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fix: restore 5 files
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‎ai-platform/snippets/create-dataset.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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async function main(
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datasetDisplayName,
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metadataSchemaUri,
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project,
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location = 'us-central1'
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) {
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// [START aiplatform_create_dataset_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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// const datasetDisplayName = 'YOUR_DATASET_DISPLAY_NAME';
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// const metadataSchemaUri = 'YOUR_METADATA_SCHEMA_URI';
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// const project = 'YOUR_PROJECT_ID';
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// const location = 'YOUR_PROJECT_LOCATION';
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// Imports the Google Cloud Dataset Service Client library
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const {DatasetServiceClient} = 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 datasetServiceClient = new DatasetServiceClient(clientOptions);
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async function createDataset() {
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// Configure the parent resource
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const parent = `projects/${project}/locations/${location}`;
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// Configure the dataset resource
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const dataset = {
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displayName: datasetDisplayName,
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metadataSchemaUri: metadataSchemaUri,
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};
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const request = {
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parent,
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dataset,
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};
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// Create Dataset Request
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const [response] = await datasetServiceClient.createDataset(request);
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console.log(`Long running operation : ${response.name}`);
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// Wait for operation to complete
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const [createDatasetResponse] = await response.promise();
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console.log('Create dataset response');
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console.log(`\tName : ${createDatasetResponse.name}`);
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console.log(`\tDisplay name : ${createDatasetResponse.displayName}`);
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console.log(
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`\tMetadata schema uri : ${createDatasetResponse.metadataSchemaUri}`
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);
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console.log(
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`\tMetadata : ${JSON.stringify(createDatasetResponse.metadata)}`
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);
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console.log(`\tCreate time : ${createDatasetResponse.createTime}`);
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console.log(`\tUpdate time : ${createDatasetResponse.updateTime}`);
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console.log(`\tLabels : ${JSON.stringify(createDatasetResponse.labels)}`);
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}
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createDataset();
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// [END aiplatform_create_dataset_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-featurestore-fixed-nodes-sample.js‎

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/*
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* Copyright 2022 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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/*
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* Creates a new Featurestore with fixed nodes configuration in a given project and location.
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* See https://cloud.google.com/vertex-ai/docs/featurestore/setup before running
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* the code snippet
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*/
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'use strict';
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async function main(
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project,
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featurestoreId,
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fixedNodeCount = 1,
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location = 'us-central1',
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apiEndpoint = 'us-central1-aiplatform.googleapis.com',
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timeout = 900000
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) {
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// [START aiplatform_create_featurestore_fixed_nodes_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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// const project = 'YOUR_PROJECT_ID';
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// const featurestoreId = 'YOUR_FEATURESTORE_ID';
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// const fixedNodeCount = <NO_OF_NODES>;
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// const location = 'YOUR_PROJECT_LOCATION';
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// const apiEndpoint = 'YOUR_API_ENDPOINT';
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// const timeout = <TIMEOUT_IN_MILLI_SECONDS>;
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// Imports the Google Cloud Featurestore Service Client library
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const {FeaturestoreServiceClient} = 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: apiEndpoint,
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};
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// Instantiates a client
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const featurestoreServiceClient = new FeaturestoreServiceClient(
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clientOptions
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);
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async function createFeaturestoreFixedNodes() {
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// Configure the parent resource
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const parent = `projects/${project}/locations/${location}`;
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const featurestore = {
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onlineServingConfig: {fixedNodeCount: Number(fixedNodeCount)},
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};
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const request = {
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parent: parent,
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featurestore: featurestore,
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featurestoreId: featurestoreId,
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};
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// Create Featurestore request
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const [operation] = await featurestoreServiceClient.createFeaturestore(
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request,
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{timeout: Number(timeout)}
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);
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const [response] = await operation.promise();
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console.log('Create featurestore fixed nodes response');
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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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createFeaturestoreFixedNodes();
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// [END aiplatform_create_featurestore_fixed_nodes_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));

‎ai-platform/snippets/create-training-pipeline-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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async function main(
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datasetId,
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modelDisplayName,
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trainingPipelineDisplayName,
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project,
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location = 'us-central1'
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) {
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// [START aiplatform_create_training_pipeline_video_action_recognition_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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// const datasetId = 'YOUR_DATASET_ID';
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// const modelDisplayName = 'YOUR_MODEL_DISPLAY_NAME';
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// const trainingPipelineDisplayName = 'YOUR_TRAINING_PIPELINE_DISPLAY_NAME';
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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 {definition} =
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aiplatform.protos.google.cloud.aiplatform.v1.schema.trainingjob;
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// Imports the Google Cloud Pipeline Service Client library
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const {PipelineServiceClient} = 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 pipelineServiceClient = new PipelineServiceClient(clientOptions);
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async function createTrainingPipelineVideoActionRecognition() {
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// Configure the parent resource
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const parent = `projects/${project}/locations/${location}`;
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// Values should match the input expected by your model.
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const trainingTaskInputObj =
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new definition.AutoMlVideoActionRecognitionInputs({
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// modelType can be either 'CLOUD' or 'MOBILE_VERSATILE_1'
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modelType: 'CLOUD',
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});
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const trainingTaskInputs = trainingTaskInputObj.toValue();
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const modelToUpload = {displayName: modelDisplayName};
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const inputDataConfig = {datasetId: datasetId};
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const trainingPipeline = {
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displayName: trainingPipelineDisplayName,
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trainingTaskDefinition:
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'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_action_recognition_1.0.0.yaml',
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trainingTaskInputs,
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inputDataConfig,
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modelToUpload,
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};
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const request = {
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parent,
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trainingPipeline,
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};
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// Create training pipeline request
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const [response] =
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await pipelineServiceClient.createTrainingPipeline(request);
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console.log('Create training pipeline video action recognition response');
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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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createTrainingPipelineVideoActionRecognition();
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// [END aiplatform_create_training_pipeline_video_action_recognition_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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