Class TensorboardServiceAsyncClient (1.12.1) 

TensorboardServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.tensorboard_service.transports.base.TensorboardServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

TensorboardService

Inheritance

builtins.object > TensorboardServiceAsyncClient

Properties

transport

Returns the transport used by the client instance.

Returns
Type Description
TensorboardServiceTransport The transport used by the client instance.

Methods

TensorboardServiceAsyncClient

TensorboardServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.tensorboard_service.transports.base.TensorboardServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiates the tensorboard service 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 Union[str, `.TensorboardServiceTransport`]

The transport to use. If set to None, a transport is chosen automatically.
client_options ClientOptions

Custom options for the client. It won't take effect if a transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.
Exceptions
Type Description
google.auth.exceptions.MutualTlsChannelError If mutual TLS transport creation failed for any reason.

batch_create_tensorboard_runs

batch_create_tensorboard_runs(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.BatchCreateTensorboardRunsRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[Sequence[google.cloud.aiplatform_v1.types.tensorboard_service.CreateTensorboardRunRequest]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Batch create TensorboardRuns.

from google.cloud import aiplatform_v1

def sample_batch_create_tensorboard_runs():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    requests = aiplatform_v1.CreateTensorboardRunRequest()
    requests.parent = "parent_value"
    requests.tensorboard_run.display_name = "display_name_value"
    requests.tensorboard_run_id = "tensorboard_run_id_value"

    request = aiplatform_v1.BatchCreateTensorboardRunsRequest(
        parent="parent_value",
        requests=requests,
    )

    # Make the request
    response = client.batch_create_tensorboard_runs(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.BatchCreateTensorboardRunsRequest, dict]

The request object. Request message for TensorboardService.BatchCreateTensorboardRuns.
parent `str`

Required. The resource name of the TensorboardExperiment to create the TensorboardRuns in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} The parent field in the CreateTensorboardRunRequest messages must match this field. This corresponds to the parent field on the request instance; if request is provided, this should not be set.
requests :class:`Sequence[google.cloud.aiplatform_v1.types.CreateTensorboardRunRequest]`

Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch. This corresponds to the requests field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.BatchCreateTensorboardRunsResponse Response message for TensorboardService.BatchCreateTensorboardRuns.

batch_create_tensorboard_time_series

batch_create_tensorboard_time_series(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.BatchCreateTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[Sequence[google.cloud.aiplatform_v1.types.tensorboard_service.CreateTensorboardTimeSeriesRequest]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Batch create TensorboardTimeSeries that belong to a TensorboardExperiment.

from google.cloud import aiplatform_v1

def sample_batch_create_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    requests = aiplatform_v1.CreateTensorboardTimeSeriesRequest()
    requests.parent = "parent_value"
    requests.tensorboard_time_series.display_name = "display_name_value"
    requests.tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1.BatchCreateTensorboardTimeSeriesRequest(
        parent="parent_value",
        requests=requests,
    )

    # Make the request
    response = client.batch_create_tensorboard_time_series(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.BatchCreateTensorboardTimeSeriesRequest, dict]

The request object. Request message for TensorboardService.BatchCreateTensorboardTimeSeries.
parent `str`

Required. The resource name of the TensorboardExperiment to create the TensorboardTimeSeries in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} The TensorboardRuns referenced by the parent fields in the CreateTensorboardTimeSeriesRequest messages must be sub resources of this TensorboardExperiment. This corresponds to the parent field on the request instance; if request is provided, this should not be set.
requests :class:`Sequence[google.cloud.aiplatform_v1.types.CreateTensorboardTimeSeriesRequest]`

Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch. This corresponds to the requests field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.BatchCreateTensorboardTimeSeriesResponse Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

batch_read_tensorboard_time_series_data

batch_read_tensorboard_time_series_data(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.BatchReadTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Reads multiple TensorboardTimeSeries' data. The data point number limit is 1000 for scalars, 100 for tensors and blob references. If the number of data points stored is less than the limit, all data will be returned. Otherwise, that limit number of data points will be randomly selected from this time series and returned.

