AutoMlClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.automl_v1beta1.services.auto_ml.transports.base.AutoMlTransport, typing.Callable[[...], google.cloud.automl_v1beta1.services.auto_ml.transports.base.AutoMlTransport]]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
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.
Properties
api_endpoint
Return the API endpoint used by the client instance.
Returns | |
---|---|
Type | Description |
str |
The API endpoint used by the client instance. |
transport
Returns the transport used by the client instance.
Returns | |
---|---|
Type | Description |
AutoMlTransport |
The transport used by the client instance. |
universe_domain
Return the universe domain used by the client instance.
Returns | |
---|---|
Type | Description |
str |
The universe domain used by the client instance. |
Methods
AutoMlClient
AutoMlClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.automl_v1beta1.services.auto_ml.transports.base.AutoMlTransport, typing.Callable[[...], google.cloud.automl_v1beta1.services.auto_ml.transports.base.AutoMlTransport]]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
Instantiates the auto ml client.
Parameters | |
---|---|
Name | Description |
credentials |
Optional[google.auth.credentials.Credentials]
The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. |
transport |
Optional[Union[str,AutoMlTransport,Callable[..., AutoMlTransport]]]
The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the AutoMlTransport constructor. If set to None, a transport is chosen automatically. |
client_options |
Optional[Union[google.api_core.client_options.ClientOptions, dict]]
Custom options for the client. 1. The |
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 |
Exceptions | |
---|---|
Type | Description |
google.auth.exceptions.MutualTLSChannelError |
If mutual TLS transport creation failed for any reason. |
__exit__
__exit__(type, value, traceback)
Releases underlying transport's resources.
annotation_spec_path
annotation_spec_path(
project: str, location: str, dataset: str, annotation_spec: str
) -> str
Returns a fully-qualified annotation_spec string.
column_spec_path
column_spec_path(
project: str, location: str, dataset: str, table_spec: str, column_spec: str
) -> str
Returns a fully-qualified column_spec string.
common_billing_account_path
common_billing_account_path(billing_account: str) -> str
Returns a fully-qualified billing_account string.
common_folder_path
common_folder_path(folder: str) -> str
Returns a fully-qualified folder string.
common_location_path
common_location_path(project: str, location: str) -> str
Returns a fully-qualified location string.
common_organization_path
common_organization_path(organization: str) -> str
Returns a fully-qualified organization string.
common_project_path
common_project_path(project: str) -> str
Returns a fully-qualified project string.
create_dataset
create_dataset(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.CreateDatasetRequest, dict
]
] = None,
*,
parent: typing.Optional[str] = None,
dataset: typing.Optional[google.cloud.automl_v1beta1.types.dataset.Dataset] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.dataset.Dataset
Creates a dataset.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_create_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
dataset = automl_v1beta1.Dataset()
dataset.translation_dataset_metadata.source_language_code = "source_language_code_value"
dataset.translation_dataset_metadata.target_language_code = "target_language_code_value"
request = automl_v1beta1.CreateDatasetRequest(
parent="parent_value",
dataset=dataset,
)
# Make the request
response = client.create_dataset(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.CreateDatasetRequest, dict]
The request object. Request message for AutoMl.CreateDataset. |
parent |
str
Required. The resource name of the project to create the dataset for. This corresponds to the |
dataset |
google.cloud.automl_v1beta1.types.Dataset
Required. The dataset to create. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.Dataset |
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. |
create_model
create_model(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.CreateModelRequest, dict]
] = None,
*,
parent: typing.Optional[str] = None,
model: typing.Optional[google.cloud.automl_v1beta1.types.model.Model] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Creates a model. Returns a Model in the
response][google.longrunning.Operation.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.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_create_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.CreateModelRequest(
parent="parent_value",
)
# Make the request
operation = client.create_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.CreateModelRequest, dict]
The request object. Request message for AutoMl.CreateModel. |
parent |
str
Required. Resource name of the parent project where the model is being created. This corresponds to the |
model |
google.cloud.automl_v1beta1.types.Model
Required. The model to create. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be Model API proto representing a trained machine learning model. |
dataset_path
dataset_path(project: str, location: str, dataset: str) -> str
Returns a fully-qualified dataset string.
