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feat(samples): adds samples for enhanced version of library (#16)
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Lines changed: 2868 additions & 39 deletions

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‎ai-platform/snippets/create-batch-prediction-job-video-classification.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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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_classification]
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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 {
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params,
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} = aiplatform.protos.google.cloud.aiplatform.v1beta1.schema.predict;
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// Imports the Google Cloud Job 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 createBatchPredictionJobVideoClassification() {
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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.VideoClassificationPredictionParams({
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confidenceThreshold: 0.5,
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maxPredictions: 1000,
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segmentClassification: true,
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shotClassification: true,
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oneSecIntervalClassification: true,
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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('Create batch prediction job video classification 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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createBatchPredictionJobVideoClassification();
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// [END aiplatform_create_batch_prediction_job_video_classification]
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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-batch-prediction-job-video-object-tracking.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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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_object_tracking]
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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 {
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params,
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} = aiplatform.protos.google.cloud.aiplatform.v1beta1.schema.predict;
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// Imports the Google Cloud Job 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 createBatchPredictionJobVideoObjectTracking() {
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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.VideoObjectTrackingPredictionParams({
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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('Create batch prediction job video object tracking 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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createBatchPredictionJobVideoObjectTracking();
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// [END aiplatform_create_batch_prediction_job_video_object_tracking]
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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-image-classification.js‎

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);
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console.log('Create training pipeline image classification response');
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console.log(`\tName : ${response.name}`);
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console.log(`\tDisplay Name : ${response.displayName}`);
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console.log(
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`\tTraining task definition : ${response.trainingTaskDefinition}`
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);
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console.log(
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`\tTraining task inputs : \
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${JSON.stringify(response.trainingTaskInputs)}`
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);
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console.log(
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`\tTraining task metadata : \
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${JSON.stringify(response.trainingTaskMetadata)}`
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);
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console.log(`\tState ; ${response.state}`);
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console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
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console.log(`\tStart time : ${JSON.stringify(response.startTime)}`);
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console.log(`\tEnd time : ${JSON.stringify(response.endTime)}`);
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console.log(`\tUpdate time : ${JSON.stringify(response.updateTime)}`);
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console.log(`\tLabels : ${JSON.stringify(response.labels)}`);
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const error = response.error;
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console.log('\tError');
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if (error === null) {
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console.log('\t\tCode : {}');
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console.log('\t\tMessage : {}');
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} else {
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console.log(`\t\tCode : ${error.code}`);
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console.log(`\t\tMessage : ${error.message}`);
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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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createTrainingPipelineImageClassification();

‎ai-platform/snippets/create-training-pipeline-image-object-detection.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_image_object_detection]
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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 {
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definition,
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} = aiplatform.protos.google.cloud.aiplatform.v1beta1.schema.trainingjob;
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const ModelType = definition.AutoMlImageObjectDetectionInputs.ModelType;
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// Imports the Google Cloud Pipeline Service Client library
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const {PipelineServiceClient} = 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 pipelineServiceClient = new PipelineServiceClient(clientOptions);
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async function createTrainingPipelineImageObjectDetection() {
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// Configure the parent resource
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const parent = `projects/${project}/locations/${location}`;
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const trainingTaskInputsObj = new definition.AutoMlImageObjectDetectionInputs(
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{
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disableEarlyStopping: false,
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modelType: ModelType.CLOUD_HIGH_ACCURACY_1,
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budgetMilliNodeHours: 20000,
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}
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);
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const trainingTaskInputs = trainingTaskInputsObj.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_image_object_detection_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] = await pipelineServiceClient.createTrainingPipeline(
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request
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);
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console.log('Create training pipeline image object detection 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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createTrainingPipelineImageObjectDetection();
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// [END aiplatform_create_training_pipeline_image_object_detection]
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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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