Add GPU support #3257

samos123 · 2023-05-30T06:59:50Z

WIP, still need to add podman support

This implements GPU support as discussed in #3164

k8s-ci-robot · 2023-05-30T06:59:57Z

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neolit123 · 2023-05-30T08:49:47Z

pkg/apis/config/v1alpha4/types.go

@@ -118,6 +118,10 @@ type Node struct {
 	// binded to a host Port
 	ExtraPortMappings []PortMapping `yaml:"extraPortMappings,omitempty" json:"extraPortMappings,omitempty"`

+	// GPUs allows to access GPU devices from the kind node. Setting this to
+	// "all" will pass all the available GPUs to the kind node.
+	Gpus string `yaml:"gpus,omitempty" json:"gpus,omitempty"`


use GPUs string?

All letters in the acronym should have the same case, using the appropriate case for the situation

https://github.com/zecke/Kubernetes/blob/master/docs/devel/api-conventions.md#naming-conventions

+1, also this field should be validated (with "all" being the only valid value currently), and we should put a note that in the future we'll look at supporting specifying specific devices.

Commented more below and on the issue.

neolit123 · 2023-05-30T08:51:47Z

pkg/internal/apis/config/types.go

@@ -98,6 +98,10 @@ type Node struct {
 	// binded to a host Port
 	ExtraPortMappings []PortMapping

+	// GPUs allows to access GPU devices from the kind node. Setting this to
+	// "all" will pass all the available GPUs to the kind node.
+	Gpus string


neolit123 · 2023-05-30T08:53:11Z

site/content/docs/user/configuration.md

@@ -285,6 +285,20 @@ nodes:

 **Note**: Kubernetes versions are expressed as x.y.z, where x is the major version, y is the minor version, and z is the patch version, following [Semantic Versioning](https://semver.org/) terminology. For more information, see [Kubernetes Release Versioning.](https://github.com/kubernetes/sig-release/blob/master/release-engineering/versioning.md#kubernetes-release-versioning)

+### GPU Support
+
+Kind nodes can utilize GPUs by setting the following:


Suggested change

Kind nodes can utilize GPUs by setting the following:

Kind nodes can utilize GPU devices from the host, by setting the following:

aligning with the API godoc comment

BenTheElder · 2023-05-30T20:23:36Z

pkg/cluster/internal/providers/docker/provision.go

@@ -255,6 +255,10 @@ func runArgsForNode(node *config.Node, clusterIPFamily config.ClusterIPFamily, n
 		args = append(args, "-e", "KUBECONFIG=/etc/kubernetes/admin.conf")
 	}

+	if len(node.Gpus) > 0 {
+		args = append(args, fmt.Sprintf("--gpus=%v", node.Gpus))


let's not plumb this directly since the values are incompatible across backends

instead we can have a more structured format in the internal type (for now gpus true/false) and we need valdiatino on the external type.

I added validation to ensure right now only "all" can be passed so it's now safe to to use node.Gpus directly in the docker provisioner. I prefer to keep the code simpler and passthrough instead of adding another layer of validation in the docker provisioning code. Happy to add additional validation in docker/povision.go if you have strong opinion on it.

For podman we can not plumb it through directly. So I will work out a way to convert node.Gpus into a podman compatible format. I don't have podman installed myself.

Only 'all' will be supported for now

samos123 · 2023-06-01T06:35:51Z

One interesting thing I had to do was to run the following after kidn cluster came up:

helm install --wait --generate-name \
     -n gpu-operator --create-namespace \
     nvidia/gpu-operator \
     --set driver.enabled=false

docker exec -ti kind-control-plane bash
systemctl restart containerd
ln -s /sbin/ldconfig /sbin/ldconfig.real

So I'm thinking there might needs to be a change to the base image to include that symlink. This could also be due to my system being on Archlinux.

k8s-ci-robot · 2023-06-01T07:13:38Z

@samos123: The following test failed, say /retest to rerun all failed tests or /retest-required to rerun all mandatory failed tests:

Test name	Commit	Details	Required	Rerun command
pull-kind-conformance-parallel-dual-stack-ipv4-ipv6	`cb9b34b`	link	true	`/test pull-kind-conformance-parallel-dual-stack-ipv4-ipv6`

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klueska · 2023-06-01T09:48:00Z

Sorry to be a party pooper, but I do not believe this is the right approach for adding GPU support to kind.