delete_dataset
delete_dataset(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.DeleteDatasetRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Deletes a dataset and all of its contents. Returns empty
response in the
response][google.longrunning.Operation.response]
field when it
completes, and delete_details
in the
metadata][google.longrunning.Operation.metadata]
field.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_delete_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.DeleteDatasetRequest(
name="name_value",
)
# Make the request
operation = client.delete_dataset(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.DeleteDatasetRequest, dict]
The request object. Request message for AutoMl.DeleteDataset. |
name |
str
Required. The resource name of the dataset to delete. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_model
delete_model(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.DeleteModelRequest, dict]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Deletes a model. Returns google.protobuf.Empty
in the
response][google.longrunning.Operation.response]
field when it
completes, and delete_details
in the
metadata][google.longrunning.Operation.metadata]
field.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_delete_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.DeleteModelRequest(
name="name_value",
)
# Make the request
operation = client.delete_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.DeleteModelRequest, dict]
The request object. Request message for AutoMl.DeleteModel. |
name |
str
Required. Resource name of the model being deleted. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
deploy_model
deploy_model(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.DeployModelRequest, dict]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
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
xref_node_number) will reset the deployment state without pausing the model's availability.
Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage deployment automatically.
Returns an empty response in the
response][google.longrunning.Operation.response]
field when it
completes.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_deploy_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.DeployModelRequest(
name="name_value",
)
# Make the request
operation = client.deploy_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.DeployModelRequest, dict]
The request object. Request message for AutoMl.DeployModel. |
name |
str
Required. Resource name of the model to deploy. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
export_data
export_data(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.ExportDataRequest, dict]
] = None,
*,
name: typing.Optional[str] = None,
output_config: typing.Optional[
google.cloud.automl_v1beta1.types.io.OutputConfig
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Exports dataset's data to the provided output location. Returns
an empty response in the
response][google.longrunning.Operation.response]
field when it
completes.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_export_data():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ExportDataRequest(
name="name_value",
)
# Make the request
operation = client.export_data(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ExportDataRequest, dict]
The request object. Request message for AutoMl.ExportData. |
name |
str
Required. The resource name of the dataset. This corresponds to the |
output_config |
google.cloud.automl_v1beta1.types.OutputConfig
Required. The desired output location. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
export_evaluated_examples
export_evaluated_examples(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.ExportEvaluatedExamplesRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
output_config: typing.Optional[
google.cloud.automl_v1beta1.types.io.ExportEvaluatedExamplesOutputConfig
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
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][google.longrunning.Operation.response]
field when it
completes.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_export_evaluated_examples():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ExportEvaluatedExamplesRequest(
name="name_value",
)
# Make the request
operation = client.export_evaluated_examples(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesRequest, dict]
The request object. Request message for AutoMl.ExportEvaluatedExamples. |
name |
str
Required. The resource name of the model whose evaluated examples are to be exported. This corresponds to the |
output_config |
google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesOutputConfig
Required. The desired output location and configuration. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
export_model
export_model(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.ExportModelRequest, dict]
] = None,
*,
name: typing.Optional[str] = None,
output_config: typing.Optional[
google.cloud.automl_v1beta1.types.io.ModelExportOutputConfig
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
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
xref_ModelExportOutputConfig.
Returns an empty response in the
response][google.longrunning.Operation.response]
field when it
completes.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_export_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ExportModelRequest(
name="name_value",
)
# Make the request
operation = client.export_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ExportModelRequest, dict]
The request object. Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned. |
name |
str
Required. The resource name of the model to export. This corresponds to the |
output_config |
google.cloud.automl_v1beta1.types.ModelExportOutputConfig
Required. The desired output location and configuration. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
from_service_account_file
from_service_account_file(filename: str, *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_info
from_service_account_info(info: dict, *args, **kwargs)
Creates an instance of this client using the provided credentials info.
