Class AutoMlClient (1.0.3)

AutoMlClient(
    transport=None,
    channel=None,
    credentials=None,
    client_config=None,
    client_info=None,
    client_options=None,
)

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.

Methods

AutoMlClient

AutoMlClient(
    transport=None,
    channel=None,
    credentials=None,
    client_config=None,
    client_info=None,
    client_options=None,
)

Constructor.

Parameters

Name	Description
`channel`	`grpc.Channel` DEPRECATED. A `Channel` instance through which to make calls. This argument is mutually exclusive with `credentials`; providing both will raise an exception.
`credentials`	`google.auth.credentials.Credentials` The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment. This argument is mutually exclusive with providing a transport instance to `transport`; doing so will raise an exception.
`client_config`	`dict` DEPRECATED. A dictionary of call options for each method. If not specified, the default configuration is used.
`client_info`	`google.api_core.gapic_v1.client_info.ClientInfo` The client info used to send a user-agent string along with API requests. If `None`, then default info will be used. Generally, you only need to set this if you're developing your own client library.
`client_options`	`Union[dict, google.api_core.client_options.ClientOptions]` Client options used to set user options on the client. API Endpoint should be set through client_options.

annotation_spec_path

annotation_spec_path(project, location, dataset, annotation_spec)

Return a fully-qualified annotation_spec string.

create_dataset

create_dataset(parent, dataset, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Creates a dataset.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

parent = client.location_path('[PROJECT]', '[LOCATION]')

TODO: Initialize dataset:

dataset = {}

response = client.create_dataset(parent, dataset)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`parent`	`str` The resource name of the project to create the dataset for.
`dataset`	`Union[dict, Dataset]` The dataset to create. If a dict is provided, it must be of the same form as the protobuf message Dataset
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

create_model

create_model(parent, model, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

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.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

parent = client.location_path('[PROJECT]', '[LOCATION]')

TODO: Initialize model:

model = {}

response = client.create_model(parent, model)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`parent`	`str` Resource name of the parent project where the model is being created.
`model`	`Union[dict, Model]` The model to create. If a dict is provided, it must be of the same form as the protobuf message Model
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

dataset_path

dataset_path(project, location, dataset)

Return a fully-qualified dataset string.

delete_dataset

delete_dataset(name, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

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.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]')

response = client.delete_dataset(name)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`name`	`str` The resource name of the dataset to delete.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

delete_model

delete_model(name, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

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

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')

response = client.delete_model(name)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`name`	`str` Resource name of the model being deleted.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

deploy_model

deploy_model(name, image_object_detection_model_deployment_metadata=None, image_classification_model_deployment_metadata=None, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

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; all other domains manage deployment automatically.

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

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')

response = client.deploy_model(name)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`name`	`str` Resource name of the model to deploy.
`image_object_detection_model_deployment_metadata`	`Union[dict, ImageObjectDetectionModelDeploymentMetadata]` Model deployment metadata specific to Image Object Detection. If a dict is provided, it must be of the same form as the protobuf message ImageObjectDetectionModelDeploymentMetadata
`image_classification_model_deployment_metadata`	`Union[dict, ImageClassificationModelDeploymentMetadata]` Model deployment metadata specific to Image Classification. If a dict is provided, it must be of the same form as the protobuf message ImageClassificationModelDeploymentMetadata
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

export_data

export_data(name, output_config, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

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

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]')

TODO: Initialize output_config:

output_config = {}

response = client.export_data(name, output_config)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`name`	`str` Required. The resource name of the dataset.
`output_config`	`Union[dict, OutputConfig]` Required. The desired output location. If a dict is provided, it must be of the same form as the protobuf message OutputConfig
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

export_model

export_model(name, output_config, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

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.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')

TODO: Initialize output_config:

output_config = {}

response = client.export_model(name, output_config)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`name`	`str` Required. The resource name of the model to export.
`output_config`	`Union[dict, ModelExportOutputConfig]` Required. The desired output location and configuration. If a dict is provided, it must be of the same form as the protobuf message ModelExportOutputConfig
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

from_service_account_file

from_service_account_file(filename, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter

Name	Description
`filename`	`str` The path to the service account private key json file.

