Running Ray Tasks

The Ray task offers the capability to execute a Ray job either on a pre-existing Ray cluster or by creating a new Ray cluster using the Ray operator.


Version Compatibility

  • flyte >= 1.11.1-b1 only works with kuberay 1.1.0

  • Although flyte < 1.11.0 can work with kuberay 0.6.0 and 1.1.0, we strongly recommend upgrading to the latest flyte and kuberay 1.1.0 for stability and usability

To begin, load the libraries.

import typing

from flytekit import ImageSpec, Resources, task, workflow

Create an ImageSpec to encompass all the dependencies needed for the Ray task.


Replace with a container registry you’ve access to publish to.

To upload the image to the local registry in the demo cluster, indicate the registry as localhost:30000.


custom_image = ImageSpec(
    # kuberay operator needs wget for readiness probe.

import ray
from flytekitplugins.ray import HeadNodeConfig, RayJobConfig, WorkerNodeConfig

In this example, we define a remote function that will be executed asynchronously in the Ray cluster.

def f(x):
    return x * x

Include both HeadNodeConfig and WorkerNodeConfig in RayJobConfig. These configurations will subsequently be employed by the Ray operator to establish a Ray cluster before task execution.

Here’s a breakdown of the parameters:

  • ray_start_params: These are the parameters used in the Ray init method, encompassing the address and object-store-memory settings.

  • replicas: Specifies the desired number of replicas for the worker group. The default is 1.

  • group_name: A RayCluster can host multiple worker groups, each differentiated by its name.

  • runtime_env: The runtime environment definition outlines the necessary dependencies for your Ray application to function. This environment is dynamically installed on the cluster at runtime.

ray_config = RayJobConfig(
    head_node_config=HeadNodeConfig(ray_start_params={"log-color": "True"}),
    worker_node_config=[WorkerNodeConfig(group_name="ray-group", replicas=1)],
    runtime_env={"pip": ["numpy", "pandas"]},  # or runtime_env="./requirements.txt"

Create a Ray task. The task is invoked on the Ray head node, while f.remote(i) executes asynchronously on distinct Ray workers.

    requests=Resources(mem="2Gi", cpu="2"),
def ray_task(n: int) -> typing.List[int]:
    futures = [f.remote(i) for i in range(n)]
    return ray.get(futures)


The Resources section here is utilized to specify the resources allocated to the worker nodes.

Lastly, define a workflow to call the Ray task.

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

You have the option to execute the code locally, during which Flyte generates a self-contained Ray cluster on your local environment.

if __name__ == "__main__":


If you observe that the head and worker pods are not being generated, you need to ensure that ray[default] is installed since it supports the cluster and dashboard launcher.

Another potential error might involve ingress issues, as indicated in the kuberay-operator logs. If you encounter an error resembling the following:

ERROR controllers.RayCluster Ingress create error!
    "Ingress.Error": "Internal error occurred: failed calling webhook "": failed to call webhook: Post "<https://nginx-ingress-ingress-nginx-controller-admission.default.svc:443/networking/v1/ingresses?timeout=10s>": no endpoints available for service "nginx-ingress-ingress-nginx-controller-admission"",
    "error": "Internal error occurred: failed calling webhook "": failed to call webhook: Post "<https://nginx-ingress-ingress-nginx-controller-admission.default.svc:443/networking/v1/ingresses?timeout=10s>": no endpoints available for service "nginx-ingress-ingress-nginx-controller-admission""

You must ensure that the ingress controller is installed.