Created
March 17, 2020 21:43
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Create an Argo Pipeline YAML using Kubeflow pipelines SDK
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| import kfp | |
| @kfp.components.func_to_container_op | |
| def print_func(param: int): | |
| print(str(param)) | |
| @kfp.components.func_to_container_op | |
| def list_func(param: int) -> list: | |
| return list(range(param)) | |
| @kfp.dsl.pipeline(name='pipeline') | |
| def pipeline(param: int): | |
| list_func_op = list_func(param) | |
| with kfp.dsl.ParallelFor(list_func_op.output) as param: | |
| print_func(param) | |
| if __name__ == '__main__': | |
| artifact_location = ArtifactLocation.s3( | |
| bucket="__argo_bucket_name__", | |
| endpoint="s3.amazonaws.com", | |
| region="us-west-2", | |
| insecure = False, | |
| access_key_secret=V1SecretKeySelector(name="__secret_name__", key="aws-access-key-id"), | |
| secret_key_secret=V1SecretKeySelector(name="__secret_name__", key="aws-secret-access-key")) | |
| # config pipeline level artifact location | |
| conf = dsl.PipelineConf() | |
| conf = conf.set_artifact_location(artifact_location) | |
| workflow_dict = kfp.compiler.Compiler()._create_workflow(pipeline,pipeline_conf=conf) | |
| workflow_dict['metadata']['namespace'] = "default" | |
| del workflow_dict['spec']['serviceAccountName'] | |
| kfp.compiler.Compiler._write_workflow(workflow_dict, "pipe.yaml") |
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