The --gpus flag in docker has always been a bit of a hack, and not something that we (NVIDIA) would like to support long term. The proper way to support GPUs (and any device for that matter) in kind is to expose the --device flag through kind config file rather than the specialized --gpus flag.

With the upcoming CDI support that will be available in Docker 25, having the --device flag exposed will be sufficient to get equivalent functionality that the --gpus flag provides today (and more).

Once this is available in docker, both of these will work in a unified way:

podman run --rm --device nvidia.com/gpu=all ubuntu nvidia-smi -L
docker run --rm --device nvidia.com/gpu=all ubuntu nvidia-smi -L

samos123 · 2023-08-20T08:12:14Z

Edit: I posted a full tutorial on how to configure Kind + GPU support here: https://www.substratus.ai/blog/kind-with-gpus

I confirmed the steps provided by @klueska worked except hitting 1 minor issue. The only thing I had to do was run this:

docker exec -ti kind-control-plane ln -s /sbin/ldconfig /sbin/ldconfig.real

Should that be something that should be included in the base image?

Here were the full steps I used to verify:

nvidia_config_file="/etc/nvidia-container-runtime/config.toml"
if [ -e "${nvidia_config_file}" ]; then
sudo sed -i '/accept-nvidia-visible-devices-as-volume-mounts/c\accept-nvidia-visible-devices-as-volume-mounts = true' ${nvidia_config_file}
fi

kind create cluster --name kind --config - <<EOF
apiVersion: kind.x-k8s.io/v1alpha4
kind: Cluster
nodes:
- role: control-plane
  image: kindest/node:v1.27.3@sha256:3966ac761ae0136263ffdb6cfd4db23ef8a83cba8a463690e98317add2c9ba72
  extraPortMappings:
  - containerPort: 30080
    hostPort: 30080
  # part of GPU workaround
  extraMounts:
    - hostPath: /dev/null
      containerPath: /var/run/nvidia-container-devices/all
EOF

docker exec -ti kind-control-plane ln -s /sbin/ldconfig /sbin/ldconfig.real || true

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia || true
helm repo update
helm install --wait --generate-name \
     -n gpu-operator --create-namespace \
     nvidia/gpu-operator --set driver.enabled=false

For more context see: kubernetes-sigs/kind#3257 (comment)

klueska · 2023-08-21T20:47:40Z

@samos123 You shouldn't need to create that symlink. Can you show me the contents of your /etc/nvidia-container-runtime/config.toml file?

samos123 · 2023-08-22T06:00:18Z

@klueska content of my file: https://gist.github.com/samos123/cc816b91a7a03651c71441e0949c3bb6

Note I don't seem to be the only one hitting this issue: NVIDIA/nvidia-docker#614 (comment) That's the source of the workaround

xussof · 2023-09-12T18:16:58Z

Following @samos123 steps I can't fully deploy nvidia-device-plugin-daemonset and nvidia-operator-validator when I have more than one node deployed by using this kind-config:

kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
name: siv-dev-gpu
nodes:
  - role: control-plane
    image: kindest/node:v1.26.0
    extraMounts:
      - hostPath: /data/string-in-video/repos/git/github/string-in-video
        containerPath: /data/string-in-video/repos/git/github/string-in-video
      - hostPath: /data/string-in-video/files/dev
        containerPath: /data/string-in-video/files/dev
      - hostPath: /data/string-in-video/volumes/dev
        containerPath: /data/string-in-video/volumes/dev
      - hostPath: /dev/null
        containerPath: /var/run/nvidia-container-devices/all
    kubeadmConfigPatches:
      - |
        kind: InitConfiguration
        nodeRegistration:
          kubeletExtraArgs:
            node-labels: "ingress-ready=true"        
    extraPortMappings:
      - containerPort: 80
        hostPort: 80
        protocol: TCP
      - containerPort: 443
        hostPort: 443
        protocol: TCP

