[release-1.5] Cherry-pick commits for v1.5.1

[andrewsykim]

Why are these changes needed?

Cherry-picks for v1.5.1

Related issue number

Checks

• I've made sure the tests are passing.
• Testing Strategy
  • Unit tests
  • Manual tests
  • This PR is not tested :(

[Future-Outlier]

my plan for testing this branch
all combinations from here #4203
with and without using kubernetes proxy in kuberay

[Future-Outlier]

my test when using kubernetes proxy in kuberay

args: "-leader-election-namespace default -use-kubernetes-proxy"
branch: this one
image: rayproject/ray:2.52.0.9527a5-extra-py310-cpu

HTTP mode, k8s job mode, cluster selector, and sidecar mode

light weight job submitter (k8s mode)

rayservice

my test when not using kubernetes proxy in kuberay

my example

apiVersion: ray.io/v1
kind: RayJob
metadata:
  name: rayjob-sample-http-mode-v6
spec:
  # submissionMode specifies how RayJob submits the Ray job to the RayCluster.
  # The default value is "K8sJobMode", meaning RayJob will submit the Ray job via a submitter Kubernetes Job.
  # The alternative value is "HTTPMode", indicating that KubeRay will submit the Ray job by sending an HTTP request to the RayCluster.
  submissionMode: "HTTPMode"
  entrypoint: python /home/ray/samples/sample_code.py
  # shutdownAfterJobFinishes specifies whether the RayCluster should be deleted after the RayJob finishes. Default is false.
  # shutdownAfterJobFinishes: false

  # ttlSecondsAfterFinished specifies the number of seconds after which the RayCluster will be deleted after the RayJob finishes.
  # ttlSecondsAfterFinished: 10

  # activeDeadlineSeconds is the duration in seconds that the RayJob may be active before
  # KubeRay actively tries to terminate the RayJob; value must be positive integer.
  # activeDeadlineSeconds: 120

  # RuntimeEnvYAML represents the runtime environment configuration provided as a multi-line YAML string.
  # See https://docs.ray.io/en/latest/ray-core/handling-dependencies.html for details.
  # (New in KubeRay version 1.0.)
  runtimeEnvYAML: |
    pip:
      - requests==2.26.0
      - pendulum==2.1.2
    env_vars:
      counter_name: "test_counter"

  # Suspend specifies whether the RayJob controller should create a RayCluster instance.
  # If a job is applied with the suspend field set to true, the RayCluster will not be created and we will wait for the transition to false.
  # If the RayCluster is already created, it will be deleted. In the case of transition to false, a new RayCluster will be created.
  # suspend: false

  # rayClusterSpec specifies the RayCluster instance to be created by the RayJob controller.
  rayClusterSpec:
    rayVersion: "2.52.0" # should match the Ray version in the image of the containers+
    authOptions:
      mode: "token"
    # Ray head pod template
    headGroupSpec:
      # The `rayStartParams` are used to configure the `ray start` command.
      # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
      # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
      rayStartParams: {}
      #pod template
      template:
        spec:
          containers:
          - name: ray-head
            # image: rayproject/ray:nightly-py311-cpu
            image: rayproject/ray:2.52.0.9527a5-extra-py310-cpu
            ports:
            - containerPort: 6379
              name: gcs-server
            - containerPort: 8265 # Ray dashboard
              name: dashboard
            - containerPort: 10001
              name: client
            resources:
              requests:
                cpu: "2"
            volumeMounts:
            - mountPath: /home/ray/samples
              name: code-sample
          volumes:
          # You set volumes at the Pod level, then mount them into containers inside that Pod
          - name: code-sample
            configMap:
              # Provide the name of the ConfigMap you want to mount.
              name: ray-job-code-sample
              # An array of keys from the ConfigMap to create as files
              items:
              - key: sample_code.py
                path: sample_code.py
    workerGroupSpecs:
    # the pod replicas in this group typed worker
    - replicas: 1
      minReplicas: 1
      maxReplicas: 5
      # logical group name, for this called small-group, also can be functional
      groupName: small-group
      # The `rayStartParams` are used to configure the `ray start` command.
      # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
      # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
      rayStartParams: {}
      #pod template
      template:
        spec:
          containers:
          - name: ray-worker # must consist of lower case alphanumeric characters or '-', and must start and end with an alphanumeric character (e.g. 'my-name',  or '123-abc'
            # image: rayproject/ray:nightly-py311-cpu
            image: rayproject/ray:2.52.0.9527a5-extra-py310-cpu
            resources:
              requests:
                cpu: "2"

