kfp package

• kfp.Client class
  • Generated APIs
• kfp.compiler package
• kfp.components package
  • kfp.components.structures subpackage
• kfp.containers package
• kfp.dsl package
  • kfp.dsl.types module
• KFP extension modules
  • kfp.onprem module
  • kfp.gcp module
  • kfp.aws module
  • kfp.azure module

class kfp.LocalClient(pipeline_root: Optional[str] = None)

Bases: object

class ExecutionMode(mode: str = 'docker', images_to_exclude: List[str] = [], ops_to_exclude: List[str] = [], docker_options: List[str] = [])

Bases: object

Configuration to decide whether the client executes a component in docker or in local process.

DOCKER = 'docker'

LOCAL = 'local'

docker_options

images_to_exclude

mode

ops_to_exclude

create_run_from_pipeline_func(pipeline_func: Callable, arguments: Mapping[str, str], execution_mode: kfp._local_client.LocalClient.ExecutionMode = <kfp._local_client.LocalClient.ExecutionMode object>)

Runs a pipeline locally, either using Docker or in a local process.

Parameters:

• pipeline_func – pipeline function
• arguments – Arguments to the pipeline function provided as a dict, reference to kfp.client.create_run_from_pipeline_func
• execution_mode – Configuration to decide whether the client executes component in docker or in local process.

kfp.run_pipeline_func_locally(pipeline_func: Callable, arguments: Mapping[str, str], local_client: Optional[kfp._local_client.LocalClient] = None, pipeline_root: Optional[str] = None, execution_mode: kfp._local_client.LocalClient.ExecutionMode = <kfp._local_client.LocalClient.ExecutionMode object>)

Runs a pipeline locally, either using Docker or in a local process.

Feature stage: [Alpha](https://github.com/kubeflow/pipelines/blob/master/docs/release/feature-stages.md#alpha)

In this alpha implementation, we support:

• Control flow: Condition, ParallelFor
• Data passing: InputValue, InputPath, OutputPath

And we don’t support:

• Control flow: ExitHandler, Graph, SubGraph
• ContainerOp with environment variables, init containers, sidecars, pvolumes
• ResourceOp
• VolumeOp
• Caching

Parameters:

• pipeline_func – A function that describes a pipeline by calling components and composing them into execution graph.
• arguments – Arguments to the pipeline function provided as a dict. reference to kfp.client.create_run_from_pipeline_func.
• local_client – Optional. An instance of kfp.LocalClient.
• pipeline_root – Optional. The root directory where the output artifact of component will be saved.
• execution_mode – Configuration to decide whether the client executes component in docker or in local process.

kfp.run_pipeline_func_on_cluster(pipeline_func: Callable, arguments: Mapping[str, str], run_name: str = None, experiment_name: str = None, kfp_client: kfp._client.Client = None, pipeline_conf: kfp.dsl._pipeline.PipelineConf = None)

Runs pipeline on KFP-enabled Kubernetes cluster.

This command compiles the pipeline function, creates or gets an experiment and submits the pipeline for execution.

Feature stage: [Alpha](https://github.com/kubeflow/pipelines/blob/07328e5094ac2981d3059314cc848fbb71437a76/docs/release/feature-stages.md#alpha)

Parameters:

• pipeline_func – A function that describes a pipeline by calling components
• composing them into execution graph. (and) –
• arguments – Arguments to the pipeline function provided as a dict.
• run_name – Optional. Name of the run to be shown in the UI.
• experiment_name – Optional. Name of the experiment to add the run to.
• kfp_client – Optional. An instance of kfp.Client configured for the desired KFP cluster.
• pipeline_conf – Optional. kfp.dsl.PipelineConf instance. Can specify op transforms, image pull secrets and other pipeline-level configuration options.