from google.cloud import aiplatform_v1

def sample_batch_read_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.BatchReadTensorboardTimeSeriesDataRequest(
        tensorboard="tensorboard_value",
        time_series=['time_series_value_1', 'time_series_value_2'],
    )

    # Make the request
    response = client.batch_read_tensorboard_time_series_data(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.BatchReadTensorboardTimeSeriesDataRequest, dict]

The request object. Request message for TensorboardService.BatchReadTensorboardTimeSeriesData.
tensorboard `str`

Required. The resource name of the Tensorboard containing TensorboardTimeSeries to read data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}. The TensorboardTimeSeries referenced by time_series must be sub resources of this Tensorboard. This corresponds to the tensorboard field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.BatchReadTensorboardTimeSeriesDataResponse Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

create_tensorboard

create_tensorboard(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.CreateTensorboardRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard: Optional[google.cloud.aiplatform_v1.types.tensorboard.Tensorboard] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a Tensorboard.

from google.cloud import aiplatform_v1

def sample_create_tensorboard():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard = aiplatform_v1.Tensorboard()
    tensorboard.display_name = "display_name_value"

    request = aiplatform_v1.CreateTensorboardRequest(
        parent="parent_value",
        tensorboard=tensorboard,
    )

    # Make the request
    operation = client.create_tensorboard(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateTensorboardRequest, dict]

The request object. Request message for TensorboardService.CreateTensorboard.
parent `str`

Required. The resource name of the Location to create the Tensorboard in. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
tensorboard Tensorboard

Required. The Tensorboard to create. This corresponds to the tensorboard field on the request instance; if request is provided, this should not be set.
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_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be Tensorboard Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a GCP project. If needed users can also create extra Tensorboards in their projects.

create_tensorboard_experiment

create_tensorboard_experiment(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.CreateTensorboardExperimentRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_experiment: Optional[google.cloud.aiplatform_v1.types.tensorboard_experiment.TensorboardExperiment] = None, tensorboard_experiment_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a TensorboardExperiment.

from google.cloud import aiplatform_v1

def sample_create_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateTensorboardExperimentRequest(
        parent="parent_value",
        tensorboard_experiment_id="tensorboard_experiment_id_value",
    )

    # Make the request
    response = client.create_tensorboard_experiment(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateTensorboardExperimentRequest, dict]

The request object. Request message for TensorboardService.CreateTensorboardExperiment.
parent `str`

Required. The resource name of the Tensorboard to create the TensorboardExperiment in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
tensorboard_experiment TensorboardExperiment

The TensorboardExperiment to create. This corresponds to the tensorboard_experiment field on the request instance; if request is provided, this should not be set.
tensorboard_experiment_id `str`

Required. The ID to use for the Tensorboard experiment, which will become the final component of the Tensorboard experiment's resource name. This value should be 1-128 characters, and valid characters are /a-z][0-9]-/. This corresponds to the tensorboard_experiment_id field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardExperiment A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

create_tensorboard_run

create_tensorboard_run(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.CreateTensorboardRunRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_run: Optional[google.cloud.aiplatform_v1.types.tensorboard_run.TensorboardRun] = None, tensorboard_run_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a TensorboardRun.

from google.cloud import aiplatform_v1

def sample_create_tensorboard_run():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_run = aiplatform_v1.TensorboardRun()
    tensorboard_run.display_name = "display_name_value"

    request = aiplatform_v1.CreateTensorboardRunRequest(
        parent="parent_value",
        tensorboard_run=tensorboard_run,
        tensorboard_run_id="tensorboard_run_id_value",
    )

    # Make the request
    response = client.create_tensorboard_run(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateTensorboardRunRequest, dict]

The request object. Request message for TensorboardService.CreateTensorboardRun.
parent `str`

Required. The resource name of the TensorboardExperiment to create the TensorboardRun in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
tensorboard_run TensorboardRun

Required. The TensorboardRun to create. This corresponds to the tensorboard_run field on the request instance; if request is provided, this should not be set.
tensorboard_run_id `str`

Required. The ID to use for the Tensorboard run, which will become the final component of the Tensorboard run's resource name. This value should be 1-128 characters, and valid characters are /a-z][0-9]-/. This corresponds to the tensorboard_run_id field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardRun TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

create_tensorboard_time_series

create_tensorboard_time_series(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.CreateTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_time_series: Optional[google.cloud.aiplatform_v1.types.tensorboard_time_series.TensorboardTimeSeries] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a TensorboardTimeSeries.

