As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

TensorboardService¶

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.TensorboardServiceAsyncClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1beta1.services.tensorboard_service.transports.base.TensorboardServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1beta1.services.tensorboard_service.transports.base.TensorboardServiceTransport]]] = 'grpc_asyncio', client_options: ~typing.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>)[source]¶

TensorboardService

Instantiates the tensorboard service async client.

Parameters:

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,TensorboardServiceTransport,Callable[..., TensorboardServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport to use. If a Callable is given, it will be called with the same set of initialization arguments as used in the TensorboardServiceTransport 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 api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “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).

2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS 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.

3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.
client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises:

google.auth.exceptions.MutualTlsChannelError – If mutual TLS transport creation failed for any reason.

property api_endpoint¶

Return the API endpoint used by the client instance.

Returns:: The API endpoint used by the client instance.
Return type:: str

async batch_create_tensorboard_runs(request: Optional[Union[BatchCreateTensorboardRunsRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[MutableSequence[CreateTensorboardRunRequest]] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchCreateTensorboardRunsResponse[source]¶

Batch create TensorboardRuns.

# 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 aiplatform_v1beta1

async def sample_batch_create_tensorboard_runs():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    requests = aiplatform_v1beta1.CreateTensorboardRunRequest()
    requests.parent = "parent_value"
    requests.tensorboard_run.display_name = "display_name_value"
    requests.tensorboard_run_id = "tensorboard_run_id_value"

    request = aiplatform_v1beta1.BatchCreateTensorboardRunsRequest(
        parent="parent_value",
        requests=requests,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardRunsRequest, dict]]) – The request object. Request message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardRuns].

Return type:

google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardRunsResponse

async batch_create_tensorboard_time_series(request: Optional[Union[BatchCreateTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[MutableSequence[CreateTensorboardTimeSeriesRequest]] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchCreateTensorboardTimeSeriesResponse[source]¶

Batch create TensorboardTimeSeries that belong to a TensorboardExperiment.

# 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 aiplatform_v1beta1

async def sample_batch_create_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    requests = aiplatform_v1beta1.CreateTensorboardTimeSeriesRequest()
    requests.parent = "parent_value"
    requests.tensorboard_time_series.display_name = "display_name_value"
    requests.tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.BatchCreateTensorboardTimeSeriesRequest(
        parent="parent_value",
        requests=requests,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardTimeSeriesRequest, dict]]) – The request object. Request message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardTimeSeries].

Return type:

google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardTimeSeriesResponse

async batch_read_tensorboard_time_series_data(request: Optional[Union[BatchReadTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchReadTensorboardTimeSeriesDataResponse[source]¶

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 is returned. Otherwise, the number limit of data points is randomly selected from this time series and returned.

# 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 aiplatform_v1beta1

async def sample_batch_read_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.BatchReadTensorboardTimeSeriesDataRequest(
        tensorboard="tensorboard_value",
        time_series=['time_series_value1', 'time_series_value2'],
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.BatchReadTensorboardTimeSeriesDataRequest, dict]]) – The request object. Request message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.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][google.cloud.aiplatform.v1beta1.BatchReadTensorboardTimeSeriesDataRequest.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.BatchReadTensorboardTimeSeriesData].

Return type:

google.cloud.aiplatform_v1beta1.types.BatchReadTensorboardTimeSeriesDataResponse

async cancel_operation(request: Optional[CancelOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (CancelOperationRequest) – The request object. Request message for CancelOperation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

None

static common_billing_account_path(billing_account: str) → str¶: Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str¶: Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str¶: Returns a fully-qualified location string.

static common_organization_path(organization: str) → str¶: Returns a fully-qualified organization string.

static common_project_path(project: str) → str¶: Returns a fully-qualified project string.

async create_tensorboard(request: Optional[Union[CreateTensorboardRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard: Optional[Tensorboard] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Creates a Tensorboard.

# 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 aiplatform_v1beta1

async def sample_create_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    tensorboard = aiplatform_v1beta1.Tensorboard()
    tensorboard.display_name = "display_name_value"

    request = aiplatform_v1beta1.CreateTensorboardRequest(
        parent="parent_value",
        tensorboard=tensorboard,
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardRequest, dict]]) – The request object. Request message for [TensorboardService.CreateTensorboard][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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_async.AsyncRetry) – 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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.Tensorboard Tensorboard is a physical database that stores users’ training metrics.: A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Return type:

google.api_core.operation_async.AsyncOperation

async create_tensorboard_experiment(request: Optional[Union[CreateTensorboardExperimentRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_experiment: Optional[TensorboardExperiment] = None, tensorboard_experiment_id: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardExperiment[source]¶

Creates a TensorboardExperiment.

# 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 aiplatform_v1beta1

async def sample_create_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateTensorboardExperimentRequest(
        parent="parent_value",
        tensorboard_experiment_id="tensorboard_experiment_id_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardExperimentRequest, dict]]) – The request object. Request message for [TensorboardService.CreateTensorboardExperiment][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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 becomes 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_async.AsyncRetry) – 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:

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardExperiment

async create_tensorboard_run(request: Optional[Union[CreateTensorboardRunRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_run: Optional[TensorboardRun] = None, tensorboard_run_id: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardRun[source]¶

Creates a TensorboardRun.

# 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 aiplatform_v1beta1

async def sample_create_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    tensorboard_run = aiplatform_v1beta1.TensorboardRun()
    tensorboard_run.display_name = "display_name_value"

    request = aiplatform_v1beta1.CreateTensorboardRunRequest(
        parent="parent_value",
        tensorboard_run=tensorboard_run,
        tensorboard_run_id="tensorboard_run_id_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardRunRequest, dict]]) – The request object. Request message for [TensorboardService.CreateTensorboardRun][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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 becomes 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_async.AsyncRetry) – 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:

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardRun

async create_tensorboard_time_series(request: Optional[Union[CreateTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_time_series: Optional[TensorboardTimeSeries] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardTimeSeries[source]¶

Creates a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

async def sample_create_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    tensorboard_time_series = aiplatform_v1beta1.TensorboardTimeSeries()
    tensorboard_time_series.display_name = "display_name_value"
    tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.CreateTensorboardTimeSeriesRequest(
        parent="parent_value",
        tensorboard_time_series=tensorboard_time_series,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardTimeSeriesRequest, dict]]) – The request object. Request message for [TensorboardService.CreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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_async.AsyncRetry) – 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:

TensorboardTimeSeries maps to times series produced in training runs

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries

async delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (DeleteOperationRequest) – The request object. Request message for DeleteOperation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

None

async delete_tensorboard(request: Optional[Union[DeleteTensorboardRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a Tensorboard.