Parameter | |
---|---|
Name | Description |
info |
dict
The service account private key info. |
Returns | |
---|---|
Type | Description |
AutoMlClient |
The constructed client. |
from_service_account_json
from_service_account_json(filename: str, *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(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.GetAnnotationSpecRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.annotation_spec.AnnotationSpec
Gets an annotation spec.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_get_annotation_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetAnnotationSpecRequest(
name="name_value",
)
# Make the request
response = client.get_annotation_spec(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.GetAnnotationSpecRequest, dict]
The request object. Request message for AutoMl.GetAnnotationSpec. |
name |
str
Required. The resource name of the annotation spec to retrieve. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.AnnotationSpec |
A definition of an annotation spec. |
get_column_spec
get_column_spec(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.GetColumnSpecRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.column_spec.ColumnSpec
Gets a column spec.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_get_column_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetColumnSpecRequest(
name="name_value",
)
# Make the request
response = client.get_column_spec(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.GetColumnSpecRequest, dict]
The request object. Request message for AutoMl.GetColumnSpec. |
name |
str
Required. The resource name of the column spec to retrieve. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.ColumnSpec |
A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: \* Tables |
get_dataset
get_dataset(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.GetDatasetRequest, dict]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.dataset.Dataset
Gets a dataset.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_get_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetDatasetRequest(
name="name_value",
)
# Make the request
response = client.get_dataset(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.GetDatasetRequest, dict]
The request object. Request message for AutoMl.GetDataset. |
name |
str
Required. The resource name of the dataset to retrieve. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.Dataset |
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. |
get_model
get_model(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.GetModelRequest, dict]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.model.Model
Gets a model.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_get_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetModelRequest(
name="name_value",
)
# Make the request
response = client.get_model(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.GetModelRequest, dict]
The request object. Request message for AutoMl.GetModel. |
name |
str
Required. Resource name of the model. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.Model |
API proto representing a trained machine learning model. |
get_model_evaluation
get_model_evaluation(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.GetModelEvaluationRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.model_evaluation.ModelEvaluation
Gets a model evaluation.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_get_model_evaluation():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetModelEvaluationRequest(
name="name_value",
)
# Make the request
response = client.get_model_evaluation(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.GetModelEvaluationRequest, dict]
The request object. Request message for AutoMl.GetModelEvaluation. |
name |
str
Required. Resource name for the model evaluation. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.ModelEvaluation |
Evaluation results of a model. |
get_mtls_endpoint_and_cert_source
get_mtls_endpoint_and_cert_source(
client_options: typing.Optional[
google.api_core.client_options.ClientOptions
] = None,
)
Deprecated. Return the API endpoint and client cert source for mutual TLS.
The client cert source is determined in the following order:
(1) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is not "true", the
client cert source is None.
(2) if client_options.client_cert_source
is provided, use the provided one; if the
default client cert source exists, use the default one; otherwise the client cert
source is None.
The API endpoint is determined in the following order:
(1) if client_options.api_endpoint
if provided, use the provided one.
(2) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is "always", use the
default mTLS endpoint; if the environment variable is "never", use the default API
endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
use the default API endpoint.
More details can be found at https://google.aip.dev/auth/4114.
Parameter | |
---|---|
Name | Description |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
Exceptions | |
---|---|
Type | Description |
google.auth.exceptions.MutualTLSChannelError |
If any errors happen. |
Returns | |
---|---|
Type | Description |
Tuple[str, Callable[[], Tuple[bytes, bytes]]] |
returns the API endpoint and the client cert source to use. |
get_table_spec
get_table_spec(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.GetTableSpecRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.table_spec.TableSpec
Gets a table spec.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_get_table_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetTableSpecRequest(
name="name_value",
)
# Make the request
response = client.get_table_spec(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.GetTableSpecRequest, dict]
The request object. Request message for AutoMl.GetTableSpec. |
name |
str
Required. The resource name of the table spec to retrieve. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.TableSpec |
A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: \* Tables |
import_data
import_data(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.ImportDataRequest, dict]
] = None,
*,
name: typing.Optional[str] = None,
input_config: typing.Optional[
google.cloud.automl_v1beta1.types.io.InputConfig
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Imports data into a dataset. For Tables this method can only be called on an empty Dataset.