  - role: worker
    image: kindest/node:v1.26.0
    extraMounts:
      - hostPath: /data/string-in-video/repos/git/github/string-in-video
        containerPath: /data/string-in-video/repos/git/github/string-in-video
      - hostPath: /data/string-in-video/files/dev
        containerPath: /data/string-in-video/files/dev
      - hostPath: /data/string-in-video/volumes/dev
        containerPath: /data/string-in-video/volumes/dev

  - role: worker
    image: kindest/node:v1.26.0
    extraMounts:
      - hostPath: /data/string-in-video/repos/git/github/string-in-video
        containerPath: /data/string-in-video/repos/git/github/string-in-video
      - hostPath: /data/string-in-video/files/dev
        containerPath: /data/string-in-video/files/dev
      - hostPath: /data/string-in-video/volumes/dev
        containerPath: /data/string-in-video/volumes/dev
      - hostPath: /dev/null
        containerPath: /var/run/nvidia-container-devices/all

This is the error I get:

Failed to create pod sandbox: rpc error: code = Unknown desc = failed to get sandbox runtime: no runtime for "nvidia" is configured

But when I deploy it over a kind with a control-plane and a single worker it works.

samos123 · 2023-09-12T18:21:27Z

I think thats a limitation of the approach. Maybe @klueska as another cool workaround to solve that? :D

klueska · 2023-09-12T18:24:38Z

Only one of your workers has:

      - hostPath: /dev/null
        containerPath: /var/run/nvidia-container-devices/all

xussof · 2023-09-12T18:35:31Z

Only one of your workers has:

      - hostPath: /dev/null
        containerPath: /var/run/nvidia-container-devices/all

Yes because I only one one node to has gpu, the others I want to emulate that doesn't have GPU. Still, it fails if I add the hostpath to the another worker:

kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
name: siv-dev-gpu
nodes:
  - role: control-plane
    image: kindest/node:v1.26.0
    extraMounts:
      - hostPath: /data/string-in-video/repos/git/github/string-in-video
        containerPath: /data/string-in-video/repos/git/github/string-in-video
      - hostPath: /data/string-in-video/files/dev
        containerPath: /data/string-in-video/files/dev
      - hostPath: /data/string-in-video/volumes/dev
        containerPath: /data/string-in-video/volumes/dev
      - hostPath: /dev/null
        containerPath: /var/run/nvidia-container-devices/all
    kubeadmConfigPatches:
      - |
        kind: InitConfiguration
        nodeRegistration:
          kubeletExtraArgs:
            node-labels: "ingress-ready=true"        
    extraPortMappings:
      - containerPort: 80
        hostPort: 80
        protocol: TCP
      - containerPort: 443
        hostPort: 443
        protocol: TCP

  - role: worker
    image: kindest/node:v1.26.0
    extraMounts:
      - hostPath: /data/string-in-video/repos/git/github/string-in-video
        containerPath: /data/string-in-video/repos/git/github/string-in-video
      - hostPath: /data/string-in-video/files/dev
        containerPath: /data/string-in-video/files/dev
      - hostPath: /data/string-in-video/volumes/dev
        containerPath: /data/string-in-video/volumes/dev
      - hostPath: /dev/null
        containerPath: /var/run/nvidia-container-devices/all
        
  - role: worker
    image: kindest/node:v1.26.0
    extraMounts:
      - hostPath: /data/string-in-video/repos/git/github/string-in-video
        containerPath: /data/string-in-video/repos/git/github/string-in-video
      - hostPath: /data/string-in-video/files/dev
        containerPath: /data/string-in-video/files/dev
      - hostPath: /data/string-in-video/volumes/dev
        containerPath: /data/string-in-video/volumes/dev
      - hostPath: /dev/null
        containerPath: /var/run/nvidia-container-devices/all

Error of nvidia-device-plugin-daemonset:

NVIDIA_DRIVER_ROOT=/ CONTAINER_DRIVER_ROOT=/host Starting nvidia-device-plugin I0912 18:33:00.988910 1 main.go:154] Starting FS watcher. E0912 18:33:00.988987 1 main.go:123] failed to create FS watcher: too many open files