from google.cloud import aiplatform_v1

def sample_create_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_time_series = aiplatform_v1.TensorboardTimeSeries()
    tensorboard_time_series.display_name = "display_name_value"
    tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1.CreateTensorboardTimeSeriesRequest(
        parent="parent_value",
        tensorboard_time_series=tensorboard_time_series,
    )

    # Make the request
    response = client.create_tensorboard_time_series(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateTensorboardTimeSeriesRequest, dict]

The request object. Request message for TensorboardService.CreateTensorboardTimeSeries.
parent `str`

Required. The resource name of the TensorboardRun to create the TensorboardTimeSeries in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
tensorboard_time_series TensorboardTimeSeries

Required. The TensorboardTimeSeries to create. This corresponds to the tensorboard_time_series field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardTimeSeries TensorboardTimeSeries maps to times series produced in training runs

delete_tensorboard

delete_tensorboard(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.DeleteTensorboardRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a Tensorboard.

from google.cloud import aiplatform_v1

def sample_delete_tensorboard():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteTensorboardRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_tensorboard(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteTensorboardRequest, dict]

The request object. Request message for TensorboardService.DeleteTensorboard.
name `str`

Required. The name of the Tensorboard to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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_async.AsyncOperation 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); } The JSON representation for Empty is empty JSON object {}.

delete_tensorboard_experiment

delete_tensorboard_experiment(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.DeleteTensorboardExperimentRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a TensorboardExperiment.

from google.cloud import aiplatform_v1

def sample_delete_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteTensorboardExperimentRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_tensorboard_experiment(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteTensorboardExperimentRequest, dict]

The request object. Request message for TensorboardService.DeleteTensorboardExperiment.
name `str`

Required. The name of the TensorboardExperiment to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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_async.AsyncOperation 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); } The JSON representation for Empty is empty JSON object {}.

delete_tensorboard_run

delete_tensorboard_run(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.DeleteTensorboardRunRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a TensorboardRun.

from google.cloud import aiplatform_v1

def sample_delete_tensorboard_run():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteTensorboardRunRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_tensorboard_run(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteTensorboardRunRequest, dict]

The request object. Request message for TensorboardService.DeleteTensorboardRun.
name `str`

Required. The name of the TensorboardRun to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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_async.AsyncOperation 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); } The JSON representation for Empty is empty JSON object {}.

delete_tensorboard_time_series

delete_tensorboard_time_series(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.DeleteTensorboardTimeSeriesRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a TensorboardTimeSeries.

from google.cloud import aiplatform_v1

def sample_delete_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteTensorboardTimeSeriesRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_tensorboard_time_series(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteTensorboardTimeSeriesRequest, dict]

The request object. Request message for TensorboardService.DeleteTensorboardTimeSeries.
name `str`

Required. The name of the TensorboardTimeSeries to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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_async.AsyncOperation 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); } The JSON representation for Empty is empty JSON object {}.

export_tensorboard_time_series_data

export_tensorboard_time_series_data(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.ExportTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard_time_series: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Exports a TensorboardTimeSeries' data. Data is returned in paginated responses.

from google.cloud import aiplatform_v1

def sample_export_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ExportTensorboardTimeSeriesDataRequest(
        tensorboard_time_series="tensorboard_time_series_value",
    )

    # Make the request
    page_result = client.export_tensorboard_time_series_data(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ExportTensorboardTimeSeriesDataRequest, dict]

The request object. Request message for TensorboardService.ExportTensorboardTimeSeriesData.
tensorboard_time_series `str`

Required. The resource name of the TensorboardTimeSeries to export data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series} This corresponds to the tensorboard_time_series field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.services.tensorboard_service.pagers.ExportTensorboardTimeSeriesDataAsyncPager Response message for TensorboardService.ExportTensorboardTimeSeriesData. Iterating over this object will yield results and resolve additional pages automatically.

