pipelines/components/gcp/dataflow/launch_python/component.yaml

75 lines
2.6 KiB
YAML

# Copyright 2018 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.
name: Launch Python
description: |
Launch a self-executing beam python file.
metadata:
labels:
add-pod-env: 'true'
inputs:
- name: python_file_path
description: 'The gcs or local path to the python file to run.'
type: String
- name: project_id
description: 'The ID of the GCP project to run the Dataflow job.'
type: String
- name: region
description: 'The GCP region to run the Dataflow job.'
type: String
- name: staging_dir
description: >-
Optional. The GCS directory for keeping staging files.
A random subdirectory will be created under the directory to keep job info
for resuming the job in case of failure and it will be passed as
`staging_location` and `temp_location` command line args of the beam code.
default: ''
type: GCSPath
- name: requirements_file_path
description: 'Optional, the gcs or local path to the pip requirements file'
default: ''
type: GCSPath
- name: args
description: 'The list of args to pass to the python file.'
default: '[]'
type: List
- name: wait_interval
default: '30'
description: 'Optional wait interval between calls to get job status. Defaults to 30.'
type: Integer
outputs:
- name: job_id
description: 'The id of the created dataflow job.'
type: String
- name: MLPipeline UI metadata
type: UI metadata
implementation:
container:
image: gcr.io/ml-pipeline/ml-pipeline-gcp:1.7.0-rc.3
command: ["python", -u, -m, "kfp_component.launcher"]
args: [
--ui_metadata_path, {outputPath: MLPipeline UI metadata},
kfp_component.google.dataflow, launch_python,
--python_file_path, {inputValue: python_file_path},
--project_id, {inputValue: project_id},
--region, {inputValue: region},
--staging_dir, {inputValue: staging_dir},
--requirements_file_path, {inputValue: requirements_file_path},
--args, {inputValue: args},
--wait_interval, {inputValue: wait_interval},
--job_id_output_path, {outputPath: job_id},
]
env:
KFP_POD_NAME: "{{pod.name}}"