Error on nvidia-operator-validator:
Failed to create pod sandbox: rpc error: code = Unknown desc = failed to get sandbox runtime: no runtime for "nvidia" is configured

xussof · 2023-09-12T18:43:19Z

Nevermind, it was a problem related to my sysctl limit related to this topic:
NVIDIA/gpu-operator#441
The solution was to increase the max:
NVIDIA/gpu-operator#441 (comment)

Now it works on 2 workers node! Thanks

cceyda · 2023-11-20T07:10:29Z

I had to downgrade the nvidia driver from 545->535 for it to work. Based on the compatibility stuff of the gpu-operator:
https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/release-notes.html
then followed the tutorial #3257 (comment) (Thank you)

jiangxiaobin96 · 2023-12-19T07:41:13Z

It occurred to me over the weekend that we can actually enable GPU support in kind today by leveraging a feature we added to the nvidia-container-toolkit quite some time ago:

Read list of GPU devices from volume mounts instead of NVIDIA_VISIBLE_DEVICES

It relies on passing the list of GPUs you want to inject as volume mounts rather than via an environment variable.

Steps to enable this:

Add nvidia as your default runtime in /etc/docker/daemon.json

Restart docker (as necessary)

Set accept-nvidia-visible-devices-as-volume-mounts = true in /etc/nvidia-container-runtime/config.toml

Add the following to any kind nodes you want to have access to all GPUs in the system:
  extraMounts:
    - hostPath: /dev/null
      containerPath: /var/run/nvidia-container-devices/all
I've tested this in my local environment and it works as expected. The only caveat is that it only works for passing all GPUs into a node (i.e. I can't pick and choose some GPUs for one node and another set of GPUs for another node).

In general the nvidia-visible-devices-as-volume-mount feature allows you to do fine-grained injection of a subset of GPUs into a container. However, kind runs all of its nodes in --privileged containers, meaning that there is no way to prevent it from seeing all GPUs (even if you tell it you only want it to see a subset of the GPUs). If / when kind ever supports running worker nodes without --privileged this restriction should go away.

Hello, I follow this comment and meet new error

Error: failed to create containerd task: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running hook #0: error running hook: exit status 1, stdout: , stderr: nvidia-container-cli.real: mount error: stat failed: /proc/driver/nvidia/capabilities: no such file or directory: unknown

How to solve this problem /proc/driver/nvidia/capabilities no such file or directory?

xihajun · 2023-12-20T00:48:44Z

Anyone can help with this issue?

Creating cluster "new-cluster" ...
 ✓ Ensuring node image (kindest/node:v1.27.3) 🖼
 ✓ Preparing nodes 📦 📦 📦
 ✓ Writing configuration 📜
 ✓ Starting control-plane 🕹️
 ✓ Installing CNI 🔌
 ✓ Installing StorageClass 💾
 ✗ Joining worker nodes 🚜
Deleted nodes: ["new-cluster-control-plane" "new-cluster-worker" "new-cluster-worker2"]
ERROR: failed to create cluster: failed to join node with kubeadm: command "docker exec --privileged new-cluster-worker kubeadm join --config /kind/kubeadm.conf --skip-phases=preflight --v=6" failed with error: exit status 1
Command Output: I1220 00:26:42.732281     158 join.go:412] [preflight] found NodeName empty; using OS hostname as NodeName
I1220 00:26:42.732373     158 joinconfiguration.go:76] loading configuration from "/kind/kubeadm.conf"
I1220 00:26:42.734232     158 controlplaneprepare.go:225] [download-certs] Skipping certs download
I1220 00:26:42.734258     158 join.go:529] [preflight] Discovering cluster-info
I1220 00:26:42.734283     158 token.go:80] [discovery] Created cluster-info discovery client, requesting info from "new-cluster-control-plane:6443"
I1220 00:26:42.754681     158 round_trippers.go:553] GET https://new-cluster-control-plane:6443/api/v1/namespaces/kube-public/configmaps/cluster-info?timeout=10s 200 OK in 19 milliseconds

Solved it, out of RAM

But now got

│ Failed to allocate device vector A (error code CUDA driver version is insufficient for CUDA runtime version)!                  │

jiangxiaobin96 · 2023-12-21T08:58:44Z

Anyone can help with this issue?

