# Copyright 2019 The Kubeflow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = [
"run_pipeline_func_on_cluster",
"run_pipeline_func_locally",
]
from typing import Callable, List, Mapping, Optional
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: Optional[LocalClient] = None,
pipeline_root: Optional[str] = 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.
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.
"""
local_client = local_client or LocalClient(pipeline_root)
return local_client.create_run_from_pipeline_func(
pipeline_func, arguments, execution_mode=execution_mode)