For Tables:
- A
xref_schema_inference_version
parameter must be explicitly set. Returns an empty response
in the
response][google.longrunning.Operation.response]
field when it completes.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_import_data():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ImportDataRequest(
name="name_value",
)
# Make the request
operation = client.import_data(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ImportDataRequest, dict]
The request object. Request message for AutoMl.ImportData. |
name |
str
Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. This corresponds to the |
input_config |
google.cloud.automl_v1beta1.types.InputConfig
Required. The desired input location and its domain specific semantics, if any. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
list_column_specs
list_column_specs(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.ListColumnSpecsRequest, dict
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.services.auto_ml.pagers.ListColumnSpecsPager
Lists column specs in a table spec.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_list_column_specs():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListColumnSpecsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_column_specs(request=request)
# Handle the response
for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ListColumnSpecsRequest, dict]
The request object. Request message for AutoMl.ListColumnSpecs. |
parent |
str
Required. The resource name of the table spec to list column specs from. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.services.auto_ml.pagers.ListColumnSpecsPager |
Response message for AutoMl.ListColumnSpecs. Iterating over this object will yield results and resolve additional pages automatically. |
list_datasets
list_datasets(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.ListDatasetsRequest, dict
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.services.auto_ml.pagers.ListDatasetsPager
Lists datasets in a project.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_list_datasets():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListDatasetsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_datasets(request=request)
# Handle the response
for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ListDatasetsRequest, dict]
The request object. Request message for AutoMl.ListDatasets. |
parent |
str
Required. The resource name of the project from which to list datasets. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.services.auto_ml.pagers.ListDatasetsPager |
Response message for AutoMl.ListDatasets. Iterating over this object will yield results and resolve additional pages automatically. |
list_model_evaluations
list_model_evaluations(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.ListModelEvaluationsRequest, dict
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.services.auto_ml.pagers.ListModelEvaluationsPager
Lists model evaluations.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_list_model_evaluations():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListModelEvaluationsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_model_evaluations(request=request)
# Handle the response
for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ListModelEvaluationsRequest, dict]
The request object. Request message for AutoMl.ListModelEvaluations. |
parent |
str
Required. Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.services.auto_ml.pagers.ListModelEvaluationsPager |
Response message for AutoMl.ListModelEvaluations. Iterating over this object will yield results and resolve additional pages automatically. |
list_models
list_models(
request: typing.Optional[
typing.Union[google.cloud.automl_v1beta1.types.service.ListModelsRequest, dict]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.services.auto_ml.pagers.ListModelsPager
Lists models.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_list_models():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListModelsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_models(request=request)
# Handle the response
for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ListModelsRequest, dict]
The request object. Request message for AutoMl.ListModels. |
parent |
str
Required. Resource name of the project, from which to list the models. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.services.auto_ml.pagers.ListModelsPager |
Response message for AutoMl.ListModels. Iterating over this object will yield results and resolve additional pages automatically. |
list_table_specs
list_table_specs(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.ListTableSpecsRequest, dict
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.services.auto_ml.pagers.ListTableSpecsPager
Lists table specs in a dataset.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_list_table_specs():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListTableSpecsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_table_specs(request=request)
# Handle the response
for response in page_result:
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.ListTableSpecsRequest, dict]
The request object. Request message for AutoMl.ListTableSpecs. |
parent |
str
Required. The resource name of the dataset to list table specs from. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.services.auto_ml.pagers.ListTableSpecsPager |
Response message for AutoMl.ListTableSpecs. Iterating over this object will yield results and resolve additional pages automatically. |
model_evaluation_path
model_evaluation_path(
project: str, location: str, model: str, model_evaluation: str
) -> str
Returns a fully-qualified model_evaluation string.
model_path
model_path(project: str, location: str, model: str) -> str
Returns a fully-qualified model string.
parse_annotation_spec_path
parse_annotation_spec_path(path: str) -> typing.Dict[str, str]
Parses a annotation_spec path into its component segments.
parse_column_spec_path
parse_column_spec_path(path: str) -> typing.Dict[str, str]
Parses a column_spec path into its component segments.