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
TensorboardServiceAsyncClient 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
TensorboardServiceAsyncClient 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
TensorboardServiceAsyncClient The constructed client.

get_mtls_endpoint_and_cert_source

get_mtls_endpoint_and_cert_source(
    client_options: Optional[google.api_core.client_options.ClientOptions] = None,
)

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 variabel 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 api_endpoint and client_cert_source properties may be used in this method.
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_tensorboard

get_tensorboard(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.GetTensorboardRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a Tensorboard.

from google.cloud import aiplatform_v1

def sample_get_tensorboard():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetTensorboardRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_tensorboard(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetTensorboardRequest, dict]

The request object. Request message for TensorboardService.GetTensorboard.
name `str`

Required. The name of the Tensorboard resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.Tensorboard Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a GCP project. If needed users can also create extra Tensorboards in their projects.

get_tensorboard_experiment

get_tensorboard_experiment(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.GetTensorboardExperimentRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a TensorboardExperiment.

from google.cloud import aiplatform_v1

def sample_get_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetTensorboardExperimentRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_tensorboard_experiment(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetTensorboardExperimentRequest, dict]

The request object. Request message for TensorboardService.GetTensorboardExperiment.
name `str`

Required. The name of the TensorboardExperiment resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardExperiment A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

get_tensorboard_run

get_tensorboard_run(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.GetTensorboardRunRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a TensorboardRun.

from google.cloud import aiplatform_v1

def sample_get_tensorboard_run():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetTensorboardRunRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_tensorboard_run(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetTensorboardRunRequest, dict]

The request object. Request message for TensorboardService.GetTensorboardRun.
name `str`

Required. The name of the TensorboardRun resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardRun TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

get_tensorboard_time_series

get_tensorboard_time_series(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.GetTensorboardTimeSeriesRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a TensorboardTimeSeries.

from google.cloud import aiplatform_v1

def sample_get_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetTensorboardTimeSeriesRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_tensorboard_time_series(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetTensorboardTimeSeriesRequest, dict]

The request object. Request message for TensorboardService.GetTensorboardTimeSeries.
name `str`

Required. The name of the TensorboardTimeSeries resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series} This corresponds to the name field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardTimeSeries TensorboardTimeSeries maps to times series produced in training runs

get_transport_class

get_transport_class()

Returns an appropriate transport class.

list_tensorboard_experiments

list_tensorboard_experiments(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.ListTensorboardExperimentsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists TensorboardExperiments in a Location.

from google.cloud import aiplatform_v1

def sample_list_tensorboard_experiments():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListTensorboardExperimentsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_tensorboard_experiments(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListTensorboardExperimentsRequest, dict]

The request object. Request message for TensorboardService.ListTensorboardExperiments.
parent `str`

Required. The resource name of the Tensorboard to list TensorboardExperiments. Format: 'projects/{project}/locations/{location}/tensorboards/{tensorboard}' This corresponds to the parent field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.services.tensorboard_service.pagers.ListTensorboardExperimentsAsyncPager Response message for TensorboardService.ListTensorboardExperiments. Iterating over this object will yield results and resolve additional pages automatically.

list_tensorboard_runs

list_tensorboard_runs(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.ListTensorboardRunsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists TensorboardRuns in a Location.

from google.cloud import aiplatform_v1

def sample_list_tensorboard_runs():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListTensorboardRunsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_tensorboard_runs(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListTensorboardRunsRequest, dict]

The request object. Request message for TensorboardService.ListTensorboardRuns.
parent `str`

Required. The resource name of the TensorboardExperiment to list TensorboardRuns. Format: 'projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}' This corresponds to the parent field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.services.tensorboard_service.pagers.ListTensorboardRunsAsyncPager Response message for TensorboardService.ListTensorboardRuns. Iterating over this object will yield results and resolve additional pages automatically.

list_tensorboard_time_series

list_tensorboard_time_series(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.ListTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists TensorboardTimeSeries in a Location.

from google.cloud import aiplatform_v1

def sample_list_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListTensorboardTimeSeriesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_tensorboard_time_series(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListTensorboardTimeSeriesRequest, dict]

The request object. Request message for TensorboardService.ListTensorboardTimeSeries.
parent `str`