# 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 aiplatform_v1beta1

async def sample_delete_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteTensorboardRequest(
        name="name_value",
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardRequest, dict]]) – The request object. Request message for [TensorboardService.DeleteTensorboard][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

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);

}

Return type:

google.api_core.operation_async.AsyncOperation

async delete_tensorboard_experiment(request: Optional[Union[DeleteTensorboardExperimentRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a TensorboardExperiment.

# 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 aiplatform_v1beta1

async def sample_delete_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteTensorboardExperimentRequest(
        name="name_value",
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardExperimentRequest, dict]]) – The request object. Request message for [TensorboardService.DeleteTensorboardExperiment][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

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);

}

Return type:

google.api_core.operation_async.AsyncOperation

async delete_tensorboard_run(request: Optional[Union[DeleteTensorboardRunRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a TensorboardRun.

# 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 aiplatform_v1beta1

async def sample_delete_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteTensorboardRunRequest(
        name="name_value",
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardRunRequest, dict]]) – The request object. Request message for [TensorboardService.DeleteTensorboardRun][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

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);

}

Return type:

google.api_core.operation_async.AsyncOperation

async delete_tensorboard_time_series(request: Optional[Union[DeleteTensorboardTimeSeriesRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

async def sample_delete_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteTensorboardTimeSeriesRequest(
        name="name_value",
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardTimeSeriesRequest, dict]]) – The request object. Request message for [TensorboardService.DeleteTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

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);

}

Return type:

google.api_core.operation_async.AsyncOperation

async export_tensorboard_time_series_data(request: Optional[Union[ExportTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard_time_series: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ExportTensorboardTimeSeriesDataAsyncPager[source]¶

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

# 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 aiplatform_v1beta1

async def sample_export_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ExportTensorboardTimeSeriesDataRequest(
        tensorboard_time_series="tensorboard_time_series_value",
    )

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

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataRequest, dict]]) – The request object. Request message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ExportTensorboardTimeSeriesData].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ExportTensorboardTimeSeriesDataAsyncPager

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]¶

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

TensorboardServiceAsyncClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]¶

Creates an instance of this client using the provided credentials: info.

Parameters:

info (dict) – The service account private key info.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

TensorboardServiceAsyncClient

classmethod from_service_account_json(filename: str, *args, **kwargs)¶

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

TensorboardServiceAsyncClient

async get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Parameters:

request (GetIamPolicyRequest) – The request object. Request message for GetIamPolicy method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

async get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location[source]¶

Gets information about a location.

Parameters:

request (GetLocationRequest) – The request object. Request message for GetLocation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Location object.

Return type:

Location

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)[source]¶

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.

Parameters:

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.

Returns:

returns the API endpoint and the: client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

async get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Gets the latest state of a long-running operation.

Parameters:

request (GetOperationRequest) – The request object. Request message for GetOperation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

An Operation object.

Return type:

Operation

async get_tensorboard(request: Optional[Union[GetTensorboardRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Tensorboard[source]¶

Gets a Tensorboard.

# 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 aiplatform_v1beta1

async def sample_get_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardRequest, dict]]) – The request object. Request message for [TensorboardService.GetTensorboard][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Tensorboard is a physical database that stores users’ training metrics. A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Return type:

google.cloud.aiplatform_v1beta1.types.Tensorboard

async get_tensorboard_experiment(request: Optional[Union[GetTensorboardExperimentRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardExperiment[source]¶

Gets a TensorboardExperiment.

# 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 aiplatform_v1beta1

async def sample_get_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardExperimentRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardExperimentRequest, dict]]) – The request object. Request message for [TensorboardService.GetTensorboardExperiment][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardExperiment

async get_tensorboard_run(request: Optional[Union[GetTensorboardRunRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardRun[source]¶

Gets a TensorboardRun.

# 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 aiplatform_v1beta1

async def sample_get_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardRunRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardRunRequest, dict]]) – The request object. Request message for [TensorboardService.GetTensorboardRun][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardRun

async get_tensorboard_time_series(request: Optional[Union[GetTensorboardTimeSeriesRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardTimeSeries[source]¶

Gets a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

async def sample_get_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardTimeSeriesRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardTimeSeriesRequest, dict]]) – The request object. Request message for [TensorboardService.GetTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

TensorboardTimeSeries maps to times series produced in training runs

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries

classmethod get_transport_class(label: Optional[str] = None) → Type[TensorboardServiceTransport]¶

Returns an appropriate transport class.

Parameters:: label – The name of the desired transport. If none is provided, then the first transport in the registry is used.
Returns:: The transport class to use.

async list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse[source]¶

Lists information about the supported locations for this service.

Parameters:

request (ListLocationsRequest) – The request object. Request message for ListLocations method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for ListLocations method.

Return type:

ListLocationsResponse

async list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse[source]¶

Lists operations that match the specified filter in the request.

Parameters:

request (ListOperationsRequest) – The request object. Request message for ListOperations method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for ListOperations method.

Return type:

ListOperationsResponse

async list_tensorboard_experiments(request: Optional[Union[ListTensorboardExperimentsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardExperimentsAsyncPager[source]¶

Lists TensorboardExperiments in a Location.

# 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 aiplatform_v1beta1

async def sample_list_tensorboard_experiments():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListTensorboardExperimentsRequest(
        parent="parent_value",
    )

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

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsRequest, dict]]) – The request object. Request message for [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardExperiments].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardExperimentsAsyncPager

async list_tensorboard_runs(request: Optional[Union[ListTensorboardRunsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardRunsAsyncPager[source]¶

Lists TensorboardRuns in a Location.

# 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 aiplatform_v1beta1

async def sample_list_tensorboard_runs():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListTensorboardRunsRequest(
        parent="parent_value",
    )

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

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsRequest, dict]]) – The request object. Request message for [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardRuns].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardRunsAsyncPager

async list_tensorboard_time_series(request: Optional[Union[ListTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardTimeSeriesAsyncPager[source]¶

Lists TensorboardTimeSeries in a Location.