parse_common_billing_account_path
parse_common_billing_account_path(path: str) -> typing.Dict[str, str]
Parse a billing_account path into its component segments.
parse_common_folder_path
parse_common_folder_path(path: str) -> typing.Dict[str, str]
Parse a folder path into its component segments.
parse_common_location_path
parse_common_location_path(path: str) -> typing.Dict[str, str]
Parse a location path into its component segments.
parse_common_organization_path
parse_common_organization_path(path: str) -> typing.Dict[str, str]
Parse a organization path into its component segments.
parse_common_project_path
parse_common_project_path(path: str) -> typing.Dict[str, str]
Parse a project path into its component segments.
parse_dataset_path
parse_dataset_path(path: str) -> typing.Dict[str, str]
Parses a dataset path into its component segments.
parse_model_evaluation_path
parse_model_evaluation_path(path: str) -> typing.Dict[str, str]
Parses a model_evaluation path into its component segments.
parse_model_path
parse_model_path(path: str) -> typing.Dict[str, str]
Parses a model path into its component segments.
parse_table_spec_path
parse_table_spec_path(path: str) -> typing.Dict[str, str]
Parses a table_spec path into its component segments.
table_spec_path
table_spec_path(project: str, location: str, dataset: str, table_spec: str) -> str
Returns a fully-qualified table_spec string.
undeploy_model
undeploy_model(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.UndeployModelRequest, dict
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
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][google.longrunning.Operation.response]
field when it
completes.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_undeploy_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.UndeployModelRequest(
name="name_value",
)
# Make the request
operation = client.undeploy_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.UndeployModelRequest, dict]
The request object. Request message for AutoMl.UndeployModel. |
name |
str
Required. Resource name of the model to undeploy. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
update_column_spec
update_column_spec(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.UpdateColumnSpecRequest, dict
]
] = None,
*,
column_spec: typing.Optional[
google.cloud.automl_v1beta1.types.column_spec.ColumnSpec
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.column_spec.ColumnSpec
Updates a column spec.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_update_column_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.UpdateColumnSpecRequest(
)
# Make the request
response = client.update_column_spec(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.UpdateColumnSpecRequest, dict]
The request object. Request message for AutoMl.UpdateColumnSpec |
column_spec |
google.cloud.automl_v1beta1.types.ColumnSpec
Required. The column spec which replaces the resource on the server. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.ColumnSpec |
A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: \* Tables |
update_dataset
update_dataset(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.UpdateDatasetRequest, dict
]
] = None,
*,
dataset: typing.Optional[google.cloud.automl_v1beta1.types.dataset.Dataset] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.dataset.Dataset
Updates a dataset.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_update_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
dataset = automl_v1beta1.Dataset()
dataset.translation_dataset_metadata.source_language_code = "source_language_code_value"
dataset.translation_dataset_metadata.target_language_code = "target_language_code_value"
request = automl_v1beta1.UpdateDatasetRequest(
dataset=dataset,
)
# Make the request
response = client.update_dataset(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.UpdateDatasetRequest, dict]
The request object. Request message for AutoMl.UpdateDataset |
dataset |
google.cloud.automl_v1beta1.types.Dataset
Required. The dataset which replaces the resource on the server. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.Dataset |
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. |
update_table_spec
update_table_spec(
request: typing.Optional[
typing.Union[
google.cloud.automl_v1beta1.types.service.UpdateTableSpecRequest, dict
]
] = None,
*,
table_spec: typing.Optional[
google.cloud.automl_v1beta1.types.table_spec.TableSpec
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.automl_v1beta1.types.table_spec.TableSpec
Updates a table spec.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_update_table_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.UpdateTableSpecRequest(
)
# Make the request
response = client.update_table_spec(request=request)
# Handle the response
print(response)
Parameters | |
---|---|
Name | Description |
request |
Union[google.cloud.automl_v1beta1.types.UpdateTableSpecRequest, dict]
The request object. Request message for AutoMl.UpdateTableSpec |
table_spec |
google.cloud.automl_v1beta1.types.TableSpec
Required. The table spec which replaces the resource on the server. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.automl_v1beta1.types.TableSpec |
A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: \* Tables |