Ray¶
KubeRay is an open-source toolkit designed to facilitate the execution of Ray applications on Kubernetes. It offers a range of tools that enhance the operational aspects of running and overseeing Ray on Kubernetes.
Key components include:
Ray Operator
Backend services for cluster resource creation and deletion
Kubectl plugin/CLI for CRD object management
Seamless integration of Jobs and Serving functionality with Clusters
Install the plugin¶
To install the Ray plugin, run the following command:
pip install flytekitplugins-ray
To enable the plugin in the backend, refer to the instructions provided in the Configure Kubernetes Plugins guide.
Implementation details¶
Submit a Ray job to existing cluster¶
@ray.remote
def f(x):
return x * x
@task(
task_config=RayJobConfig(
address=<RAY_CLUSTER_ADDRESS>
runtime_env={"pip": ["numpy", "pandas"]}
)
)
def ray_task() -> typing.List[int]:
futures = [f.remote(i) for i in range(5)]
return ray.get(futures)
Create a Ray cluster managed by Flyte and run a Ray Job on the cluster¶
@task(task_config=RayJobConfig(worker_node_config=[WorkerNodeConfig(group_name="test-group", replicas=10)]))
def ray_task() -> typing.List[int]:
futures = [f.remote(i) for i in range(5)]
return ray.get(futures)
Run the example on the Flyte cluster¶
To run the provided example on the Flyte cluster, use the following command:
pyflyte run --remote ray_example.py \
ray_workflow --n 10