Required. The resource name of the TensorboardRun to list TensorboardTimeSeries. Format: 'projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}' This corresponds to the parent field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.services.tensorboard_service.pagers.ListTensorboardTimeSeriesAsyncPager Response message for TensorboardService.ListTensorboardTimeSeries. Iterating over this object will yield results and resolve additional pages automatically.

list_tensorboards

list_tensorboards(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.ListTensorboardsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists Tensorboards in a Location.

from google.cloud import aiplatform_v1

def sample_list_tensorboards():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListTensorboardsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_tensorboards(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListTensorboardsRequest, dict]

The request object. Request message for TensorboardService.ListTensorboards.
parent `str`

Required. The resource name of the Location to list Tensorboards. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.services.tensorboard_service.pagers.ListTensorboardsAsyncPager Response message for TensorboardService.ListTensorboards. Iterating over this object will yield results and resolve additional pages automatically.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_tensorboard_experiment_path

parse_tensorboard_experiment_path(path: str)

Parses a tensorboard_experiment path into its component segments.

parse_tensorboard_path

parse_tensorboard_path(path: str)

Parses a tensorboard path into its component segments.

parse_tensorboard_run_path

parse_tensorboard_run_path(path: str)

Parses a tensorboard_run path into its component segments.

parse_tensorboard_time_series_path

parse_tensorboard_time_series_path(path: str)

Parses a tensorboard_time_series path into its component segments.

read_tensorboard_blob_data

read_tensorboard_blob_data(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.ReadTensorboardBlobDataRequest, dict]] = None, *, time_series: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets bytes of TensorboardBlobs. This is to allow reading blob data stored in consumer project's Cloud Storage bucket without users having to obtain Cloud Storage access permission.

from google.cloud import aiplatform_v1

def sample_read_tensorboard_blob_data():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ReadTensorboardBlobDataRequest(
        time_series="time_series_value",
    )

    # Make the request
    stream = client.read_tensorboard_blob_data(request=request)

    # Handle the response
    for response in stream:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ReadTensorboardBlobDataRequest, dict]

The request object. Request message for TensorboardService.ReadTensorboardBlobData.
time_series `str`

Required. The resource name of the TensorboardTimeSeries to list Blobs. Format: 'projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}' This corresponds to the time_series field on the request instance; if request is provided, this should not be set.
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
AsyncIterable[google.cloud.aiplatform_v1.types.ReadTensorboardBlobDataResponse] Response message for TensorboardService.ReadTensorboardBlobData.

read_tensorboard_time_series_data

read_tensorboard_time_series_data(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.ReadTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard_time_series: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Reads a TensorboardTimeSeries' data. By default, if the number of data points stored is less than 1000, all data will be returned. Otherwise, 1000 data points will be randomly selected from this time series and returned. This value can be changed by changing max_data_points, which can't be greater than 10k.

from google.cloud import aiplatform_v1

def sample_read_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ReadTensorboardTimeSeriesDataRequest(
        tensorboard_time_series="tensorboard_time_series_value",
    )

    # Make the request
    response = client.read_tensorboard_time_series_data(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ReadTensorboardTimeSeriesDataRequest, dict]

The request object. Request message for TensorboardService.ReadTensorboardTimeSeriesData.
tensorboard_time_series `str`

Required. The resource name of the TensorboardTimeSeries to read data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series} This corresponds to the tensorboard_time_series field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.ReadTensorboardTimeSeriesDataResponse Response message for TensorboardService.ReadTensorboardTimeSeriesData.

tensorboard_experiment_path

tensorboard_experiment_path(
    project: str, location: str, tensorboard: str, experiment: str
)

Returns a fully-qualified tensorboard_experiment string.

tensorboard_path

tensorboard_path(project: str, location: str, tensorboard: str)

Returns a fully-qualified tensorboard string.

tensorboard_run_path

tensorboard_run_path(
    project: str, location: str, tensorboard: str, experiment: str, run: str
)

Returns a fully-qualified tensorboard_run string.

tensorboard_time_series_path

tensorboard_time_series_path(
    project: str,
    location: str,
    tensorboard: str,
    experiment: str,
    run: str,
    time_series: str,
)

Returns a fully-qualified tensorboard_time_series string.

