AutoMlClient

AutoMlClient

AutoML Server API.

The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.

An ID of a resource is the last element of the item's resource name. For projects/{project_id}/locations/{location_id}/datasets/{dataset_id}, then the id for the item is {dataset_id}.

Currently the only supported location_id is "us-central1".

On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.

Constructor

new AutoMlClient(optionsopt)

Construct an instance of AutoMlClient.

Parameters:
Name Type Attributes Description
options object <optional>

The configuration object. See the subsequent parameters for more details.

Properties
Name Type Attributes Description
credentials object <optional>

Credentials object.

Properties
Name Type Attributes Description
client_email string <optional>
private_key string <optional>
email string <optional>

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

keyFilename string <optional>

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 <optional>

The port on which to connect to the remote host.

projectId string <optional>

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.

promise function <optional>

Custom promise module to use instead of native Promises.

apiEndpoint string <optional>

The domain name of the API remote host.

Source:

Members

(static) apiEndpoint

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

Source:

(static) port

The port for this API service.

Source:

(static) scopes

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

Source:

(static) servicePath

The DNS address for this API service.

Source:

Methods

annotationSpecPath(project, location, dataset, annotationSpec) → {String}

Return a fully-qualified annotation_spec resource name string.

Parameters:
Name Type Description
project String
location String
dataset String
annotationSpec String
Source:

columnSpecPath(project, location, dataset, tableSpec, columnSpec) → {String}

Return a fully-qualified column_spec resource name string.

Parameters:
Name Type Description
project String
location String
dataset String
tableSpec String
columnSpec String
Source:

createDataset(request, optionsopt, callbackopt) → {Promise}

Creates a dataset.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
parent string

The resource name of the project to create the dataset for.

dataset Object

The dataset to create.

This object should have the same structure as Dataset

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing Dataset.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');
const dataset = {};
const request = {
  parent: formattedParent,
  dataset: dataset,
};
client.createDataset(request)
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

createModel(request, optionsopt, callbackopt) → {Promise}

Creates a model. Returns a Model in the response field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
parent string

Resource name of the parent project where the model is being created.

model Object

The model to create.

This object should have the same structure as Model

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');
const model = {};
const request = {
  parent: formattedParent,
  model: model,
};

// Handle the operation using the promise pattern.
client.createModel(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');
const model = {};
const request = {
  parent: formattedParent,
  model: model,
};

// Handle the operation using the event emitter pattern.
client.createModel(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');
const model = {};
const request = {
  parent: formattedParent,
  model: model,
};

// Handle the operation using the await pattern.
const [operation] = await client.createModel(request);

const [response] = await operation.promise();

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

Return a fully-qualified dataset resource name string.

Parameters:
Name Type Description
project String
location String
dataset String
Source:

deleteDataset(request, optionsopt, callbackopt) → {Promise}

Deletes a dataset and all of its contents. Returns empty response in the response field when it completes, and delete_details in the metadata field.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

The resource name of the dataset to delete.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');

// Handle the operation using the promise pattern.
client.deleteDataset({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');

// Handle the operation using the event emitter pattern.
client.deleteDataset({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');

// Handle the operation using the await pattern.
const [operation] = await client.deleteDataset({name: formattedName});

const [response] = await operation.promise();

deleteModel(request, optionsopt, callbackopt) → {Promise}

Deletes a model. Returns google.protobuf.Empty in the response field when it completes, and delete_details in the metadata field.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Resource name of the model being deleted.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the promise pattern.
client.deleteModel({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the event emitter pattern.
client.deleteModel({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the await pattern.
const [operation] = await client.deleteModel({name: formattedName});

const [response] = await operation.promise();

deployModel(request, optionsopt, callbackopt) → {Promise}

Deploys a model. If a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parametrs (as e.g. changing

node_number) will reset the deployment state without pausing the model's availability.

Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically.

Returns an empty response in the response field when it completes.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
name string

Resource name of the model to deploy.

imageObjectDetectionModelDeploymentMetadata Object <optional>

Model deployment metadata specific to Image Object Detection.

This object should have the same structure as ImageObjectDetectionModelDeploymentMetadata

imageClassificationModelDeploymentMetadata Object <optional>

Model deployment metadata specific to Image Classification.

