AutoMlClient - Documentation

Constructor

new AutoMlClient(optionsopt, gaxInstanceopt)

Construct an instance of AutoMlClient.

Parameters:

Name Type Attributes Description

options

object

The configuration object. The options accepted by the constructor are described in detail in this document. The common options are:

Properties

Name Type Attributes Description

credentials

object

Credentials object.

Properties

Name	Type	Attributes	Description
`client_email`	string	<optional>
`private_key`	string	<optional>

email

string

Account email address. Required when using a .pem or .p12 keyFilename.

keyFilename

string

Full path to the a .json, .pem, or .p12 key downloaded from the Google Developers Console. If you provide a path to a JSON file, the projectId option below is not necessary. NOTE: .pem and .p12 require you to specify options.email as well.

port

number

The port on which to connect to the remote host.

projectId

string

The project ID from the Google Developer's Console, e.g. 'grape-spaceship-123'. We will also check the environment variable GCLOUD_PROJECT for your project ID. If your app is running in an environment which supports Application Default Credentials, your project ID will be detected automatically.

apiEndpoint

string

The domain name of the API remote host.

clientConfig

gax.ClientConfig

Client configuration override. Follows the structure of gapicConfig.

fallback

boolean

Use HTTP/1.1 REST mode. For more information, please check the documentation.

gaxInstance

gax

loaded instance of google-gax. Useful if you need to avoid loading the default gRPC version and want to use the fallback HTTP implementation. Load only fallback version and pass it to the constructor: const gax = require('google-gax/build/src/fallback'); // avoids loading google-gax with gRPC const client = new AutoMlClient({fallback: true}, gax);

Members

apiEndpoint

The DNS address for this API service - same as servicePath(), exists for compatibility reasons.

port

The port for this API service.

scopes

The scopes needed to make gRPC calls for every method defined in this service.

servicePath

The DNS address for this API service.

Methods

annotationSpecPath(project, location, dataset, annotation_spec) → {string}

Return a fully-qualified annotationSpec resource name string.

Parameters:

Name	Type	Description
`project`	string
`location`	string
`dataset`	string
`annotation_spec`	string

Returns:

Type	Description
string	Resource name string.

(async) checkCreateModelProgress(name) → {Promise}

Check the status of the long running operation returned by createModel().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Resource name of the parent project where the model is being created.
   */
  // const parent = 'abc123'
  /**
   *  Required. The model to create.
   */
  // const model = {}

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callCreateModel() {
    // Construct request
    const request = {
      parent,
      model,
    };

    // Run request
    const [operation] = await automlClient.createModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callCreateModel();

(async) checkDeleteDatasetProgress(name) → {Promise}

Check the status of the long running operation returned by deleteDataset().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the dataset to delete.
   */
  // const name = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callDeleteDataset() {
    // Construct request
    const request = {
      name,
    };

    // Run request
    const [operation] = await automlClient.deleteDataset(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callDeleteDataset();

(async) checkDeleteModelProgress(name) → {Promise}

Check the status of the long running operation returned by deleteModel().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Resource name of the model being deleted.
   */
  // const name = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callDeleteModel() {
    // Construct request
    const request = {
      name,
    };

    // Run request
    const [operation] = await automlClient.deleteModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callDeleteModel();

(async) checkDeployModelProgress(name) → {Promise}

Check the status of the long running operation returned by deployModel().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Model deployment metadata specific to Image Object Detection.
   */
  // const imageObjectDetectionModelDeploymentMetadata = {}
  /**
   *  Model deployment metadata specific to Image Classification.
   */
  // const imageClassificationModelDeploymentMetadata = {}
  /**
   *  Required. Resource name of the model to deploy.
   */
  // const name = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callDeployModel() {
    // Construct request
    const request = {
      name,
    };

    // Run request
    const [operation] = await automlClient.deployModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callDeployModel();

(async) checkExportDataProgress(name) → {Promise}

Check the status of the long running operation returned by exportData().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the dataset.
   */
  // const name = 'abc123'
  /**
   *  Required. The desired output location.
   */
  // const outputConfig = {}

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callExportData() {
    // Construct request
    const request = {
      name,
      outputConfig,
    };

    // Run request
    const [operation] = await automlClient.exportData(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callExportData();

