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Merge pull request #439 from GoogleCloudPlatform/kfp-prebuilt-update
Update KFP prebuilt components pipeline
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...books/kubeflow_pipelines/pipelines/labs/pipeline_vertex/retrieve_best_hptune_component.py
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| # Copyright 2021 Google LLC | ||
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| # 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 | ||
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| # https://www.apache.org/licenses/LICENSE-2.0 | ||
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| # 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. | ||
| """Lightweight component tuning function.""" | ||
| from typing import Dict, List, NamedTuple | ||
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| from kfp.dsl import component | ||
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| @component( | ||
| base_image="python:3.8", | ||
| packages_to_install=["google-cloud-aiplatform"], | ||
| ) | ||
| def retrieve_best_hptune_result( | ||
| project: str, | ||
| location: str, | ||
| gcp_resources: str, | ||
| container_uri: str, | ||
| training_file_path: str, | ||
| validation_file_path: str, | ||
| ) -> NamedTuple( | ||
| "Outputs", | ||
| [ | ||
| ("best_parameters", Dict), | ||
| ("best_metrics", Dict), | ||
| ("best_worker_pool_spec", List), | ||
| ], | ||
| ): | ||
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| # pylint: disable=import-outside-toplevel | ||
| import json | ||
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| from google.cloud import aiplatform | ||
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| aiplatform.init(project=project, location=location) | ||
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| # Retrieve the hyperparameter tuning job result | ||
| gcp_resources = json.loads(gcp_resources) | ||
| job_id = gcp_resources["resources"][0]["resourceUri"].split("/")[-1] | ||
| hp_job = aiplatform.HyperparameterTuningJob.get(job_id) | ||
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| # Retrieve the best trial | ||
| metrics = [ | ||
| trial.final_measurement.metrics[0].value for trial in hp_job.trials | ||
| ] | ||
| goal = hp_job.to_dict()["studySpec"]["metrics"][0]["goal"] | ||
| goal_f = min if goal == "MINIMIZE" else max # MINIMIZE or MAXIMIZE | ||
| best_result = goal_f(metrics) | ||
| best_trial = hp_job.trials[metrics.index(best_result)] | ||
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| best_parameters = { | ||
| param.parameter_id: param.value for param in best_trial.parameters | ||
| } | ||
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| best_metrics = { | ||
| m.metric_id: m.value for m in best_trial.final_measurement.metrics | ||
| } | ||
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| # Construct worker_pool_spec | ||
| best_worker_pool_spec = [ | ||
| { | ||
| "machine_spec": {"machine_type": "n1-standard-4"}, | ||
| "replica_count": 1, | ||
| "container_spec": { | ||
| "image_uri": container_uri, | ||
| "args": [ | ||
| f"--training_dataset_path={training_file_path}", | ||
| f"--validation_dataset_path={validation_file_path}", | ||
| "--nohptune", | ||
| ], | ||
| }, | ||
| } | ||
| ] | ||
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| for k, v in best_parameters.items(): | ||
| best_worker_pool_spec[0]["container_spec"]["args"].append(f"--{k}={v}") | ||
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| return best_parameters, best_metrics, best_worker_pool_spec |
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