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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>)
TensorboardService
Properties
api_endpoint
Return the API endpoint used by the client instance.
Returns | |
---|---|
Type | Description |
str |
The API endpoint used by the client instance. |
transport
Returns the transport used by the client instance.
Returns | |
---|---|
Type | Description |
TensorboardServiceTransport |
The transport used by the client instance. |
universe_domain
Return the universe domain used by the client instance.
Returns | |
---|---|
Type | Description |
str |
The universe domain used by the client instance. |
Methods
TensorboardServiceClient
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>)
Instantiates the tensorboard service client.
Parameters | |
---|---|
Name | Description |
credentials |
Optional[google.auth.credentials.Credentials]
The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. |
transport |
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 |
client_info |
google.api_core.gapic_v1.client_info.ClientInfo
The client info used to send a user-agent string along with API requests. If |
Exceptions | |
---|---|
Type | Description |
google.auth.exceptions.MutualTLSChannelError |
If mutual TLS transport creation failed for any reason. |
__exit__
__exit__(type, value, traceback)
Releases underlying transport's resources.
batch_create_tensorboard_runs
batch_create_tensorboard_runs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.BatchCreateTensorboardRunsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
requests: typing.Optional[
typing.MutableSequence[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.CreateTensorboardRunRequest
]
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.BatchCreateTensorboardRunsResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardRunsRequest, dict]
The request object. Request message for TensorboardService.BatchCreateTensorboardRuns. |
parent |
str
Required. The resource name of the TensorboardExperiment to create the TensorboardRuns in. Format: |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardRunsResponse |
Response message for TensorboardService.BatchCreateTensorboardRuns. |
batch_create_tensorboard_time_series
batch_create_tensorboard_time_series(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.BatchCreateTensorboardTimeSeriesRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
requests: typing.Optional[
typing.MutableSequence[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.CreateTensorboardTimeSeriesRequest
]
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.BatchCreateTensorboardTimeSeriesResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardTimeSeriesRequest, dict]
The request object. Request message for TensorboardService.BatchCreateTensorboardTimeSeries. |
parent |
str
Required. The resource name of the TensorboardExperiment to create the TensorboardTimeSeries in. Format: |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.BatchCreateTensorboardTimeSeriesResponse |
Response message for TensorboardService.BatchCreateTensorboardTimeSeries. |
batch_read_tensorboard_time_series_data
batch_read_tensorboard_time_series_data(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.BatchReadTensorboardTimeSeriesDataRequest,
dict,
]
] = None,
*,
tensorboard: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.BatchReadTensorboardTimeSeriesDataResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.BatchReadTensorboardTimeSeriesDataRequest, dict]
The request object. Request message for TensorboardService.BatchReadTensorboardTimeSeriesData. |
tensorboard |
str
Required. The resource name of the Tensorboard containing TensorboardTimeSeries to read data from. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.BatchReadTensorboardTimeSeriesDataResponse |
Response message for TensorboardService.BatchReadTensorboardTimeSeriesData. |
cancel_operation
cancel_operation(
request: typing.Optional[
google.longrunning.operations_pb2.CancelOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
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 | |
---|---|
Name | Description |
request |
The request object. Request message for |
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. |
common_billing_account_path
common_billing_account_path(billing_account: str) -> str
Returns a fully-qualified billing_account string.
common_folder_path
common_folder_path(folder: str) -> str
Returns a fully-qualified folder string.
common_location_path
common_location_path(project: str, location: str) -> str
Returns a fully-qualified location string.
common_organization_path
common_organization_path(organization: str) -> str
Returns a fully-qualified organization string.
common_project_path
common_project_path(project: str) -> str
Returns a fully-qualified project string.
