Distributed KubeFlow Pipelines Example

This is an example KFP pipeline that uses resource_from_app to launch a distributed operator using the kubernetes/volcano job scheduler. This only works in Kubernetes KFP clusters with https://volcano.sh/en/docs/ installed on them.

import kfp
from torchx import specs
from torchx.pipelines.kfp.adapter import resource_from_app


def pipeline() -> None:
    # First we define our AppDef for the component, we set
    echo_app = specs.AppDef(
        name="test-dist",
        roles=[
            specs.Role(
                name="dist-echo",
                image="alpine",
                entrypoint="/bin/echo",
                args=["hello dist!"],
                num_replicas=3,
            ),
        ],
    )

    # To convert the TorchX AppDef into a KFP container we use
    # the resource_from_app adapter. This takes generates a KFP Kubernetes
    # resource operator definition from the TorchX app def and instantiates it.
    echo_container: kfp.dsl.BaseOp = resource_from_app(echo_app, queue="default")

To generate the pipeline definition file we need to call into the KFP compiler with our pipeline function.

kfp.compiler.Compiler().compile(
    pipeline_func=pipeline,
    package_path="pipeline.yaml",
)

with open("pipeline.yaml", "rt") as f:
    print(f.read())

Once this has all run you should have a pipeline file (typically pipeline.yaml) that you can upload to your KFP cluster via the UI or a kfp.Client.

See the KFP SDK Examples for more info on launching KFP pipelines.

See the Advanced KubeFlow Pipelines Example for how to chain multiple components together and use builtin components.

# sphinx_gallery_thumbnail_path = '_static/img/gallery-kfp.png'