This is the Flyte SDK (v2) version of this plugin. The Flytekit version is available as flytekitplugins-ray.
Ray
flyteplugins-ray
Union can execute Ray jobs natively on a Kubernetes Cluster,
pip install flyteplugins-rayQuick Start(example, may need adjustment)
See full examplespip install flyteplugins-ray
from flytekit import task, workflow
from flyteplugins.ray import HeadNodeConfig, RayJobConfig, WorkerNodeConfig
@task(task_config=RayJobConfig(...))
def my_task() -> None:
...
@workflow
def my_workflow() -> None:
my_task()Available Imports (3)
Configuration type for Ray.
extends dataclass — configuration or data structure for plugin setup
from flyteplugins.ray import HeadNodeConfig
Task for Ray.
extends dataclass — configuration or data structure for plugin setup
from flyteplugins.ray import RayJobConfig
Configuration type for Ray.
extends dataclass — configuration or data structure for plugin setup
from flyteplugins.ray import WorkerNodeConfig
Related Plugins
Dask
Flyte can execute dask jobs natively on a Kubernetes Cluster, which manages the virtual dask cluster's lifecycle
Dask
Flyte can execute dask jobs natively on a Kubernetes Cluster, which manages the virtual dask cluster's lifecycle
Ray
Flyte backend can be connected with Ray. Once enabled, it allows you to run flyte task on Ray cluster
Kubeflow MPI
This plugin uses the Kubeflow MPI Operator and provides an extremely simplified interface for executing distributed training.