create_tensorboard
create_tensorboard(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.CreateTensorboardRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
tensorboard: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard.Tensorboard
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Creates a 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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardRequest, dict]
The request object. Request message for TensorboardService.CreateTensorboard. |
parent |
str
Required. The resource name of the Location to create the Tensorboard in. Format: |
tensorboard |
google.cloud.aiplatform_v1beta1.types.Tensorboard
Required. The Tensorboard to create. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be 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. |
create_tensorboard_experiment
create_tensorboard_experiment(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.CreateTensorboardExperimentRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
tensorboard_experiment: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard_experiment.TensorboardExperiment
] = None,
tensorboard_experiment_id: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.tensorboard_experiment.TensorboardExperiment
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardExperimentRequest, dict]
The request object. Request message for TensorboardService.CreateTensorboardExperiment. |
parent |
str
Required. The resource name of the Tensorboard to create the TensorboardExperiment in. Format: |
tensorboard_experiment |
google.cloud.aiplatform_v1beta1.types.TensorboardExperiment
The TensorboardExperiment to create. This corresponds to the |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardExperiment |
A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard. |
create_tensorboard_run
create_tensorboard_run(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.CreateTensorboardRunRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
tensorboard_run: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard_run.TensorboardRun
] = None,
tensorboard_run_id: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.tensorboard_run.TensorboardRun
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardRunRequest, dict]
The request object. Request message for TensorboardService.CreateTensorboardRun. |
parent |
str
Required. The resource name of the TensorboardExperiment to create the TensorboardRun in. Format: |
tensorboard_run |
google.cloud.aiplatform_v1beta1.types.TensorboardRun
Required. The TensorboardRun to create. This corresponds to the |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardRun |
TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc |
create_tensorboard_time_series
create_tensorboard_time_series(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.CreateTensorboardTimeSeriesRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
tensorboard_time_series: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard_time_series.TensorboardTimeSeries
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_time_series.TensorboardTimeSeries
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateTensorboardTimeSeriesRequest, dict]
The request object. Request message for TensorboardService.CreateTensorboardTimeSeries. |
parent |
str
Required. The resource name of the TensorboardRun to create the TensorboardTimeSeries in. Format: |
tensorboard_time_series |
google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries
Required. The TensorboardTimeSeries to create. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries |
TensorboardTimeSeries maps to times series produced in training runs |
delete_operation
delete_operation(
request: typing.Optional[
google.longrunning.operations_pb2.DeleteOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None
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 | |
---|---|
Name | Description |
request |
The request object. Request message for |
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. |
delete_tensorboard
delete_tensorboard(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.DeleteTensorboardRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Deletes a 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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardRequest, dict]
The request object. Request message for TensorboardService.DeleteTensorboard. |
name |
str
Required. The name of the Tensorboard to be deleted. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_tensorboard_experiment
delete_tensorboard_experiment(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.DeleteTensorboardExperimentRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Deletes a 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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardExperimentRequest, dict]
The request object. Request message for TensorboardService.DeleteTensorboardExperiment. |
name |
str
Required. The name of the TensorboardExperiment to be deleted. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_tensorboard_run
delete_tensorboard_run(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.DeleteTensorboardRunRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Deletes a 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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardRunRequest, dict]
The request object. Request message for TensorboardService.DeleteTensorboardRun. |
name |
str
Required. The name of the TensorboardRun to be deleted. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_tensorboard_time_series
delete_tensorboard_time_series(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.DeleteTensorboardTimeSeriesRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
Deletes a 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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteTensorboardTimeSeriesRequest, dict]
The request object. Request message for TensorboardService.DeleteTensorboardTimeSeries. |
name |
str
Required. The name of the TensorboardTimeSeries to be deleted. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
export_tensorboard_time_series_data
export_tensorboard_time_series_data(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ExportTensorboardTimeSeriesDataRequest,
dict,
]
] = None,
*,
tensorboard_time_series: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ExportTensorboardTimeSeriesDataPager
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ExportTensorboardTimeSeriesDataRequest, dict]
The request object. Request message for TensorboardService.ExportTensorboardTimeSeriesData. |
tensorboard_time_series |
str
Required. The resource name of the TensorboardTimeSeries to export data from. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ExportTensorboardTimeSeriesDataPager |
Response message for TensorboardService.ExportTensorboardTimeSeriesData. Iterating over this object will yield results and resolve additional pages automatically. |
from_service_account_file
from_service_account_file(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Parameter | |
---|---|
Name | Description |
filename |
str
The path to the service account private key json file. |
Returns | |
---|---|
Type | Description |
TensorboardServiceClient |
The constructed client. |
from_service_account_info
from_service_account_info(info: dict, *args, **kwargs)
Creates an instance of this client using the provided credentials info.