# 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 aiplatform_v1beta1

async def sample_list_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListTensorboardTimeSeriesRequest(
        parent="parent_value",
    )

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

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesRequest, dict]]) – The request object. Request message for [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardTimeSeries].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardTimeSeriesAsyncPager

async list_tensorboards(request: Optional[Union[ListTensorboardsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardsAsyncPager[source]¶

Lists Tensorboards in a Location.

# 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 aiplatform_v1beta1

async def sample_list_tensorboards():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListTensorboardsRequest(
        parent="parent_value",
    )

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

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardsRequest, dict]]) – The request object. Request message for [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboards].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardsAsyncPager

static parse_common_billing_account_path(path: str) → Dict[str, str]¶: Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]¶: Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]¶: Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]¶: Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]¶: Parse a project path into its component segments.

static parse_tensorboard_experiment_path(path: str) → Dict[str, str]¶: Parses a tensorboard_experiment path into its component segments.

static parse_tensorboard_path(path: str) → Dict[str, str]¶: Parses a tensorboard path into its component segments.

static parse_tensorboard_run_path(path: str) → Dict[str, str]¶: Parses a tensorboard_run path into its component segments.

static parse_tensorboard_time_series_path(path: str) → Dict[str, str]¶: Parses a tensorboard_time_series path into its component segments.

read_tensorboard_blob_data(request: Optional[Union[ReadTensorboardBlobDataRequest, dict]] = None, *, time_series: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Awaitable[AsyncIterable[ReadTensorboardBlobDataResponse]][source]¶

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.

# 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 aiplatform_v1beta1

async def sample_read_tensorboard_blob_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ReadTensorboardBlobDataRequest(
        time_series="time_series_value",
    )

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

    # Handle the response
    async for response in stream:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardBlobDataRequest, dict]]) – The request object. Request message for [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardBlobData].

Return type:

AsyncIterable[google.cloud.aiplatform_v1beta1.types.ReadTensorboardBlobDataResponse]

async read_tensorboard_size(request: Optional[Union[ReadTensorboardSizeRequest, dict]] = None, *, tensorboard: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ReadTensorboardSizeResponse[source]¶

Returns the storage size for a given TensorBoard instance.

# 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 aiplatform_v1beta1

async def sample_read_tensorboard_size():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ReadTensorboardSizeRequest(
        tensorboard="tensorboard_value",
    )

    # Make the request
    response = await client.read_tensorboard_size(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardSizeRequest, dict]]) – The request object. Request message for [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardSize].
tensorboard (str) –
Required. The name of the Tensorboard resource. 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.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardSize].

Return type:

google.cloud.aiplatform_v1beta1.types.ReadTensorboardSizeResponse

async read_tensorboard_time_series_data(request: Optional[Union[ReadTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard_time_series: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ReadTensorboardTimeSeriesDataResponse[source]¶

Reads a TensorboardTimeSeries’ data. By default, if the number of data points stored is less than 1000, all data is returned. Otherwise, 1000 data points is 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.

# 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 aiplatform_v1beta1

async def sample_read_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ReadTensorboardTimeSeriesDataRequest(
        tensorboard_time_series="tensorboard_time_series_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardTimeSeriesDataRequest, dict]]) – The request object. Request message for [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardTimeSeriesData].

Return type:

google.cloud.aiplatform_v1beta1.types.ReadTensorboardTimeSeriesDataResponse

async read_tensorboard_usage(request: Optional[Union[ReadTensorboardUsageRequest, dict]] = None, *, tensorboard: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ReadTensorboardUsageResponse[source]¶

Returns a list of monthly active users for a given TensorBoard instance.

# 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 aiplatform_v1beta1

async def sample_read_tensorboard_usage():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ReadTensorboardUsageRequest(
        tensorboard="tensorboard_value",
    )

    # Make the request
    response = await client.read_tensorboard_usage(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardUsageRequest, dict]]) – The request object. Request message for [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardUsage].
tensorboard (str) –
Required. The name of the Tensorboard resource. 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.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for: [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardUsage].

Return type:

google.cloud.aiplatform_v1beta1.types.ReadTensorboardUsageResponse

async set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

request (SetIamPolicyRequest) – The request object. Request message for SetIamPolicy method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

static tensorboard_experiment_path(project: str, location: str, tensorboard: str, experiment: str) → str¶: Returns a fully-qualified tensorboard_experiment string.

static tensorboard_path(project: str, location: str, tensorboard: str) → str¶: Returns a fully-qualified tensorboard string.

static tensorboard_run_path(project: str, location: str, tensorboard: str, experiment: str, run: str) → str¶: Returns a fully-qualified tensorboard_run string.

static tensorboard_time_series_path(project: str, location: str, tensorboard: str, experiment: str, run: str, time_series: str) → str¶: Returns a fully-qualified tensorboard_time_series string.

async test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse[source]¶

Tests the specified IAM permissions against the IAM access control: policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Parameters:

request (TestIamPermissionsRequest) – The request object. Request message for TestIamPermissions method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

property transport: TensorboardServiceTransport¶

Returns the transport used by the client instance.

Returns:: The transport used by the client instance.
Return type:: TensorboardServiceTransport

property universe_domain: str¶

Return the universe domain used by the client instance.

Returns:

The universe domain used: by the client instance.

Return type:

str

async update_tensorboard(request: Optional[Union[UpdateTensorboardRequest, dict]] = None, *, tensorboard: Optional[Tensorboard] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Updates a Tensorboard.

# 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 aiplatform_v1beta1

async def sample_update_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    tensorboard = aiplatform_v1beta1.Tensorboard()
    tensorboard.display_name = "display_name_value"

    request = aiplatform_v1beta1.UpdateTensorboardRequest(
        tensorboard=tensorboard,
    )

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

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

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardRequest, dict]]) – The request object. Request message for [TensorboardService.UpdateTensorboard][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboard].
tensorboard (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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_async.AsyncRetry) – 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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.Tensorboard Tensorboard is a physical database that stores users’ training metrics.: A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Return type:

google.api_core.operation_async.AsyncOperation

async update_tensorboard_experiment(request: Optional[Union[UpdateTensorboardExperimentRequest, dict]] = None, *, tensorboard_experiment: Optional[TensorboardExperiment] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardExperiment[source]¶

Updates a TensorboardExperiment.

