async def sample_update_tensorboard_experiment(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.UpdateTensorboardExperimentRequest() # Make the request response = await client.update_tensorboard_experiment(request=request) # Handle the response print(response)
async def sample_get_tensorboard(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.GetTensorboardRequest(name="name_value", ) # Make the request response = await client.get_tensorboard(request=request) # Handle the response print(response)
async def sample_read_tensorboard_time_series_data(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.ReadTensorboardTimeSeriesDataRequest( tensorboard_time_series="tensorboard_time_series_value", ) # Make the request response = await client.read_tensorboard_time_series_data(request=request) # Handle the response print(response)
async def sample_list_tensorboards(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.ListTensorboardsRequest( parent="parent_value", ) # Make the request page_result = client.list_tensorboards(request=request) # Handle the response async for response in page_result: print(response)
async def sample_export_tensorboard_time_series_data(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.ExportTensorboardTimeSeriesDataRequest( tensorboard_time_series="tensorboard_time_series_value", ) # Make the request page_result = client.export_tensorboard_time_series_data(request=request) # Handle the response async for response in page_result: print(response)
async def sample_create_tensorboard_experiment(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.CreateTensorboardExperimentRequest( parent="parent_value", tensorboard_experiment_id="tensorboard_experiment_id_value", ) # Make the request response = await client.create_tensorboard_experiment(request=request) # Handle the response print(response)
async def sample_delete_tensorboard(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.DeleteTensorboardRequest(name="name_value", ) # Make the request operation = client.delete_tensorboard(request=request) print("Waiting for operation to complete...") response = await operation.result() # Handle the response print(response)
async def sample_update_tensorboard_run(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) tensorboard_run = aiplatform_v1beta1.TensorboardRun() tensorboard_run.display_name = "display_name_value" request = aiplatform_v1beta1.UpdateTensorboardRunRequest( tensorboard_run=tensorboard_run, ) # Make the request response = await client.update_tensorboard_run(request=request) # Handle the response print(response)
async def sample_update_tensorboard_time_series(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) tensorboard_time_series = aiplatform_v1beta1.TensorboardTimeSeries() tensorboard_time_series.display_name = "display_name_value" tensorboard_time_series.value_type = "BLOB_SEQUENCE" request = aiplatform_v1beta1.UpdateTensorboardTimeSeriesRequest( tensorboard_time_series=tensorboard_time_series, ) # Make the request response = await client.update_tensorboard_time_series(request=request) # Handle the response print(response)
async def sample_write_tensorboard_run_data(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) time_series_data = aiplatform_v1beta1.TimeSeriesData() time_series_data.tensorboard_time_series_id = "tensorboard_time_series_id_value" time_series_data.value_type = "BLOB_SEQUENCE" request = aiplatform_v1beta1.WriteTensorboardRunDataRequest( tensorboard_run="tensorboard_run_value", time_series_data=time_series_data, ) # Make the request response = await client.write_tensorboard_run_data(request=request) # Handle the response print(response)
async def sample_batch_create_tensorboard_runs(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) requests = aiplatform_v1beta1.CreateTensorboardRunRequest() requests.parent = "parent_value" requests.tensorboard_run.display_name = "display_name_value" requests.tensorboard_run_id = "tensorboard_run_id_value" request = aiplatform_v1beta1.BatchCreateTensorboardRunsRequest( parent="parent_value", requests=requests, ) # Make the request response = await client.batch_create_tensorboard_runs(request=request) # Handle the response print(response)
async def sample_batch_create_tensorboard_time_series(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) requests = aiplatform_v1beta1.CreateTensorboardTimeSeriesRequest() requests.parent = "parent_value" requests.tensorboard_time_series.display_name = "display_name_value" requests.tensorboard_time_series.value_type = "BLOB_SEQUENCE" request = aiplatform_v1beta1.BatchCreateTensorboardTimeSeriesRequest( parent="parent_value", requests=requests, ) # Make the request response = await client.batch_create_tensorboard_time_series(request=request) # Handle the response print(response)
async def sample_create_tensorboard(): # Create a client client = aiplatform_v1beta1.TensorboardServiceAsyncClient() # Initialize request argument(s) tensorboard = aiplatform_v1beta1.Tensorboard() tensorboard.display_name = "display_name_value" request = aiplatform_v1beta1.CreateTensorboardRequest( parent="parent_value", tensorboard=tensorboard, ) # Make the request operation = client.create_tensorboard(request=request) print("Waiting for operation to complete...") response = await operation.result() # Handle the response print(response)