generate_yaml.py

# Copyright 2022 IBM, Red Hat
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
This sub-module exists primarily to be used internally by the Cluster object
(in the cluster sub-module) for AppWrapper generation.
"""

from typing import Optional
import typing
import yaml
import sys
import os
import argparse
import uuid
from kubernetes import client, config
from .kube_api_helpers import _kube_api_error_handling
from ..cluster.auth import api_config_handler, config_check
from os import urandom
from base64 import b64encode
from urllib3.util import parse_url
from mergedeep import merge


def read_template(template):
    with open(template, "r") as stream:
        try:
            return yaml.safe_load(stream)
        except yaml.YAMLError as exc:
            print(exc)


def gen_names(name):
    if not name:
        gen_id = str(uuid.uuid4())
        appwrapper_name = "appwrapper-" + gen_id
        cluster_name = "cluster-" + gen_id
        return appwrapper_name, cluster_name
    else:
        return name, name


# Check if the routes api exists
def is_openshift_cluster():
    try:
        config_check()
        for api in client.ApisApi(api_config_handler()).get_api_versions().groups:
            for v in api.versions:
                if "route.openshift.io/v1" in v.group_version:
                    return True
        else:
            return False
    except Exception as e:  # pragma: no cover
        return _kube_api_error_handling(e)


def is_kind_cluster():
    try:
        config_check()
        v1 = client.CoreV1Api()
        label_selector = "kubernetes.io/hostname=kind-control-plane"
        nodes = v1.list_node(label_selector=label_selector)
        # If we find one or more nodes with the label, assume it's a KinD cluster
        return len(nodes.items) > 0
    except Exception as e:
        print(f"Error checking if cluster is KinD: {e}")
        return False


def update_names(cluster_yaml, cluster_name, namespace):
    meta = cluster_yaml.get("metadata")
    meta["name"] = cluster_name
    meta["namespace"] = namespace


def update_image(spec, image):
    containers = spec.get("containers")
    for container in containers:
        container["image"] = image


def update_image_pull_secrets(spec, image_pull_secrets):
    template_secrets = spec.get("imagePullSecrets", [])
    spec["imagePullSecrets"] = template_secrets + [
        {"name": x} for x in image_pull_secrets
    ]


def update_volume_mounts(spec, volume_mounts: list):
    containers = spec.get("containers")
    for volume_mount in volume_mounts:
        for container in containers:
            volumeMount = client.ApiClient().sanitize_for_serialization(volume_mount)
            container["volumeMounts"].append(volumeMount)


def update_volumes(spec, volumes: list):
    for volume in volumes:
        new_volume = client.ApiClient().sanitize_for_serialization(volume)
        spec["volumes"].append(new_volume)


def update_env(spec, env):
    containers = spec.get("containers")
    for container in containers:
        if env:
            if "env" in container:
                container["env"].extend(env)
            else:
                container["env"] = env


def update_resources(spec, min_cpu, max_cpu, min_memory, max_memory, gpu):
    container = spec.get("containers")
    for resource in container:
        requests = resource.get("resources").get("requests")
        if requests is not None:
            requests["cpu"] = min_cpu
            requests["memory"] = min_memory
            requests["nvidia.com/gpu"] = gpu
        limits = resource.get("resources").get("limits")
        if limits is not None:
            limits["cpu"] = max_cpu
            limits["memory"] = max_memory
            limits["nvidia.com/gpu"] = gpu


def update_nodes(
    cluster_yaml,
    appwrapper_name,
    min_cpu,
    max_cpu,
    min_memory,
    max_memory,
    gpu,
    workers,
    image,
    env,
    image_pull_secrets,
    head_cpus,
    head_memory,
    head_gpus,
    volumes,
    volume_mounts,
):
    head = cluster_yaml.get("spec").get("headGroupSpec")
    head["rayStartParams"]["num-gpus"] = str(int(head_gpus))

    worker = cluster_yaml.get("spec").get("workerGroupSpecs")[0]
    # Head counts as first worker
    worker["replicas"] = workers
    worker["minReplicas"] = workers
    worker["maxReplicas"] = workers
    worker["groupName"] = "small-group-" + appwrapper_name
    worker["rayStartParams"]["num-gpus"] = str(int(gpu))

    for comp in [head, worker]:
        spec = comp.get("template").get("spec")
        update_volume_mounts(spec, volume_mounts)
        update_volumes(spec, volumes)
        update_image_pull_secrets(spec, image_pull_secrets)
        update_image(spec, image)
        update_env(spec, env)
        if comp == head:
            # TODO: Eventually add head node configuration outside of template
            update_resources(
                spec, head_cpus, head_cpus, head_memory, head_memory, head_gpus
            )
        else:
            update_resources(spec, min_cpu, max_cpu, min_memory, max_memory, gpu)


def del_from_list_by_name(l: list, target: typing.List[str]) -> list:
    return [x for x in l if x["name"] not in target]


