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samples: adds missing Node.js samples (#128)
* samples: adds missing Node.js samples
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‎ai-platform/snippets/create-batch-prediction-job-video-action-recognition.js‎

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/*
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* Copyright 2020 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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function main(
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batchPredictionDisplayName,
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modelId,
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gcsSourceUri,
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gcsDestinationOutputUriPrefix,
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project,
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location = 'us-central1'
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) {
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// [START aiplatform_create_batch_prediction_job_video_action_recognition]
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/**
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* TODO(developer): Uncomment these variables before running the sample.\
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* (Not necessary if passing values as arguments)
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*/
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// const batchPredictionDisplayName = 'YOUR_BATCH_PREDICTION_DISPLAY_NAME';
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// const modelId = 'YOUR_MODEL_ID';
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// const gcsSourceUri = 'YOUR_GCS_SOURCE_URI';
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// const gcsDestinationOutputUriPrefix = 'YOUR_GCS_DEST_OUTPUT_URI_PREFIX';
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// eg. "gs://<your-gcs-bucket>/destination_path"
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// const project = 'YOUR_PROJECT_ID';
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// const location = 'YOUR_PROJECT_LOCATION';
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const aiplatform = require('@google-cloud/aiplatform');
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const {params} = aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;
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// Imports the Google Cloud Job Service Client library
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const {JobServiceClient} = require('@google-cloud/aiplatform').v1;
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// Specifies the location of the api endpoint
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const clientOptions = {
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apiEndpoint: 'us-central1-aiplatform.googleapis.com',
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};
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// Instantiates a client
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const jobServiceClient = new JobServiceClient(clientOptions);
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async function createBatchPredictionJobVideoActionRecognition() {
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// Configure the parent resource
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const parent = `projects/${project}/locations/${location}`;
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const modelName = `projects/${project}/locations/${location}/models/${modelId}`;
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// For more information on how to configure the model parameters object, see
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// https://cloud.google.com/ai-platform-unified/docs/predictions/batch-predictions
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const modelParamsObj = new params.VideoActionRecognitionPredictionParams({
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confidenceThreshold: 0.5,
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});
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const modelParameters = modelParamsObj.toValue();
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const inputConfig = {
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instancesFormat: 'jsonl',
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gcsSource: {uris: [gcsSourceUri]},
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};
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const outputConfig = {
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predictionsFormat: 'jsonl',
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gcsDestination: {outputUriPrefix: gcsDestinationOutputUriPrefix},
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};
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const batchPredictionJob = {
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displayName: batchPredictionDisplayName,
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model: modelName,
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modelParameters,
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inputConfig,
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outputConfig,
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};
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const request = {
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parent,
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batchPredictionJob,
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};
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// Create batch prediction job request
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const [response] = await jobServiceClient.createBatchPredictionJob(request);
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console.log(
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'Create batch prediction job video action recognition response'
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);
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console.log(`Name : ${response.name}`);
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console.log('Raw response:');
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console.log(JSON.stringify(response, null, 2));
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}
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createBatchPredictionJobVideoActionRecognition();
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// [END aiplatform_create_batch_prediction_job_video_action_recognition]
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}
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process.on('unhandledRejection', err => {
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console.error(err.message);
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process.exitCode = 1;
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});
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main(...process.argv.slice(2));