(async) checkExportEvaluatedExamplesProgress(name) → {Promise}

Check the status of the long running operation returned by exportEvaluatedExamples().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the model whose evaluated examples are to
   *  be exported.
   */
  // const name = 'abc123'
  /**
   *  Required. The desired output location and configuration.
   */
  // const outputConfig = {}

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callExportEvaluatedExamples() {
    // Construct request
    const request = {
      name,
      outputConfig,
    };

    // Run request
    const [operation] = await automlClient.exportEvaluatedExamples(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callExportEvaluatedExamples();

(async) checkExportModelProgress(name) → {Promise}

Check the status of the long running operation returned by exportModel().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the model to export.
   */
  // const name = 'abc123'
  /**
   *  Required. The desired output location and configuration.
   */
  // const outputConfig = {}

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callExportModel() {
    // Construct request
    const request = {
      name,
      outputConfig,
    };

    // Run request
    const [operation] = await automlClient.exportModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callExportModel();

(async) checkImportDataProgress(name) → {Promise}

Check the status of the long running operation returned by importData().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Dataset name. Dataset must already exist. All imported
   *  annotations and examples will be added.
   */
  // const name = 'abc123'
  /**
   *  Required. The desired input location and its domain specific semantics,
   *  if any.
   */
  // const inputConfig = {}

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callImportData() {
    // Construct request
    const request = {
      name,
      inputConfig,
    };

    // Run request
    const [operation] = await automlClient.importData(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callImportData();

(async) checkUndeployModelProgress(name) → {Promise}

Check the status of the long running operation returned by undeployModel().

Parameters:

Name	Type	Description
`name`	String	The operation name that will be passed.

Returns:

Type	Description
Promise	The promise which resolves to an object. The decoded operation object has result and metadata field to get information from. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Resource name of the model to undeploy.
   */
  // const name = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callUndeployModel() {
    // Construct request
    const request = {
      name,
    };

    // Run request
    const [operation] = await automlClient.undeployModel(request);
    const [response] = await operation.promise();
    console.log(response);
  }

  callUndeployModel();

close() → {Promise}

Terminate the gRPC channel and close the client.

The client will no longer be usable and all future behavior is undefined.

Returns:

Type	Description
Promise	A promise that resolves when the client is closed.

columnSpecPath(project, location, dataset, table_spec, column_spec) → {string}

Return a fully-qualified columnSpec resource name string.

Parameters:

Name	Type	Description
`project`	string
`location`	string
`dataset`	string
`table_spec`	string
`column_spec`	string

Returns:

Type	Description
string	Resource name string.

datasetPath(project, location, dataset) → {string}

Return a fully-qualified dataset resource name string.

Parameters:

Name	Type	Description
`project`	string
`location`	string
`dataset`	string

Returns:

Type	Description
string	Resource name string.

getProjectId() → {Promise}

Return the project ID used by this class.

Returns:

Type	Description
Promise	A promise that resolves to string containing the project ID.

initialize() → {Promise}

Initialize the client. Performs asynchronous operations (such as authentication) and prepares the client. This function will be called automatically when any class method is called for the first time, but if you need to initialize it before calling an actual method, feel free to call initialize() directly.

You can await on this method if you want to make sure the client is initialized.

Returns:

Type	Description
Promise	A promise that resolves to an authenticated service stub.

listColumnSpecsAsync(request, optionsopt) → {Object}

Equivalent to listColumnSpecs, but returns an iterable object.

for-await-of syntax is used with the iterable to get response elements on-demand.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. The resource name of the table spec to list column specs from.
`fieldMask`	google.protobuf.FieldMask	Mask specifying which fields to read.
`filter`	string	Filter expression, see go/filtering.
`pageSize`	number	Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
`pageToken`	string	A token identifying a page of results for the server to return. Typically obtained from the ListColumnSpecsResponse.next_page_token field of the previous AutoMl.ListColumnSpecs call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Object	An iterable Object that allows async iteration. When you iterate the returned iterable, each element will be an object representing ColumnSpec. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the table spec to list column specs from.
   */
  // const parent = 'abc123'
  /**
   *  Mask specifying which fields to read.
   */
  // const fieldMask = {}
  /**
   *  Filter expression, see go/filtering.
   */
  // const filter = 'abc123'
  /**
   *  Requested page size. The server can return fewer results than requested.
   *  If unspecified, the server will pick a default size.
   */
  // const pageSize = 1234
  /**
   *  A token identifying a page of results for the server to return.
   *  Typically obtained from the
   *  ListColumnSpecsResponse.next_page_token google.cloud.automl.v1beta1.ListColumnSpecsResponse.next_page_token  field of the previous
   *  AutoMl.ListColumnSpecs google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs  call.
   */
  // const pageToken = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callListColumnSpecs() {
    // Construct request
    const request = {
      parent,
    };