def get_default_kueue_name(namespace: str):
    # If the local queue is set, use it. Otherwise, try to use the default queue.
    try:
        config_check()
        api_instance = client.CustomObjectsApi(api_config_handler())
        local_queues = api_instance.list_namespaced_custom_object(
            group="kueue.x-k8s.io",
            version="v1beta1",
            namespace=namespace,
            plural="localqueues",
        )
    except Exception as e:  # pragma: no cover
        return _kube_api_error_handling(e)
    for lq in local_queues["items"]:
        if (
            "annotations" in lq["metadata"]
            and "kueue.x-k8s.io/default-queue" in lq["metadata"]["annotations"]
            and lq["metadata"]["annotations"]["kueue.x-k8s.io/default-queue"].lower()
            == "true"
        ):
            return lq["metadata"]["name"]
    raise ValueError(
        "Default Local Queue with kueue.x-k8s.io/default-queue: true annotation not found please create a default Local Queue or provide the local_queue name in Cluster Configuration"
    )


def local_queue_exists(namespace: str, local_queue_name: str):
    # get all local queues in the namespace
    try:
        config_check()
        api_instance = client.CustomObjectsApi(api_config_handler())
        local_queues = api_instance.list_namespaced_custom_object(
            group="kueue.x-k8s.io",
            version="v1beta1",
            namespace=namespace,
            plural="localqueues",
        )
    except Exception as e:  # pragma: no cover
        return _kube_api_error_handling(e)
    # check if local queue with the name provided in cluster config exists
    for lq in local_queues["items"]:
        if lq["metadata"]["name"] == local_queue_name:
            return True
    return False


def add_queue_label(item: dict, namespace: str, local_queue: Optional[str]):
    lq_name = local_queue or get_default_kueue_name(namespace)
    if not local_queue_exists(namespace, lq_name):
        raise ValueError(
            "local_queue provided does not exist or is not in this namespace. Please provide the correct local_queue name in Cluster Configuration"
        )
    if not "labels" in item["metadata"]:
        item["metadata"]["labels"] = {}
    item["metadata"]["labels"].update({"kueue.x-k8s.io/queue-name": lq_name})


def augment_labels(item: dict, labels: dict):
    if not "labels" in item["metadata"]:
        item["metadata"]["labels"] = {}
    item["metadata"]["labels"].update(labels)


def notebook_annotations(item: dict):
    nb_prefix = os.environ.get("NB_PREFIX")
    if nb_prefix:
        if not "annotations" in item["metadata"]:
            item["metadata"]["annotations"] = {}
        item["metadata"]["annotations"].update(
            {"app.kubernetes.io/managed-by": nb_prefix}
        )


def wrap_cluster(cluster_yaml: dict, appwrapper_name: str, namespace: str):
    return {
        "apiVersion": "workload.codeflare.dev/v1beta2",
        "kind": "AppWrapper",
        "metadata": {"name": appwrapper_name, "namespace": namespace},
        "spec": {"components": [{"template": cluster_yaml}]},
    }


def write_user_yaml(user_yaml, output_file_name):
    # Create the directory if it doesn't exist
    directory_path = os.path.dirname(output_file_name)
    if not os.path.exists(directory_path):
        os.makedirs(directory_path)

    with open(output_file_name, "w") as outfile:
        yaml.dump(user_yaml, outfile, default_flow_style=False)

    print(f"Written to: {output_file_name}")


def generate_appwrapper(
    name: str,
    namespace: str,
    head_cpus: int,
    head_memory: int,
    head_gpus: int,
    min_cpu: int,
    max_cpu: int,
    min_memory: int,
    max_memory: int,
    gpu: int,
    workers: int,
    template: str,
    image: str,
    appwrapper: bool,
    env,
    image_pull_secrets: list,
    write_to_file: bool,
    local_queue: Optional[str],
    labels,
    volumes: list[client.V1Volume],
    volume_mounts: list[client.V1VolumeMount],
    template_update_dict={},
):
    cluster_yaml = read_template(template)
    cluster_yaml = merge(cluster_yaml, template_update_dict)
    appwrapper_name, cluster_name = gen_names(name)
    update_names(cluster_yaml, cluster_name, namespace)
    update_nodes(
        cluster_yaml,
        appwrapper_name,
        min_cpu,
        max_cpu,
        min_memory,
        max_memory,
        gpu,
        workers,
        image,
        env,
        image_pull_secrets,
        head_cpus,
        head_memory,
        head_gpus,
        volumes,
        volume_mounts,
    )
    augment_labels(cluster_yaml, labels)
    notebook_annotations(cluster_yaml)

    user_yaml = (
        wrap_cluster(cluster_yaml, appwrapper_name, namespace)
        if appwrapper
        else cluster_yaml
    )

    add_queue_label(user_yaml, namespace, local_queue)

    if write_to_file:
        directory_path = os.path.expanduser("~/.codeflare/resources/")
        outfile = os.path.join(directory_path, appwrapper_name + ".yaml")
        write_user_yaml(user_yaml, outfile)
        return outfile
    else:
        user_yaml = yaml.dump(user_yaml)
        print(f"Yaml resources loaded for {name}")
        return user_yaml