn-sample-mm-frontend-go:0.17.3
+kubectl expose deployment frontend --name=frontend --port=8090
+

Backend

The backend application component is an ML model. Release it using the Iter8 release chart:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: backend
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
+EOF
+

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/backend-0 --timeout=600s
+

Generate load

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090
+

In another shell, run a script to generate load from multiple users: 

curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --
+

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: backend-0__isvc-3642375d03
+

Deploy candidate

A candidate version of the model can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: backend
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
+EOF
+
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

Until the candidate version is ready, calls to Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: backend-1__isvc-3642375d03
+

Compare versions using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source modelmesh-serving/backend with the following parameters:

  • URL: http://iter8.modelmesh-serving:8080/abnDashboard
  • Query string: namespace=modelmesh-serving&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

A/B dashboard

Once you identify a winner, it can be promoted, and the candidate version deleted.

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: backend
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend-0 (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: backend-0__isvc-3642375d03
+

Cleanup

Delete the backend:

helm delete backend
+

Delete the frontend:

kubectl delete deploy/frontend svc/frontend
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve-mm/blue-green/index.html b/1.1/tutorials/integrations/kserve-mm/blue-green/index.html new file mode 100644 index 00000000..e1d5ead6 --- /dev/null +++ b/1.1/tutorials/integrations/kserve-mm/blue-green/index.html @@ -0,0 +1,97 @@ + Blue-green - Iter8

Blue-green release of a ML model

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe ModelMesh environment using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

Blue-green release

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe ModelMesh Serving. For example, you can create a modelmesh-serving Quickstart environment. If using the Quickstart environment, your default namespace will be changed to modelmesh-serving. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
+  strategy: blue-green
+EOF
+

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s
+
What happens?
  • Because environment is set to kserve-modelmesh-istio, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a ServiceEntry named default/wisdom is deployed. Further, an Iter8 routemap is created.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

Verify routing

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml
+

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. Send requests: 

    cat grpc_input.json \
+| grpcurl -vv -plaintext -proto kserve.proto -d @ \
+  -authority wisdom.modelmesh-serving \
+  modelmesh-serving.modelmesh-serving:8033 \
+  inference.GRPCInferenceService.ModelInfer \
+| grep -e app-version
+

The output includes the version of the application that responded (in the app-version response header). In this example:

app-version: wisdom-0
+

Deploy candidate

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "s3://modelmesh-example-models/sklearn/mnist-svm.joblib"
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "s3://modelmesh-example-models/sklearn/mnist-svm.joblib"
+  strategy: blue-green
+EOF
+
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that requests are sent to both versions.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Requests will be handled equally by both versions. Output will be something like:

app-version: wisdom-0
+...
+app-version: wisdom-1
+

Modify weights (optional)

To modify the request distribution between versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "s3://modelmesh-example-models/sklearn/mnist-svm.joblib"
+    weight: 30
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "s3://modelmesh-example-models/sklearn/mnist-svm.joblib"
+    weight: 70
+  strategy: blue-green
+EOF
+

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version (wisdom-1); the remaining 30 percent by the primary version (wisdom-0).

Promote candidate

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
+  strategy: blue-green
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

app-version: wisdom-0
+

Cleanup

Delete the models are their routing:

helm delete wisdom
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve-mm/canary/index.html b/1.1/tutorials/integrations/kserve-mm/canary/index.html new file mode 100644 index 00000000..dc1f16f9 --- /dev/null +++ b/1.1/tutorials/integrations/kserve-mm/canary/index.html @@ -0,0 +1,95 @@ + Canary - Iter8

Canary release of a ML model

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe ModelMesh environment using a canary release strategy. In a canary release, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

Canary release

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe ModelMesh Serving. For example, you can create a modelmesh-serving Quickstart environment. If using the Quickstart environment, your default namespace will be changed to modelmesh-serving. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: s3://modelmesh-example-models/sklearn/mnist-svm.joblib
+  strategy: canary
+EOF
+

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s
+
What happens?
  • Because environment is set to kserve-modelmesh-istio, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a ServiceEntry named default/wisdom is deployed. Further, an Iter8 routemap is created.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

Verify routing

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml
+

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. To send requests without the header traffic: 

    cat grpc_input.json \
+| grpcurl -vv -plaintext -proto kserve.proto -d @ \
+  -authority wisdom.modelmesh-serving \
+  modelmesh-serving.modelmesh-serving:8033 \
+  inference.GRPCInferenceService.ModelInfer \
+| grep -e app-version
+

  4. Requests can also be sent with the header traffic: test. When a candidate is deployed, requests with this header will be routed to the candidate. When no candidate is deployed, all requests will be routed to the primary version. 
    cat grpc_input.json \
+| grpcurl -vv -plaintext -proto kserve.proto -d @ \
+  -H 'traffic: test' \
+  -authority wisdom.modelmesh-serving \
+  modelmesh-serving.modelmesh-serving:8033 \
+  inference.GRPCInferenceService.ModelInfer \
+| grep -e app-version
+

The output includes the version of the application that responded (in the app-version response header). In this example:

app-version: wisdom-0
+
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the Istio ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80
+
(b) Download the proto file and sample input: 
curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/kserve.proto
+curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/modelmesh-serving/grpc_input.json
+
(c) To send requests without the header traffic: 
cat grpc_input.json | \
+grpcurl -vv -plaintext -proto kserve.proto -d @ \
+-authority wisdom.modelmesh-serving \
+localhost:8080 inference.GRPCInferenceService.ModelInfer \
+| grep -e app-version
+
(d) To send requests with the header traffic: test: 
cat grpc_input.json | \
+grpcurl -vv -plaintext -proto kserve.proto -d @ \
+-H 'traffic: test' \
+-authority wisdom.modelmesh-serving \
+localhost:8080 inference.GRPCInferenceService.ModelInfer \
+| grep -e app-version
+

Deploy candidate

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "s3://modelmesh-example-models/sklearn/mnist-svm.joblib"
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "s3://modelmesh-example-models/sklearn/mnist-svm.joblib"
+  strategy: canary
+EOF
+
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that inference requests with the header traffic set to test will be sent to the candidate model. All other requests will be sent to the primary model.

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate model (wisdom-1):

app-version: wisdom-1
+

All others will be handled by the primary version (wisdom-0):

app-version: wisdom-0
+

Promote candidate

Redefine the primary to use the candidate model remove the candidate:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve-modelmesh-istio
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+    annotations:
+      serving.kserve.io/secretKey: localMinIO
+  modelFormat: sklearn
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "s3://modelmesh-example-models/sklearn/mnist-svm.joblib"
+  strategy: canary
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

app-version: wisdom-0
+

Cleanup

Delete the models and their routing:

helm delete wisdom
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve-mm/performance/index.html b/1.1/tutorials/integrations/kserve-mm/performance/index.html new file mode 100644 index 00000000..d1783495 --- /dev/null +++ b/1.1/tutorials/integrations/kserve-mm/performance/index.html @@ -0,0 +1 @@ + Performance testing - Iter8
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve/abn-grpc/index.html b/1.1/tutorials/integrations/kserve/abn-grpc/index.html new file mode 100644 index 00000000..b39d53bd --- /dev/null +++ b/1.1/tutorials/integrations/kserve/abn-grpc/index.html @@ -0,0 +1,81 @@ + Inference request by gRPC - Iter8

A/B Testing a backend ML model

This tutorial describes how to do A/B testing as part of the release of a backend ML model hosted on KServe using the Iter8 SDK. In this tutorial, communication with the model is via gRPC calls.

A/B/n testing

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
+
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy the sample application

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components:

Frontend

The frontend component uses the Iter8 SDK method Lookup() before each call to the backend (ML model). The frontend uses the returned version number to route the request to the recommended version of backend.

Deploy the frontend:

kubectl create deployment frontend --image=iter8/abn-sample-kserve-grpc-frontend-go:0.17.3
+kubectl expose deployment frontend --name=frontend --port=8090
+

Backend

The backend application component is an ML model. Release it using the Iter8 release chart:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/version: backend
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  protocolVersion: v2
+  ports:
+  - containerPort: 9000
+    name: h2c
+    protocol: TCP
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/backend-0 --timeout=600s
+

Generate load

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090
+

In another shell, run a script to generate load from multiple users: 

curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --
+

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: backend-0
+

Deploy candidate

A candidate version of the model can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/version: backend
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  protocolVersion: v2
+  ports:
+  - containerPort: 9000
+    name: h2c
+    protocol: TCP
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

Until the candidate version is ready, calls to Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: backend-1
+

Compare versions using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source default/backend with the following parameters:

  • URL: http://iter8.default:8080/abnDashboard
  • Query string: namespace=default&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

A/B dashboard

Once you identify a winner, it can be promoted, and the candidate version deleted.

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/version: backend
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  protocolVersion: v2
+  ports:
+  - containerPort: 9000
+    name: h2c
+    protocol: TCP
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend-0 (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: backend-0
+

Cleanup

Delete the backend:

helm delete backend
+

Delete the frontend:

kubectl delete deploy/frontend svc/frontend
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve/abn-http/index.html b/1.1/tutorials/integrations/kserve/abn-http/index.html new file mode 100644 index 00000000..bf206a8f --- /dev/null +++ b/1.1/tutorials/integrations/kserve/abn-http/index.html @@ -0,0 +1,66 @@ + Inference request by HTTP - Iter8

A/B Testing a backend ML model

This tutorial describes how to do A/B testing as part of the release of a backend ML model hosted on KServe when using the Iter8 SDK. In this tutorial, communication from the frontend to the backend model is via HTTP calls.

A/B/n testing

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
+
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy the sample application

A simple sample two-tier application using the Iter8 SDK is provided. Note that only the frontend component uses the Iter8 SDK. Deploy both the frontend and backend components:

Frontend

The frontend component uses the Iter8 SDK method Lookup() before each call to the backend (ML model). The frontend uses the returned version number to route the request to the recommended version of backend.

Deploy the frontend:

kubectl create deployment frontend --image=iter8/abn-sample-kserve-http-frontend-go:0.17.3
+kubectl expose deployment frontend --name=frontend --port=8090
+

Backend

The backend application component is an ML model. Release it using the Iter8 release chart:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/version: backend
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/backend-0 --timeout=600s
+

Generate load

In one shell, port-forward requests to the frontend component: 

kubectl port-forward service/frontend 8090:8090
+

In another shell, run a script to generate load from multiple users: 

curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.3/samples/abn-sample/generate_load.sh | sh -s --
+

The load generator and sample frontend application outputs the backend that handled each recommendation. With just one version is deployed, all requests are handled by backend-0. In the output you will see something like:

Recommendation: backend-0
+

Deploy candidate

A candidate version of the model can be deployed simply by adding a second version to the list of versions:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/version: backend
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

Until the candidate version is ready, calls to Lookup() will return only the version index number 0; that is, the first, or primary, version of the model. Once the candidate version is ready, Lookup() will return both 0 and 1, the indices of both versions, so that requests can be distributed across both versions.

Once both backend versions are responding to requests, the output of the load generator will include recommendations from the candidate version. In this example, you should see something like:

Recommendation: backend-1
+

Compare versions using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source default/backend with the following parameters:

  • URL: http://iter8.default:8080/abnDashboard
  • Query string: namespace=default&application=backend

Create a new dashboard by import. Copy and paste the contents of the abn Grafana dashboard into the text box and load it. Associate it with the JSON API data source above.

The Iter8 dashboard allows you to compare the behavior of the two versions of the backend component against each other and select a winner. Since user requests are being sent by the load generation script, the values in the report may change over time. The Iter8 dashboard will look like the following:

A/B dashboard

Once you identify a winner, it can be promoted, and the candidate version deleted.