Parameter | |
---|---|
Name | Description |
info |
dict
The service account private key info. |
Returns | |
---|---|
Type | Description |
TensorboardServiceClient |
The constructed client. |
from_service_account_json
from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Parameter | |
---|---|
Name | Description |
filename |
str
The path to the service account private key json file. |
Returns | |
---|---|
Type | Description |
TensorboardServiceClient |
The constructed client. |
get_iam_policy
get_iam_policy(
request: typing.Optional[google.iam.v1.iam_policy_pb2.GetIamPolicyRequest] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.policy_pb2.Policy
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 | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
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 __. |
get_location
get_location(
request: typing.Optional[
google.cloud.location.locations_pb2.GetLocationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.location.locations_pb2.Location
Gets information about a location.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Location object. |
get_mtls_endpoint_and_cert_source
get_mtls_endpoint_and_cert_source(
client_options: typing.Optional[
google.api_core.client_options.ClientOptions
] = None,
)
Deprecated. Return the API endpoint and client cert source for mutual TLS.
The client cert source is determined in the following order:
(1) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is not "true", the
client cert source is None.
(2) if client_options.client_cert_source
is provided, use the provided one; if the
default client cert source exists, use the default one; otherwise the client cert
source is None.
The API endpoint is determined in the following order:
(1) if client_options.api_endpoint
if provided, use the provided one.
(2) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is "always", use the
default mTLS endpoint; if the environment variable is "never", use the default API
endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
use the default API endpoint.
More details can be found at https://google.aip.dev/auth/4114.
Parameter | |
---|---|
Name | Description |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
Exceptions | |
---|---|
Type | Description |
google.auth.exceptions.MutualTLSChannelError |
If any errors happen. |
Returns | |
---|---|
Type | Description |
Tuple[str, Callable[[], Tuple[bytes, bytes]]] |
returns the API endpoint and the client cert source to use. |
get_operation
get_operation(
request: typing.Optional[
google.longrunning.operations_pb2.GetOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.Operation
Gets the latest state of a long-running operation.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
An Operation object. |
get_tensorboard
get_tensorboard(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.GetTensorboardRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.tensorboard.Tensorboard
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardRequest, dict]
The request object. Request message for TensorboardService.GetTensorboard. |
name |
str
Required. The name of the Tensorboard resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_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. |
get_tensorboard_experiment
get_tensorboard_experiment(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.GetTensorboardExperimentRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.tensorboard_experiment.TensorboardExperiment
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardExperimentRequest, dict]
The request object. Request message for TensorboardService.GetTensorboardExperiment. |
name |
str
Required. The name of the TensorboardExperiment resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardExperiment |
A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard. |
get_tensorboard_run
get_tensorboard_run(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.GetTensorboardRunRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.tensorboard_run.TensorboardRun
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardRunRequest, dict]
The request object. Request message for TensorboardService.GetTensorboardRun. |
name |
str
Required. The name of the TensorboardRun resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardRun |
TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc |
get_tensorboard_time_series
get_tensorboard_time_series(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.GetTensorboardTimeSeriesRequest,
dict,
]
] = None,
*,
name: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_time_series.TensorboardTimeSeries
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetTensorboardTimeSeriesRequest, dict]
The request object. Request message for TensorboardService.GetTensorboardTimeSeries. |
name |
str
Required. The name of the TensorboardTimeSeries resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries |
