Cloud AutoML: Node.js Client

release level npm version

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: ${resu result 
playName}`);
  console.log(`Predicted class score: ${resu result 
ssification.score}`);
}); 

Samples

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

Sample Source Code Try it
Batch_predict
source code Open in Cloud Shell
Delete_dataset
source code Open in Cloud Shell
Delete_model
source code Open in Cloud Shell
Deploy_model
source code Open in Cloud Shell
Export_dataset
source code Open in Cloud Shell
Get_dataset
source code Open in Cloud Shell
Get_model
source code Open in Cloud Shell
Get_model_evaluation
source code Open in Cloud Shell
Get_operation_status
source code Open in Cloud Shell
Import_dataset
source code Open in Cloud Shell
Language_entity_extraction_create_dataset
source code Open in Cloud Shell
Language_entity_extraction_create_model
source code Open in Cloud Shell
Language_entity_extraction_predict
source code Open in Cloud Shell
Language_sentiment_analysis_create_dataset
source code Open in Cloud Shell
Language_sentiment_analysis_create_model
source code Open in Cloud Shell
Language_sentiment_analysis_predict
source code Open in Cloud Shell
Language_text_classification_create_dataset
source code Open in Cloud Shell
Language_text_classification_create_model
source code Open in Cloud Shell
Language_text_classification_predict
source code Open in Cloud Shell
List_datasets
source code Open in Cloud Shell
List_model_evaluations
source code Open in Cloud Shell
List_models
source code Open in Cloud Shell
List_operation_status
source code Open in Cloud Shell
Quickstart
source code Open in Cloud Shell
Translate_create_dataset
source code Open in Cloud Shell
Translate_create_model
source code Open in Cloud Shell
Translate_predict
source code Open in Cloud Shell
Undeploy_model
source code Open in Cloud Shell
Vision_classification_create_dataset
source code Open in Cloud Shell
Vision_classification_create_model
source code Open in Cloud Shell
Vision_classification_deploy_model_node_count
source code Open in Cloud Shell
Vision_classification_predict
source code Open in Cloud Shell
Vision_object_detection_create_dataset
source code Open in Cloud Shell
Vision_object_detection_create_model
source code Open in Cloud Shell
Vision_object_detection_deploy_model_node_count
source code Open in Cloud Shell
Vision_object_detection_predict
source code Open 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.

Client libraries targeting some end-of-life versions of Node.js are available, and can be installed via npm dist-tags . The dist-tags follow the naming convention legacy-(version) .

Legacy Node.js versions are supported as a best effort:

  • Legacy versions will not be tested in continuous integration.
  • Some security patches may not be able to be backported.
  • Dependencies will not be kept up-to-date, and features will not be backported.

Legacy tags available

  • legacy-8 : install client libraries from this dist-tag for versions compatible with Node.js 8.

Versioning

This library follows Semantic Versioning .

This library is considered to be General Availability (GA). This means it is 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 GAlibraries 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

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