    // Run request
    const iterable = await automlClient.listColumnSpecsAsync(request);
    for await (const response of iterable) {
        console.log(response);
    }
  }

  callListColumnSpecs();

listColumnSpecsStream(request, optionsopt) → {Stream}

Equivalent to method.name.toCamelCase(), but returns a NodeJS Stream object.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. The resource name of the table spec to list column specs from.
`fieldMask`	google.protobuf.FieldMask	Mask specifying which fields to read.
`filter`	string	Filter expression, see go/filtering.
`pageSize`	number	Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
`pageToken`	string	A token identifying a page of results for the server to return. Typically obtained from the ListColumnSpecsResponse.next_page_token field of the previous AutoMl.ListColumnSpecs call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Stream	An object stream which emits an object representing ColumnSpec on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using `listColumnSpecsAsync()` method described below for async iteration which you can stop as needed. Please see the documentation for more details and examples.

listDatasetsAsync(request, optionsopt) → {Object}

Equivalent to listDatasets, but returns an iterable object.

for-await-of syntax is used with the iterable to get response elements on-demand.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. The resource name of the project from which to list datasets.
`filter`	string	An expression for filtering the results of the request. * `dataset_metadata` - for existence of the case (e.g. `image_classification_dataset_metadata:`). Some examples of using the filter are: `translation_dataset_metadata:*` --> The dataset has `translation_dataset_metadata`.
`pageSize`	number	Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
`pageToken`	string	A token identifying a page of results for the server to return Typically obtained via ListDatasetsResponse.next_page_token of the previous AutoMl.ListDatasets call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Object	An iterable Object that allows async iteration. When you iterate the returned iterable, each element will be an object representing Dataset. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the project from which to list datasets.
   */
  // const parent = 'abc123'
  /**
   *  An expression for filtering the results of the request.
   *    * `dataset_metadata` - for existence of the case (e.g.
   *              `image_classification_dataset_metadata:*`). Some examples of
   *              using the filter are:
   *    * `translation_dataset_metadata:*` --> The dataset has
   *                                           `translation_dataset_metadata`.
   */
  // const filter = 'abc123'
  /**
   *  Requested page size. Server may return fewer results than requested.
   *  If unspecified, server will pick a default size.
   */
  // const pageSize = 1234
  /**
   *  A token identifying a page of results for the server to return
   *  Typically obtained via
   *  ListDatasetsResponse.next_page_token google.cloud.automl.v1beta1.ListDatasetsResponse.next_page_token  of the previous
   *  AutoMl.ListDatasets google.cloud.automl.v1beta1.AutoMl.ListDatasets  call.
   */
  // const pageToken = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callListDatasets() {
    // Construct request
    const request = {
      parent,
    };

    // Run request
    const iterable = await automlClient.listDatasetsAsync(request);
    for await (const response of iterable) {
        console.log(response);
    }
  }

  callListDatasets();

listDatasetsStream(request, optionsopt) → {Stream}

Equivalent to method.name.toCamelCase(), but returns a NodeJS Stream object.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. The resource name of the project from which to list datasets.
`filter`	string	An expression for filtering the results of the request. * `dataset_metadata` - for existence of the case (e.g. `image_classification_dataset_metadata:`). Some examples of using the filter are: `translation_dataset_metadata:*` --> The dataset has `translation_dataset_metadata`.
`pageSize`	number	Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
`pageToken`	string	A token identifying a page of results for the server to return Typically obtained via ListDatasetsResponse.next_page_token of the previous AutoMl.ListDatasets call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Stream	An object stream which emits an object representing Dataset on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using `listDatasetsAsync()` method described below for async iteration which you can stop as needed. Please see the documentation for more details and examples.

listModelEvaluationsAsync(request, optionsopt) → {Object}

Equivalent to listModelEvaluations, but returns an iterable object.