TensorboardTimeSeries maps to times series produced in training runs |
list_locations
list_locations(
request: typing.Optional[
google.cloud.location.locations_pb2.ListLocationsRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.location.locations_pb2.ListLocationsResponse
Lists information about the supported locations for this service.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Response message for ListLocations method. |
list_operations
list_operations(
request: typing.Optional[
google.longrunning.operations_pb2.ListOperationsRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.ListOperationsResponse
Lists operations that match the specified filter in the request.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Response message for ListOperations method. |
list_tensorboard_experiments
list_tensorboard_experiments(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ListTensorboardExperimentsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardExperimentsPager
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardExperimentsRequest, dict]
The request object. Request message for TensorboardService.ListTensorboardExperiments. |
parent |
str
Required. The resource name of the Tensorboard to list TensorboardExperiments. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardExperimentsPager |
Response message for TensorboardService.ListTensorboardExperiments. Iterating over this object will yield results and resolve additional pages automatically. |
list_tensorboard_runs
list_tensorboard_runs(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ListTensorboardRunsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardRunsPager
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardRunsRequest, dict]
The request object. Request message for TensorboardService.ListTensorboardRuns. |
parent |
str
Required. The resource name of the TensorboardExperiment to list TensorboardRuns. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardRunsPager |
Response message for TensorboardService.ListTensorboardRuns. Iterating over this object will yield results and resolve additional pages automatically. |
list_tensorboard_time_series
list_tensorboard_time_series(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ListTensorboardTimeSeriesRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardTimeSeriesPager
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardTimeSeriesRequest, dict]
The request object. Request message for TensorboardService.ListTensorboardTimeSeries. |
parent |
str
Required. The resource name of the TensorboardRun to list TensorboardTimeSeries. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardTimeSeriesPager |
Response message for TensorboardService.ListTensorboardTimeSeries. Iterating over this object will yield results and resolve additional pages automatically. |
list_tensorboards
list_tensorboards(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ListTensorboardsRequest,
dict,
]
] = None,
*,
parent: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardsPager
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListTensorboardsRequest, dict]
The request object. Request message for TensorboardService.ListTensorboards. |
parent |
str
Required. The resource name of the Location to list Tensorboards. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.services.tensorboard_service.pagers.ListTensorboardsPager |
Response message for TensorboardService.ListTensorboards. Iterating over this object will yield results and resolve additional pages automatically. |
parse_common_billing_account_path
parse_common_billing_account_path(path: str) -> typing.Dict[str, str]
Parse a billing_account path into its component segments.
parse_common_folder_path
parse_common_folder_path(path: str) -> typing.Dict[str, str]
Parse a folder path into its component segments.
parse_common_location_path
parse_common_location_path(path: str) -> typing.Dict[str, str]
Parse a location path into its component segments.
parse_common_organization_path
parse_common_organization_path(path: str) -> typing.Dict[str, str]
Parse a organization path into its component segments.
parse_common_project_path
parse_common_project_path(path: str) -> typing.Dict[str, str]
Parse a project path into its component segments.
parse_tensorboard_experiment_path
parse_tensorboard_experiment_path(path: str) -> typing.Dict[str, str]
Parses a tensorboard_experiment path into its component segments.
parse_tensorboard_path
parse_tensorboard_path(path: str) -> typing.Dict[str, str]
Parses a tensorboard path into its component segments.
parse_tensorboard_run_path
parse_tensorboard_run_path(path: str) -> typing.Dict[str, str]
Parses a tensorboard_run path into its component segments.
parse_tensorboard_time_series_path
parse_tensorboard_time_series_path(path: str) -> typing.Dict[str, str]
Parses a tensorboard_time_series path into its component segments.