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install backend --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/version: backend
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Calls to Lookup() will now recommend that all traffic be sent to the new primary version backend-0 (currently serving the promoted version of the code).

The output of the load generator will again show just backend_0:

Recommendation: backend-0
+

Cleanup

Delete the backend:

helm delete backend
+

Delete the frontend:

kubectl delete deploy/frontend svc/frontend
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve/blue-green/index.html b/1.1/tutorials/integrations/kserve/blue-green/index.html new file mode 100644 index 00000000..e56cfe7d --- /dev/null +++ b/1.1/tutorials/integrations/kserve/blue-green/index.html @@ -0,0 +1,100 @@ + Blue-green - Iter8

Blue-green release of a KServe ML model

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe environment using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

Blue-green release

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
+
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  strategy: blue-green
+EOF
+

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s
+
What happens?
  • Because environment is set to kserve, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a Service (of type ExternalName) named default/wisdom pointing at the KNative gateway, knative-local-gateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created. Finally, to support the blue-green release, a ConfigMap (wisdom-0-weight-config) is created to be used to manage the proportion of traffic sent to this version.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

Verify routing

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml
+

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. Send requests: 

    curl -H 'Content-Type: application/json' \
+http://wisdom.default -d @input.json -s -D - \
+| grep -e HTTP -e app-version
+

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK
+app-version: wisdom-0
+
To send requests from outside the cluster

To configure the release for traffic from outside the cluster:

(a) In a separate terminal, port-forward the Istio ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80
+
(b) Download the sample input: 
curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/kserve-serving/input.json
+
(c) Send inference requests using the Host header: 
curl -H 'Content-Type: application/json' \
+-H 'Host: wisdom.default' \
+localhost:8080 -d @input.json -s -D - \
+| grep -e '^HTTP' -e app-version
+

Deploy candidate

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  strategy: blue-green
+EOF
+
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate model is ready, Iter8 will automatically reconfigure the routing so that inference requests are sent to both versions.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Requests will be handled equally by both versions. Output will be something like:

HTTP/1.1 200 OK
+app-version: wisdom-0
+...
+HTTP/1.1 200 OK
+app-version: wisdom-1
+

Modify weights (optional)

To modify the request distribution between versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+    weight: 30
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+    weight: 70
+  strategy: blue-green
+EOF
+

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version; the remaining 30 percent by the primary version.

Promote candidate

The candidate model can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  strategy: blue-green
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK
+app-version: wisdom-0
+

Cleanup

Delete the models are their routing:

helm delete wisdom
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve/canary/index.html b/1.1/tutorials/integrations/kserve/canary/index.html new file mode 100644 index 00000000..fe2f41f8 --- /dev/null +++ b/1.1/tutorials/integrations/kserve/canary/index.html @@ -0,0 +1,87 @@ + Canary - Iter8

Canary release of a KServe ML model

This tutorial shows how Iter8 can be used to release ML models hosted in a KServe environment using a canary release strategy. In a canary release, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

Canary release

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
+
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  strategy: canary
+EOF
+

Wait for the backend model to be ready:

kubectl wait --for condition=ready isvc/wisdom-0 --timeout=600s
+
What happens?
  • Because environment is set to kserve, an InferenceService object is created.
  • The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata.
  • The name wisdom-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case.
  • Alternatively, an inferenceServiceSpecification could have been provided.

To support routing, a Service (of type ExternalName) named default/wisdom pointing at the KNative gateway, knative-local-gateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the InferenceService is ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all inference requests to the only deployed version, wisdom-0.

Verify routing

You can send verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice wisdom -o yaml
+

You can also send inference requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. To send requests without the header traffic: 

    curl -H 'Content-Type: application/json' \
+http://wisdom.default -d @input.json -s -D - \
+| grep -e HTTP -e app-version
+

  4. Requests can also be sent with the header traffic: test. When a candidate is deployed, requests with this header will be routed to the candidate. When no candidate is deployed, all requests will be routed to the primary version. 

    curl -H 'Content-Type: application/json' \
+-H 'traffic: test' \
+http://wisdom.default -d @input.json -s -D - \
+| grep -e HTTP -e app-version
+

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK
+app-version: wisdom-0
+
To send requests from outside the cluster

To configure the release for traffic from outside the cluster:

(a) In a separate terminal, port-forward the Istio ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80
+
(b) Download the sample input: 
curl -sO https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/kserve-serving/input.json
+
(c) To send requests without the header traffic: 
curl -H 'Content-Type: application/json' \
+-H 'Host: wisdom.default' \
+localhost:8080 -d @input.json -s -D - \
+| grep -e '^HTTP' -e app-version
+
(d) To send requests with the header traffic: test: 
curl -H 'Content-Type: application/json' \
+-H 'Host: wisdom.default' \
+-H 'traffic: test' \
+localhost:8080 -d @input.json -s -D - \
+| grep -e HTTP -e app-version
+

Deploy candidate

A candidate version of the model can be deployed simply by adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  strategy: canary
+EOF
+
About the candidate

In this tutorial, the model source (field storageUri) for the candidate version is the same as for the primary version of the model. In a real example, this would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the candidate version is ready, the Iter8 controller will Iter8 will automatically reconfigure the routing so that inference requests with the header traffic set to test will be sent to the candidate model:

HTTP/1.1 200 OK
+app-version: wisdom-1
+

All other requests will be sent to the primary model (wisdom-0):

HTTP/1.1 200 OK
+app-version: wisdom-0
+

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate model (wisdom-1) while all others will be handled by the primary version.

Promote candidate

The candidate model can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install wisdom --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: kserve
+application: 
+  metadata:
+    labels:
+      app.kubernetes.io/name: wisdom
+  modelFormat: sklearn
+  runtime: kserve-mlserver
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+  strategy: canary
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, storageUri would also be updated (with that from the candidate version).

Once the (reconfigured) primary InferenceService ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify Routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK
+app-version: wisdom-0
+

Cleanup

Delete the models and their routing:

helm delete wisdom
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve/grpc/index.html b/1.1/tutorials/integrations/kserve/grpc/index.html new file mode 100644 index 00000000..19d94a11 --- /dev/null +++ b/1.1/tutorials/integrations/kserve/grpc/index.html @@ -0,0 +1,41 @@ + Inference request by gRPC - Iter8

Load test a KServe model (via gRPC)

This tutorial shows how easy it is to run a load test for KServe when using gRPC to make requests. We use a sklearn model to demonstrate. The same approach works for any model type.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
+
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy an InferenceService

Create an InferenceService which exposes a gRPC port. The following serves the sklearn irisv2 model:

cat <<EOF | kubectl create -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "sklearn-irisv2"
+spec:
+  predictor:
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      protocolVersion: v2
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+      ports:
+      - containerPort: 9000
+        name: h2c
+        protocol: TCP
+EOF
+

Launch performance test

GRPC_HOST=$(kubectl get isvc sklearn-irisv2 -o jsonpath='{.status.components.predictor.address.url}' | sed 's#.*//##')
+GRPC_PORT=80
+
helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 model-test iter8 \
+--set "tasks={ready,grpc}" \
+--set ready.isvc=sklearn-irisv2 \
+--set ready.timeout=180s \
+--set grpc.protoURL=https://raw.githubusercontent.com/kserve/kserve/master/docs/predict-api/v2/grpc_predict_v2.proto \
+--set grpc.host=${GRPC_HOST}:${GRPC_PORT} \
+--set grpc.call=inference.GRPCInferenceService.ModelInfer \
+--set grpc.dataURL=https://gist.githubusercontent.com/kalantar/6e9eaa03cad8f4e86b20eeb712efef45/raw/56496ed5fa9078b8c9cdad590d275ab93beaaee4/sklearn-irisv2-input-grpc.json
+
About this performance test

This performance test consists of two tasks, namely, ready and grpc.

The ready task checks if the sklearn-irisv2 InferenceService exists and is Ready.

The grpc task sends call requests to the inference.GRPCInferenceService.ModelInfer method of the cluster-local gRPC service with host address ${GRPC_HOST}:${GRPC_PORT}, and collects Iter8's built-in gRPC load test metrics.

View results using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source model-test with the following parameters:

  • URL: http://iter8.default:8080/grpcDashboard
  • Query string: namespace=default&test=model-test

Create a new dashboard by import. Paste the contents of the grpc Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

grpc Iter8 dashboard

Cleanup

helm delete model-test
+kubectl delete inferenceservice sklearn-irisv2
+

Uninstall the Iter8 controller

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Some variations and extensions of this performance test
  1. The grpc task can be configured with load related parameters such as the number of requests, requests per second, or number of concurrent connections.
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kserve/http/index.html b/1.1/tutorials/integrations/kserve/http/index.html new file mode 100644 index 00000000..78a5c836 --- /dev/null +++ b/1.1/tutorials/integrations/kserve/http/index.html @@ -0,0 +1,33 @@ + Inference request by HTTP - Iter8

Load test a KServe model (via HTTP)

This tutorial shows how easy it is to run a load test for KServe when using HTTP to make requests. We use a sklearn model to demonstrate. The same approach works for any model type.

Before you begin
  1. Ensure that you have the kubectl and helm CLIs installed.
  2. Have access to a cluster running KServe. You can create a KServe Quickstart environment as follows: 
    curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
+
    If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy an InferenceService

Create an InferenceService which exposes an HTTP port. The following serves the sklearn irisv2 model:

cat <<EOF | kubectl apply -f -
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "sklearn-irisv2"
+spec:
+  predictor:
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-mlserver
+      storageUri: "gs://seldon-models/sklearn/mms/lr_model"
+EOF
+

Launch performance test

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 model-test iter8 \
+--set "tasks={ready,http}" \
+--set ready.isvc=sklearn-irisv2 \
+--set ready.timeout=180s \
+--set http.url=http://sklearn-irisv2.default.svc.cluster.local/v2/models/sklearn-irisv2/infer \
+--set http.payloadURL=https://gist.githubusercontent.com/kalantar/d2dd03e8ebff2c57c3cfa992b44a54ad/raw/97a0480d0dfb1deef56af73a0dd31c80dc9b71f4/sklearn-irisv2-input.json \
+--set http.contentType="application/json"
+
About this performance test

This performance test consists of two tasks, namely, ready and http.

The ready task checks if the sklearn-irisv2 InferenceService exists and is Ready.

The http task sends requests to the cluster-local HTTP service whose URL exposed by the InferenceService, http://sklearn-irisv2.default.svc.cluster.local/v2/models/sklearn-irisv2/infer, and collects Iter8's built-in HTTP load test metrics.

View results using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source model-test with the following parameters:

  • URL: http://iter8.default:8080/httpDashboard
  • Query string: namespace=default&test=model-test

Create a new dashboard by import. Paste the contents of the http Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

http Iter8 dashboard

Cleanup

helm delete model-test
+kubectl delete inferenceservice sklearn-irisv2
+

Uninstall the Iter8 controller

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Some variations and extensions of this performance test
  1. The http task can be configured with load related parameters such as the number of requests, queries per second, or number of parallel connections.
  2. The http task can be configured to send various types of content as payload.
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kubernetes-gateway-api/blue-green/index.html b/1.1/tutorials/integrations/kubernetes-gateway-api/blue-green/index.html new file mode 100644 index 00000000..24f080b1 --- /dev/null +++ b/1.1/tutorials/integrations/kubernetes-gateway-api/blue-green/index.html @@ -0,0 +1,72 @@ + Blue-green - Iter8

Blue-green release

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a blue-green release strategy. In a blue-green release, a percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the blue-green release strategy.