This object should have the same structure as ImageClassificationModelDeploymentMetadata

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the promise pattern.
client.deployModel({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the event emitter pattern.
client.deployModel({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the await pattern.
const [operation] = await client.deployModel({name: formattedName});

const [response] = await operation.promise();

exportData(request, optionsopt, callbackopt) → {Promise}

Exports dataset's data to the provided output location. Returns an empty response in the response field when it completes.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Required. The resource name of the dataset.

outputConfig Object

Required. The desired output location.

This object should have the same structure as OutputConfig

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the promise pattern.
client.exportData(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the event emitter pattern.
client.exportData(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the await pattern.
const [operation] = await client.exportData(request);

const [response] = await operation.promise();

exportEvaluatedExamples(request, optionsopt, callbackopt) → {Promise}

Exports examples on which the model was evaluated (i.e. which were in the TEST set of the dataset the model was created from), together with their ground truth annotations and the annotations created (predicted) by the model. The examples, ground truth and predictions are exported in the state they were at the moment the model was evaluated.

This export is available only for 30 days since the model evaluation is created.

Currently only available for Tables.

Returns an empty response in the response field when it completes.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Required. The resource name of the model whose evaluated examples are to be exported.

outputConfig Object

Required. The desired output location and configuration.

This object should have the same structure as ExportEvaluatedExamplesOutputConfig

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the promise pattern.
client.exportEvaluatedExamples(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the event emitter pattern.
client.exportEvaluatedExamples(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the await pattern.
const [operation] = await client.exportEvaluatedExamples(request);

const [response] = await operation.promise();

exportModel(request, optionsopt, callbackopt) → {Promise}

Exports a trained, "export-able", model to a user specified Google Cloud Storage location. A model is considered export-able if and only if it has an export format defined for it in

ModelExportOutputConfig.

Returns an empty response in the response field when it completes.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Required. The resource name of the model to export.

outputConfig Object

Required. The desired output location and configuration.

This object should have the same structure as ModelExportOutputConfig

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the promise pattern.
client.exportModel(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the event emitter pattern.
client.exportModel(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
const outputConfig = {};
const request = {
  name: formattedName,
  outputConfig: outputConfig,
};

// Handle the operation using the await pattern.
const [operation] = await client.exportModel(request);

const [response] = await operation.promise();

getAnnotationSpec(request, optionsopt, callbackopt) → {Promise}

Gets an annotation spec.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

The resource name of the annotation spec to retrieve.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing AnnotationSpec.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.annotationSpecPath('[PROJECT]', '[LOCATION]', '[DATASET]', '[ANNOTATION_SPEC]');
client.getAnnotationSpec({name: formattedName})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

getColumnSpec(request, optionsopt, callbackopt) → {Promise}

Gets a column spec.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
name string

The resource name of the column spec to retrieve.

fieldMask Object <optional>

Mask specifying which fields to read.

This object should have the same structure as FieldMask

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing ColumnSpec.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.columnSpecPath('[PROJECT]', '[LOCATION]', '[DATASET]', '[TABLE_SPEC]', '[COLUMN_SPEC]');
client.getColumnSpec({name: formattedName})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

getDataset(request, optionsopt, callbackopt) → {Promise}

Gets a dataset.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

The resource name of the dataset to retrieve.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing Dataset.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
client.getDataset({name: formattedName})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

getModel(request, optionsopt, callbackopt) → {Promise}

Gets a model.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Resource name of the model.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing Model.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
client.getModel({name: formattedName})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

getModelEvaluation(request, optionsopt, callbackopt) → {Promise}

Gets a model evaluation.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Resource name for the model evaluation.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing ModelEvaluation.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.modelEvaluationPath('[PROJECT]', '[LOCATION]', '[MODEL]', '[MODEL_EVALUATION]');
client.getModelEvaluation({name: formattedName})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

getProjectId(callback)

Return the project ID used by this class.

Parameters:
Name Type Description
callback function

the callback to be called with the current project Id.

Source:

getTableSpec(request, optionsopt, callbackopt) → {Promise}

Gets a table spec.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
name string

The resource name of the table spec to retrieve.

fieldMask Object <optional>

Mask specifying which fields to read.