read_tensorboard_blob_data
read_tensorboard_blob_data(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardBlobDataRequest,
dict,
]
] = None,
*,
time_series: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> typing.Iterable[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardBlobDataResponse
]
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardBlobDataRequest, dict]
The request object. Request message for TensorboardService.ReadTensorboardBlobData. |
time_series |
str
Required. The resource name of the TensorboardTimeSeries to list Blobs. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
Iterable[google.cloud.aiplatform_v1beta1.types.ReadTensorboardBlobDataResponse] |
Response message for TensorboardService.ReadTensorboardBlobData. |
read_tensorboard_size
read_tensorboard_size(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardSizeRequest,
dict,
]
] = None,
*,
tensorboard: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardSizeResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardSizeRequest, dict]
The request object. Request message for TensorboardService.ReadTensorboardSize. |
tensorboard |
str
Required. The name of the Tensorboard resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.ReadTensorboardSizeResponse |
Response message for TensorboardService.ReadTensorboardSize. |
read_tensorboard_time_series_data
read_tensorboard_time_series_data(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardTimeSeriesDataRequest,
dict,
]
] = None,
*,
tensorboard_time_series: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardTimeSeriesDataResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardTimeSeriesDataRequest, dict]
The request object. Request message for TensorboardService.ReadTensorboardTimeSeriesData. |
tensorboard_time_series |
str
Required. The resource name of the TensorboardTimeSeries to read data from. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.ReadTensorboardTimeSeriesDataResponse |
Response message for TensorboardService.ReadTensorboardTimeSeriesData. |
read_tensorboard_usage
read_tensorboard_usage(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardUsageRequest,
dict,
]
] = None,
*,
tensorboard: typing.Optional[str] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.ReadTensorboardUsageResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ReadTensorboardUsageRequest, dict]
The request object. Request message for TensorboardService.ReadTensorboardUsage. |
tensorboard |
str
Required. The name of the Tensorboard resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.ReadTensorboardUsageResponse |
Response message for TensorboardService.ReadTensorboardUsage. |
set_iam_policy
set_iam_policy(
request: typing.Optional[google.iam.v1.iam_policy_pb2.SetIamPolicyRequest] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.policy_pb2.Policy
Sets the IAM access control policy on the specified function.
Replaces any existing policy.
Parameters | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
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 __. |
tensorboard_experiment_path
tensorboard_experiment_path(
project: str, location: str, tensorboard: str, experiment: str
) -> str
Returns a fully-qualified tensorboard_experiment string.
tensorboard_path
tensorboard_path(project: str, location: str, tensorboard: str) -> str
Returns a fully-qualified tensorboard string.
tensorboard_run_path
tensorboard_run_path(
project: str, location: str, tensorboard: str, experiment: str, run: str
) -> str
Returns a fully-qualified tensorboard_run string.
tensorboard_time_series_path
tensorboard_time_series_path(
project: str,
location: str,
tensorboard: str,
experiment: str,
run: str,
time_series: str,
) -> str
Returns a fully-qualified tensorboard_time_series string.
test_iam_permissions
test_iam_permissions(
request: typing.Optional[
google.iam.v1.iam_policy_pb2.TestIamPermissionsRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.iam_policy_pb2.TestIamPermissionsResponse
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 | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
Response message for TestIamPermissions method. |
update_tensorboard
update_tensorboard(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.UpdateTensorboardRequest,
dict,
]
] = None,
*,
tensorboard: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard.Tensorboard
] = None,
update_mask: typing.Optional[google.protobuf.field_mask_pb2.FieldMask] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardRequest, dict]
The request object. Request message for TensorboardService.UpdateTensorboard. |
tensorboard |
google.cloud.aiplatform_v1beta1.types.Tensorboard
Required. The Tensorboard's |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.api_core.operation.Operation |
An object representing a long-running operation. The result type for the operation will be 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. |
update_tensorboard_experiment
update_tensorboard_experiment(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.UpdateTensorboardExperimentRequest,
dict,
]
] = None,
*,
tensorboard_experiment: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard_experiment.TensorboardExperiment