# 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 aiplatform_v1beta1

async def sample_update_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateTensorboardExperimentRequest(
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardExperimentRequest, dict]]) – The request object. Request message for [TensorboardService.UpdateTensorboardExperiment][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardExperiment].
tensorboard_experiment (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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_async.AsyncRetry) – 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:

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardExperiment

async update_tensorboard_run(request: Optional[Union[UpdateTensorboardRunRequest, dict]] = None, *, tensorboard_run: Optional[TensorboardRun] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardRun[source]¶

Updates a TensorboardRun.

# 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 aiplatform_v1beta1

async def sample_update_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    tensorboard_run = aiplatform_v1beta1.TensorboardRun()
    tensorboard_run.display_name = "display_name_value"

    request = aiplatform_v1beta1.UpdateTensorboardRunRequest(
        tensorboard_run=tensorboard_run,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardRunRequest, dict]]) – The request object. Request message for [TensorboardService.UpdateTensorboardRun][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardRun].
tensorboard_run (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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_async.AsyncRetry) – 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:

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardRun

async update_tensorboard_time_series(request: Optional[Union[UpdateTensorboardTimeSeriesRequest, dict]] = None, *, tensorboard_time_series: Optional[TensorboardTimeSeries] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardTimeSeries[source]¶

Updates a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

async def sample_update_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    tensorboard_time_series = aiplatform_v1beta1.TensorboardTimeSeries()
    tensorboard_time_series.display_name = "display_name_value"
    tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.UpdateTensorboardTimeSeriesRequest(
        tensorboard_time_series=tensorboard_time_series,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardTimeSeriesRequest, dict]]) – The request object. Request message for [TensorboardService.UpdateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardTimeSeries].
tensorboard_time_series (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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_async.AsyncRetry) – 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:

TensorboardTimeSeries maps to times series produced in training runs

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries

async wait_operation(request: Optional[WaitOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (WaitOperationRequest) – The request object. Request message for WaitOperation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

An Operation object.

Return type:

Operation

async write_tensorboard_experiment_data(request: Optional[Union[WriteTensorboardExperimentDataRequest, dict]] = None, *, tensorboard_experiment: Optional[str] = None, write_run_data_requests: Optional[MutableSequence[WriteTensorboardRunDataRequest]] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → WriteTensorboardExperimentDataResponse[source]¶

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

# 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 aiplatform_v1beta1

async def sample_write_tensorboard_experiment_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    write_run_data_requests = aiplatform_v1beta1.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_v1beta1.WriteTensorboardExperimentDataRequest(
        tensorboard_experiment="tensorboard_experiment_value",
        write_run_data_requests=write_run_data_requests,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.WriteTensorboardExperimentDataRequest, dict]]) – The request object. Request message for [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardExperimentData].

Return type:

google.cloud.aiplatform_v1beta1.types.WriteTensorboardExperimentDataResponse

async write_tensorboard_run_data(request: Optional[Union[WriteTensorboardRunDataRequest, dict]] = None, *, tensorboard_run: Optional[str] = None, time_series_data: Optional[MutableSequence[TimeSeriesData]] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → WriteTensorboardRunDataResponse[source]¶

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

# 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 aiplatform_v1beta1

async def sample_write_tensorboard_run_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceAsyncClient()

    # Initialize request argument(s)
    time_series_data = aiplatform_v1beta1.TimeSeriesData()
    time_series_data.tensorboard_time_series_id = "tensorboard_time_series_id_value"
    time_series_data.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.WriteTensorboardRunDataRequest(
        tensorboard_run="tensorboard_run_value",
        time_series_data=time_series_data,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.WriteTensorboardRunDataRequest, dict]]) – The request object. Request message for [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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_async.AsyncRetry) – 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:

Response message for: [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardRunData].

Return type:

google.cloud.aiplatform_v1beta1.types.WriteTensorboardRunDataResponse

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.TensorboardServiceClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1beta1.services.tensorboard_service.transports.base.TensorboardServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1beta1.services.tensorboard_service.transports.base.TensorboardServiceTransport]]] = 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>)[source]¶

TensorboardService

Instantiates the tensorboard service client.

Parameters:

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,TensorboardServiceTransport,Callable[..., TensorboardServiceTransport]]]) – 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 TensorboardServiceTransport 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 api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “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).

2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS 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.

3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.
client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises:

google.auth.exceptions.MutualTLSChannelError – If mutual TLS transport creation failed for any reason.

__exit__(type, value, traceback)[source]¶: Releases underlying transport’s resources.

Warning

ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients!

property api_endpoint¶

Return the API endpoint used by the client instance.

Returns:: The API endpoint used by the client instance.
Return type:: str

batch_create_tensorboard_runs(request: Optional[Union[BatchCreateTensorboardRunsRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[MutableSequence[CreateTensorboardRunRequest]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchCreateTensorboardRunsResponse[source]¶

Batch create TensorboardRuns.

# 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 aiplatform_v1beta1

def sample_batch_create_tensorboard_runs():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    requests = aiplatform_v1beta1.CreateTensorboardRunRequest()
    requests.parent = "parent_value"
    requests.tensorboard_run.display_name = "display_name_value"
    requests.tensorboard_run_id = "tensorboard_run_id_value"

    request = aiplatform_v1beta1.BatchCreateTensorboardRunsRequest(
        parent="parent_value",
        requests=requests,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardRunsRequest, dict]) – The request object. Request message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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:

Response message for: [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardRuns].

Return type:

google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardRunsResponse

batch_create_tensorboard_time_series(request: Optional[Union[BatchCreateTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[MutableSequence[CreateTensorboardTimeSeriesRequest]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchCreateTensorboardTimeSeriesResponse[source]¶

Batch create TensorboardTimeSeries that belong to a TensorboardExperiment.

# 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 aiplatform_v1beta1

def sample_batch_create_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    requests = aiplatform_v1beta1.CreateTensorboardTimeSeriesRequest()
    requests.parent = "parent_value"
    requests.tensorboard_time_series.display_name = "display_name_value"
    requests.tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.BatchCreateTensorboardTimeSeriesRequest(
        parent="parent_value",
        requests=requests,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardTimeSeriesRequest, dict]) – The request object. Request message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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:

Response message for: [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.BatchCreateTensorboardTimeSeries].

