A Flyte SDK (v2) version of this plugin is available as flyteplugins-ray.
Ray
flytekitplugins-ray
Flyte backend can be connected with Ray. Once enabled, it allows you to run flyte task on Ray cluster
pip install flytekitplugins-rayQuick Start(example, may need adjustment)
See full examplespip install flytekitplugins-ray
from flytekit import task, workflow
from flytekitplugins.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 flytekitplugins.ray import HeadNodeConfig
Task for Ray.
extends dataclass — configuration or data structure for plugin setup
from flytekitplugins.ray import RayJobConfig
Configuration type for Ray.
extends dataclass — configuration or data structure for plugin setup
from flytekitplugins.ray import WorkerNodeConfig
Related Plugins
Dask
Flyte can execute dask jobs natively on a Kubernetes Cluster, which manages the virtual dask cluster's lifecycle
Ray
Union can execute Ray jobs natively on a Kubernetes Cluster,
Dask
Flyte can execute dask jobs natively on a Kubernetes Cluster, which manages the virtual dask cluster's lifecycle
Kubeflow MPI
This plugin uses the Kubeflow MPI Operator and provides an extremely simplified interface for executing distributed training.
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