This object should have the same structure as FieldMask

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing TableSpec.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.tableSpecPath('[PROJECT]', '[LOCATION]', '[DATASET]', '[TABLE_SPEC]');
client.getTableSpec({name: formattedName})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

importData(request, optionsopt, callbackopt) → {Promise}

Imports data into a dataset. For Tables this method can only be called on an empty Dataset.

For Tables:

  • A schema_inference_version parameter must be explicitly set. Returns an empty response in the response field when it completes.
Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.

inputConfig Object

Required. The desired input location and its domain specific semantics, if any.

This object should have the same structure as InputConfig

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
const inputConfig = {};
const request = {
  name: formattedName,
  inputConfig: inputConfig,
};

// Handle the operation using the promise pattern.
client.importData(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
const inputConfig = {};
const request = {
  name: formattedName,
  inputConfig: inputConfig,
};

// Handle the operation using the event emitter pattern.
client.importData(request)
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
const inputConfig = {};
const request = {
  name: formattedName,
  inputConfig: inputConfig,
};

// Handle the operation using the await pattern.
const [operation] = await client.importData(request);

const [response] = await operation.promise();

listColumnSpecs(request, optionsopt, callbackopt) → {Promise}

Lists column specs in a table spec.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

The resource name of the table spec to list column specs from.

fieldMask Object <optional>

Mask specifying which fields to read.

This object should have the same structure as FieldMask

filter string <optional>

Filter expression, see go/filtering.

pageSize number <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is Array of ColumnSpec.

When autoPaginate: false is specified through options, it contains the result in a single response. If the response indicates the next page exists, the third parameter is set to be used for the next request object. The fourth parameter keeps the raw response object of an object representing ListColumnSpecsResponse.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

// Iterate over all elements.
const formattedParent = client.tableSpecPath('[PROJECT]', '[LOCATION]', '[DATASET]', '[TABLE_SPEC]');

client.listColumnSpecs({parent: formattedParent})
  .then(responses => {
    const resources = responses[0];
    for (const resource of resources) {
      // doThingsWith(resource)
    }
  })
  .catch(err => {
    console.error(err);
  });

// Or obtain the paged response.
const formattedParent = client.tableSpecPath('[PROJECT]', '[LOCATION]', '[DATASET]', '[TABLE_SPEC]');


const options = {autoPaginate: false};
const callback = responses => {
  // The actual resources in a response.
  const resources = responses[0];
  // The next request if the response shows that there are more responses.
  const nextRequest = responses[1];
  // The actual response object, if necessary.
  // const rawResponse = responses[2];
  for (const resource of resources) {
    // doThingsWith(resource);
  }
  if (nextRequest) {
    // Fetch the next page.
    return client.listColumnSpecs(nextRequest, options).then(callback);
  }
}
client.listColumnSpecs({parent: formattedParent}, options)
  .then(callback)
  .catch(err => {
    console.error(err);
  });

listColumnSpecsStream(request, optionsopt) → {Stream}

Equivalent to listColumnSpecs, but returns a NodeJS Stream object.

This fetches the paged responses for listColumnSpecs continuously and invokes the callback registered for 'data' event for each element in the responses.

The returned object has 'end' method when no more elements are required.

autoPaginate option will be ignored.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

The resource name of the table spec to list column specs from.

fieldMask Object <optional>

Mask specifying which fields to read.

This object should have the same structure as FieldMask

filter string <optional>

Filter expression, see go/filtering.

pageSize number <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

Source:
See:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedParent = client.tableSpecPath('[PROJECT]', '[LOCATION]', '[DATASET]', '[TABLE_SPEC]');
client.listColumnSpecsStream({parent: formattedParent})
  .on('data', element => {
    // doThingsWith(element)
  }).on('error', err => {
    console.log(err);
  });

listDatasets(request, optionsopt, callbackopt) → {Promise}

Lists datasets in a project.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

The resource name of the project from which to list datasets.

filter string <optional>

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 <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is Array of Dataset.