Return type:

google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardTimeSeriesResponse

batch_read_tensorboard_time_series_data(request: Optional[Union[BatchReadTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchReadTensorboardTimeSeriesDataResponse[source]¶

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 is returned. Otherwise, the number limit of data points is randomly selected from this time series and returned.

# 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 aiplatform_v1beta1

def sample_batch_read_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.BatchReadTensorboardTimeSeriesDataRequest(
        tensorboard="tensorboard_value",
        time_series=['time_series_value1', 'time_series_value2'],
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.BatchReadTensorboardTimeSeriesDataRequest, dict]) – The request object. Request message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.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][google.cloud.aiplatform.v1beta1.BatchReadTensorboardTimeSeriesDataRequest.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:

Response message for: [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.BatchReadTensorboardTimeSeriesData].

Return type:

google.cloud.aiplatform_v1beta1.types.BatchReadTensorboardTimeSeriesDataResponse

cancel_operation(request: Optional[CancelOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (CancelOperationRequest) – The request object. Request message for CancelOperation method.
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:

None

static common_billing_account_path(billing_account: str) → str[source]¶: Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str[source]¶: Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str[source]¶: Returns a fully-qualified location string.

static common_organization_path(organization: str) → str[source]¶: Returns a fully-qualified organization string.

static common_project_path(project: str) → str[source]¶: Returns a fully-qualified project string.

create_tensorboard(request: Optional[Union[CreateTensorboardRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard: Optional[Tensorboard] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Creates a Tensorboard.

# 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 aiplatform_v1beta1

def sample_create_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard = aiplatform_v1beta1.Tensorboard()
    tensorboard.display_name = "display_name_value"

    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardRequest, dict]) – The request object. Request message for [TensorboardService.CreateTensorboard][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.Tensorboard Tensorboard is a physical database that stores users’ training metrics.: A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Return type:

google.api_core.operation.Operation

create_tensorboard_experiment(request: Optional[Union[CreateTensorboardExperimentRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_experiment: Optional[TensorboardExperiment] = None, tensorboard_experiment_id: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardExperiment[source]¶

Creates a TensorboardExperiment.

# 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 aiplatform_v1beta1

def sample_create_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardExperimentRequest, dict]) – The request object. Request message for [TensorboardService.CreateTensorboardExperiment][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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 becomes 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:

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardExperiment

create_tensorboard_run(request: Optional[Union[CreateTensorboardRunRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_run: Optional[TensorboardRun] = None, tensorboard_run_id: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardRun[source]¶

Creates a TensorboardRun.

# 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 aiplatform_v1beta1

def sample_create_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_run = aiplatform_v1beta1.TensorboardRun()
    tensorboard_run.display_name = "display_name_value"

    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardRunRequest, dict]) – The request object. Request message for [TensorboardService.CreateTensorboardRun][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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 becomes 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:

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardRun

create_tensorboard_time_series(request: Optional[Union[CreateTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, tensorboard_time_series: Optional[TensorboardTimeSeries] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardTimeSeries[source]¶

Creates a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

def sample_create_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_time_series = aiplatform_v1beta1.TensorboardTimeSeries()
    tensorboard_time_series.display_name = "display_name_value"
    tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardTimeSeriesRequest, dict]) – The request object. Request message for [TensorboardService.CreateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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 (google.cloud.aiplatform_v1beta1.types.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:

TensorboardTimeSeries maps to times series produced in training runs

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries

delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn’t support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (DeleteOperationRequest) – The request object. Request message for DeleteOperation method.
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:

None

delete_tensorboard(request: Optional[Union[DeleteTensorboardRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a Tensorboard.

# 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 aiplatform_v1beta1

def sample_delete_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardRequest, dict]) – The request object. Request message for [TensorboardService.DeleteTensorboard][google.cloud.aiplatform.v1beta1.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:

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);

}

Return type:

google.api_core.operation.Operation

delete_tensorboard_experiment(request: Optional[Union[DeleteTensorboardExperimentRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a TensorboardExperiment.

# 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 aiplatform_v1beta1

def sample_delete_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardExperimentRequest, dict]) – The request object. Request message for [TensorboardService.DeleteTensorboardExperiment][google.cloud.aiplatform.v1beta1.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:

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);

}

Return type:

google.api_core.operation.Operation

delete_tensorboard_run(request: Optional[Union[DeleteTensorboardRunRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a TensorboardRun.

# 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 aiplatform_v1beta1

def sample_delete_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardRunRequest, dict]) – The request object. Request message for [TensorboardService.DeleteTensorboardRun][google.cloud.aiplatform.v1beta1.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:

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);

}

Return type:

google.api_core.operation.Operation

delete_tensorboard_time_series(request: Optional[Union[DeleteTensorboardTimeSeriesRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

def sample_delete_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardTimeSeriesRequest, dict]) – The request object. Request message for [TensorboardService.DeleteTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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:

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);

}

Return type:

google.api_core.operation.Operation

export_tensorboard_time_series_data(request: Optional[Union[ExportTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard_time_series: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ExportTensorboardTimeSeriesDataPager[source]¶

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

# 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 aiplatform_v1beta1

def sample_export_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataRequest, dict]) – The request object. Request message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.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:

Response message for: [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ExportTensorboardTimeSeriesData].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ExportTensorboardTimeSeriesDataPager

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]¶

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

TensorboardServiceClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]¶

Creates an instance of this client using the provided credentials: info.

Parameters:

info (dict) – The service account private key info.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

TensorboardServiceClient

classmethod from_service_account_json(filename: str, *args, **kwargs)¶

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

TensorboardServiceClient

get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Parameters:

request (GetIamPolicyRequest) – The request object. Request message for GetIamPolicy method.
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:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location[source]¶

Gets information about a location.

Parameters:

request (GetLocationRequest) – The request object. Request message for GetLocation method.
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:

Location object.

Return type:

Location

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)[source]¶

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.

Parameters:

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.

Returns:

returns the API endpoint and the: client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Gets the latest state of a long-running operation.

Parameters:

request (GetOperationRequest) – The request object. Request message for GetOperation method.
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:

An Operation object.

Return type:

Operation

get_tensorboard(request: Optional[Union[GetTensorboardRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Tensorboard[source]¶

Gets a Tensorboard.

