def run_pipeline(request, pipeline_metadata, pipeline_package_path=None, pipeline_id=None): """Run a pipeline.""" client = _get_client(pipeline_metadata.get("kfp_host", None)) experiment = client.create_experiment(pipeline_metadata["experiment_name"]) run_name = kfp_utils.generate_run_name(pipeline_metadata["pipeline_name"]) run = client.run_pipeline(experiment.id, run_name, pipeline_package_path=pipeline_package_path, pipeline_id=pipeline_id) return {"id": run.id, "name": run.name, "status": run.status}
results_task = results_op()\ .add_pvolumes(pvolumes_dict)\ .after(randomforest_task, logisticregression_task, naivebayes_task, svm_task, decisiontree_task) results_task.container.working_dir = "/kale" results_task.container.set_security_context( k8s_client.V1SecurityContext(run_as_user=0)) output_artifacts = {} output_artifacts.update( {'mlpipeline-ui-metadata': '/mlpipeline-ui-metadata.json'}) output_artifacts.update({'results': '/results.html'}) results_task.output_artifact_paths.update(output_artifacts) if __name__ == "__main__": pipeline_func = auto_generated_pipeline pipeline_filename = pipeline_func.__name__ + '.pipeline.tar.gz' import kfp.compiler as compiler compiler.Compiler().compile(pipeline_func, pipeline_filename) # Get or create an experiment and submit a pipeline run import kfp client = kfp.Client() experiment = client.create_experiment('titanic') # Submit a pipeline run from kale.utils.kfp_utils import generate_run_name run_name = generate_run_name('titanic-ml-rnd') run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, {})
{'mlpipeline-ui-metadata': '/mlpipeline-ui-metadata.json'}) output_artifacts.update({'explain': '/explain.html'}) explain_task.output_artifact_paths.update(output_artifacts) explain_task.add_pod_label("pipelines.kubeflow.org/metadata_written", "true") dep_names = explain_task.dependent_names + volume_step_names explain_task.add_pod_annotation("kubeflow-kale.org/dependent-templates", json.dumps(dep_names)) if volume_name_parameters: explain_task.add_pod_annotation( "kubeflow-kale.org/volume-name-parameters", json.dumps(volume_name_parameters)) if __name__ == "__main__": pipeline_func = auto_generated_pipeline pipeline_filename = pipeline_func.__name__ + '.pipeline.tar.gz' import kfp.compiler as compiler compiler.Compiler().compile(pipeline_func, pipeline_filename) # Get or create an experiment and submit a pipeline run import kfp client = kfp.Client() experiment = client.create_experiment('seldon-e2e-adult') # Submit a pipeline run from kale.utils.kfp_utils import generate_run_name run_name = generate_run_name('seldon-e2e-adult-ttonn') run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, {})
{'mlpipeline-ui-metadata': '/mlpipeline-ui-metadata.json'}) output_artifacts.update({'results': '/results.html'}) results_task.output_artifact_paths.update(output_artifacts) results_task.add_pod_label( "pipelines.kubeflow.org/metadata_written", "true") dep_names = results_task.dependent_names + volume_step_names results_task.add_pod_annotation( "kubeflow-kale.org/dependent-templates", json.dumps(dep_names)) if volume_name_parameters: results_task.add_pod_annotation( "kubeflow-kale.org/volume-name-parameters", json.dumps(volume_name_parameters)) if __name__ == "__main__": pipeline_func = auto_generated_pipeline pipeline_filename = pipeline_func.__name__ + '.pipeline.tar.gz' import kfp.compiler as compiler compiler.Compiler().compile(pipeline_func, pipeline_filename) # Get or create an experiment and submit a pipeline run import kfp client = kfp.Client() experiment = client.create_experiment('titanic') # Submit a pipeline run from kale.utils.kfp_utils import generate_run_name run_name = generate_run_name('titanic-ml-gxj28') run_result = client.run_pipeline( experiment.id, run_name, pipeline_filename, {})
{'mlpipeline-ui-metadata': '/mlpipeline-ui-metadata.json'}) output_artifacts.update({'sum_matrix': '/sum_matrix.html'}) sum_matrix_task.output_artifact_paths.update(output_artifacts) sum_matrix_task.add_pod_label("pipelines.kubeflow.org/metadata_written", "true") dep_names = sum_matrix_task.dependent_names + volume_step_names sum_matrix_task.add_pod_annotation("kubeflow-kale.org/dependent-templates", json.dumps(dep_names)) if volume_name_parameters: sum_matrix_task.add_pod_annotation( "kubeflow-kale.org/volume-name-parameters", json.dumps(volume_name_parameters)) if __name__ == "__main__": pipeline_func = auto_generated_pipeline pipeline_filename = pipeline_func.__name__ + '.pipeline.tar.gz' import kfp.compiler as compiler compiler.Compiler().compile(pipeline_func, pipeline_filename) # Get or create an experiment and submit a pipeline run import kfp client = kfp.Client() experiment = client.create_experiment('hp-tuning') # Submit a pipeline run from kale.utils.kfp_utils import generate_run_name run_name = generate_run_name('hp-test-rnd') run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, {})