When autoPaginate: false is specified through options, it contains the result in a single response. If the response indicates the next page exists, the third parameter is set to be used for the next request object. The fourth parameter keeps the raw response object of an object representing ListDatasetsResponse.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

// Iterate over all elements.
const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');

client.listDatasets({parent: formattedParent})
  .then(responses => {
    const resources = responses[0];
    for (const resource of resources) {
      // doThingsWith(resource)
    }
  })
  .catch(err => {
    console.error(err);
  });

// Or obtain the paged response.
const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');


const options = {autoPaginate: false};
const callback = responses => {
  // The actual resources in a response.
  const resources = responses[0];
  // The next request if the response shows that there are more responses.
  const nextRequest = responses[1];
  // The actual response object, if necessary.
  // const rawResponse = responses[2];
  for (const resource of resources) {
    // doThingsWith(resource);
  }
  if (nextRequest) {
    // Fetch the next page.
    return client.listDatasets(nextRequest, options).then(callback);
  }
}
client.listDatasets({parent: formattedParent}, options)
  .then(callback)
  .catch(err => {
    console.error(err);
  });

listDatasetsStream(request, optionsopt) → {Stream}

Equivalent to listDatasets, but returns a NodeJS Stream object.

This fetches the paged responses for listDatasets continuously and invokes the callback registered for 'data' event for each element in the responses.

The returned object has 'end' method when no more elements are required.

autoPaginate option will be ignored.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

The resource name of the project from which to list datasets.

filter string <optional>

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 <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

Source:
See:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');
client.listDatasetsStream({parent: formattedParent})
  .on('data', element => {
    // doThingsWith(element)
  }).on('error', err => {
    console.log(err);
  });

listModelEvaluations(request, optionsopt, callbackopt) → {Promise}

Lists model evaluations.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

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 <optional>

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 <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is Array of ModelEvaluation.

When autoPaginate: false is specified through options, it contains the result in a single response. If the response indicates the next page exists, the third parameter is set to be used for the next request object. The fourth parameter keeps the raw response object of an object representing ListModelEvaluationsResponse.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

// Iterate over all elements.
const formattedParent = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

client.listModelEvaluations({parent: formattedParent})
  .then(responses => {
    const resources = responses[0];
    for (const resource of resources) {
      // doThingsWith(resource)
    }
  })
  .catch(err => {
    console.error(err);
  });

// Or obtain the paged response.
const formattedParent = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');


const options = {autoPaginate: false};
const callback = responses => {
  // The actual resources in a response.
  const resources = responses[0];
  // The next request if the response shows that there are more responses.
  const nextRequest = responses[1];
  // The actual response object, if necessary.
  // const rawResponse = responses[2];
  for (const resource of resources) {
    // doThingsWith(resource);
  }
  if (nextRequest) {
    // Fetch the next page.
    return client.listModelEvaluations(nextRequest, options).then(callback);
  }
}
client.listModelEvaluations({parent: formattedParent}, options)
  .then(callback)
  .catch(err => {
    console.error(err);
  });

listModelEvaluationsStream(request, optionsopt) → {Stream}

Equivalent to listModelEvaluations, but returns a NodeJS Stream object.

This fetches the paged responses for listModelEvaluations continuously and invokes the callback registered for 'data' event for each element in the responses.

The returned object has 'end' method when no more elements are required.

autoPaginate option will be ignored.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

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 <optional>

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 <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

Source:
See:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedParent = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');
client.listModelEvaluationsStream({parent: formattedParent})
  .on('data', element => {
    // doThingsWith(element)
  }).on('error', err => {
    console.log(err);
  });

listModels(request, optionsopt, callbackopt) → {Promise}

Lists models.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

Resource name of the project, from which to list the models.

filter string <optional>

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 <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is Array of Model.

When autoPaginate: false is specified through options, it contains the result in a single response. If the response indicates the next page exists, the third parameter is set to be used for the next request object. The fourth parameter keeps the raw response object of an object representing ListModelsResponse.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

// Iterate over all elements.
const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');

client.listModels({parent: formattedParent})
  .then(responses => {
    const resources = responses[0];
    for (const resource of resources) {
      // doThingsWith(resource)
    }
  })
  .catch(err => {
    console.error(err);
  });

// Or obtain the paged response.
const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');


const options = {autoPaginate: false};
const callback = responses => {
  // The actual resources in a response.
  const resources = responses[0];
  // The next request if the response shows that there are more responses.
  const nextRequest = responses[1];
  // The actual response object, if necessary.
  // const rawResponse = responses[2];
  for (const resource of resources) {
    // doThingsWith(resource);
  }
  if (nextRequest) {
    // Fetch the next page.
    return client.listModels(nextRequest, options).then(callback);
  }
}
client.listModels({parent: formattedParent}, options)
  .then(callback)
  .catch(err => {
    console.error(err);
  });

listModelsStream(request, optionsopt) → {Stream}

Equivalent to listModels, but returns a NodeJS Stream object.

