Cloud AutoML: Node.js Client

release level npm version

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Cloud AutoML API client for Node.js

A comprehensive list of changes in each version may be found in the CHANGELOG.

Read more about the client libraries for Cloud APIs, including the older Google APIs Client Libraries, in Client Libraries Explained.

Table of contents:

Quickstart

Before you begin

  1. Select or create a Cloud Platform project.
  2. Enable billing for your project.
  3. Enable the Cloud AutoML API.
  4. Set up authentication with a service account so you can access the API from your local workstation.

Installing the client library

npm install @google-cloud/automl

Using the client library

const automl = require('@google-cloud/automl');
const fs = require('fs');

// Create client for prediction service.
const client = new automl.PredictionServiceClient();

/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const projectId = `The GCLOUD_PROJECT string, e.g. "my-gcloud-project"`;
// const computeRegion = `region-name, e.g. "us-central1"`;
// const modelId = `id of the model, e.g. “ICN723541179344731436”`;
// const filePath = `local text file path of content to be classified, e.g. "./resources/flower.png"`;
// const scoreThreshold = `value between 0.0 and 1.0, e.g. "0.5"`;

// Get the full path of the model.
const modelFullId = client.modelPath(projectId, computeRegion, modelId);

// Read the file content for prediction.
const content = fs.readFileSync(filePath, 'base64');

const params = {};

if (scoreThreshold) {
  params.score_threshold = scoreThreshold;
}

// Set the payload by giving the content and type of the file.
const payload = {};
payload.image = {imageBytes: content};

// params is additional domain-specific parameters.
// currently there is no additional parameters supported.
const [response] = await client.predict({
  name: modelFullId,
  payload: payload,
  params: params,
});
console.log('Prediction results:');
response.payload.forEach(result => {
  console.log(`Predicted class name: ${result.displayName}`);
  console.log(`Predicted class score: ${result.classification.score}`);
});

Samples

Samples are in the samples/ directory. Each sample's README.md has instructions for running its sample.

SampleSource CodeTry it
Batch_predictsource codeOpen in Cloud Shell
Delete_datasetsource codeOpen in Cloud Shell
Delete_modelsource codeOpen in Cloud Shell
Deploy_modelsource codeOpen in Cloud Shell
Export_datasetsource codeOpen in Cloud Shell
Get_datasetsource codeOpen in Cloud Shell
Get_modelsource codeOpen in Cloud Shell
Get_model_evaluationsource codeOpen in Cloud Shell
Get_operation_statussource codeOpen in Cloud Shell
Import_datasetsource codeOpen in Cloud Shell
Language_entity_extraction_create_datasetsource codeOpen in Cloud Shell
Language_entity_extraction_create_modelsource codeOpen in Cloud Shell
Language_entity_extraction_predictsource codeOpen in Cloud Shell
Language_sentiment_analysis_create_datasetsource codeOpen in Cloud Shell
Language_sentiment_analysis_create_modelsource codeOpen in Cloud Shell
Language_sentiment_analysis_predictsource codeOpen in Cloud Shell
Language_text_classification_create_datasetsource codeOpen in Cloud Shell
Language_text_classification_create_modelsource codeOpen in Cloud Shell
Language_text_classification_predictsource codeOpen in Cloud Shell
List_datasetssource codeOpen in Cloud Shell
List_model_evaluationssource codeOpen in Cloud Shell
List_modelssource codeOpen in Cloud Shell
List_operation_statussource codeOpen in Cloud Shell
Quickstartsource codeOpen in Cloud Shell
Translate_create_datasetsource codeOpen in Cloud Shell
Translate_create_modelsource codeOpen in Cloud Shell
Translate_predictsource codeOpen in Cloud Shell
Undeploy_modelsource codeOpen in Cloud Shell
Vision_classification_create_datasetsource codeOpen in Cloud Shell
Vision_classification_create_modelsource codeOpen in Cloud Shell
Vision_classification_deploy_model_node_countsource codeOpen in Cloud Shell
Vision_classification_predictsource codeOpen in Cloud Shell
Vision_object_detection_create_datasetsource codeOpen in Cloud Shell
Vision_object_detection_create_modelsource codeOpen in Cloud Shell
Vision_object_detection_deploy_model_node_countsource codeOpen in Cloud Shell
Vision_object_detection_predictsource codeOpen in Cloud Shell

The Cloud AutoML Node.js Client API Reference documentation also contains samples.

Supported Node.js Versions

Our client libraries follow the Node.js release schedule. Libraries are compatible with all current active and maintenance versions of Node.js. If you are using an end-of-life version of Node.js, we recommend that you update as soon as possible to an actively supported LTS version.

Google's client libraries support legacy versions of Node.js runtimes on a best-efforts basis with the following warnings:

  • Legacy versions are not tested in continuous integration.
  • Some security patches and features cannot be backported.
  • Dependencies cannot be kept up-to-date.

Client libraries targeting some end-of-life versions of Node.js are available, and can be installed through npm dist-tags. The dist-tags follow the naming convention legacy-(version). For example, npm install @google-cloud/automl@legacy-8 installs client libraries for versions compatible with Node.js 8.

Versioning

This library follows Semantic Versioning.

This library is considered to be stable. The code surface will not change in backwards-incompatible ways unless absolutely necessary (e.g. because of critical security issues) or with an extensive deprecation period. Issues and requests against stable libraries are addressed with the highest priority.

More Information: Google Cloud Platform Launch Stages

Contributing

Contributions welcome! See the Contributing Guide.

Please note that this README.md, the samples/README.md, and a variety of configuration files in this repository (including .nycrc and tsconfig.json) are generated from a central template. To edit one of these files, make an edit to its templates in directory.

License

Apache Version 2.0

See LICENSE