for-await-of syntax is used with the iterable to get response elements on-demand.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location.
`filter`	string	An expression for filtering the results of the request. * `annotation_spec_id` - for =, != or existence. See example below for the last. Some examples of using the filter are: * `annotation_spec_id!=4` --> The model evaluation was done for annotation spec with ID different than 4. * `NOT annotation_spec_id:*` --> The model evaluation was done for aggregate of all annotation specs.
`pageSize`	number	Requested page size.
`pageToken`	string	A token identifying a page of results for the server to return. Typically obtained via ListModelEvaluationsResponse.next_page_token of the previous AutoMl.ListModelEvaluations call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Object	An iterable Object that allows async iteration. When you iterate the returned iterable, each element will be an object representing ModelEvaluation. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Resource name of the model to list the model evaluations for.
   *  If modelId is set as "-", this will list model evaluations from across all
   *  models of the parent location.
   */
  // const parent = 'abc123'
  /**
   *  An expression for filtering the results of the request.
   *    * `annotation_spec_id` - for =, !=  or existence. See example below for
   *                           the last.
   *  Some examples of using the filter are:
   *    * `annotation_spec_id!=4` --> The model evaluation was done for
   *                              annotation spec with ID different than 4.
   *    * `NOT annotation_spec_id:*` --> The model evaluation was done for
   *                                 aggregate of all annotation specs.
   */
  // const filter = 'abc123'
  /**
   *  Requested page size.
   */
  // const pageSize = 1234
  /**
   *  A token identifying a page of results for the server to return.
   *  Typically obtained via
   *  ListModelEvaluationsResponse.next_page_token google.cloud.automl.v1beta1.ListModelEvaluationsResponse.next_page_token  of the previous
   *  AutoMl.ListModelEvaluations google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations  call.
   */
  // const pageToken = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callListModelEvaluations() {
    // Construct request
    const request = {
      parent,
    };

    // Run request
    const iterable = await automlClient.listModelEvaluationsAsync(request);
    for await (const response of iterable) {
        console.log(response);
    }
  }

  callListModelEvaluations();

listModelEvaluationsStream(request, optionsopt) → {Stream}

Equivalent to method.name.toCamelCase(), but returns a NodeJS Stream object.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location.
`filter`	string	An expression for filtering the results of the request. * `annotation_spec_id` - for =, != or existence. See example below for the last. Some examples of using the filter are: * `annotation_spec_id!=4` --> The model evaluation was done for annotation spec with ID different than 4. * `NOT annotation_spec_id:*` --> The model evaluation was done for aggregate of all annotation specs.
`pageSize`	number	Requested page size.
`pageToken`	string	A token identifying a page of results for the server to return. Typically obtained via ListModelEvaluationsResponse.next_page_token of the previous AutoMl.ListModelEvaluations call.

options

object

Call options. See CallOptions for more details.

Returns:

Type Description

Type	Description
Stream	An object stream which emits an object representing ModelEvaluation on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using `listModelEvaluationsAsync()` method described below for async iteration which you can stop as needed. Please see the documentation for more details and examples.

Stream

An object stream which emits an object representing ModelEvaluation on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using listModelEvaluationsAsync() method described below for async iteration which you can stop as needed. Please see the documentation for more details and examples.

listModelsAsync(request, optionsopt) → {Object}

Equivalent to listModels, but returns an iterable object.

for-await-of syntax is used with the iterable to get response elements on-demand.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. Resource name of the project, from which to list the models.
`filter`	string	An expression for filtering the results of the request. * `model_metadata` - for existence of the case (e.g. `video_classification_model_metadata:`). `dataset_id` - for = or !=. Some examples of using the filter are: * `image_classification_model_metadata:` --> The model has `image_classification_model_metadata`. `dataset_id=5` --> The model was created from a dataset with ID 5.
`pageSize`	number	Requested page size.
`pageToken`	string	A token identifying a page of results for the server to return Typically obtained via ListModelsResponse.next_page_token of the previous AutoMl.ListModels call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Object	An iterable Object that allows async iteration. When you iterate the returned iterable, each element will be an object representing Model. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Resource name of the project, from which to list the models.
   */
  // const parent = 'abc123'
  /**
   *  An expression for filtering the results of the request.
   *    * `model_metadata` - for existence of the case (e.g.
   *              `video_classification_model_metadata:*`).
   *    * `dataset_id` - for = or !=. Some examples of using the filter are:
   *    * `image_classification_model_metadata:*` --> The model has
   *                                       `image_classification_model_metadata`.
   *    * `dataset_id=5` --> The model was created from a dataset with ID 5.
   */
  // const filter = 'abc123'
  /**
   *  Requested page size.
   */
  // const pageSize = 1234
  /**
   *  A token identifying a page of results for the server to return
   *  Typically obtained via
   *  ListModelsResponse.next_page_token google.cloud.automl.v1beta1.ListModelsResponse.next_page_token  of the previous
   *  AutoMl.ListModels google.cloud.automl.v1beta1.AutoMl.ListModels  call.
   */
  // const pageToken = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callListModels() {
    // Construct request
    const request = {
      parent,
    };