Returns

Type	Description
`AutoMlClient`	The constructed client.

from_service_account_json

from_service_account_json(filename, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter

Name	Description
`filename`	`str` The path to the service account private key json file.

Returns

Type	Description
`AutoMlClient`	The constructed client.

get_annotation_spec

get_annotation_spec(name, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Gets an annotation spec.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.annotation_spec_path('[PROJECT]', '[LOCATION]', '[DATASET]', '[ANNOTATION_SPEC]')

response = client.get_annotation_spec(name)

Parameters

Name	Description
`name`	`str` The resource name of the annotation spec to retrieve.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

get_dataset

get_dataset(name, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Gets a dataset.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]')

response = client.get_dataset(name)

Parameters

Name	Description
`name`	`str` The resource name of the dataset to retrieve.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

get_model

get_model(name, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Gets a model.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')

response = client.get_model(name)

Parameters

Name	Description
`name`	`str` Resource name of the model.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

get_model_evaluation

get_model_evaluation(name, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Gets a model evaluation.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.model_evaluation_path('[PROJECT]', '[LOCATION]', '[MODEL]', '[MODEL_EVALUATION]')

response = client.get_model_evaluation(name)

Parameters

Name	Description
`name`	`str` Resource name for the model evaluation.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

import_data

import_data(name, input_config, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Imports data into a dataset.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.dataset_path('[PROJECT]', '[LOCATION]', '[DATASET]')

TODO: Initialize input_config:

input_config = {}

response = client.import_data(name, input_config)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`name`	`str` Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.
`input_config`	`Union[dict, InputConfig]` Required. The desired input location and its domain specific semantics, if any. If a dict is provided, it must be of the same form as the protobuf message InputConfig
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

list_datasets

list_datasets(parent, filter_=None, page_size=None, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Lists datasets in a project.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

parent = client.location_path('[PROJECT]', '[LOCATION]')

Iterate over all results

for element in client.list_datasets(parent): ... # process element ... pass

Alternatively:

Iterate over results one page at a time

for page in client.list_datasets(parent).pages: ... for element in page: ... # process element ... pass

Parameters

Name	Description
`parent`	`str` The resource name of the project from which to list datasets.
`filter_`	`str` 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.
`page_size`	`int` 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.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

list_model_evaluations

list_model_evaluations(parent, filter_, page_size=None, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Lists model evaluations.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

parent = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')

TODO: Initialize filter_:

filter_ = ''

Iterate over all results

for element in client.list_model_evaluations(parent, filter_): ... # process element ... pass

Alternatively:

Iterate over results one page at a time

for page in client.list_model_evaluations(parent, filter_).pages: ... for element in page: ... # process element ... pass

Parameters

Name	Description
`parent`	`str` 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_`	`str` 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.
`page_size`	`int` 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.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

list_models

list_models(parent, filter_=None, page_size=None, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Lists models.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

parent = client.location_path('[PROJECT]', '[LOCATION]')

Iterate over all results

for element in client.list_models(parent): ... # process element ... pass

Alternatively:

Iterate over results one page at a time

for page in client.list_models(parent).pages: ... for element in page: ... # process element ... pass

Parameters

Name	Description
`parent`	`str` Resource name of the project, from which to list the models.
`filter_`	`str` An expression for filtering the results of the request. - `model_metadata` - for existence of the case (e.g. image_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.
`page_size`	`int` 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.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

location_path

location_path(project, location)

Return a fully-qualified location string.

model_evaluation_path

model_evaluation_path(project, location, model, model_evaluation)

Return a fully-qualified model_evaluation string.

model_path

model_path(project, location, model)