Creating cluster "new-cluster" ...
 ✓ Ensuring node image (kindest/node:v1.27.3) 🖼
 ✓ Preparing nodes 📦 📦 📦
 ✓ Writing configuration 📜
 ✓ Starting control-plane 🕹️
 ✓ Installing CNI 🔌
 ✓ Installing StorageClass 💾
 ✗ Joining worker nodes 🚜
Deleted nodes: ["new-cluster-control-plane" "new-cluster-worker" "new-cluster-worker2"]
ERROR: failed to create cluster: failed to join node with kubeadm: command "docker exec --privileged new-cluster-worker kubeadm join --config /kind/kubeadm.conf --skip-phases=preflight --v=6" failed with error: exit status 1
Command Output: I1220 00:26:42.732281     158 join.go:412] [preflight] found NodeName empty; using OS hostname as NodeName
I1220 00:26:42.732373     158 joinconfiguration.go:76] loading configuration from "/kind/kubeadm.conf"
I1220 00:26:42.734232     158 controlplaneprepare.go:225] [download-certs] Skipping certs download
I1220 00:26:42.734258     158 join.go:529] [preflight] Discovering cluster-info
I1220 00:26:42.734283     158 token.go:80] [discovery] Created cluster-info discovery client, requesting info from "new-cluster-control-plane:6443"
I1220 00:26:42.754681     158 round_trippers.go:553] GET https://new-cluster-control-plane:6443/api/v1/namespaces/kube-public/configmaps/cluster-info?timeout=10s 200 OK in 19 milliseconds

Solved it, out of RAM

But now got

│ Failed to allocate device vector A (error code CUDA driver version is insufficient for CUDA runtime version)!                  │

cuda version and driver version do not match.

Add nvidia-driver host map to kind node. Edited `generate_kind_config.py` to add: ```yaml # part of GPU workaround extraMounts: - hostPath: /dev/null containerPath: /var/run/nvidia-container-devices/all ``` For more details, see: kubernetes-sigs/kind#3257 (comment)

alexeadem · 2024-01-30T20:58:36Z

Steps to enable this:

Add nvidia as your default runtime in /etc/docker/daemon.json
[...]

Thanks @klueska I have linux server at home. But I have been running K8s training for my team using kind on their W11 laptops. Would like to dive into Kubeflow/Notebooks-with-GPUs scenario (I know, an overkill if not for training purpose, could have just used WSL2+CUDA). Would you be able translate the above to Docker Desktop on Windows environment?

The daemon config is accessible via the Docker Desktop UI. But I need some help on the nvidia-container-runtime/config.toml location on Windows. Though, as I read the Nvidia page on container runtime and toolkit I came away thinking they may not be applicable to WSL2, as:
docker run -it --rm --gpus all ubuntu nvidia-smi
seems to work with just installing CUDA support. But Kubeflow seems different in that Nvidia runtime is still needed for the new containers I would be building... if I understand the container runtime concept correctly?

You may want to look at this comment running Kubeflow, gpu-operator and wsl2 qbo with kind mages or directly into kind

…gs/kind/pull/3257\#issuecomment-1607287275

…/kubernetes-sigs/kind/pull/3257\#issuecomment-1607287275

utsumi-fj · 2025-01-20T08:29:03Z

FYI, in addition to #3257 (comment), the workaround NVIDIA/nvidia-container-toolkit#198 (comment) is needed.

I installed GPU Operator in kind as follows:

Add GPU support #3257 (comment)
/proc/driver/nvidia/capabilities: no such file or directory: unknown NVIDIA/nvidia-container-toolkit#198 (comment) (docker exec -it {{ kind node }} umount -R /proc/driver/nvidia)
helm install --wait --generate-name -n gpu-operator --create-namespace nvidia/gpu-operator --version=v24.9.1 --set driver.enabled=false

Maybe, NVIDIA/gpu-operator#441 (comment) is needed too.