Blue-green release

This tutorial uses the Kubernetes Gateway API to allow the use any service mesh that supports this API. In this case, we use demonstrate with Linkerd.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Linkerd.

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the application (httpbin) using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used. Note that we deploy the application to the namespace test.

About creating a namespace for Linkerd deployments

When creating a namespace, it should be annotated so that all created pods are injected with the Linkerd proxy. This can be done, for example, by using the Linkerd CLI: 

kubectl create ns test --dry-run=client -o yaml | linkerd inject - | kubectl apply -f -
+

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-gtw
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  strategy: blue-green
+EOF
+
What happens?

The environment defines what kind of application is deployed. In this case (deployment-gtw), a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (httpbin) is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created. Finally, to support the blue-green release, a ConfigMap (httpbin-0-weight-config) is created to be used to manage the proportion of traffic sent to this version.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an HTTPRoute. It is configured to route all traffic to the only deployed version, httpbin-0.

Verify routing

You can verify the routing configuration by inspecting the HTTPRoute:

kubectl -n test get httproute.gateway.networking.k8s.io httpbin -o yaml
+

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    export SERVICE_MESH=linkerd
+curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. Send requests: 

    curl httpbin.test -s -D - | grep -e '^HTTP' -e app-version
+

The output includes the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Deploy candidate

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-gtw
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: blue-green
+EOF
+
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing; the HTTPRoute is updated to distribute traffic between versions based on the weights.

Verify routing

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. Requests will now be handled equally by both versions. Output will be something like:

HTTP/1.1 200 OK
+app-version: httpbin-0
+...
+HTTP/1.1 200 OK
+app-version: httpbin-1
+

Modify weights (optional)

To modify the request distribution between the versions, add a weight to each version. The weights are relative to each other.

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-gtw
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+    weight: 30
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+    weight: 70
+  strategy: blue-green
+EOF
+

Iter8 automatically reconfigures the routing to distribute traffic between the versions based on the new weights.

Verify routing

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. 70 percent of requests will now be handled by the candidate version; the remaining 30 percent by the primary version.

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-gtw
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: blue-green
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify routing

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Cleanup

Delete the application and its routing configuration:

helm -n test delete httpbin
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/kubernetes-gateway-api/canary/index.html b/1.1/tutorials/integrations/kubernetes-gateway-api/canary/index.html new file mode 100644 index 00000000..8172cdb5 --- /dev/null +++ b/1.1/tutorials/integrations/kubernetes-gateway-api/canary/index.html @@ -0,0 +1,60 @@ + Canary - Iter8

Canary release

This tutorial shows how Iter8 can be used to release a basic Kubernetes application using a canary release strategy. In a canary release, inference requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary, or initial, version of the model. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the canary release strategy.

Canary release

This tutorial uses the Kubernetes Gateway API to allow the use any service mesh that supports this API. In this case, we use demonstrate with Linkerd.

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Linkerd.

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the application (httpbin) using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used. Note that we deploy the application to the namespace test.

About creating a namespace for Linkerd deployments

When creating a namespace, it should be annotated so that all created pods are injected with the Linkerd proxy. This can be done, for example, by using the Linkerd CLI: 

kubectl create ns test --dry-run=client -o yaml | linkerd inject - | kubectl apply -f -
+

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-gtw
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  strategy: canary
+EOF
+
What happens?

The environment defines what kind of application is deployed. In this case (deployment-gtw), a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (httpbin) is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an HTTPRoute. It is configured to route all traffic to the only deployed version, httpbin-0.

Verify routing

You can verify the routing configuration by inspecting the HTTPRoute:

kubectl -n test get httproute.gateway.networking.k8s.io httpbin -o yaml
+

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    export SERVICE_MESH=linkerd
+curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. To send requests without the header traffic: 

    curl httpbin.test -s -D - | grep -e '^HTTP' -e app-version
+

  4. Requests can also be sent with the header traffic: test. When a candidate is deployed, requests with this header will be routed to the candidate. When no candidate is deployed, all requests will be routed to the primary version. 

    curl httpbin.test -H 'traffic: test' -s -D - | grep -e '^HTTP' -e app-version
+

The output includes the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Deploy candidate

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-gtw
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+    matches:
+    - headers:
+      - name: traffic
+        value: test
+  strategy: canary
+EOF
+
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing so that requests with the header traffic set to test will be sent to the candidate. All other requests will be sent to the primary version.

Verify routing

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. Those with header traffic set to test will be handled by the candidate version:

HTTP/1.1 200 OK
+app-version: httpbin-1
+

All others will be handled by the primary version:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm -n test upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-gtw
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: canary
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify routing

You can verify the routing configuration by inspecting the HTTPRoute and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Cleanup

Delete the application and its routing configuration:

helm -n test delete httpbin
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/overview/index.html b/1.1/tutorials/integrations/overview/index.html new file mode 100644 index 00000000..c8a50ff7 --- /dev/null +++ b/1.1/tutorials/integrations/overview/index.html @@ -0,0 +1 @@ + Integrations - Iter8

Integrations

The tutorials under the integrations section are maintained by members of the Iter8 community. They may become outdated.

If you find that something is not working, please lend a helping hand and fix it in a PR.

More integrations and examples are always welcome.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/integrations/slack/index.html b/1.1/tutorials/integrations/slack/index.html new file mode 100644 index 00000000..ac50d2e3 --- /dev/null +++ b/1.1/tutorials/integrations/slack/index.html @@ -0,0 +1,12 @@ + Slack - Iter8

Use Iter8 to send a message to a Slack channel

Iter8 provides a slack task that sends a message to a Slack channel using a webhook.

Example

In this example, you will run the Your first performance test but at the end of the performance test, Iter8 will send a message on Slack.

The message will simply contain a summary of the performance test in text form. However, you can easily construct a more sophisticated message by providing your own payload template.

This task could provide important updates on a performance test over Slack, for example a summary at the end of the test.

To summarize what will happen, you will create a new channel on Slack and configure a webhook, set up and run a performance test, and check if a message was sent to the channel.

The slack task requires the URL of the Slack webhook. To see a full list of the github task parameters, see here.

  1. Create a new channel in your Slack organization.
  2. Create a Slack app, enable incoming webhooks, and create a new incoming webhook. See here.
  3. Ensure that you have a Kubernetes cluster and the kubectl CLI. You can create a local Kubernetes cluster using tools like Kind or Minikube.

  4. Install the Iter8 controller

    Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
    helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

    For additional install options, see Iter8 Installation.

  5. Deploy the sample HTTP service in the Kubernetes cluster. 

    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80
+kubectl expose deploy httpbin --port=80
+

  6. Launch the performance test with the slack task with the appropriate values. 
    helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http,slack}" \
+--set http.url=http://httpbin.default/get \
+--set slack.url=<Slack webhook> \
+--set slack.method=POST
+
  7. Verify that the message has been sent after the performance test has completed.
Some variations and extensions of the slack task

The default slack task payload sends a summary of the performance test.

However, you do not need to use the default payload. You can provide your own payload by overriding the default of the payloadTemplateURL.

For example, you can also use Slack's Block Kit to create more sophisticated messages. You can use markdown, create different sections, or add interactivity, such as buttons.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/mirror/index.html b/1.1/tutorials/mirror/index.html new file mode 100644 index 00000000..ee0d4f26 --- /dev/null +++ b/1.1/tutorials/mirror/index.html @@ -0,0 +1,70 @@ + Mirrored - Iter8

Mirrored release

This tutorial shows how Iter8 can be used to release a new version of an application using mirroring. When an application is mirrored, all requests are sent to the primary version of the application. A percentage of requests is replicated and sent to the candidate version of the model. This percentage can be changed over time. Only the responses from the primary version are returned to the user. The user declaratively describes the desired application state at any given moment. An Iter8 release chart assists users who describe the application state at any given moment. The chart provides the configuration needed for Iter8 to automatically deploy application versions and configure the routing to implement the mirrored release strategy.

Mirrored release

Before you begin
  1. Ensure that you have a Kubernetes cluster and the kubectl and helm CLIs. If using a local cluster (for example, Kind or Minikube), we recommend providing the cluster with at least 16GB of memory.
  2. Install Istio. It suffices to install the demo profile, for example by using: 
    istioctl install --set profile=demo -y
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Deploy initial version

Deploy the initial version of the model using the Iter8 release chart by identifying the environment into which it should be deployed, a list of the versions to be deployed (only one here), and the release strategy to be used:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  strategy: mirror
+EOF
+
What happens?

Because environment is set to deployment-istio, a Deployment and a Service object are created. - The namespace default is inherited from the Helm release namespace since it is not specified in the version or in application.metadata. - The name httpbin-0 is derived from the Helm release name since it is not specified in the version or in application.metadata. The name is derived by appending the index of the version in the list of versions; -0 in this case. - Alternatively, a deploymentSpecification and/or a serviceSpecification could have been specified.

To support routing, a Service (of type ExternalName) named default/httpbin pointing at the Istio gateway, istio-ingressgateway.istio-system, is deployed. The name is the Helm release name since it not specified in application.metadata. Further, an Iter8 routemap is created.

Once the application components are ready, the Iter8 controller automatically configures the routing by creating an Istio VirtualService. It is configured to route all traffic to the only deployed version, httpbin-0.

Verify routing

You can verify the routing configuration by inspecting the VirtualService:

kubectl get virtualservice httpbin -o yaml
+

You can also send requests from a pod within the cluster:

  1. Create a sleep pod in the cluster from which requests can be made: 

    curl -s https://raw.githubusercontent.com/iter8-tools/docs/v0.18.4/samples/kserve-serving/sleep.sh | sh -
+

  2. Exec into the sleep pod: 

    kubectl exec --stdin --tty "$(kubectl get pod --sort-by={metadata.creationTimestamp} -l app=sleep -o jsonpath={.items..metadata.name} | rev | cut -d' ' -f 1 | rev)" -c sleep -- /bin/sh
+

  3. Send requests: 

    curl httpbin.default -s -D - | grep -e '^HTTP' -e app-version
+

The output includes the success of the request (the HTTP return code) and the version of the application that responded (in the app-version response header). In this example:

HTTP/1.1 200 OK
+app-version: httpbin-0
+
To send requests from outside the cluster

To configure the release for traffic from outside the cluster, a suitable Istio Gateway is required (for example). When using the Iter8 release chart, set the gateway field to the name of your Gateway. Finally, to send traffic:

(a) In a separate terminal, port-forward the ingress gateway: 

kubectl -n istio-system port-forward svc/istio-ingressgateway 8080:80
+
(b) Send requests using the Host header: 
curl -H 'Host: httpbin.default' localhost:8080 -s -D - | grep -e '^HTTP' -e app-version
+

Deploy candidate

A candidate can deployed by simply adding a second version to the list of versions comprising the application:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+    weight: 100
+  strategy: mirror
+EOF
+
About the candidate version

In this tutorial, the candidate image is the same as the one for the primary version. In a real world example, it would be different. The version label (app.kubernetes.io/version) can be used to distinguish between versions.

To support modifying the percentage of traffic sent to the candidate, a ConfigMap (httpbin-1-weight-config) is created to be used to manage the proportion of traffic sent to this version.

When the second version is deployed and ready, the Iter8 controller automatically reconfigures the routing; the VirtualService is updated to mirror a portion of the traffic to the candidate version.

Verify routing

You can verify the routing configuration by inspecting the VirtualService. With mirroring, only the response of the primary is returned to the user so output will continue to be:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Modify mirrored weight (optional)

To modify the percentage of traffic mirrored, set the weight in the candidate version:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v0
+    image: kennethreitz/httpbin
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+    weight: 50
+  strategy: mirror
+EOF
+

Iter8 automatically reconfigures the routing to send the desired percentage of requests to the candidate version.