# 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 aiplatform_v1beta1

def sample_get_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardRequest, dict]) – The request object. Request message for [TensorboardService.GetTensorboard][google.cloud.aiplatform.v1beta1.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:

Tensorboard is a physical database that stores users’ training metrics. A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Return type:

google.cloud.aiplatform_v1beta1.types.Tensorboard

get_tensorboard_experiment(request: Optional[Union[GetTensorboardExperimentRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardExperiment[source]¶

Gets a TensorboardExperiment.

# 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 aiplatform_v1beta1

def sample_get_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardExperimentRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardExperimentRequest, dict]) – The request object. Request message for [TensorboardService.GetTensorboardExperiment][google.cloud.aiplatform.v1beta1.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:

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardExperiment

get_tensorboard_run(request: Optional[Union[GetTensorboardRunRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardRun[source]¶

Gets a TensorboardRun.

# 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 aiplatform_v1beta1

def sample_get_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardRunRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardRunRequest, dict]) – The request object. Request message for [TensorboardService.GetTensorboardRun][google.cloud.aiplatform.v1beta1.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:

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardRun

get_tensorboard_time_series(request: Optional[Union[GetTensorboardTimeSeriesRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardTimeSeries[source]¶

Gets a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

def sample_get_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetTensorboardTimeSeriesRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardTimeSeriesRequest, dict]) – The request object. Request message for [TensorboardService.GetTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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:

TensorboardTimeSeries maps to times series produced in training runs

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries

list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse[source]¶

Lists information about the supported locations for this service.

Parameters:

request (ListLocationsRequest) – The request object. Request message for ListLocations method.
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:

Response message for ListLocations method.

Return type:

ListLocationsResponse

list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse[source]¶

Lists operations that match the specified filter in the request.

Parameters:

request (ListOperationsRequest) – The request object. Request message for ListOperations method.
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:

Response message for ListOperations method.

Return type:

ListOperationsResponse

list_tensorboard_experiments(request: Optional[Union[ListTensorboardExperimentsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardExperimentsPager[source]¶

Lists TensorboardExperiments in a Location.

# 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 aiplatform_v1beta1

def sample_list_tensorboard_experiments():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsRequest, dict]) – The request object. Request message for [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1beta1.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:

Response message for: [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardExperiments].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardExperimentsPager

list_tensorboard_runs(request: Optional[Union[ListTensorboardRunsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardRunsPager[source]¶

Lists TensorboardRuns in a Location.

# 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 aiplatform_v1beta1

def sample_list_tensorboard_runs():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsRequest, dict]) – The request object. Request message for [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1beta1.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:

Response message for: [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardRuns].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardRunsPager

list_tensorboard_time_series(request: Optional[Union[ListTensorboardTimeSeriesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardTimeSeriesPager[source]¶

Lists TensorboardTimeSeries in a Location.

# 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 aiplatform_v1beta1

def sample_list_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesRequest, dict]) – The request object. Request message for [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.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:

Response message for: [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboardTimeSeries].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardTimeSeriesPager

list_tensorboards(request: Optional[Union[ListTensorboardsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListTensorboardsPager[source]¶

Lists Tensorboards in a Location.

# 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 aiplatform_v1beta1

def sample_list_tensorboards():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardsRequest, dict]) – The request object. Request message for [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1beta1.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:

Response message for: [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1beta1.TensorboardService.ListTensorboards].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardsPager

static parse_common_billing_account_path(path: str) → Dict[str, str][source]¶: Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str][source]¶: Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str][source]¶: Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str][source]¶: Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str][source]¶: Parse a project path into its component segments.

static parse_tensorboard_experiment_path(path: str) → Dict[str, str][source]¶: Parses a tensorboard_experiment path into its component segments.

static parse_tensorboard_path(path: str) → Dict[str, str][source]¶: Parses a tensorboard path into its component segments.

static parse_tensorboard_run_path(path: str) → Dict[str, str][source]¶: Parses a tensorboard_run path into its component segments.

static parse_tensorboard_time_series_path(path: str) → Dict[str, str][source]¶: Parses a tensorboard_time_series path into its component segments.

read_tensorboard_blob_data(request: Optional[Union[ReadTensorboardBlobDataRequest, dict]] = None, *, time_series: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Iterable[ReadTensorboardBlobDataResponse][source]¶

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.

# 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 aiplatform_v1beta1

def sample_read_tensorboard_blob_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardBlobDataRequest, dict]) – The request object. Request message for [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1beta1.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:

Response message for: [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardBlobData].

Return type:

Iterable[google.cloud.aiplatform_v1beta1.types.ReadTensorboardBlobDataResponse]

read_tensorboard_size(request: Optional[Union[ReadTensorboardSizeRequest, dict]] = None, *, tensorboard: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ReadTensorboardSizeResponse[source]¶

Returns the storage size for a given TensorBoard instance.

# 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 aiplatform_v1beta1

def sample_read_tensorboard_size():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ReadTensorboardSizeRequest(
        tensorboard="tensorboard_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardSizeRequest, dict]) – The request object. Request message for [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardSize].
tensorboard (str) –
Required. The name of the Tensorboard resource. 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.
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:

Response message for: [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardSize].

Return type:

google.cloud.aiplatform_v1beta1.types.ReadTensorboardSizeResponse

read_tensorboard_time_series_data(request: Optional[Union[ReadTensorboardTimeSeriesDataRequest, dict]] = None, *, tensorboard_time_series: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ReadTensorboardTimeSeriesDataResponse[source]¶

Reads a TensorboardTimeSeries’ data. By default, if the number of data points stored is less than 1000, all data is returned. Otherwise, 1000 data points is 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.

# 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 aiplatform_v1beta1

def sample_read_tensorboard_time_series_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardTimeSeriesDataRequest, dict]) – The request object. Request message for [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.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:

Response message for: [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardTimeSeriesData].

Return type:

google.cloud.aiplatform_v1beta1.types.ReadTensorboardTimeSeriesDataResponse

read_tensorboard_usage(request: Optional[Union[ReadTensorboardUsageRequest, dict]] = None, *, tensorboard: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ReadTensorboardUsageResponse[source]¶

Returns a list of monthly active users for a given TensorBoard instance.

# 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 aiplatform_v1beta1

def sample_read_tensorboard_usage():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ReadTensorboardUsageRequest(
        tensorboard="tensorboard_value",
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardUsageRequest, dict]) – The request object. Request message for [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardUsage].
tensorboard (str) –
Required. The name of the Tensorboard resource. 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.
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:

Response message for: [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1beta1.TensorboardService.ReadTensorboardUsage].