This fetches the paged responses for listModels continuously and invokes the callback registered for 'data' event for each element in the responses.

The returned object has 'end' method when no more elements are required.

autoPaginate option will be ignored.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

Resource name of the project, from which to list the models.

filter string <optional>

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 <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

Source:
See:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedParent = client.locationPath('[PROJECT]', '[LOCATION]');
client.listModelsStream({parent: formattedParent})
  .on('data', element => {
    // doThingsWith(element)
  }).on('error', err => {
    console.log(err);
  });

listTableSpecs(request, optionsopt, callbackopt) → {Promise}

Lists table specs in a dataset.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

The resource name of the dataset to list table specs from.

fieldMask Object <optional>

Mask specifying which fields to read.

This object should have the same structure as FieldMask

filter string <optional>

Filter expression, see go/filtering.

pageSize number <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is Array of TableSpec.

When autoPaginate: false is specified through options, it contains the result in a single response. If the response indicates the next page exists, the third parameter is set to be used for the next request object. The fourth parameter keeps the raw response object of an object representing ListTableSpecsResponse.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

// Iterate over all elements.
const formattedParent = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');

client.listTableSpecs({parent: formattedParent})
  .then(responses => {
    const resources = responses[0];
    for (const resource of resources) {
      // doThingsWith(resource)
    }
  })
  .catch(err => {
    console.error(err);
  });

// Or obtain the paged response.
const formattedParent = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');


const options = {autoPaginate: false};
const callback = responses => {
  // The actual resources in a response.
  const resources = responses[0];
  // The next request if the response shows that there are more responses.
  const nextRequest = responses[1];
  // The actual response object, if necessary.
  // const rawResponse = responses[2];
  for (const resource of resources) {
    // doThingsWith(resource);
  }
  if (nextRequest) {
    // Fetch the next page.
    return client.listTableSpecs(nextRequest, options).then(callback);
  }
}
client.listTableSpecs({parent: formattedParent}, options)
  .then(callback)
  .catch(err => {
    console.error(err);
  });

listTableSpecsStream(request, optionsopt) → {Stream}

Equivalent to listTableSpecs, but returns a NodeJS Stream object.

This fetches the paged responses for listTableSpecs continuously and invokes the callback registered for 'data' event for each element in the responses.

The returned object has 'end' method when no more elements are required.

autoPaginate option will be ignored.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
parent string

The resource name of the dataset to list table specs from.

fieldMask Object <optional>

Mask specifying which fields to read.

This object should have the same structure as FieldMask

filter string <optional>

Filter expression, see go/filtering.

pageSize number <optional>

The maximum number of resources contained in the underlying API response. If page streaming is performed per-resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

Source:
See:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedParent = client.datasetPath('[PROJECT]', '[LOCATION]', '[DATASET]');
client.listTableSpecsStream({parent: formattedParent})
  .on('data', element => {
    // doThingsWith(element)
  }).on('error', err => {
    console.log(err);
  });

locationPath(project, location) → {String}

Return a fully-qualified location resource name string.

Parameters:
Name Type Description
project String
location String
Source:

matchAnnotationSpecFromAnnotationSpecName(annotationSpecName) → {String}

Parse the annotationSpecName from a annotation_spec resource.

Parameters:
Name Type Description
annotationSpecName String

A fully-qualified path representing a annotation_spec resources.

Source:

matchColumnSpecFromColumnSpecName(columnSpecName) → {String}

Parse the columnSpecName from a column_spec resource.

Parameters:
Name Type Description
columnSpecName String

A fully-qualified path representing a column_spec resources.

Source:

matchDatasetFromAnnotationSpecName(annotationSpecName) → {String}

Parse the annotationSpecName from a annotation_spec resource.

Parameters:
Name Type Description
annotationSpecName String

A fully-qualified path representing a annotation_spec resources.

Source:

matchDatasetFromColumnSpecName(columnSpecName) → {String}

Parse the columnSpecName from a column_spec resource.

