deploy-model-custom-trained-model.js

/*
 * Copyright 2021 Google LLC
 *
 * 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
 *
 *     https://www.apache.org/licenses/LICENSE-2.0
 *
 * 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.
 */

'use strict';

function main(
  modelId,
  deployedModelDisplayName,
  endpointId,
  project,
  location = 'us-central1'
) {
  // [START aiplatform_deploy_model_custom_trained_model_sample]
  /**
   * TODO(developer): Uncomment these variables before running the sample.\
   * (Not necessary if passing values as arguments)
   */

  // const modelId = "YOUR_MODEL_ID";
  // const endpointId = 'YOUR_ENDPOINT_ID';
  // const deployedModelDisplayName = 'YOUR_DEPLOYED_MODEL_DISPLAY_NAME';
  // const project = 'YOUR_PROJECT_ID';
  // const location = 'YOUR_PROJECT_LOCATION';

  const modelName = `projects/${project}/locations/${location}/models/${modelId}`;
  const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
  // Imports the Google Cloud Endpoint Service Client library
  const {EndpointServiceClient} = require('@google-cloud/aiplatform');

  // Specifies the location of the api endpoint:
  const clientOptions = {
    apiEndpoint: 'us-central1-aiplatform.googleapis.com',
  };

  // Instantiates a client
  const endpointServiceClient = new EndpointServiceClient(clientOptions);

  async function deployModelCustomTrainedModel() {
    // Configure the parent resource
    // key '0' assigns traffic for the newly deployed model
    // Traffic percentage values must add up to 100
    // Leave dictionary empty if endpoint should not accept any traffic
    const trafficSplit = {0: 100};
    const deployedModel = {
      // format: 'projects/{project}/locations/{location}/models/{model}'
      model: modelName,
      displayName: deployedModelDisplayName,
      // `dedicatedResources` must be used for non-AutoML models
      dedicatedResources: {
        minReplicaCount: 1,
        machineSpec: {
          machineType: 'n1-standard-2',
          // Accelerators can be used only if the model specifies a GPU image.
          // acceleratorType: 'NVIDIA_TESLA_K80',
          // acceleratorCount: 1,
        },
      },
    };
    const request = {
      endpoint,
      deployedModel,
      trafficSplit,
    };

    // Get and print out a list of all the endpoints for this resource
    const [response] = await endpointServiceClient.deployModel(request);
    console.log(`Long running operation : ${response.name}`);

    // Wait for operation to complete
    await response.promise();
    const result = response.result;

    console.log('Deploy model response');
    const modelDeployed = result.deployedModel;
    console.log(`\t\tId : ${modelDeployed.id}`);
    console.log(modelDeployed);
  }
  deployModelCustomTrainedModel();
  // [END aiplatform_deploy_model_custom_trained_model_sample]
}

process.on('unhandledRejection', err => {
  console.error(err.message);
  process.exitCode = 1;
});

main(...process.argv.slice(2));