    // Run request
    const iterable = await automlClient.listModelsAsync(request);
    for await (const response of iterable) {
        console.log(response);
    }
  }

  callListModels();

listModelsStream(request, optionsopt) → {Stream}

Equivalent to method.name.toCamelCase(), but returns a NodeJS Stream object.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. Resource name of the project, from which to list the models.
`filter`	string	An expression for filtering the results of the request. * `model_metadata` - for existence of the case (e.g. `video_classification_model_metadata:`). `dataset_id` - for = or !=. Some examples of using the filter are: * `image_classification_model_metadata:` --> The model has `image_classification_model_metadata`. `dataset_id=5` --> The model was created from a dataset with ID 5.
`pageSize`	number	Requested page size.
`pageToken`	string	A token identifying a page of results for the server to return Typically obtained via ListModelsResponse.next_page_token of the previous AutoMl.ListModels call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Stream	An object stream which emits an object representing Model on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using `listModelsAsync()` method described below for async iteration which you can stop as needed. Please see the documentation for more details and examples.

listTableSpecsAsync(request, optionsopt) → {Object}

Equivalent to listTableSpecs, but returns an iterable object.

for-await-of syntax is used with the iterable to get response elements on-demand.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. The resource name of the dataset to list table specs from.
`fieldMask`	google.protobuf.FieldMask	Mask specifying which fields to read.
`filter`	string	Filter expression, see go/filtering.
`pageSize`	number	Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
`pageToken`	string	A token identifying a page of results for the server to return. Typically obtained from the ListTableSpecsResponse.next_page_token field of the previous AutoMl.ListTableSpecs call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Object	An iterable Object that allows async iteration. When you iterate the returned iterable, each element will be an object representing TableSpec. The API will be called under the hood as needed, once per the page, so you can stop the iteration when you don't need more results. Please see the documentation for more details and examples.

Example

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. The resource name of the dataset to list table specs from.
   */
  // const parent = 'abc123'
  /**
   *  Mask specifying which fields to read.
   */
  // const fieldMask = {}
  /**
   *  Filter expression, see go/filtering.
   */
  // const filter = 'abc123'
  /**
   *  Requested page size. The server can return fewer results than requested.
   *  If unspecified, the server will pick a default size.
   */
  // const pageSize = 1234
  /**
   *  A token identifying a page of results for the server to return.
   *  Typically obtained from the
   *  ListTableSpecsResponse.next_page_token google.cloud.automl.v1beta1.ListTableSpecsResponse.next_page_token  field of the previous
   *  AutoMl.ListTableSpecs google.cloud.automl.v1beta1.AutoMl.ListTableSpecs  call.
   */
  // const pageToken = 'abc123'

  // Imports the Automl library
  const {AutoMlClient} = require('@google-cloud/automl').v1beta1;

  // Instantiates a client
  const automlClient = new AutoMlClient();

  async function callListTableSpecs() {
    // Construct request
    const request = {
      parent,
    };

    // Run request
    const iterable = await automlClient.listTableSpecsAsync(request);
    for await (const response of iterable) {
        console.log(response);
    }
  }

  callListTableSpecs();

listTableSpecsStream(request, optionsopt) → {Stream}

Equivalent to method.name.toCamelCase(), but returns a NodeJS Stream object.

Parameters:

Name Type Attributes Description

request

Object

The request object that will be sent.