update_tensorboard

update_tensorboard(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.UpdateTensorboardRequest, dict]] = None, *, tensorboard: Optional[google.cloud.aiplatform_v1.types.tensorboard.Tensorboard] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a Tensorboard.

from google.cloud import aiplatform_v1

def sample_update_tensorboard():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard = aiplatform_v1.Tensorboard()
    tensorboard.display_name = "display_name_value"

    request = aiplatform_v1.UpdateTensorboardRequest(
        tensorboard=tensorboard,
    )

    # Make the request
    operation = client.update_tensorboard(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateTensorboardRequest, dict]

The request object. Request message for TensorboardService.UpdateTensorboard.
tensorboard Tensorboard

Required. The Tensorboard's name field is used to identify the Tensorboard to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} This corresponds to the tensorboard field on the request instance; if request is provided, this should not be set.
update_mask `google.protobuf.field_mask_pb2.FieldMask`

Required. Field mask is used to specify the fields to be overwritten in the Tensorboard resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then all fields will be overwritten if new values are specified. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
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_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be Tensorboard Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a GCP project. If needed users can also create extra Tensorboards in their projects.

update_tensorboard_experiment

update_tensorboard_experiment(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.UpdateTensorboardExperimentRequest, dict]] = None, *, tensorboard_experiment: Optional[google.cloud.aiplatform_v1.types.tensorboard_experiment.TensorboardExperiment] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a TensorboardExperiment.

from google.cloud import aiplatform_v1

def sample_update_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateTensorboardExperimentRequest(
    )

    # Make the request
    response = client.update_tensorboard_experiment(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateTensorboardExperimentRequest, dict]

The request object. Request message for TensorboardService.UpdateTensorboardExperiment.
tensorboard_experiment TensorboardExperiment

Required. The TensorboardExperiment's name field is used to identify the TensorboardExperiment to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} This corresponds to the tensorboard_experiment field on the request instance; if request is provided, this should not be set.
update_mask `google.protobuf.field_mask_pb2.FieldMask`

Required. Field mask is used to specify the fields to be overwritten in the TensorboardExperiment resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then all fields will be overwritten if new values are specified. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardExperiment A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

update_tensorboard_run

update_tensorboard_run(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.UpdateTensorboardRunRequest, dict]] = None, *, tensorboard_run: Optional[google.cloud.aiplatform_v1.types.tensorboard_run.TensorboardRun] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a TensorboardRun.

from google.cloud import aiplatform_v1

def sample_update_tensorboard_run():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_run = aiplatform_v1.TensorboardRun()
    tensorboard_run.display_name = "display_name_value"

    request = aiplatform_v1.UpdateTensorboardRunRequest(
        tensorboard_run=tensorboard_run,
    )

    # Make the request
    response = client.update_tensorboard_run(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateTensorboardRunRequest, dict]

The request object. Request message for TensorboardService.UpdateTensorboardRun.
tensorboard_run TensorboardRun

Required. The TensorboardRun's name field is used to identify the TensorboardRun to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run} This corresponds to the tensorboard_run field on the request instance; if request is provided, this should not be set.
update_mask `google.protobuf.field_mask_pb2.FieldMask`

Required. Field mask is used to specify the fields to be overwritten in the TensorboardRun resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then all fields will be overwritten if new values are specified. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardRun TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

update_tensorboard_time_series

update_tensorboard_time_series(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.UpdateTensorboardTimeSeriesRequest, dict]] = None, *, tensorboard_time_series: Optional[google.cloud.aiplatform_v1.types.tensorboard_time_series.TensorboardTimeSeries] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a TensorboardTimeSeries.

from google.cloud import aiplatform_v1

def sample_update_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_time_series = aiplatform_v1.TensorboardTimeSeries()
    tensorboard_time_series.display_name = "display_name_value"
    tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1.UpdateTensorboardTimeSeriesRequest(
        tensorboard_time_series=tensorboard_time_series,
    )

    # Make the request
    response = client.update_tensorboard_time_series(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateTensorboardTimeSeriesRequest, dict]

The request object. Request message for TensorboardService.UpdateTensorboardTimeSeries.
tensorboard_time_series TensorboardTimeSeries

