kfp package¶
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class
kfp.
LocalClient
(pipeline_root: Optional[str] = None)[source]¶ Bases:
object
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class
ExecutionMode
(mode: str = 'docker', images_to_exclude: List[str] = [], ops_to_exclude: List[str] = [], docker_options: List[str] = [])[source]¶ Bases:
object
Configuration to decide whether the client executes a component in docker or in local process.
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DOCKER
= 'docker'¶
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LOCAL
= 'local'¶
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docker_options
¶
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images_to_exclude
¶
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mode
¶
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ops_to_exclude
¶
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-
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>)[source]¶ 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.
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class
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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>)[source]¶ 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.
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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)[source]¶ 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.