Parameters:
Name Type Description
columnSpecName String

A fully-qualified path representing a column_spec resources.

Source:

matchDatasetFromDatasetName(datasetName) → {String}

Parse the datasetName from a dataset resource.

Parameters:
Name Type Description
datasetName String

A fully-qualified path representing a dataset resources.

Source:

matchDatasetFromTableSpecName(tableSpecName) → {String}

Parse the tableSpecName from a table_spec resource.

Parameters:
Name Type Description
tableSpecName String

A fully-qualified path representing a table_spec resources.

Source:

matchLocationFromAnnotationSpecName(annotationSpecName) → {String}

Parse the annotationSpecName from a annotation_spec resource.

Parameters:
Name Type Description
annotationSpecName String

A fully-qualified path representing a annotation_spec resources.

Source:

matchLocationFromColumnSpecName(columnSpecName) → {String}

Parse the columnSpecName from a column_spec resource.

Parameters:
Name Type Description
columnSpecName String

A fully-qualified path representing a column_spec resources.

Source:

matchLocationFromDatasetName(datasetName) → {String}

Parse the datasetName from a dataset resource.

Parameters:
Name Type Description
datasetName String

A fully-qualified path representing a dataset resources.

Source:

matchLocationFromLocationName(locationName) → {String}

Parse the locationName from a location resource.

Parameters:
Name Type Description
locationName String

A fully-qualified path representing a location resources.

Source:

matchLocationFromModelEvaluationName(modelEvaluationName) → {String}

Parse the modelEvaluationName from a model_evaluation resource.

Parameters:
Name Type Description
modelEvaluationName String

A fully-qualified path representing a model_evaluation resources.

Source:

matchLocationFromModelName(modelName) → {String}

Parse the modelName from a model resource.

Parameters:
Name Type Description
modelName String

A fully-qualified path representing a model resources.

Source:

matchLocationFromTableSpecName(tableSpecName) → {String}

Parse the tableSpecName from a table_spec resource.

Parameters:
Name Type Description
tableSpecName String

A fully-qualified path representing a table_spec resources.

Source:

matchModelEvaluationFromModelEvaluationName(modelEvaluationName) → {String}

Parse the modelEvaluationName from a model_evaluation resource.

Parameters:
Name Type Description
modelEvaluationName String

A fully-qualified path representing a model_evaluation resources.

Source:

matchModelFromModelEvaluationName(modelEvaluationName) → {String}

Parse the modelEvaluationName from a model_evaluation resource.

Parameters:
Name Type Description
modelEvaluationName String

A fully-qualified path representing a model_evaluation resources.

Source:

matchModelFromModelName(modelName) → {String}

Parse the modelName from a model resource.

Parameters:
Name Type Description
modelName String

A fully-qualified path representing a model resources.

Source:

matchProjectFromAnnotationSpecName(annotationSpecName) → {String}

Parse the annotationSpecName from a annotation_spec resource.

Parameters:
Name Type Description
annotationSpecName String

A fully-qualified path representing a annotation_spec resources.

Source:

matchProjectFromColumnSpecName(columnSpecName) → {String}

Parse the columnSpecName from a column_spec resource.

Parameters:
Name Type Description
columnSpecName String

A fully-qualified path representing a column_spec resources.

Source:

matchProjectFromDatasetName(datasetName) → {String}

Parse the datasetName from a dataset resource.

Parameters:
Name Type Description
datasetName String

A fully-qualified path representing a dataset resources.

Source:

matchProjectFromLocationName(locationName) → {String}

Parse the locationName from a location resource.

Parameters:
Name Type Description
locationName String

A fully-qualified path representing a location resources.

Source:

matchProjectFromModelEvaluationName(modelEvaluationName) → {String}

Parse the modelEvaluationName from a model_evaluation resource.

Parameters:
Name Type Description
modelEvaluationName String

A fully-qualified path representing a model_evaluation resources.

Source:

matchProjectFromModelName(modelName) → {String}

Parse the modelName from a model resource.

Parameters:
Name Type Description
modelName String

A fully-qualified path representing a model resources.

Source:

matchProjectFromTableSpecName(tableSpecName) → {String}

Parse the tableSpecName from a table_spec resource.

Parameters:
Name Type Description
tableSpecName String

A fully-qualified path representing a table_spec resources.