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. Again, user requests will always be responded to by the primary version.

Promote candidate

The candidate can be promoted by redefining the primary version and removing the candidate:

cat <<EOF | helm upgrade --install httpbin --repo https://iter8-tools.github.io/iter8 release --version 1.1 -f -
+environment: deployment-istio
+application: 
+  versions:
+  - metadata:
+      labels:
+        app.kubernetes.io/version: v1
+    image: kennethreitz/httpbin
+  strategy: mirror
+EOF
+
What is different?

The version label (app.kubernetes.io/version) of the primary version was updated. In a real world example, the image would also have been updated (with that from the candidate version).

Once the (reconfigured) primary version ready, the Iter8 controller will automatically reconfigure the routing to send all requests to it.

Verify routing

You can verify the routing configuration by inspecting the VirtualService and/or by sending requests as described above. They will all be handled by the primary version. Output will be something like:

HTTP/1.1 200 OK
+app-version: httpbin-0
+

Cleanup

Delete the application and its routing configuration:

helm delete httpbin
+

If you used the sleep pod to generate load, remove it:

kubectl delete deploy sleep
+

Uninstall Iter8 controller:

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/performance/load-test-grpc-multiple/index.html b/1.1/tutorials/performance/load-test-grpc-multiple/index.html new file mode 100644 index 00000000..273d4cc6 --- /dev/null +++ b/1.1/tutorials/performance/load-test-grpc-multiple/index.html @@ -0,0 +1,28 @@ + Multiple gRPC methods - Iter8

Load test multiple gRPC methods

This tutorial expands on the tutorial Single gRPC method by describing how to load test multiple gRPC methods.

load-test-grpc

Before you begin
  1. Try Your first performance test. Understand the main concepts behind Iter8.
  2. Deploy the sample gRPC service in the Kubernetes cluster. 
    kubectl create deployment routeguide --image=golang --port=50051 \
+-- bash -c "git clone -b v1.52.0 --depth 1 https://github.com/grpc/grpc-go; cd grpc-go/examples/route_guide; sed -i "''" "'"s/localhost//"'" server/server.go; go run server/server.go"
+kubectl expose deployment routeguide --port=50051
+
  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Launch performance test

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={ready,grpc}" \
+--set ready.deploy=routeguide \
+--set ready.service=routeguide \
+--set ready.timeout=60s \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \
+--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \
+--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json
+
About this performance test

This performance test consists of two tasks, namely, ready, and grpc.

The ready task checks if the routeguide deployment exists and is available, and the routeguide service exists.

The grpc task sends call requests to two methods of the cluster-local gRPC service, and collects Iter8's built-in gRPC load test metrics. The two methods are routeguide.RouteGuide.GetFeature and routeguide.RouteGuide.ListFeatures. Note that each method also has its own dataURL for the request payload.

View results using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source routeguide-test with the following parameters:

  • URL: http://iter8.default:8080/grpcDashboard
  • Query string: namespace=default&test=routeguide-test

Create a new dashboard by import. Paste the contents of the grpc Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

grpc Iter8 dashboard with multiple endpoints

View logs

Logs are useful for debugging.

kubectl logs -l iter8.tools/test=routeguide-test
+

Cleanup

Remove the performance test and the sample app from the Kubernetes cluster.

helm delete routeguide-test
+kubectl delete svc/routeguide
+kubectl delete deploy/routeguide
+

Uninstall the Iter8 controller

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Some variations and extensions of this performance test
  1. The grpc task can be configured with load related parameters such as the total number of requests, requests per second, or number of concurrent connections.
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/performance/load-test-grpc/index.html b/1.1/tutorials/performance/load-test-grpc/index.html new file mode 100644 index 00000000..bf2e8b02 --- /dev/null +++ b/1.1/tutorials/performance/load-test-grpc/index.html @@ -0,0 +1,56 @@ + Single gRPC method - Iter8

Load test gRPC method

Load test a Kubernetes gRPC service and visualize the performance metrics with an Iter8 Grafana dashboard.

load-test-grpc

Before you begin
  1. Try Your first performance test. Understand the main concepts behind.

  2. Deploy the sample gRPC service in the Kubernetes cluster. 

    kubectl create deployment routeguide --image=golang --port=50051 \
+-- bash -c "git clone -b v1.52.0 --depth 1 https://github.com/grpc/grpc-go; cd grpc-go/examples/route_guide; sed -i "''" "'"s/localhost//"'" server/server.go; go run server/server.go"
+kubectl expose deployment routeguide --port=50051
+

  3. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 

    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Launch performance test

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={ready,grpc}" \
+--set ready.deploy=routeguide \
+--set ready.service=routeguide \
+--set ready.timeout=60s \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.call=routeguide.RouteGuide.GetFeature \
+--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json
+
helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={ready,grpc}" \
+--set ready.deploy=routeguide \
+--set ready.service=routeguide \
+--set ready.timeout=60s \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json
+
helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={ready,grpc}" \
+--set ready.deploy=routeguide \
+--set ready.service=routeguide \
+--set ready.timeout=60s \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.call=routeguide.RouteGuide.RecordRoute \
+--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json
+
helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={ready,grpc}" \
+--set ready.deploy=routeguide \
+--set ready.service=routeguide \
+--set ready.timeout=60s \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.call=routeguide.RouteGuide.RouteChat \
+--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/bidirectional.json
+
About this performance test

This performance test consists of two tasks, namely, ready, and grpc.

The ready task checks if the routeguide deployment exists and is available, and the routeguide service exists.

The grpc task sends call requests to the specified method of the cluster-local gRPC service with host address routeguide.default:50051 and collects Iter8's built-in gRPC load test metrics. This task supports all four gRPC service methods: unary, server streaming, client streaming, and bidirectional streaming, and will provide payload in the appropriate manner using dataURL.

View results using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source routeguide-test with the following parameters:

  • URL: http://iter8.default:8080/grpcDashboard
  • Query string: namespace=default&test=routeguide-test

Create a new dashboard by import. Paste the contents of the grpc Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

grpc Iter8 dashboard

View logs

Logs are useful for debugging.

kubectl logs -l iter8.tools/test=routeguide-test
+

Cleanup

Remove the performance test and the sample app from the Kubernetes cluster.

helm delete routeguide-test
+kubectl delete svc/routeguide
+kubectl delete deploy/routeguide
+

Uninstall the Iter8 controller

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Some variations and extensions of this performance test
  1. The grpc task can be configured with load related parameters such as the total number of requests, requests per second, or number of concurrent connections.
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/performance/load-test-http-multiple/index.html b/1.1/tutorials/performance/load-test-http-multiple/index.html new file mode 100644 index 00000000..54111ffe --- /dev/null +++ b/1.1/tutorials/performance/load-test-http-multiple/index.html @@ -0,0 +1,25 @@ + Multiple HTTP endpoints - Iter8

Load test multiple HTTP endpoints

Your first performance test describes how to load test a single HTTP service. This tutorial expands on the previous tutorial and describes how to load test multiple HTTP endpoints.

load-test-http

Before you begin
  1. Try Your first performance test. Understand the main concepts behind Iter8.
  2. Ensure that you have a Kubernetes cluster and the kubectl CLI. You can create a local Kubernetes cluster using tools like Kind or Minikube.
  3. Deploy the sample HTTP service in the Kubernetes cluster. 
    kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80
+kubectl expose deploy httpbin --port=80
+
  4. Have Grafana available. For example, Grafana can be installed on your cluster as follows: 
    kubectl create deploy grafana --image=grafana/grafana
+kubectl expose deploy grafana --port=3000
+

Install the Iter8 controller

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

For additional install options, see Iter8 Installation.

Launch performance test

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={ready,http}" \
+--set ready.deploy=httpbin \
+--set ready.service=httpbin \
+--set ready.timeout=60s \
+--set http.endpoints.get.url=http://httpbin.default/get \
+--set http.endpoints.getAnything.url=http://httpbin.default/anything \
+--set http.endpoints.post.url=http://httpbin.default/post \
+--set http.endpoints.post.payloadStr=hello
+
About this performance test

This performance test consists of two tasks, namely, ready and http.

The ready task checks if the httpbin deployment exists and is available, and the httpbin service exists.

The http task sends requests to three endpoints from the cluster-local HTTP service, and collects Iter8's built-in HTTP load test metrics. The three endpoints are http://httpbin.default/get, http://httpbin.default/anything, and http://httpbin.default/post. The last endpoint also has a payload string hello.

View results using Grafana

Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-forward requests as follows:

kubectl port-forward service/grafana 3000:3000
+

Open Grafana in a browser by going to http://localhost:3000 and login. The default username/password are admin/admin.

Add a JSON API data source httpbin-test with the following parameters:

  • URL: http://iter8.default:8080/httpDashboard
  • Query string: namespace=default&test=httpbin-test

Create a new dashboard by import. Paste the contents of the http Grafana dashboard into the text box and load it. Associate it with the JSON API data source defined above.

The Iter8 dashboard will look like the following:

http Iter8 dashboard with multiple endpoints

View logs

Logs are useful for debugging.

kubectl logs -l iter8.tools/test=httpbin-test
+

Cleanup

Remove the performance test and the sample app from the Kubernetes cluster.

helm delete httpbin-test
+kubectl delete svc/httpbin
+kubectl delete deploy/httpbin
+

Uninstall the Iter8 controller

helm delete iter8
+

For additional uninstall options, see Iter8 Uninstall.

If you installed Grafana, you can delete it as follows:

kubectl delete svc/grafana deploy/grafana
+
Some variations and extensions of this performance test
  1. The http task can be configured with load related parameters such as the number of requests, queries per second, or number of parallel connections.
  2. The http task can be configured to send various types of content as payload.
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/tutorials/performance/load-test-http/index.html b/1.1/tutorials/performance/load-test-http/index.html new file mode 100644 index 00000000..abdd3839 --- /dev/null +++ b/1.1/tutorials/performance/load-test-http/index.html @@ -0,0 +1 @@ + Single HTTP endpoint - Iter8
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/abn/about/index.html b/1.1/user-guide/abn/about/index.html new file mode 100644 index 00000000..8d412d14 --- /dev/null +++ b/1.1/user-guide/abn/about/index.html @@ -0,0 +1,105 @@ + About - Iter8

A/B/n Testing

A/B/n testing relies on business metrics typically computed by a frontend, user-facing, application component.

A/B/n test

Metric values often depend on one or more interactions with backend (not user-facing) application components. To run an A/B/n test on a backend component, it is necessary to be able to associate a metric value (computed by the frontend component) to the version of the backend component that contributed to its computation. The challenge is that the frontend component often does not know which version of the backend component processed a given request. To address this challenge, Iter8 introduces an A/B/n SDK.

The Iter8 SDK uses a fixed set of versions numbers (0, 1, etc.) as a way to refer to the current set of versions of a Kubernetes application or ML model. The version of the application associated with a given version number changes over time as new versions are developed, deployed for testing, and either promoted or deleted. Since the set of version numbers is fixed, they can be used to configure routing to the application.

The Iter8 SDK provides two APIs to frontend application components:

a. Lookup() - Given an application and user session, returns a version number to be used as an index to a table of routes. So long as there are no changes in configuration, the version number (and hence the route) will be same for the same user session, guaranteeing session stickiness.

b. WriteMetric() - Given an application, a user session, a metric name its value, WriteMetric() associates the metric value with the appropriate version of the application.

Configuring the Iter8 controller

The Iter8 controller is implemented using gRPC. The service is configured to watch a given set of Kubernetes resource types. The default set of types Iter8 can watch are identified in the default values.yaml file. Other configuration options are described in the same file.

