Hi, I am using the master version of kuberay-operator. Commands used to deploy kuberay-operator are:

git clone <https://github.com/ray-project/kuberay.git>
cd kuberay
kubectl create -k ray-operator/config/default

When I submit the Ray workflow, rayjob, head and worker pods are getting created and also is up and running. But the Workflow is not getting submitted in the cluster and the job is in queue in console for more than 1 hour. Script:

import typing
import ray
import time
from flytekit import Resources, task, workflow
from flytekitplugins.ray import HeadNodeConfig, RayJobConfig, WorkerNodeConfig


@ray.remote
def square(x):
    return x * x

ray_config = RayJobConfig(
      head_node_config=HeadNodeConfig(ray_start_params={"log-color": "True"}),
      worker_node_config=[WorkerNodeConfig(group_name="ray-group", replicas=2)],
      runtime_env={"pip": ["numpy", "pandas"]},
)


@task(task_config=ray_config, requests=Resources(mem="2000Mi", cpu="1"), limits=Resources(mem="3000Mi", cpu="2"))
def ray_task(n: int) -> typing.List[int]:
    futures = [square.remote(i) for i in range(n)]
    return ray.get(futures)


@workflow
def ray_workflow(n: int) -> typing.List[int]:
    return ray_task(n=n)

if __name__ == "__main__":
    print(ray_workflow(n=10))

Hi, I am also facing the same issue. Anyone please tell us the solution

cc @Kevin Su

Hey @Padma Priya M are you using sandbox?

I am using EKS cluster

Thanks

export KUBERAY_VERSION=v0.5.2
kubectl create -k "<http://github.com/ray-project/kuberay/manifests/cluster-scope-resources?ref=${KUBERAY_VERSION}&timeout=90s|github.com/ray-project/kuberay/manifests/cluster-scope-resources?ref=${KUBERAY_VERSION}&timeout=90s>"
kubectl apply -k "<http://github.com/ray-project/kuberay/manifests/base?ref=${KUBERAY_VERSION}&timeout=90s|github.com/ray-project/kuberay/manifests/base?ref=${KUBERAY_VERSION}&timeout=90s>"

can you confirm on the installation steps

Yes, correct

sure thanks. v0.5.2 is working fine for me. v0.5.2 is stable version only right?

yes, it is.