# 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 Dataflow Template description: | Launchs a dataflow job from template. metadata: labels: add-pod-env: 'true' inputs: - name: project_id description: 'Required. The ID of the Cloud Platform project that the job belongs to.' type: GCPProjectID - name: gcs_path description: >- Required. A Cloud Storage path to the template from which to create the job. Must be valid Cloud Storage URL, beginning with `gs://`. type: GCSPath - name: launch_parameters description: >- Parameters to provide to the template being launched. Schema defined in https://cloud.google.com/dataflow/docs/reference/rest/v1b3/LaunchTemplateParameters. `jobName` will be replaced by generated name.' type: Dict default: '{}' - name: location description: 'The regional endpoint to which to direct the request.' default: '' type: GCPRegion - name: validate_only description: >- If true, the request is validated but not actually executed. Defaults to false. default: 'False' type: Bool - 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. default: '' type: GCSPath - name: wait_interval description: >- Optional wait interval between calls to get job status. Defaults to 30. default: '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 args: [ --ui_metadata_path, {outputPath: MLPipeline UI metadata}, kfp_component.google.dataflow, launch_template, --project_id, {inputValue: project_id}, --gcs_path, {inputValue: gcs_path}, --launch_parameters, {inputValue: launch_parameters}, --location, {inputValue: location}, --validate_only, {inputValue: validate_only}, --staging_dir, {inputValue: staging_dir}, --wait_interval, {inputValue: wait_interval}, --job_id_output_path, {outputPath: job_id}, ] env: KFP_POD_NAME: "{{pod.name}}"