To configure the specific resources to watch for a given application, a Kubernetes ConfigMap is created. It identifies the specific resources that comprise each version. For example, consider the ConfigMap:

apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: backend
+  labels:
+    app.kubernetes.io/managed-by: iter8
+    iter8.tools/kind: routemap
+    iter8.tools/version: "v1.1"
+immutable: true
+data:
+  strSpec: |
+    versions:
+    - resources:
+      - gvrShort: svc
+        name: backend
+        namespace: default
+      - gvrShort: deploy
+        name: backend
+        namespace: default
+    - resources:
+      - gvrShort: svc
+        name: backend-candidate-1
+        namespace: default
+      - gvrShort: deploy
+        name: backend-candidate-1
+        namespace: default
+

This ConfigMap describes an application backend. It identifies two versions of the application. The first is comprised of a Kubernetes Deployment and a Service object both named backend in the default namespace. The second is comprised of the same resource types named backend-candidate-1 in the same namespace.

Deployment time configuration of backend components

As versions of a watched application are deployed or deleted, the Iter8 controller keeps track of which versions are available enabling it to respond appropriately to Lookup() and WriteMetric() requests.

Developing frontend components: Using the SDK

The basic steps to author a frontend application component using the Iter8 SDK are outlined below for Node.js and Go. Similar steps would be required for any gRPC supported language.

Use/import language specific libraries

The gRPC protocol buffer definition is used to generate language specific implementation. These files can be used directly or packaged and imported as a library. As examples, the Node.js sample uses manually generated files directly. On the other hand, the Go sample imports the library provided by the core Iter8 service implementation. In addition to the API specific methods, some general gRPC libraries are required.

The manually generated node files abn_pd.js and abn_grpc_pb.js used in the sample application can be copied and used without modification.

var grpc = require('@grpc/grpc-js');
+
+var messages = require('./abn_pb.js');
+var services = require('./abn_grpc_pb.js');
+
import (
+    "google.golang.org/grpc"
+    "google.golang.org/grpc/credentials/insecure"
+
+    pb "github.com/iter8-tools/iter8/abn/grpc"
+)
+

Instantiate a gRPC client

Instantiate a client to the Iter8 controller:

var client = new services.ABNClient(abnEndpoint, grpc.credentials.createInsecure());
+
opts := []grpc.DialOption{grpc.WithTransportCredentials(insecure.NewCredentials())}
+conn, err := grpc.Dial(fmt.Sprintf("%s:%s", getAbnService(), getAbnServicePort()), opts...)
+if err != nil {
+    panic("Cannot establish connection with abn service")
+}
+c := pb.NewABNClient(conn)
+client = &c
+

Define routing

Track identifiers are mapped to a static set of endpoints. One approach is to maintain a map from track identifier to endpoint:

const versionNumberToRoute = [
+    "http://backend.default.svc.cluster.local:8091",
+    "http://backend-candidate-1.default.svc.cluster.local:8091",
+]
+
versionNumberToRoute = []string{
+    "http://backend.default.svc.cluster.local:8091",
+    "http://backend-candidate-1.default.svc.cluster.local:8091",
+}
+

Using Lookup()

Given a user session identifier, Lookup() returns a version number that can be used to route requests. In code sample below, the user session identifier is assumed to be passed in the X-User header of user requests. The version number is used as an index to the versionNumberToRoute map defined above. A default is used if the call to Lookup() fails for any reason.

var application = new messages.Application();
+application.setName('default/backend');
+application.setUser(req.header('X-User'));
+client.lookup(application, function(err, versionRecommendation) {
+    if (err) {
+        // use default route (see above)
+        console.warn("error calling Lookup()")
+    } else {
+        // use route determined by recommended track
+        console.info('lookup suggested track %d', versionRecommendation.getVersionnumber())
+        versionNumber = versionRecommendation.getVersionnumber()
+        if (versionNumber != NaN && 0 <= versionNumber && versionNumber < versionNumberToRoute.length) {
+            route = versionNumberToRoute[versionNumber]
+        }
+    }
+
+    // call backend service using route
+    ...
+});
+
route := versionNumberToRoute[0]
+user := req.Header["X-User"][0]
+s, err := (*client).Lookup(
+    ctx,
+    &pb.Application{
+        Name: "default/backend",
+        User: user,
+    },
+)
+if err == nil && s != nil {
+    versionNumber := int(s.GetVersionNumber())
+    if err == nil && 0 <= versionNumber && versionNumber < len(versionNumberToRoute) {
+        route = versionNumberToRoute[versionNumber]
+    } // else use default value for route
+}
+
+// call backend service using route
+...
+

Using WriteMetric()

As an example, a single metric named sample_metric is assigned a random value between 0 and 100 and written.

var mv = new messages.MetricValue();
+mv.setName('sample_metric');
+mv.setValue(random({min: 0, max: 100, integer: true}).toString());
+mv.setApplication('default/backend');
+mv.setUser(user);
+client.writeMetric(mv, function(err, session) {});
+
_, _ = (*client).WriteMetric(
+    ctx,
+    &pb.MetricValue{
+        Name:        "sample_metric",
+        Value:       fmt.Sprintf("%f", rand.Float64()*100.0),
+        Application: "default/backend",
+        User:        user,
+    },
+)
+
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/abn/extension/index.html b/1.1/user-guide/abn/extension/index.html new file mode 100644 index 00000000..ea69779d --- /dev/null +++ b/1.1/user-guide/abn/extension/index.html @@ -0,0 +1,7 @@ + Using new resource types - Iter8

Using new resource types

Like progressive release, A/B/n tests use the Iter8 release chart. Extending this chart for a new resource type is similar to extending it for progressive release. The key difference is in the definition of the routemap. We briefly describe how to extend the chart for an Knative application.

Example (Knative Service)

For example, to extend the chart to support A/B/n testing for Knative services, the following files could be added:

  • _knative-istio.tpl - wrapper for all objects that should be deployed
  • _knative-istio.version.ksvc.tpl - the Knative service object that should be deployed for a version
  • _knative-istio.none.tpl - wrapper for all objects that should be deployed to support the no automated traffic pattern
  • _knative-istio.none.routemap.tpl - the routemap definition
  • _knative.helpers.tpl - supporting functions

An implementation of these is here.

Note that many of these additions are the same as in the example for progressive release. Of the above files, only _knative-istio.tpl is different.

Finally, update release.yaml to include knative-istio as a valid option:

{{- else if eq "knative-istio" .Values.environment }}
+  {{- include "env.knative-istio" . }}
+

Extend the controller

The Iter8 controller will need to be restarted with permission to watch Knative service objects. Re-install the controller using the following additional options:

--set resourceTypes.ksvc.Group=serving.knative.dev \
+--set resourceTypes.ksvc.Version=v1 \
+--set resourceTypes.ksvc.Resource=services \
+--set "resourceTypes.ksvc.conditions[0]=Ready"
+

Using the modified chart

To use the modified chart to run A/B tests for Knative services, see the example for progressive release. Unset the strategy and weight fields.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/abn/using-sdk/index.html b/1.1/user-guide/abn/using-sdk/index.html new file mode 100644 index 00000000..f20df01f --- /dev/null +++ b/1.1/user-guide/abn/using-sdk/index.html @@ -0,0 +1,79 @@ + Using the SDK - Iter8

Using the SDK

The basic steps to author a frontend application component using the Iter8 SDK are outlined below for Node.js and Go. Similar steps would be required for any gRPC-supported language.

Use/import language specific libraries

The gRPC protocol buffer definition is used to generate a language-specific implementation. These files can be used directly or packaged and imported as a library. As examples, the Node.js sample uses manually generated files directly. On the other hand, the Go sample imports the library provided by the core Iter8 service implementation. In addition to the API specific methods, some general gRPC libraries are required.

The manually generated node files abn_pd.js and abn_grpc_pb.js used in the sample application can be copied and used without modification.

var grpc = require('@grpc/grpc-js');
+
+var messages = require('./abn_pb.js');
+var services = require('./abn_grpc_pb.js');
+
import (
+    "google.golang.org/grpc"
+    "google.golang.org/grpc/credentials/insecure"
+
+    pb "github.com/iter8-tools/iter8/abn/grpc"
+)
+

Instantiate a gRPC client

Instantiate a client to the Iter8 controller:

var client = new services.ABNClient(abnEndpoint, grpc.credentials.createInsecure());
+
opts := []grpc.DialOption{grpc.WithTransportCredentials(insecure.NewCredentials())}
+conn, err := grpc.Dial(fmt.Sprintf("%s:%s", getAbnService(), getAbnServicePort()), opts...)
+if err != nil {
+    panic("Cannot establish connection with abn service")
+}
+c := pb.NewABNClient(conn)
+client = &c
+

Define routing

Track identifiers are mapped to a static set of endpoints. One approach is to maintain a map from track identifier to endpoint:

const versionNumberToRoute = [
+    "http://backend.default.svc.cluster.local:8091",
+    "http://backend-candidate-1.default.svc.cluster.local:8091",
+]
+
versionNumberToRoute = []string{
+    "http://backend.default.svc.cluster.local:8091",
+    "http://backend-candidate-1.default.svc.cluster.local:8091",
+}
+

Using Lookup()

Given a user session identifier, Lookup() returns a version number that can be used to route requests. In code sample below, the user session identifier is assumed to be passed in the X-User header of user requests. The version number is used as an index to the versionNumberToRoute map defined above. A default is used if the call to Lookup() fails for any reason.

var application = new messages.Application();
+application.setName('default/backend');
+application.setUser(req.header('X-User'));
+client.lookup(application, function(err, versionRecommendation) {
+    if (err) {
+        // use default route (see above)
+        console.warn("error calling Lookup()")
+    } else {
+        // use route determined by recommended track
+        console.info('lookup suggested track %d', versionRecommendation.getVersionnumber())
+        versionNumber = versionRecommendation.getVersionnumber()
+        if (versionNumber != NaN && 0 <= versionNumber && versionNumber < versionNumberToRoute.length) {
+            route = versionNumberToRoute[versionNumber]
+        }
+    }
+
+    // call backend service using route
+    ...
+});
+
route := versionNumberToRoute[0]
+user := req.Header["X-User"][0]
+s, err := (*client).Lookup(
+    ctx,
+    &pb.Application{
+        Name: "default/backend",
+        User: user,
+    },
+)
+if err == nil && s != nil {
+    versionNumber := int(s.GetVersionNumber())
+    if err == nil && 0 <= versionNumber && versionNumber < len(versionNumberToRoute) {
+        route = versionNumberToRoute[versionNumber]
+    } // else use default value for route
+}
+
+// call backend service using route
+...
+

Using WriteMetric()

As an example, a single metric named sample_metric is assigned a random value between 0 and 100 and written.

var mv = new messages.MetricValue();
+mv.setName('sample_metric');
+mv.setValue(random({min: 0, max: 100, integer: true}).toString());
+mv.setApplication('default/backend');
+mv.setUser(user);
+client.writeMetric(mv, function(err, session) {});
+
_, _ = (*client).WriteMetric(
+    ctx,
+    &pb.MetricValue{
+        Name:        "sample_metric",
+        Value:       fmt.Sprintf("%f", rand.Float64()*100.0),
+        Application: "default/backend",
+        User:        user,
+    },
+)
+
Iter8 is open-source and available under the Apache 2 License.
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"gtV-BnMxjVMRMSeikfrav", + "type": "arrow" + } + ], + "updated": 1675782450218, + "link": null, + "locked": false + }, + { + "type": "text", + "version": 3103, + "versionNonce": 2063935590, + "isDeleted": false, + "id": "9kVNIwcclMIXrIQFNfcR1", + "fillStyle": "hachure", + "strokeWidth": 1, + "strokeStyle": "solid", + "roughness": 1, + "opacity": 100, + "angle": 0, + "x": 949.5355215962472, + "y": 329.8528437036799, + "strokeColor": "#0b7285", + "backgroundColor": "transparent", + "width": 129, + "height": 23, + "seed": 1681710646, + "groupIds": [], + "roundness": null, + "boundElements": [], + "updated": 1675782450218, + "link": null, + "locked": false, + "fontSize": 20, + "fontFamily": 1, + "text": "backend - v2", + "baseline": 16, + "textAlign": "center", + "verticalAlign": "middle", + "containerId": "_0TV30RL3kcH4MSm-ihJ9", + "originalText": "backend - v2" + } + ], + "appState": { + "gridSize": null, + "viewBackgroundColor": "#ffffff" + }, + "files": {} +} \ No newline at end of file diff --git a/1.1/user-guide/install/index.html b/1.1/user-guide/install/index.html new file mode 100644 index 00000000..cc968619 --- /dev/null +++ b/1.1/user-guide/install/index.html @@ -0,0 +1,8 @@ + Installation - Iter8

Install options

By default, Iter8 uses BadgerDB to store metrics from A/B/n and performance tests. BadgerDB is not suitable for production use. To install for production, use Redis.