Return a fully-qualified model string.

undeploy_model

undeploy_model(name, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

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

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

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

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

name = client.model_path('[PROJECT]', '[LOCATION]', '[MODEL]')

response = client.undeploy_model(name)

def callback(operation_future): ... # Handle result. ... result = operation_future.result()

response.add_done_callback(callback)

Handle metadata.

metadata = response.metadata()

Parameters

Name	Description
`name`	`str` Resource name of the model to undeploy.
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

update_dataset

update_dataset(dataset, update_mask, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Updates a dataset.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

TODO: Initialize dataset:

dataset = {}

TODO: Initialize update_mask:

update_mask = {}

response = client.update_dataset(dataset, update_mask)

Parameters

Name	Description
`dataset`	`Union[dict, Dataset]` The dataset which replaces the resource on the server. If a dict is provided, it must be of the same form as the protobuf message Dataset
`update_mask`	`Union[dict, FieldMask]` Required. The update mask applies to the resource. If a dict is provided, it must be of the same form as the protobuf message FieldMask
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

update_model

update_model(model, update_mask, retry=<_MethodDefault._DEFAULT_VALUE: <object object>>, timeout=<_MethodDefault._DEFAULT_VALUE: <object object>>, metadata=None)

Updates a model.

.. rubric:: Example

from google.cloud import automl_v1

client = automl_v1.AutoMlClient()

TODO: Initialize model:

model = {}

TODO: Initialize update_mask:

update_mask = {}

response = client.update_model(model, update_mask)

Parameters

Name	Description
`model`	`Union[dict, Model]` The model which replaces the resource on the server. If a dict is provided, it must be of the same form as the protobuf message Model
`update_mask`	`Union[dict, FieldMask]` Required. The update mask applies to the resource. If a dict is provided, it must be of the same form as the protobuf message FieldMask
`retry`	`Optional[google.api_core.retry.Retry]` A retry object used to retry requests. If `None` is specified, requests will be retried using a default configuration.
`timeout`	`Optional[float]` The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt.
`metadata`	`Optional[Sequence[Tuple[str, str]]]` Additional metadata that is provided to the method.

Exceptions

Type	Description
`google.api_core.exceptions.GoogleAPICallError`	If the request failed for any reason.
`google.api_core.exceptions.RetryError`	If the request failed due to a retryable error and retry attempts failed.
`ValueError`	If the parameters are invalid.

Class AutoMlClient (1.0.3)

Methods

AutoMlClient

annotation_spec_path

create_dataset

TODO: Initialize dataset:

Handle metadata.

create_model

TODO: Initialize model:

Handle metadata.

dataset_path

delete_dataset

Handle metadata.

delete_model

Handle metadata.

deploy_model

Handle metadata.

export_data

TODO: Initialize output_config:

Handle metadata.

export_model

TODO: Initialize output_config:

Handle metadata.

from_service_account_file

from_service_account_json

get_annotation_spec

get_dataset

get_model

get_model_evaluation

import_data

TODO: Initialize input_config:

Handle metadata.

list_datasets

Iterate over all results

Alternatively:

Iterate over results one page at a time

list_model_evaluations

TODO: Initialize filter_:

Iterate over all results

Alternatively:

Iterate over results one page at a time

list_models

Iterate over all results

Alternatively:

Iterate over results one page at a time

location_path

model_evaluation_path

model_path

undeploy_model

Handle metadata.

update_dataset

TODO: Initialize dataset:

TODO: Initialize update_mask:

update_model

TODO: Initialize model:

TODO: Initialize update_mask:

TODO: Initialize `dataset`:

TODO: Initialize `model`:

TODO: Initialize `output_config`:

TODO: Initialize `output_config`:

TODO: Initialize `input_config`:

TODO: Initialize `filter_`:

TODO: Initialize `dataset`:

TODO: Initialize `update_mask`:

TODO: Initialize `model`:

TODO: Initialize `update_mask`: