Source code for kfp._runners

# Copyright 2019 The Kubeflow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
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#      http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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__all__ = [
    "run_pipeline_func_on_cluster",
    "run_pipeline_func_locally",
]


from typing import Callable, List, Mapping

from . import Client, LocalClient, dsl


[docs]def run_pipeline_func_on_cluster( pipeline_func: Callable, arguments: Mapping[str, str], run_name: str = None, experiment_name: str = None, kfp_client: Client = None, pipeline_conf: dsl.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) Args: 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. 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. """ kfp_client = kfp_client or Client() return kfp_client.create_run_from_pipeline_func( pipeline_func, arguments, run_name, experiment_name, pipeline_conf )
[docs]def run_pipeline_func_locally( pipeline_func: Callable, arguments: Mapping[str, str], local_client: LocalClient = None, execution_mode: LocalClient.ExecutionMode = LocalClient.ExecutionMode(), ): """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 Args: 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 execution_mode: Configuration to decide whether the client executes component in docker or in local process. """ local_client = local_client or LocalClient() return local_client.create_run_from_pipeline_func( pipeline_func, arguments, execution_mode=execution_mode )