Install with helm

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller
+
helm install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller \
+--set clusterScoped=true
+

To install Iter8 in a non-default namespace, use the -n option.

Install with kustomize

Iter8 can be installed and configured to watch resources either in a single namespace (namespace-scoped) or in the whole cluster (cluster-scoped). 

kubectl apply -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/namespaceScoped?ref=v1.1.1'
+
kubectl apply -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/clusterScoped?ref=v1.1.1'
+

To install Iter8 in a non-default namespace, download the kustomize folder and modify the namespace field in the kustomization.yaml file.

Install using Rancher Desktop

Rancher does not support a standard storage class by default. Install the local path provisioner:

kubectl apply -f https://raw.githubusercontent.com/rancher/local-path-provisioner/v0.0.26/deploy/local-path-storage.yaml
+

And set metrics.badgerdb.storageClassName when starting the Iter8 controller:

--set metrics.badgerdb.storageClassName=local-path
+

Install on OpenDataHub

See https://github.com/opendatahub-io-contrib/odh-contrib-manifests/tree/main/iter8

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/metrics_store/index.html b/1.1/user-guide/metrics_store/index.html new file mode 100644 index 00000000..29dc5f09 --- /dev/null +++ b/1.1/user-guide/metrics_store/index.html @@ -0,0 +1,7 @@ + Metrics store options - Iter8

Metrics store

Iter8 provides a metrics store for A/B/n and performance metrics. This simplifies the set up and execution of test scenarios. Iter8 currently supports the following databases:

By default, Iter8 uses BadgerDB. Note, however, that BadgerDB is not suitable for production use and is only suitable for a single instance of Iter8. Support for other databases is in the works. See below for details on how you can contribute additional implementations.

BadgerDB configuration options

BadgerDB is a file system-based database. When installing Iter8, the following options can be configured:

  • bagderdb.storage - The amount of space allocated to BadgerDB (default 50Mi).
  • badgerdb.storageClassName - The Kubernetes storage class that should be used. The default (standard) may not work for all clusters.

Redis configuration options

We assume that Redis is deployed. For example, for a basic deployment:

kubectl create deploy redis --image=redis/redis-stack:latest --port=6379
+kubectl expose deploy redis --port=6379
+

Run Iter8 with the metrics store implementation set to redis and specify its endpoint:

helm upgrade --install --repo https://iter8-tools.github.io/iter8 --version 1.1 iter8 controller  \
+--set clusterScoped=true \
+--set metrics.implementation=redis \
+--set metrics.redis.addresss=redis:6379
+

Contribute a new metrics store implementation

To contribute a new metrics store implementation:

  1. Create an issue for discussion.

  2. Submit a pull request on the Iter8 project with the following updates:

  3. Submit a second pull request on the Iter8 docs project updating the list of available implementations.

  4. Alert the project reviewers on Slack #development channel.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/performance/extension/index.html b/1.1/user-guide/performance/extension/index.html new file mode 100644 index 00000000..0e580bfb --- /dev/null +++ b/1.1/user-guide/performance/extension/index.html @@ -0,0 +1,11 @@ + Using new resource types - Iter8

Using new resource types

The Iter8 performance test task ready ensures that an object exists and is ready. To use this task with new resource types, including CRDs, add the new resource type to the list of known types defined in the default values.yaml file for the chart. Alternatively, the new type can be specified at run time with the --set option.

Example

To include a Knative service as part of a version definition, add the following to the map of resourceTypes in the values.yaml file used to configure the controller. The addition identifies the Kubernetes group, version, and resource (GVR) and the status condition that should be checked for readiness.

ksvc:
+    Group: serving.knative.dev
+    Version: v1
+    Resource: services
+    conditions:
+    - Ready
+

Alternatively, to set the values at run time:

--set resourceTypes.ksvc.Group=serving.knative.dev \
+--set resourceTypes.ksvc.Version=v1 \
+--set resourceTypes.Resource=services \
+--set "resourceTypes.conditions[0]=Ready"
+
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/performance/parameters/index.html b/1.1/user-guide/performance/parameters/index.html new file mode 100644 index 00000000..1bf8e413 --- /dev/null +++ b/1.1/user-guide/performance/parameters/index.html @@ -0,0 +1,5 @@ + Parameters - Iter8

Performance test parameters

Iter8 performance tests are run using Helm. Tests can be configured with parameters in the same manner as any other Helm command -- using chart values and the --set option.

The tasks parameter is used to identify the sequence of tasks that should be executed. Each task has its own parameters. Iter8 uses the convention that the parameters of a task are nested under the name of that task. In the following example, the url parameter of the http task is nested under the http object.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.url=https://httpbin.org/get
+

All the parameters of the performance test (including of all included tasks) are optional unless otherwise documented. The task-specific parameters are documented in the task documentation.

Currently available tasks are:

  • http - generate synthetic load and capture performance metrics for HTTP endpoints
  • grpc - generate synthetic load and capture performance metrics for gRPC methods
  • ready - check readiness of an object
  • github - send a GitHub notification
  • slack - send a Slack notification

Global parameters

In addition to task-specific parameters, the following global parameters are available:

Name Type Description
serviceAccountName string Optional name of a service account to use. If specified, it is assumed the service account has the necessary permissions to run a performance test. If not specified, Iter8 will create a service account.
logLevel string Log level. Must be one of trace, debug, info (default), warning, or error.
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/performance/tasks/github/index.html b/1.1/user-guide/performance/tasks/github/index.html new file mode 100644 index 00000000..fef837e4 --- /dev/null +++ b/1.1/user-guide/performance/tasks/github/index.html @@ -0,0 +1,8 @@ + github - Iter8

github

Trigger GitHub workflows via a repository_dispatch.

A repository_dispatch will trigger workflows in the default branch of the GitHub repository. By default, an summary of the performance test will also be sent.

Usage Example

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http,github}" \
+--set http.url=http://httpbin.default/get \
+--set github.owner=<GitHub owner> \
+--set github.repo=<GitHub repository> \
+--set github.token=<GitHub token>
+

See here for a more in-depth tutorial.

Parameters

Name Type Required Default value Description
owner string Yes N/A Owner of the GitHub repository
repo string Yes N/A GitHub repository
token string Yes N/A Authorization token
payloadTemplateURL string No https://raw.githubusercontent.com/iter8-tools/iter8/v1.1.1/templates/notify/_payload-github.tpl URL to a payload template
softFailure bool No true Indicates the performance test should not fail if the task cannot successfully send the request

Default payload

A repository_dispatch requires a payload that contains the type of the event.

The default payload template will set the event_type to iter8. In addition, it will also provide a performance test summary in the client_payload, which means that this data will be accessible in the GitHub workflow via ${{ toJson(github.event.client_payload) }}.

However, if you would like to use a different payload template, simply set a payloadTemplateURL and Iter8 will not use the default.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/performance/tasks/grpc/index.html b/1.1/user-guide/performance/tasks/grpc/index.html new file mode 100644 index 00000000..d4552ec1 --- /dev/null +++ b/1.1/user-guide/performance/tasks/grpc/index.html @@ -0,0 +1,82 @@ + grpc - Iter8

grpc

Generate requests for a gRPC service and and collect latency and error-related metrics.

Usage example

In this performance test, the grpc task generates call requests for a gRPC service hosted at hello.default:50051, defined in the protobuf file located at grpc.protoURL, with a gRPC method named helloworld.Greeter.SayHello. Metrics collected by this task are viewable with a Grafana dashboard.

Single method: 

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={grpc}" \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.call=routeguide.RouteGuide.GetFeature \
+--set grpc.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json
+

Multiple methods: 

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={grpc}" \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \
+--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \
+--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \
+--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \
+--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json \
+--set grpc.endpoints.routeChat.call=routeguide.RouteGuide.RouteChat \
+--set grpc.endpoints.routeChat.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/bidirectional.json
+

Parameters

Any field in the Config struct of the ghz runner package can be used as a parameter in this task. The JSON tags of the struct fields directly correspond to the names of the parameters of this task. In the usage example, the parameters host and call correspond to the Host and Call fields respectively in the Config struct.

In addition, the following fields are defined by this task.

Name Type Description
protoURL string (URL) URL where the protobuf file that defines the gRPC service is located.
dataURL string (URL) URL where JSON data to be used in call requests is located.
binaryDataURL string (URL) URL where binary data to be used in call requests is located.
metadataURL string (URL) URL where the JSON metadata data to be used in call requests is located.
warmupNumRequests int Number of requests to be sent in a warmup task (results are ignored).
warmupDuration string Duration of warmup task (results are ignored). Specified in the Go duration string format (example, 5s). If both warmupDuration and warmupNumRequests are specified, then warmupDuration is ignored.
endpoints map[string]EndPoint Used to specify multiple endpoints and their configuration. The string is the name of the endpoint and the EndPoint struct includes all the parameters described above as well as those from the Config struct. Load testing and metric collection will be conducted separately for each endpoint.

Precedence

Some parameters have a default value, which can be overwritten. In addition, with the endpoints parameter, you can test multiple endpoints and configure parameters for each of those endpoint. In these cases, the priority order is the default value, the value set at the base level, and the value set at the endpoint value.

In the following example, all three endpoints will use the default timeout of 20s (from Config struct).

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={grpc}" \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \
+--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \
+--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \
+--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \
+--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json
+

In the following example, the getFeature and listFeatures endpoints will use the default timeout of 20s and the recordRoute endpoint will use a timeout of 30s.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={grpc}" \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \
+--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \
+--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \
+--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \
+--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json
+

In the following example, all three endpoints will use a qps of 40s.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={grpc}" \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.timeout=40s \
+--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \
+--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \
+--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \
+--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \
+--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json
+

In the following example, the getFeature and listFeatures endpoints will use a timeout of 40s and the listFeatures endpoint will use a timeout of 30s.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={grpc}" \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.timeout=40s \
+--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \
+--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \
+--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \
+--set grpc.endpoints.listFeatures.timeout=30s \
+--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \
+--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json \
+--set grpc.endpoints.recordRoute.timeout=30s
+

Further more, set parameters will trickle down to the endpoints.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 routeguide-test iter8 \
+--set "tasks={grpc}" \
+--set grpc.host=routeguide.default:50051 \
+--set grpc.protoURL=https://raw.githubusercontent.com/grpc/grpc-go/v1.52.0/examples/route_guide/routeguide/route_guide.proto \
+--set grpc.skipFirst=5 \
+--set grpc.endpoints.getFeature.call=routeguide.RouteGuide.GetFeature \
+--set grpc.endpoints.getFeature.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/unary.json \
+--set grpc.endpoints.listFeatures.call=routeguide.RouteGuide.ListFeatures \
+--set grpc.endpoints.listFeatures.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/server.json \
+--set grpc.endpoints.listFeatures.timeout=30s \
+--set grpc.endpoints.recordRoute.call=routeguide.RouteGuide.RecordRoute \
+--set grpc.endpoints.recordRoute.dataURL=https://raw.githubusercontent.com/iter8-tools/docs/v0.17.3/samples/grpc-payload/client.json
+

In this example, all three endpoints will have a skipFirst of 5.