  # SubmitterPodTemplate is the template for the pod that will run the `ray job submit` command against the RayCluster.
  # If SubmitterPodTemplate is specified, the first container is assumed to be the submitter container.
  # submitterPodTemplate:
  #   spec:
  #     restartPolicy: Never
  #     containers:
  #     - name: my-custom-rayjob-submitter-pod
  #       image: rayproject/ray:2.46.0
  #       # If Command is not specified, the correct command will be supplied at runtime using the RayJob spec `entrypoint` field.
  #       # Specifying Command is not recommended.
  #       # command: ["sh", "-c", "ray job submit --address=http://$RAY_DASHBOARD_ADDRESS --submission-id=$RAY_JOB_SUBMISSION_ID -- echo hello world"]


######################Ray code sample#################################
# this sample is from https://docs.ray.io/en/latest/cluster/job-submission.html#quick-start-example
# it is mounted into the container and executed to show the Ray job at work
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: ray-job-code-sample
data:
  sample_code.py: |
    import ray
    import os
    import requests

    ray.init()

    @ray.remote
    class Counter:
        def __init__(self):
            # Used to verify runtimeEnv
            self.name = os.getenv("counter_name")
            assert self.name == "test_counter"
            self.counter = 0

        def inc(self):
            self.counter += 1

        def get_counter(self):
            return "{} got {}".format(self.name, self.counter)

    counter = Counter.remote()

    for _ in range(5):
        ray.get(counter.inc.remote())
        print(ray.get(counter.get_counter.remote()))

    # Verify that the correct runtime env was used for the job.
    assert requests.__version__ == "2.26.0"

apiVersion: ray.io/v1
kind: RayJob
metadata:
  name: rayjob-use-existing-raycluster-auth-token
spec:
  clusterSelector:
    ray.io/cluster: rayjob-sample-spn4v
  entrypoint: python -c "import ray; ray.init(); print(ray.cluster_resources())"
  runtimeEnvYAML: |
    pip:
      - requests==2.26.0

# Make sure to increase resource requests and limits before using this example in production.
# For examples with more realistic resource configuration, see
# ray-cluster.complete.large.yaml and
# ray-cluster.autoscaler.large.yaml.
apiVersion: ray.io/v1
kind: RayService
metadata:
  name: ray-service-auth-token-3
spec:
  # serveConfigV2 takes a yaml multi-line scalar, which should be a Ray Serve multi-application config. See https://docs.ray.io/en/latest/serve/multi-app.html.
  serveConfigV2: |
    applications:
      - name: fruit_app
        import_path: fruit.deployment_graph
        route_prefix: /fruit
        runtime_env:
          working_dir: "https://github.com/ray-project/test_dag/archive/78b4a5da38796123d9f9ffff59bab2792a043e95.zip"
        deployments:
          - name: MangoStand
            num_replicas: 2
            max_replicas_per_node: 1
            user_config:
              price: 3
            ray_actor_options:
              num_cpus: 0.1
          - name: OrangeStand
            num_replicas: 1
            user_config:
              price: 2
            ray_actor_options:
              num_cpus: 0.1
          - name: PearStand
            num_replicas: 1
            user_config:
              price: 1
            ray_actor_options:
              num_cpus: 0.1
          - name: FruitMarket
            num_replicas: 1
            ray_actor_options:
              num_cpus: 0.1
      - name: math_app
        import_path: conditional_dag.serve_dag
        route_prefix: /calc
        runtime_env:
          working_dir: "https://github.com/ray-project/test_dag/archive/78b4a5da38796123d9f9ffff59bab2792a043e95.zip"
        deployments:
          - name: Adder
            num_replicas: 1
            user_config:
              increment: 3
            ray_actor_options:
              num_cpus: 0.1
          - name: Multiplier
            num_replicas: 1
            user_config:
              factor: 5
            ray_actor_options:
              num_cpus: 0.1
          - name: Router
            num_replicas: 1
  rayClusterConfig:
    rayVersion: '2.52.0' # should match the Ray version in the image of the containers
    enableInTreeAutoscaling: true
    authOptions:
      mode: token
    ######################headGroupSpecs#################################
    # Ray head pod template.
    headGroupSpec:
      # The `rayStartParams` are used to configure the `ray start` command.
      # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
      # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
      rayStartParams: {}
      #pod template
      template:
        spec:
          containers:
          - name: ray-head
            image: rayproject/ray:2.52.0.9527a5-py312-cpu
            resources:
              requests:
                cpu: 3
                memory: 4Gi      # Increased from 2Gi
    workerGroupSpecs:
    # the pod replicas in this group typed worker
    - replicas: 1
      minReplicas: 1
      maxReplicas: 5
      # logical group name, for this called small-group, also can be functional
      groupName: small-group
      # The `rayStartParams` are used to configure the `ray start` command.
      # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
      # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
      rayStartParams: {}
      #pod template
      template:
        spec:
          containers:
          - name: ray-worker # must consist of lower case alphanumeric characters or '-', and must start and end with an alphanumeric character (e.g. 'my-name',  or '123-abc'
            image: rayproject/ray:2.52.0.9527a5-py312-cpu
            resources:
              requests:
                cpu: 3
                memory: 6Gi      # Increased from 2Gi

[rueian]

LGTM