Properties

Name	Type	Description
`parent`	string	Required. The resource name of the dataset to list table specs from.
`fieldMask`	google.protobuf.FieldMask	Mask specifying which fields to read.
`filter`	string	Filter expression, see go/filtering.
`pageSize`	number	Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
`pageToken`	string	A token identifying a page of results for the server to return. Typically obtained from the ListTableSpecsResponse.next_page_token field of the previous AutoMl.ListTableSpecs call.

options

object

Call options. See CallOptions for more details.

Returns:

Type	Description
Stream	An object stream which emits an object representing TableSpec on 'data' event. The client library will perform auto-pagination by default: it will call the API as many times as needed. Note that it can affect your quota. We recommend using `listTableSpecsAsync()` method described below for async iteration which you can stop as needed. Please see the documentation for more details and examples.

locationPath(project, location) → {string}

Return a fully-qualified location resource name string.

Parameters:

Name	Type	Description
`project`	string
`location`	string

Returns:

Type	Description
string	Resource name string.

matchAnnotationSpecFromAnnotationSpecName(annotationSpecName) → {string}

Parse the annotation_spec from AnnotationSpec resource.

Parameters:

Name	Type	Description
`annotationSpecName`	string	A fully-qualified path representing AnnotationSpec resource.

Returns:

Type	Description
string	A string representing the annotation_spec.

matchColumnSpecFromColumnSpecName(columnSpecName) → {string}

Parse the column_spec from ColumnSpec resource.

Parameters:

Name	Type	Description
`columnSpecName`	string	A fully-qualified path representing ColumnSpec resource.

Returns:

Type	Description
string	A string representing the column_spec.

matchDatasetFromAnnotationSpecName(annotationSpecName) → {string}

Parse the dataset from AnnotationSpec resource.

Parameters:

Name	Type	Description
`annotationSpecName`	string	A fully-qualified path representing AnnotationSpec resource.

Returns:

Type	Description
string	A string representing the dataset.

matchDatasetFromColumnSpecName(columnSpecName) → {string}

Parse the dataset from ColumnSpec resource.

Parameters:

Name	Type	Description
`columnSpecName`	string	A fully-qualified path representing ColumnSpec resource.

Returns:

Type	Description
string	A string representing the dataset.

matchDatasetFromDatasetName(datasetName) → {string}

Parse the dataset from Dataset resource.

Parameters:

Name	Type	Description
`datasetName`	string	A fully-qualified path representing Dataset resource.

Returns:

Type	Description
string	A string representing the dataset.

matchDatasetFromTableSpecName(tableSpecName) → {string}

Parse the dataset from TableSpec resource.

Parameters:

Name	Type	Description
`tableSpecName`	string	A fully-qualified path representing TableSpec resource.

Returns:

Type	Description
string	A string representing the dataset.

matchLocationFromAnnotationSpecName(annotationSpecName) → {string}

Parse the location from AnnotationSpec resource.

Parameters:

Name	Type	Description
`annotationSpecName`	string	A fully-qualified path representing AnnotationSpec resource.

Returns:

Type	Description
string	A string representing the location.

matchLocationFromColumnSpecName(columnSpecName) → {string}

Parse the location from ColumnSpec resource.

Parameters:

Name	Type	Description
`columnSpecName`	string	A fully-qualified path representing ColumnSpec resource.

Returns:

Type	Description
string	A string representing the location.

matchLocationFromDatasetName(datasetName) → {string}

Parse the location from Dataset resource.

Parameters:

Name	Type	Description
`datasetName`	string	A fully-qualified path representing Dataset resource.

Returns:

Type	Description
string	A string representing the location.

matchLocationFromLocationName(locationName) → {string}

Parse the location from Location resource.

Parameters:

Name	Type	Description
`locationName`	string	A fully-qualified path representing Location resource.

Returns:

Type	Description
string	A string representing the location.

matchLocationFromModelEvaluationName(modelEvaluationName) → {string}

Parse the location from ModelEvaluation resource.

Parameters:

Name	Type	Description
`modelEvaluationName`	string	A fully-qualified path representing ModelEvaluation resource.

Returns:

Type	Description
string	A string representing the location.

matchLocationFromModelName(modelName) → {string}

Parse the location from Model resource.

Parameters:

Name	Type	Description
`modelName`	string	A fully-qualified path representing Model resource.

Returns:

Type	Description
string	A string representing the location.

matchLocationFromTableSpecName(tableSpecName) → {string}

Parse the location from TableSpec resource.