Required. The TensorboardTimeSeries' name field is used to identify the TensorboardTimeSeries to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series} This corresponds to the tensorboard_time_series field on the request instance; if request is provided, this should not be set.
update_mask `google.protobuf.field_mask_pb2.FieldMask`

Required. Field mask is used to specify the fields to be overwritten in the TensorboardTimeSeries resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then all fields will be overwritten if new values are specified. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.TensorboardTimeSeries TensorboardTimeSeries maps to times series produced in training runs

write_tensorboard_experiment_data

write_tensorboard_experiment_data(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.WriteTensorboardExperimentDataRequest, dict]] = None, *, tensorboard_experiment: Optional[str] = None, write_run_data_requests: Optional[Sequence[google.cloud.aiplatform_v1.types.tensorboard_service.WriteTensorboardRunDataRequest]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Write time series data points of multiple TensorboardTimeSeries in multiple TensorboardRun's. If any data fail to be ingested, an error will be returned.

from google.cloud import aiplatform_v1

def sample_write_tensorboard_experiment_data():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    write_run_data_requests = aiplatform_v1.WriteTensorboardRunDataRequest()
    write_run_data_requests.tensorboard_run = "tensorboard_run_value"
    write_run_data_requests.time_series_data.tensorboard_time_series_id = "tensorboard_time_series_id_value"
    write_run_data_requests.time_series_data.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1.WriteTensorboardExperimentDataRequest(
        tensorboard_experiment="tensorboard_experiment_value",
        write_run_data_requests=write_run_data_requests,
    )

    # Make the request
    response = client.write_tensorboard_experiment_data(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.WriteTensorboardExperimentDataRequest, dict]

The request object. Request message for TensorboardService.WriteTensorboardExperimentData.
tensorboard_experiment `str`

Required. The resource name of the TensorboardExperiment to write data to. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} This corresponds to the tensorboard_experiment field on the request instance; if request is provided, this should not be set.
write_run_data_requests :class:`Sequence[google.cloud.aiplatform_v1.types.WriteTensorboardRunDataRequest]`

Required. Requests containing per-run TensorboardTimeSeries data to write. This corresponds to the write_run_data_requests field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.WriteTensorboardExperimentDataResponse Response message for TensorboardService.WriteTensorboardExperimentData.

write_tensorboard_run_data

write_tensorboard_run_data(request: Optional[Union[google.cloud.aiplatform_v1.types.tensorboard_service.WriteTensorboardRunDataRequest, dict]] = None, *, tensorboard_run: Optional[str] = None, time_series_data: Optional[Sequence[google.cloud.aiplatform_v1.types.tensorboard_data.TimeSeriesData]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Write time series data points into multiple TensorboardTimeSeries under a TensorboardRun. If any data fail to be ingested, an error will be returned.

from google.cloud import aiplatform_v1

def sample_write_tensorboard_run_data():
    # Create a client
    client = aiplatform_v1.TensorboardServiceClient()

    # Initialize request argument(s)
    time_series_data = aiplatform_v1.TimeSeriesData()
    time_series_data.tensorboard_time_series_id = "tensorboard_time_series_id_value"
    time_series_data.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1.WriteTensorboardRunDataRequest(
        tensorboard_run="tensorboard_run_value",
        time_series_data=time_series_data,
    )

    # Make the request
    response = client.write_tensorboard_run_data(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.WriteTensorboardRunDataRequest, dict]

The request object. Request message for TensorboardService.WriteTensorboardRunData.
tensorboard_run `str`

Required. The resource name of the TensorboardRun to write data to. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run} This corresponds to the tensorboard_run field on the request instance; if request is provided, this should not be set.
time_series_data :class:`Sequence[google.cloud.aiplatform_v1.types.TimeSeriesData]`

Required. The TensorboardTimeSeries data to write. Values with in a time series are indexed by their step value. Repeated writes to the same step will overwrite the existing value for that step. The upper limit of data points per write request is 5000. This corresponds to the time_series_data field on the request instance; if request is provided, this should not be set.
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.aiplatform_v1.types.WriteTensorboardRunDataResponse Response message for TensorboardService.WriteTensorboardRunData.