Return type:

google.cloud.aiplatform_v1beta1.types.ReadTensorboardUsageResponse

set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

request (SetIamPolicyRequest) – The request object. Request message for SetIamPolicy method.
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:

Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A Policy is a collection of bindings. A binding binds one or more members to a single role. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions (defined by IAM or configured by users). A binding can optionally specify a condition, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

JSON Example

{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": ["user:eve@example.com"],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time <
        timestamp('2020-10-01T00:00:00.000Z')",
      }
    }
  ]
}

YAML Example

bindings:
- members:
  - user:mike@example.com
  - group:admins@example.com
  - domain:google.com
  - serviceAccount:my-project-id@appspot.gserviceaccount.com
  role: roles/resourcemanager.organizationAdmin
- members:
  - user:eve@example.com
  role: roles/resourcemanager.organizationViewer
  condition:
    title: expirable access
    description: Does not grant access after Sep 2020
    expression: request.time < timestamp('2020-10-01T00:00:00.000Z')

For a description of IAM and its features, see the IAM developer’s guide.

Return type:

Policy

static tensorboard_experiment_path(project: str, location: str, tensorboard: str, experiment: str) → str[source]¶: Returns a fully-qualified tensorboard_experiment string.

static tensorboard_path(project: str, location: str, tensorboard: str) → str[source]¶: Returns a fully-qualified tensorboard string.

static tensorboard_run_path(project: str, location: str, tensorboard: str, experiment: str, run: str) → str[source]¶: Returns a fully-qualified tensorboard_run string.

static tensorboard_time_series_path(project: str, location: str, tensorboard: str, experiment: str, run: str, time_series: str) → str[source]¶: Returns a fully-qualified tensorboard_time_series string.

test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse[source]¶

Tests the specified IAM permissions against the IAM access control: policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Parameters:

request (TestIamPermissionsRequest) – The request object. Request message for TestIamPermissions method.
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:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

property transport: TensorboardServiceTransport¶

Returns the transport used by the client instance.

Returns:

The transport used by the client: instance.

Return type:

TensorboardServiceTransport

property universe_domain: str¶

Return the universe domain used by the client instance.

Returns:: The universe domain used by the client instance.
Return type:: str

update_tensorboard(request: Optional[Union[UpdateTensorboardRequest, dict]] = None, *, tensorboard: Optional[Tensorboard] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Updates a Tensorboard.

# 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 aiplatform_v1beta1

def sample_update_tensorboard():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard = aiplatform_v1beta1.Tensorboard()
    tensorboard.display_name = "display_name_value"

    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardRequest, dict]) – The request object. Request message for [TensorboardService.UpdateTensorboard][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboard].
tensorboard (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.Tensorboard Tensorboard is a physical database that stores users’ training metrics.: A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Return type:

google.api_core.operation.Operation

update_tensorboard_experiment(request: Optional[Union[UpdateTensorboardExperimentRequest, dict]] = None, *, tensorboard_experiment: Optional[TensorboardExperiment] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardExperiment[source]¶

Updates a TensorboardExperiment.

# 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 aiplatform_v1beta1

def sample_update_tensorboard_experiment():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateTensorboardExperimentRequest(
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardExperimentRequest, dict]) – The request object. Request message for [TensorboardService.UpdateTensorboardExperiment][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardExperiment].
tensorboard_experiment (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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:

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardExperiment

update_tensorboard_run(request: Optional[Union[UpdateTensorboardRunRequest, dict]] = None, *, tensorboard_run: Optional[TensorboardRun] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardRun[source]¶

Updates a TensorboardRun.

# 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 aiplatform_v1beta1

def sample_update_tensorboard_run():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_run = aiplatform_v1beta1.TensorboardRun()
    tensorboard_run.display_name = "display_name_value"

    request = aiplatform_v1beta1.UpdateTensorboardRunRequest(
        tensorboard_run=tensorboard_run,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardRunRequest, dict]) – The request object. Request message for [TensorboardService.UpdateTensorboardRun][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardRun].
tensorboard_run (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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:

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardRun

update_tensorboard_time_series(request: Optional[Union[UpdateTensorboardTimeSeriesRequest, dict]] = None, *, tensorboard_time_series: Optional[TensorboardTimeSeries] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TensorboardTimeSeries[source]¶

Updates a TensorboardTimeSeries.

# 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 aiplatform_v1beta1

def sample_update_tensorboard_time_series():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    tensorboard_time_series = aiplatform_v1beta1.TensorboardTimeSeries()
    tensorboard_time_series.display_name = "display_name_value"
    tensorboard_time_series.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.UpdateTensorboardTimeSeriesRequest(
        tensorboard_time_series=tensorboard_time_series,
    )

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

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardTimeSeriesRequest, dict]) – The request object. Request message for [TensorboardService.UpdateTensorboardTimeSeries][google.cloud.aiplatform.v1beta1.TensorboardService.UpdateTensorboardTimeSeries].
tensorboard_time_series (google.cloud.aiplatform_v1beta1.types.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 is overwritten if it’s in the mask. If the user does not provide a mask then all fields are 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:

TensorboardTimeSeries maps to times series produced in training runs

Return type:

google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries

wait_operation(request: Optional[WaitOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters:

request (WaitOperationRequest) – The request object. Request message for WaitOperation method.
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:

An Operation object.

Return type:

Operation

write_tensorboard_experiment_data(request: Optional[Union[WriteTensorboardExperimentDataRequest, dict]] = None, *, tensorboard_experiment: Optional[str] = None, write_run_data_requests: Optional[MutableSequence[WriteTensorboardRunDataRequest]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → WriteTensorboardExperimentDataResponse[source]¶

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

# 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 aiplatform_v1beta1

def sample_write_tensorboard_experiment_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    write_run_data_requests = aiplatform_v1beta1.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_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.WriteTensorboardExperimentDataRequest, dict]) – The request object. Request message for [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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:

Response message for: [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardExperimentData].