Parameters:

Name	Type	Description
`tableSpecName`	string	A fully-qualified path representing TableSpec resource.

Returns:

Type	Description
string	A string representing the location.

matchModelEvaluationFromModelEvaluationName(modelEvaluationName) → {string}

Parse the model_evaluation from ModelEvaluation resource.

Parameters:

Name	Type	Description
`modelEvaluationName`	string	A fully-qualified path representing ModelEvaluation resource.

Returns:

Type	Description
string	A string representing the model_evaluation.

matchModelFromModelEvaluationName(modelEvaluationName) → {string}

Parse the model from ModelEvaluation resource.

Parameters:

Name	Type	Description
`modelEvaluationName`	string	A fully-qualified path representing ModelEvaluation resource.

Returns:

Type	Description
string	A string representing the model.

matchModelFromModelName(modelName) → {string}

Parse the model from Model resource.

Parameters:

Name	Type	Description
`modelName`	string	A fully-qualified path representing Model resource.

Returns:

Type	Description
string	A string representing the model.

matchProjectFromAnnotationSpecName(annotationSpecName) → {string}

Parse the project from AnnotationSpec resource.

Parameters:

Name	Type	Description
`annotationSpecName`	string	A fully-qualified path representing AnnotationSpec resource.

Returns:

Type	Description
string	A string representing the project.

matchProjectFromColumnSpecName(columnSpecName) → {string}

Parse the project from ColumnSpec resource.

Parameters:

Name	Type	Description
`columnSpecName`	string	A fully-qualified path representing ColumnSpec resource.

Returns:

Type	Description
string	A string representing the project.

matchProjectFromDatasetName(datasetName) → {string}

Parse the project from Dataset resource.

Parameters:

Name	Type	Description
`datasetName`	string	A fully-qualified path representing Dataset resource.

Returns:

Type	Description
string	A string representing the project.

matchProjectFromLocationName(locationName) → {string}

Parse the project from Location resource.

Parameters:

Name	Type	Description
`locationName`	string	A fully-qualified path representing Location resource.

Returns:

Type	Description
string	A string representing the project.

matchProjectFromModelEvaluationName(modelEvaluationName) → {string}

Parse the project from ModelEvaluation resource.

Parameters:

Name	Type	Description
`modelEvaluationName`	string	A fully-qualified path representing ModelEvaluation resource.

Returns:

Type	Description
string	A string representing the project.

matchProjectFromModelName(modelName) → {string}

Parse the project from Model resource.

Parameters:

Name	Type	Description
`modelName`	string	A fully-qualified path representing Model resource.

Returns:

Type	Description
string	A string representing the project.

matchProjectFromTableSpecName(tableSpecName) → {string}

Parse the project from TableSpec resource.

Parameters:

Name	Type	Description
`tableSpecName`	string	A fully-qualified path representing TableSpec resource.

Returns:

Type	Description
string	A string representing the project.

matchTableSpecFromColumnSpecName(columnSpecName) → {string}

Parse the table_spec from ColumnSpec resource.

Parameters:

Name	Type	Description
`columnSpecName`	string	A fully-qualified path representing ColumnSpec resource.

Returns:

Type	Description
string	A string representing the table_spec.

matchTableSpecFromTableSpecName(tableSpecName) → {string}

Parse the table_spec from TableSpec resource.

Parameters:

Name	Type	Description
`tableSpecName`	string	A fully-qualified path representing TableSpec resource.

Returns:

Type	Description
string	A string representing the table_spec.

modelEvaluationPath(project, location, model, model_evaluation) → {string}

Return a fully-qualified modelEvaluation resource name string.

Parameters:

Name	Type	Description
`project`	string
`location`	string
`model`	string
`model_evaluation`	string

Returns:

Type	Description
string	Resource name string.

modelPath(project, location, model) → {string}

Return a fully-qualified model resource name string.

Parameters:

Name	Type	Description
`project`	string
`location`	string
`model`	string

Returns:

Type	Description
string	Resource name string.

tableSpecPath(project, location, dataset, table_spec) → {string}

Return a fully-qualified tableSpec resource name string.

Parameters:

Name	Type	Description
`project`	string
`location`	string
`dataset`	string
`table_spec`	string

Returns:

Type	Description
string	Resource name string.