Source:

matchTableSpecFromColumnSpecName(columnSpecName) → {String}

Parse the columnSpecName from a column_spec resource.

Parameters:
Name Type Description
columnSpecName String

A fully-qualified path representing a column_spec resources.

Source:

matchTableSpecFromTableSpecName(tableSpecName) → {String}

Parse the tableSpecName from a table_spec resource.

Parameters:
Name Type Description
tableSpecName String

A fully-qualified path representing a table_spec resources.

Source:

modelEvaluationPath(project, location, model, modelEvaluation) → {String}

Return a fully-qualified model_evaluation resource name string.

Parameters:
Name Type Description
project String
location String
model String
modelEvaluation String
Source:

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

Return a fully-qualified model resource name string.

Parameters:
Name Type Description
project String
location String
model String
Source:

tableSpecPath(project, location, dataset, tableSpec) → {String}

Return a fully-qualified table_spec resource name string.

Parameters:
Name Type Description
project String
location String
dataset String
tableSpec String
Source:

undeployModel(request, optionsopt, callbackopt) → {Promise}

Undeploys a model. If the model is not deployed this method has no effect.

Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically.

Returns an empty response in the response field when it completes.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Description
name string

Resource name of the model to undeploy.

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is a gax.Operation object.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the promise pattern.
client.undeployModel({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Operation#promise starts polling for the completion of the LRO.
    return operation.promise();
  })
  .then(responses => {
    const result = responses[0];
    const metadata = responses[1];
    const finalApiResponse = responses[2];
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the event emitter pattern.
client.undeployModel({name: formattedName})
  .then(responses => {
    const [operation, initialApiResponse] = responses;

    // Adding a listener for the "complete" event starts polling for the
    // completion of the operation.
    operation.on('complete', (result, metadata, finalApiResponse) => {
      // doSomethingWith(result);
    });

    // Adding a listener for the "progress" event causes the callback to be
    // called on any change in metadata when the operation is polled.
    operation.on('progress', (metadata, apiResponse) => {
      // doSomethingWith(metadata)
    });

    // Adding a listener for the "error" event handles any errors found during polling.
    operation.on('error', err => {
      // throw(err);
    });
  })
  .catch(err => {
    console.error(err);
  });

const formattedName = client.modelPath('[PROJECT]', '[LOCATION]', '[MODEL]');

// Handle the operation using the await pattern.
const [operation] = await client.undeployModel({name: formattedName});

const [response] = await operation.promise();

updateColumnSpec(request, optionsopt, callbackopt) → {Promise}

Updates a column spec.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
columnSpec Object

The column spec which replaces the resource on the server.

This object should have the same structure as ColumnSpec

updateMask Object <optional>

The update mask applies to the resource.

This object should have the same structure as FieldMask

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing ColumnSpec.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const columnSpec = {};
client.updateColumnSpec({columnSpec: columnSpec})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

updateDataset(request, optionsopt, callbackopt) → {Promise}

Updates a dataset.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
dataset Object

The dataset which replaces the resource on the server.

This object should have the same structure as Dataset

updateMask Object <optional>

The update mask applies to the resource.

This object should have the same structure as FieldMask

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing Dataset.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const dataset = {};
client.updateDataset({dataset: dataset})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });

updateTableSpec(request, optionsopt, callbackopt) → {Promise}

Updates a table spec.

Parameters:
Name Type Attributes Description
request Object

The request object that will be sent.

Properties
Name Type Attributes Description
tableSpec Object

The table spec which replaces the resource on the server.

This object should have the same structure as TableSpec

updateMask Object <optional>

The update mask applies to the resource.

This object should have the same structure as FieldMask

options Object <optional>

Optional parameters. You can override the default settings for this call, e.g, timeout, retries, paginations, etc. See gax.CallOptions for the details.

callback function <optional>

The function which will be called with the result of the API call.

The second parameter to the callback is an object representing TableSpec.

Source:
Example
const automl = require('@google-cloud/automl');

const client = new automl.v1beta1.AutoMlClient({
  // optional auth parameters.
});

const tableSpec = {};
client.updateTableSpec({tableSpec: tableSpec})
  .then(responses => {
    const response = responses[0];
    // doThingsWith(response)
  })
  .catch(err => {
    console.error(err);
  });