Grafana Dashboard

The results of the grpc task is visualized using the grpc Iter8 Grafana dashboard. The dashboard can be found here.

Assuming the URL to the Grafana service is $GRAFANA_URL, you can install the dashboard as follows:

  1. Open Grafana in a browser.
  2. Add a new data JSON API data source with the following parameters
    • URL: $GRAFANA_URL/grpcDashboard
    • Query string: namespace=<namespace>&test=<test name>
  3. Import the grpc Iter8 Grafana dashboard
    • Copy and paste the contents of this link into the text box

You will see a visualization of the performance test like the following:

grpc Iter8 dashboard

For multiple endpoints, the visualization will look like the following:

grpc Iter8 dashboard with multiple endpoints

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/performance/tasks/http/index.html b/1.1/user-guide/performance/tasks/http/index.html new file mode 100644 index 00000000..e4ae4aa5 --- /dev/null +++ b/1.1/user-guide/performance/tasks/http/index.html @@ -0,0 +1,52 @@ + http - Iter8

http

Generate requests for an HTTP service and and collect latency and error-related metrics.

Usage example

In this performance test, the http task generates requests for https://httpbin.org/get, and collects latency and error-related metrics. Metrics collected by this task are viewable with a Grafana dashboard.

Single endpoint: 

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.url=https://httpbin.org/get
+

Multiple endpoints: 

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.endpoints.get.url=http://httpbin.default/get \
+--set http.endpoints.getAnything.url=http://httpbin.default/anything \
+--set http.endpoints.post.url=http://httpbin.default/post \
+--set http.endpoints.post.payloadStr=hello
+

Parameters

Name Type Description
url string (URL) URL where requests are sent.
headers map[string]string HTTP headers to use in the requests.
numRequests int Number of requests to be sent to the app. Default value is 100.
duration string Duration of this task. Specified in the Go duration string format (example, 5s). If both duration and numRequests are specified, then duration is ignored.
qps float qps stands for queries-per-second. Number of requests per second sent to the app. Default value is 8.0.
connections int Number of parallel connections used to send requests. Default value is 4.
payloadURL string (URL) URL from which to download the content that will be used as the request payload. If this field is specified, Iter8 will send HTTP POST requests to the app using this content as the payload.
payloadStr string String data to be used as the request payload. If this field is specified, Iter8 will send HTTP POST requests to the app using this string as the payload.
contentType string Content type of the payload. This is intended to be used in conjunction with one of the payload* fields. If this field is specified, Iter8 will send HTTP POST requests to the app using this as the Content-Type header value.
warmupNumRequests int Number of requests to be sent in a warmup task (results are ignored).
warmupDuration string Duration of warmup task (results are ignored). Specified in the Go duration string format (example, 5s). If both warmupDuration and warmupNumRequests are specified, then warmupDuration is ignored.
endpoints map[string]EndPoint Used to specify multiple endpoints and their configuration. The string is the name of the endpoint and the EndPoint struct includes all the parameters described above. Load testing and metric collection will be conducted separately for each endpoint.

Precedence

Some parameters have a default value, which can be overwritten. In addition, with the endpoints parameter, you can test multiple endpoints and configure parameters for each of those endpoint. In these cases, the priority order is the default value, the value set at the base level, and the value set at the endpoint value.

In the following example, all three endpoints will use the default qps (queries-per-second) of 8.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.endpoints.get.url=http://httpbin.default/get \
+--set http.endpoints.getAnything.url=http://httpbin.default/anything \
+--set http.endpoints.post.url=http://httpbin.default/post \
+--set http.endpoints.post.payloadStr=hello
+

In the following example, the get and getAnything endpoints will use the default qps of 8 and the post endpoint will use a qps of 15.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.endpoints.get.url=http://httpbin.default/get \
+--set http.endpoints.getAnything.url=http://httpbin.default/anything \
+--set http.endpoints.post.url=http://httpbin.default/post \
+--set http.endpoints.post.payloadStr=hello \
+--set http.endpoints.post.qps=15
+

In the following example, all three endpoints will use a qps (queries-per-second) of 10.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.qps=10 \
+--set http.endpoints.get.url=http://httpbin.default/get \
+--set http.endpoints.getAnything.url=http://httpbin.default/anything \
+--set http.endpoints.post.url=http://httpbin.default/post \
+--set http.endpoints.post.payloadStr=hello
+

In the following example, the get and getAnything endpoints will use a qps of 10 and the post endpoint will use a qps of 15.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.qps=10 \
+--set http.endpoints.get.url=http://httpbin.default/get \
+--set http.endpoints.getAnything.url=http://httpbin.default/anything \
+--set http.endpoints.post.url=http://httpbin.default/post \
+--set http.endpoints.post.payloadStr=hello \
+--set http.endpoints.post.qps=15
+

Further more, set parameters will trickle down to the endpoints.

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http}" \
+--set http.numRequests=50 \
+--set http.endpoints.get.url=http://httpbin.default/get \
+--set http.endpoints.getAnything.url=http://httpbin.default/anything \
+--set http.endpoints.post.url=http://httpbin.default/post \
+--set http.endpoints.post.payloadStr=hello
+

In this example, all three endpoints will have a numRequests of 50.

Grafana Dashboard

The results of the http task is visualized using the http Iter8 Grafana dashboard. The dashboard can be found here.

Assuming the URL to the Grafana service is $GRAFANA_URL, you can install the dashboard as follows:

  1. Open Grafana in a browser.
  2. Add a new data JSON API data source with the following parameters
    • URL: $GRAFANA_URL/httpDashboard
    • Query string: namespace=<namespace>&test=<test name>
  3. Import the http Iter8 Grafana dashboard
    • Copy and paste the contents of this link into the text box

You will see a visualization of the performance test like the following:

http Iter8 dashboard

For multiple endpoints, the visualization will look like the following:

http Iter8 dashboard with multiple endpoints

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/performance/tasks/images/grpcdashboard.png b/1.1/user-guide/performance/tasks/images/grpcdashboard.png new file mode 100644 index 00000000..07b4c25c Binary files /dev/null and b/1.1/user-guide/performance/tasks/images/grpcdashboard.png differ diff --git a/1.1/user-guide/performance/tasks/images/grpcmultipledashboard.png b/1.1/user-guide/performance/tasks/images/grpcmultipledashboard.png new file mode 100644 index 00000000..8b377d72 Binary files /dev/null and b/1.1/user-guide/performance/tasks/images/grpcmultipledashboard.png differ diff --git a/1.1/user-guide/performance/tasks/images/httpdashboard.png b/1.1/user-guide/performance/tasks/images/httpdashboard.png new file mode 100644 index 00000000..291a2464 Binary files /dev/null and b/1.1/user-guide/performance/tasks/images/httpdashboard.png differ diff --git a/1.1/user-guide/performance/tasks/images/httpmultipledashboard.png b/1.1/user-guide/performance/tasks/images/httpmultipledashboard.png new file mode 100644 index 00000000..c757ee56 Binary files /dev/null and b/1.1/user-guide/performance/tasks/images/httpmultipledashboard.png differ diff --git a/1.1/user-guide/performance/tasks/ready/index.html b/1.1/user-guide/performance/tasks/ready/index.html new file mode 100644 index 00000000..0566e3f1 --- /dev/null +++ b/1.1/user-guide/performance/tasks/ready/index.html @@ -0,0 +1,13 @@ + ready - Iter8

ready

Check if a Kubernetes object exists and is ready.

Usage example

In the following example, the ready task checks if a deployment named httpbin-prod exists and its availability condition is set to true, and a service named httpbin exists. 

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={ready,http}" \
+--set ready.deploy=httpbin-prod \
+--set ready.service=httpbin \
+--set http.url=http://httpbin.default/get
+

Parameters

Name Type Description
deploy string Name of a Kubernetes deployment. The task checks if the deployment exists and its Available condition is set to true.
service string Name of a Kubernetes service. The task checks if the service exists.
ksvc string Name of a Knative service. The task checks if the service exists and its Ready condition is set to true.
timeout string Timeout for readiness check to succeed. Default value is 60s.
namespace string The namespace under which to look for the Kubernetes objects.

Extensions

Iter8 can be easily extended to support readiness checks for any type of Kubernetes object (including objects with custom resource types). To do so, add the new resource type to the list of known types defined in the default values.yaml file for the chart.

Example

To include a Knative service as part of a version definition, add the following to the map of resourceTypes in the values.yaml file used to configure the controller. The addition identifies the Kubernetes group, version, and resource (GVR) and the status condition that should be checked for readiness.

ksvc:
+    Group: serving.knative.dev
+    Version: v1
+    Resource: services
+    conditions:
+    - Ready
+
Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/performance/tasks/slack/index.html b/1.1/user-guide/performance/tasks/slack/index.html new file mode 100644 index 00000000..656b0c78 --- /dev/null +++ b/1.1/user-guide/performance/tasks/slack/index.html @@ -0,0 +1,7 @@ + slack - Iter8

slack

Send an performance test summary in a message to a Slack channel using a incoming webhook.

Usage Example

helm upgrade --install \
+--repo https://iter8-tools.github.io/iter8 --version 1.1 httpbin-test iter8 \
+--set "tasks={http,slack}" \
+--set http.url=http://httpbin.default/get \
+--set slack.url=<Slack webhook> \
+--set slack.method=POST
+

See here for a more in-depth tutorial.

Parameters

Name Type Required Default value Description
url string Yes N/A URL to the Slack webhook
payloadTemplateURL string No https://raw.githubusercontent.com/iter8-tools/iter8/v1.1.1/templates/notify/_payload-slack.tpl URL to a payload template
softFailure bool No true Indicates the performance test should not fail if the task cannot successfully send the request

Default payload

The payload will determine what will be contained in the Slack message. The default payload template of the slack task is to send a performance test summary in text form.

However, if you would like to use a different payload template, simply set a payloadTemplateURL and Iter8 will not use the default.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/progressive-release/about/index.html b/1.1/user-guide/progressive-release/about/index.html new file mode 100644 index 00000000..e25fdadb --- /dev/null +++ b/1.1/user-guide/progressive-release/about/index.html @@ -0,0 +1 @@ + About - Iter8

About progressive release

Progressive release is an approach to application/ML model release that involves deploying a candidate version at the same time as the current, or primary, version. A portion of user traffic is directed to the candidate version allowing it to be evaluated for behavior and performance. Once approved, the candidate can be promoted -- becoming the new primary version.

There are different strategies for distributing traffic between the primary and candidate version:

  • blue-green - A percentage of requests are directed to a candidate version of the model. This percentage can be changed over time. The remaining requests go to the primary version.
  • canary - Requests that match a particular pattern, for example those that have a particular header, are directed to the candidate version of the model. The remaining requests go to the primary version.
  • mirrored - All requests are sent to the primary version. A percentage of requests is replicated and sent to a candidate version of the model. This percentage can be changed over time. Only responses from the primary version are returned to the user.