] = None,
update_mask: typing.Optional[google.protobuf.field_mask_pb2.FieldMask] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.tensorboard_experiment.TensorboardExperiment
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardExperimentRequest, dict]
The request object. Request message for TensorboardService.UpdateTensorboardExperiment. |
tensorboard_experiment |
google.cloud.aiplatform_v1beta1.types.TensorboardExperiment
Required. The TensorboardExperiment's |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardExperiment |
A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard. |
update_tensorboard_run
update_tensorboard_run(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.UpdateTensorboardRunRequest,
dict,
]
] = None,
*,
tensorboard_run: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard_run.TensorboardRun
] = None,
update_mask: typing.Optional[google.protobuf.field_mask_pb2.FieldMask] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1beta1.types.tensorboard_run.TensorboardRun
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardRunRequest, dict]
The request object. Request message for TensorboardService.UpdateTensorboardRun. |
tensorboard_run |
google.cloud.aiplatform_v1beta1.types.TensorboardRun
Required. The TensorboardRun's |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardRun |
TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc |
update_tensorboard_time_series
update_tensorboard_time_series(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.UpdateTensorboardTimeSeriesRequest,
dict,
]
] = None,
*,
tensorboard_time_series: typing.Optional[
google.cloud.aiplatform_v1beta1.types.tensorboard_time_series.TensorboardTimeSeries
] = None,
update_mask: typing.Optional[google.protobuf.field_mask_pb2.FieldMask] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_time_series.TensorboardTimeSeries
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateTensorboardTimeSeriesRequest, dict]
The request object. Request message for TensorboardService.UpdateTensorboardTimeSeries. |
tensorboard_time_series |
google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries
Required. The TensorboardTimeSeries' |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.TensorboardTimeSeries |
TensorboardTimeSeries maps to times series produced in training runs |
wait_operation
wait_operation(
request: typing.Optional[
google.longrunning.operations_pb2.WaitOperationRequest
] = None,
*,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.Operation
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 | |
---|---|
Name | Description |
request |
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
|
An Operation object. |
write_tensorboard_experiment_data
write_tensorboard_experiment_data(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.WriteTensorboardExperimentDataRequest,
dict,
]
] = None,
*,
tensorboard_experiment: typing.Optional[str] = None,
write_run_data_requests: typing.Optional[
typing.MutableSequence[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.WriteTensorboardRunDataRequest
]
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.WriteTensorboardExperimentDataResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.WriteTensorboardExperimentDataRequest, dict]
The request object. Request message for TensorboardService.WriteTensorboardExperimentData. |
tensorboard_experiment |
str
Required. The resource name of the TensorboardExperiment to write data to. Format: |
write_run_data_requests |
MutableSequence[google.cloud.aiplatform_v1beta1.types.WriteTensorboardRunDataRequest]
Required. Requests containing per-run TensorboardTimeSeries data to write. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.WriteTensorboardExperimentDataResponse |
Response message for TensorboardService.WriteTensorboardExperimentData. |
write_tensorboard_run_data
write_tensorboard_run_data(
request: typing.Optional[
typing.Union[
google.cloud.aiplatform_v1beta1.types.tensorboard_service.WriteTensorboardRunDataRequest,
dict,
]
] = None,
*,
tensorboard_run: typing.Optional[str] = None,
time_series_data: typing.Optional[
typing.MutableSequence[
google.cloud.aiplatform_v1beta1.types.tensorboard_data.TimeSeriesData
]
] = None,
retry: typing.Optional[
typing.Union[
google.api_core.retry.retry_unary.Retry,
google.api_core.gapic_v1.method._MethodDefault,
]
] = _MethodDefault._DEFAULT_VALUE,
timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
google.cloud.aiplatform_v1beta1.types.tensorboard_service.WriteTensorboardRunDataResponse
)
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 | |
---|---|
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.WriteTensorboardRunDataRequest, dict]
The request object. Request message for TensorboardService.WriteTensorboardRunData. |
tensorboard_run |
str
Required. The resource name of the TensorboardRun to write data to. Format: |
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 |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Returns | |
---|---|
Type | Description |
google.cloud.aiplatform_v1beta1.types.WriteTensorboardRunDataResponse |
Response message for TensorboardService.WriteTensorboardRunData. |