‎ai-platform/snippets/create-hyperparameter-tuning-job-sample.js‎

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/*
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* Copyright 2021 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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function main(
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displayName,
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containerImageUri,
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project,
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location = 'us-central1'
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) {
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// [START aiplatform_create_hyperparameter_tuning_job_sample]
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/**
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* TODO(developer): Uncomment these variables before running the sample.
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* (Not necessary if passing values as arguments)
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*/
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/*
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const displayName = 'YOUR HYPERPARAMETER TUNING JOB;
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const containerImageUri = 'TUNING JOB CONTAINER URI;
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const project = 'YOUR PROJECT ID';
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const location = 'us-central1';
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*/
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// Imports the Google Cloud Pipeline Service Client library
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const {JobServiceClient} = require('@google-cloud/aiplatform');
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// Specifies the location of the api endpoint
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const clientOptions = {
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apiEndpoint: 'us-central1-aiplatform.googleapis.com',
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};
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// Instantiates a client
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const jobServiceClient = new JobServiceClient(clientOptions);
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async function createHyperParameterTuningJob() {
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// Configure the parent resource
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const parent = `projects/${project}/locations/${location}`;
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// Create the hyperparameter tuning job configuration
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const hyperparameterTuningJob = {
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displayName,
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maxTrialCount: 2,
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parallelTrialCount: 1,
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maxFailedTrialCount: 1,
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studySpec: {
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metrics: [
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{
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metricId: 'accuracy',
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goal: 'MAXIMIZE',
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},
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],
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parameters: [
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{
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parameterId: 'lr',
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doubleValueSpec: {
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minValue: 0.001,
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maxValue: 0.1,
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},
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},
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],
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},
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trialJobSpec: {
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workerPoolSpecs: [
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{
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machineSpec: {
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machineType: 'n1-standard-4',
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acceleratorType: 'NVIDIA_TESLA_K80',
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acceleratorCount: 1,
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},
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replicaCount: 1,
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containerSpec: {
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imageUri: containerImageUri,
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command: [],
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args: [],
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},
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},
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],
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},
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};
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const [response] = await jobServiceClient.createHyperparameterTuningJob({
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parent,
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hyperparameterTuningJob,
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});
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console.log('Create hyperparameter tuning job response:');
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console.log(`\tDisplay name: ${response.displayName}`);
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console.log(`\tTuning job resource name: ${response.name}`);
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console.log(`\tJob status: ${response.state}`);
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}
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createHyperParameterTuningJob();
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// [END aiplatform_create_hyperparameter_tuning_job_sample]
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}
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process.on('unhandledRejection', err => {
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console.error(err.message);
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process.exitCode = 1;
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});
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main(...process.argv.slice(2));

‎ai-platform/snippets/create-hyperparameter-tuning-job.js‎

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/*
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* Copyright 2021 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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function main(
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displayName,
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containerImageUri,
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project,
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location = 'us-central1'
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) {
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// [START aiplatform_create_hyperparameter_tuning_job_sample]
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/**
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* TODO(developer): Uncomment these variables before running the sample.
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* (Not necessary if passing values as arguments)
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*/
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/*
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const displayName = 'YOUR HYPERPARAMETER TUNING JOB;
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const containerImageUri = 'TUNING JOB CONTAINER URI;
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const project = 'YOUR PROJECT ID';
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const location = 'us-central1';
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*/
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// Imports the Google Cloud Pipeline Service Client library
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const {JobServiceClient} = require('@google-cloud/aiplatform');
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// Specifies the location of the api endpoint
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const clientOptions = {
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apiEndpoint: 'us-central1-aiplatform.googleapis.com',
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};
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// Instantiates a client
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const jobServiceClient = new JobServiceClient(clientOptions);
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async function createHyperParameterTuningJob() {
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// Configure the parent resource
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const parent = `projects/${project}/locations/${location}`;
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// Create the hyperparameter tuning job configuration
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const hyperparameterTuningJob = {
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displayName,
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maxTrialCount: 2,
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parallelTrialCount: 1,
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maxFailedTrialCount: 1,
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studySpec: {
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metrics: [
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{
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metricId: 'accuracy',
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goal: 'MAXIMIZE',
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},
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],
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parameters: [
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{
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parameterId: 'lr',
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doubleValueSpec: {
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minValue: 0.001,
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maxValue: 0.1,
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},
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},
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],
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},
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trialJobSpec: {
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workerPoolSpecs: [
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{
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machineSpec: {
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machineType: 'n1-standard-4',
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acceleratorType: 'NVIDIA_TESLA_K80',
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acceleratorCount: 1,
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},
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replicaCount: 1,
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containerSpec: {
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imageUri: containerImageUri,
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command: [],
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args: [],
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},
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},
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],
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},
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};
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const [response] = await jobServiceClient.createHyperparameterTuningJob({
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parent,
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hyperparameterTuningJob,
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});
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console.log('Create hyperparameter tuning job response:');
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console.log(`\tDisplay name: ${response.displayName}`);
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console.log(`\tTuning job resource name: ${response.name}`);
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console.log(`\tJob status: ${response.state}`);
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}
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createHyperParameterTuningJob();
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// [END aiplatform_create_hyperparameter_tuning_job_sample]
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}
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process.on('unhandledRejection', err => {
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console.error(err.message);
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process.exitCode = 1;
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});
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main(...process.argv.slice(2));

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