Return type:

google.cloud.aiplatform_v1beta1.types.WriteTensorboardExperimentDataResponse

write_tensorboard_run_data(request: Optional[Union[WriteTensorboardRunDataRequest, dict]] = None, *, tensorboard_run: Optional[str] = None, time_series_data: Optional[MutableSequence[TimeSeriesData]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → WriteTensorboardRunDataResponse[source]¶

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

# 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 aiplatform_v1beta1

def sample_write_tensorboard_run_data():
    # Create a client
    client = aiplatform_v1beta1.TensorboardServiceClient()

    # Initialize request argument(s)
    time_series_data = aiplatform_v1beta1.TimeSeriesData()
    time_series_data.tensorboard_time_series_id = "tensorboard_time_series_id_value"
    time_series_data.value_type = "BLOB_SEQUENCE"

    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.WriteTensorboardRunDataRequest, dict]) – The request object. Request message for [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1beta1.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 (MutableSequence[google.cloud.aiplatform_v1beta1.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:

Response message for: [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1beta1.TensorboardService.WriteTensorboardRunData].

Return type:

google.cloud.aiplatform_v1beta1.types.WriteTensorboardRunDataResponse

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ExportTensorboardTimeSeriesDataAsyncPager(method: Callable[[...], Awaitable[ExportTensorboardTimeSeriesDataResponse]], request: ExportTensorboardTimeSeriesDataRequest, response: ExportTensorboardTimeSeriesDataResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through export_tensorboard_time_series_data requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataResponse object, and provides an __aiter__ method to iterate through its time_series_data_points field.

If there are more pages, the __aiter__ method will make additional ExportTensorboardTimeSeriesData requests and continue to iterate through the time_series_data_points field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataResponse) – The initial response object.
retry (google.api_core.retry.AsyncRetry) – 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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ExportTensorboardTimeSeriesDataPager(method: Callable[[...], ExportTensorboardTimeSeriesDataResponse], request: ExportTensorboardTimeSeriesDataRequest, response: ExportTensorboardTimeSeriesDataResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through export_tensorboard_time_series_data requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataResponse object, and provides an __iter__ method to iterate through its time_series_data_points field.

If there are more pages, the __iter__ method will make additional ExportTensorboardTimeSeriesData requests and continue to iterate through the time_series_data_points field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataResponse) – The initial response object.
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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardExperimentsAsyncPager(method: Callable[[...], Awaitable[ListTensorboardExperimentsResponse]], request: ListTensorboardExperimentsRequest, response: ListTensorboardExperimentsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboard_experiments requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsResponse object, and provides an __aiter__ method to iterate through its tensorboard_experiments field.

If there are more pages, the __aiter__ method will make additional ListTensorboardExperiments requests and continue to iterate through the tensorboard_experiments field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsResponse) – The initial response object.
retry (google.api_core.retry.AsyncRetry) – 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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardExperimentsPager(method: Callable[[...], ListTensorboardExperimentsResponse], request: ListTensorboardExperimentsRequest, response: ListTensorboardExperimentsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboard_experiments requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsResponse object, and provides an __iter__ method to iterate through its tensorboard_experiments field.

If there are more pages, the __iter__ method will make additional ListTensorboardExperiments requests and continue to iterate through the tensorboard_experiments field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsResponse) – The initial response object.
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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardRunsAsyncPager(method: Callable[[...], Awaitable[ListTensorboardRunsResponse]], request: ListTensorboardRunsRequest, response: ListTensorboardRunsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboard_runs requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsResponse object, and provides an __aiter__ method to iterate through its tensorboard_runs field.

If there are more pages, the __aiter__ method will make additional ListTensorboardRuns requests and continue to iterate through the tensorboard_runs field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsResponse) – The initial response object.
retry (google.api_core.retry.AsyncRetry) – 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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardRunsPager(method: Callable[[...], ListTensorboardRunsResponse], request: ListTensorboardRunsRequest, response: ListTensorboardRunsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboard_runs requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsResponse object, and provides an __iter__ method to iterate through its tensorboard_runs field.

If there are more pages, the __iter__ method will make additional ListTensorboardRuns requests and continue to iterate through the tensorboard_runs field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsResponse) – The initial response object.
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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardTimeSeriesAsyncPager(method: Callable[[...], Awaitable[ListTensorboardTimeSeriesResponse]], request: ListTensorboardTimeSeriesRequest, response: ListTensorboardTimeSeriesResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboard_time_series requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesResponse object, and provides an __aiter__ method to iterate through its tensorboard_time_series field.

If there are more pages, the __aiter__ method will make additional ListTensorboardTimeSeries requests and continue to iterate through the tensorboard_time_series field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesResponse) – The initial response object.
retry (google.api_core.retry.AsyncRetry) – 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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardTimeSeriesPager(method: Callable[[...], ListTensorboardTimeSeriesResponse], request: ListTensorboardTimeSeriesRequest, response: ListTensorboardTimeSeriesResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboard_time_series requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesResponse object, and provides an __iter__ method to iterate through its tensorboard_time_series field.

If there are more pages, the __iter__ method will make additional ListTensorboardTimeSeries requests and continue to iterate through the tensorboard_time_series field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesResponse) – The initial response object.
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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardsAsyncPager(method: Callable[[...], Awaitable[ListTensorboardsResponse]], request: ListTensorboardsRequest, response: ListTensorboardsResponse, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboards requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardsResponse object, and provides an __aiter__ method to iterate through its tensorboards field.

If there are more pages, the __aiter__ method will make additional ListTensorboards requests and continue to iterate through the tensorboards field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardsRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardsResponse) – The initial response object.
retry (google.api_core.retry.AsyncRetry) – 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.

class google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardsPager(method: Callable[[...], ListTensorboardsResponse], request: ListTensorboardsRequest, response: ListTensorboardsResponse, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]¶

A pager for iterating through list_tensorboards requests.

This class thinly wraps an initial google.cloud.aiplatform_v1beta1.types.ListTensorboardsResponse object, and provides an __iter__ method to iterate through its tensorboards field.

If there are more pages, the __iter__ method will make additional ListTensorboards requests and continue to iterate through the tensorboards field on the corresponding responses.

All the usual google.cloud.aiplatform_v1beta1.types.ListTensorboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters:

method (Callable) – The method that was originally called, and which instantiated this pager.
request (google.cloud.aiplatform_v1beta1.types.ListTensorboardsRequest) – The initial request object.
response (google.cloud.aiplatform_v1beta1.types.ListTensorboardsResponse) – The initial response object.
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.

TensorboardService¶

google-cloud-aiplatform

Navigation

Related Topics