Progressive release uses the Iter8 release Helm chart. Options are described in the default values.yaml file. The progressive release tutorials show how it can be used to progressively release versions of an application or ML model.

The chart provided by Iter8 supports many common deployment scenarios including:

  • Applications composed of a Deployment and a Service object using Istio as a service mesh
  • Applications composed of a Deployment and a Service object using the Kubernetes Gateway API
  • ML models deployed to KServe (using KNative)
  • ML models deploy to KServe ModelMesh using Istio as a service mesh

Other deployment environments

The release chart can be easily extended to include other deployment environments. Please consider contributing any extensions to Iter8.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/progressive-release/extension/index.html b/1.1/user-guide/progressive-release/extension/index.html new file mode 100644 index 00000000..d4855722 --- /dev/null +++ b/1.1/user-guide/progressive-release/extension/index.html @@ -0,0 +1,71 @@ + Using new resource types - Iter8

Extending progressive release to other deployment environments

The Iter8 release chart can be easily extended for applications/ML models composed of new types. We briefly describe how to extend the chart for an Knative application.

Approach

The release chart can be found in the charts/release sub-folder of the Iter8 GitHub repository. The file release.yaml is the starting point. For each valid environment, the chart contains a set of files defining the resources that should be created. These may include:

  • application object(s)
  • routemaps for different traffic patterns
  • configmaps used to specify request percentages
  • a service defining a common entry for requests (if needed)

Note that the file naming helps identify related template files.

Example (Knative Service)

For example, to implement a blue-green release for Knative services, the following files could be added.

  • _knative-istio.tpl - wrapper for all objects that should be deployed
  • _knative-istio.version.ksvc.tpl - the Knative service object that should be deployed for a version
  • _knative-istio.blue-green.tpl - wrapper for all objects that should be deployed to support the blue-green traffic pattern
  • _knative-istio.blue-green.routemap.tpl - the routemap definition
  • _knative-istio.service.tpl - a supporting external service
  • _knative.helpers.tpl - supporting functions

An implementation of these is here.

Note that this sample only implements the blue-green traffic pattern. A more complete implementation would include canary and mirroring traffic patterns.

Finally, update release.yaml to include knative-istio as a valid option:

{{- else if eq "knative-istio" .Values.environment }}
+  {{- include "env.knative-istio" . }}
+

Extend the controller

The Iter8 controller will need to be restarted with permission to watch Knative service objects. Re-install the controller using the following additional options:

--set resourceTypes.ksvc.Group=serving.knative.dev \
+--set resourceTypes.ksvc.Version=v1 \
+--set resourceTypes.ksvc.Resource=services \
+--set "resourceTypes.ksvc.conditions[0]=Ready"
+

Using the modified chart

Reference the location of the local copy of the chart instead of using the --repo and --version options. For example assuming the location is $CHART, a deployment of two versions of the Knative hello service with a 30-70 traffic split would be:

cat <<EOF | helm upgrade --install hello $CHART -f -
+environment: knative-istio
+application:
+  versions:
+  - ksvcSpecification:
+      spec:
+        template:
+          spec:
+            containers:
+            - image: ghcr.io/knative/helloworld-go:latest
+              ports:
+              - containerPort: 80
+              env:
+              - name: TARGET
+                value: "v1"
+    weight: 30
+  - ksvcSpecification:
+      spec:
+        template:
+          spec:
+            containers:
+            - image: ghcr.io/knative/helloworld-go:latest
+              ports:
+              - containerPort: 80
+              env:
+              - name: TARGET
+                value: "v2"
+    weight: 70
+  strategy: blue-green
+EOF
+

Contribute your extensions

Please consider contributing any extensions to Iter8 by submitting a pull request.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/routemap/index.html b/1.1/user-guide/routemap/index.html new file mode 100644 index 00000000..cab1dea9 --- /dev/null +++ b/1.1/user-guide/routemap/index.html @@ -0,0 +1,53 @@ + Routemaps - Iter8

Routemaps

A routemap contains a description of each version of an application and may contain one or more routing templates. The description of versions is used by Iter8 to identify which versions of the application are available at any moment. Whenever versions become available or disappear, any defined routing templates will be applied. This results in the automatic reconfiguration of the routing.

Version list

A version is a list of resources that, when available and ready, indicate that the version is available. For example, the following describes a application with two versions. In this case, each version has a Service and a Deployment. In one version, they are named httpbin-0, and in the other, httpbin-1:

versions:
+- resources:
+  - gvrShort: svc
+    name: httpbin-0
+    namespace: default
+  - gvrShort: deploy
+    name: httpbin-0
+    namespace: default
+- resources:
+  - gvrShort: svc
+    name: httpbin-1
+    namespace: default
+  - gvrShort: deploy
+    name: httpbin-1
+    namespace: default
+

Note that the resources types are specified using a short name. In this example, svc and deploy. A short name is used to simplify the specification of the resources in a version. A mapping of short name to Kubernetes group, version, resource is captured in the configuration of the Iter8 controller. This set can be extended to include any types including custom resources; that is, those defined by a CRD.

A version may optionally specify an integer weight indicating the proportion of traffic that should be sent to it relative to other versions.

Routing templates

A routing template is a Go template that is applied each time a new version becomes available or an old one goes away. Multiple templates can be defined/applied.

THe application of the templates allows Iter8 to automatically reconfigure the routing when versions come and go. For example, the template created in the blue-green release tutorial contains an Istio VirtualService. Applying the template to the available versions yields the necessary VirtualSerivce definition. In this tutorial, the template definition is:

blue-green:
+  gvrShort: vs
+  template: |
+    apiVersion: networking.istio.io/v1beta1
+    kind: VirtualService
+    metadata:
+      name: httpbin
+    spec:
+      gateways:
+      - mm-gateway
+      - mesh
+      hosts:
+      - httpbin.default
+      - httpbin.default.svc
+      - httpbin.default.svc.cluster.local
+      http:
+      - route:
+        # primary version
+        - destination:
+            host: httpbin-0.default.svc.cluster.local
+          {{- if gt (index .Weights 1) 0 }}
+          weight: {{ index .Weights 0 }}
+          {{- end }}
+          headers:
+            response:
+              add:
+                app-version: httpbin-0
+        # other versions
+        {{- if gt (index .Weights 1) 0 }}
+        - destination:
+            host: httpbin-1.default.svc.cluster.local
+          weight: {{ index .Weights 1 }}
+          headers:
+            response:
+              add:
+                app-version: httpbin-1
+        {{- end }}
+

Implementation

A routemap is implemented as a ConfigMap with the following labels: - iter8.tools/kind with value routemap - iter8.tools/version with value corresponding the version of the controller being used

The list of versions and routing templates are stored as stringified YAML.

Iter8 is open-source and available under the Apache 2 License.
\ No newline at end of file diff --git a/1.1/user-guide/uninstall/index.html b/1.1/user-guide/uninstall/index.html new file mode 100644 index 00000000..50177c46 --- /dev/null +++ b/1.1/user-guide/uninstall/index.html @@ -0,0 +1,4 @@ + Uninstall - Iter8

Uninstall

Uninstall with helm

If installed with helm, uninstall with:

helm delete iter8
+

Uninstall with kustomize

If installed with kustomize, uninstall with one of the following:

kubectl delete -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/namespaceScoped?ref=v1.1.1'
+
kubectl delete -k 'https://github.com/iter8-tools/iter8.git/kustomize/controller/clusterScoped?ref=v1.1.1'
+
Iter8 is open-source and available under the Apache 2 License.
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Redirecting to ../../0.18/contributing/... + Redirecting to ../../1.1/contributing/... \ No newline at end of file diff --git a/latest/getting-started/advantages/index.html b/latest/getting-started/advantages/index.html index 066d98a1..58bf9c82 100644 --- a/latest/getting-started/advantages/index.html +++ b/latest/getting-started/advantages/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/advantages/... + Redirecting to ../../../1.1/getting-started/advantages/... \ No newline at end of file diff --git a/latest/getting-started/concepts/index.html b/latest/getting-started/concepts/index.html index 35d932fc..25008f9d 100644 --- a/latest/getting-started/concepts/index.html +++ b/latest/getting-started/concepts/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/concepts/... + Redirecting to ../../../1.1/getting-started/concepts/... \ No newline at end of file diff --git a/latest/getting-started/first-abn/index.html b/latest/getting-started/first-abn/index.html index a29ff71f..ed7d261a 100644 --- a/latest/getting-started/first-abn/index.html +++ b/latest/getting-started/first-abn/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/first-abn/... + Redirecting to ../../../1.1/getting-started/first-abn/... \ No newline at end of file diff --git a/latest/getting-started/first-performance/index.html b/latest/getting-started/first-performance/index.html index 11f80742..b926de54 100644 --- a/latest/getting-started/first-performance/index.html +++ b/latest/getting-started/first-performance/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/first-performance/... + Redirecting to ../../../1.1/getting-started/first-performance/... \ No newline at end of file diff --git a/latest/getting-started/first-release/index.html b/latest/getting-started/first-release/index.html index 6c28cc2d..84f03dd7 100644 --- a/latest/getting-started/first-release/index.html +++ b/latest/getting-started/first-release/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/first-release/... + Redirecting to ../../../1.1/getting-started/first-release/... \ No newline at end of file diff --git a/latest/getting-started/help/index.html b/latest/getting-started/help/index.html index be43ceb0..b75365ad 100644 --- a/latest/getting-started/help/index.html +++ b/latest/getting-started/help/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/help/... + Redirecting to ../../../1.1/getting-started/help/... \ No newline at end of file diff --git a/latest/getting-started/install/index.html b/latest/getting-started/install/index.html index 33eb13df..a8c1dd94 100644 --- a/latest/getting-started/install/index.html +++ b/latest/getting-started/install/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/install/... + Redirecting to ../../../1.1/getting-started/install/... \ No newline at end of file diff --git a/latest/getting-started/logs/index.html b/latest/getting-started/logs/index.html index 04215cfc..412032f0 100644 --- a/latest/getting-started/logs/index.html +++ b/latest/getting-started/logs/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/logs/... + Redirecting to ../../../1.1/getting-started/logs/... \ No newline at end of file diff --git a/latest/getting-started/uninstall/index.html b/latest/getting-started/uninstall/index.html index b520cce9..67a14b05 100644 --- a/latest/getting-started/uninstall/index.html +++ b/latest/getting-started/uninstall/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/getting-started/uninstall/... + Redirecting to ../../../1.1/getting-started/uninstall/... \ No newline at end of file diff --git a/latest/index.html b/latest/index.html index ce144de0..e5a8fee2 100644 --- a/latest/index.html +++ b/latest/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../0.18/... + Redirecting to ../1.1/... \ No newline at end of file diff --git a/latest/roadmap/index.html b/latest/roadmap/index.html index 8a54d1f0..6781d8c3 100644 --- a/latest/roadmap/index.html +++ b/latest/roadmap/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../0.18/roadmap/... + Redirecting to ../../1.1/roadmap/... \ No newline at end of file diff --git a/latest/tutorials/abn/index.html b/latest/tutorials/abn/index.html index d32eaede..ddd8ca90 100644 --- a/latest/tutorials/abn/index.html +++ b/latest/tutorials/abn/index.html @@ -4,13 +4,13 @@ Redirecting - Redirecting to ../../../0.18/tutorials/abn/... + Redirecting to ../../../1.1/tutorials/abn/... \ No newline at end of file diff --git a/latest/tutorials/blue-green/index.html b/latest/tutorials/blue-green/index.html index 1782560d..cd84e745 100644 --- a/latest/tutorials/blue-green/index.html +++ b/latest/tutorials/blue-green/index.html @@ -4,13 +4,13 @@ Redirecting - 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