Add GPU support for docker

8ca69b3

This implements kubernetes-sigs#3164

k8s-ci-robot added the cncf-cla: yes Indicates the PR's author has signed the CNCF CLA. label May 30, 2023

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k8s-ci-robot added the size/S Denotes a PR that changes 10-29 lines, ignoring generated files. label May 30, 2023

samos123 changed the title ~~Add GPU support for docker~~ Add GPU support May 30, 2023

samos123 marked this pull request as draft May 30, 2023 07:00

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samos123 mentioned this pull request May 30, 2023

Support GPUs #3164

Open

samos123 added 2 commits May 30, 2023 22:31

change Gpus to GPUs

00b38e3

add validation for gpus

7b5a8de

Only 'all' will be supported for now

k8s-ci-robot added size/M Denotes a PR that changes 30-99 lines, ignoring generated files. and removed size/S Denotes a PR that changes 10-29 lines, ignoring generated files. labels Jun 1, 2023

samos123 added 2 commits May 31, 2023 23:32

add unit test for gpus validation

897af43

address PR comment on doc change

c2d8ead

add gpus support to podman

cb9b34b

k8s-ci-robot added the area/provider/podman Issues or PRs related to podman label Jun 1, 2023

samos123 marked this pull request as ready for review June 1, 2023 06:42

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k8s-ci-robot requested a review from neolit123 June 1, 2023 06:42

samos123 requested a review from BenTheElder June 1, 2023 06:42

klueska mentioned this pull request Jul 4, 2023

Add API for CDI --devices flag in Docker and Podman for mapping GPUs #3290

Open

klueska mentioned this pull request Jul 25, 2023

Device plugin not starting and pod showing 0/1 nodes are available: 1 node(s) had untolerated taint {gpu: }. preemption: 0/1 nodes are available: 1 Preemption is not helpful for scheduling.. NVIDIA/k8s-device-plugin#421

Closed

samos123 mentioned this pull request Aug 20, 2023

kind GPU support substratusai/runbooks#201

Merged

samos123 closed this Aug 20, 2023

samos123 added a commit to substratusai/runbooks that referenced this pull request Aug 21, 2023

kind GPU support (#201)

876a0cf

For more context see: kubernetes-sigs/kind#3257 (comment)

luiztauffer mentioned this pull request Nov 8, 2023

Possibly easier method to run Kind with GPU? Tauffer-Consulting/domino#157

Open

creatorrr mentioned this pull request Jan 1, 2024

[k8s] Make sky local up support GPU skypilot-org/skypilot#2889

Closed

ehfd mentioned this pull request Jan 9, 2024

minikube cannot detect the GPUs ehfd/nvidia-dind#2

Open

jiangxiaobin96 mentioned this pull request Jan 10, 2024

/proc/driver/nvidia/capabilities: no such file or directory: unknown NVIDIA/nvidia-container-toolkit#198

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klueska mentioned this pull request Jan 23, 2024

no runtime for "nvidia" is configured NVIDIA/gpu-operator#662

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10 tasks

alexeadem mentioned this pull request Jan 30, 2024

Getting GPU device minor number: Not Supported NVIDIA/k8s-device-plugin#332

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rohanarora referenced this pull request in rohanarora/fmperf Jun 18, 2024

bump: updates to Kind setup based on https://github.com/kubernetes-si…

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…gs/kind/pull/3257\#issuecomment-1607287275

rohanarora referenced this pull request in rohanarora/fmperf Jun 18, 2024

bump: leveraging out-of-box GPU support from kind: https://github.com…

3c3c69a

…/kubernetes-sigs/kind/pull/3257\#issuecomment-1607287275

rohanarora mentioned this pull request Jun 18, 2024

Updates to Kind with GPUs setup fmperf-project/fmperf#21

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astefanutti mentioned this pull request Jan 24, 2025

Support Local Execution of Training Jobs kubeflow/trainer#2231

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utsumi-fj commented Jan 20, 2025

	Kind nodes can utilize GPUs by setting the following:
	Kind nodes can utilize GPU devices from the host, by setting the following:

Add GPU support #3257

Add GPU support #3257

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