def sample_get_tensorboard(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) request = aiplatform_v1.GetTensorboardRequest(name="name_value", ) # Make the request response = client.get_tensorboard(request=request) # Handle the response print(response)
def sample_update_tensorboard_experiment(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) request = aiplatform_v1.UpdateTensorboardExperimentRequest() # Make the request response = client.update_tensorboard_experiment(request=request) # Handle the response print(response)
def sample_read_tensorboard_time_series_data(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) request = aiplatform_v1.ReadTensorboardTimeSeriesDataRequest( tensorboard_time_series="tensorboard_time_series_value", ) # Make the request response = client.read_tensorboard_time_series_data(request=request) # Handle the response print(response)
def sample_list_tensorboard_runs(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) request = aiplatform_v1.ListTensorboardRunsRequest(parent="parent_value", ) # Make the request page_result = client.list_tensorboard_runs(request=request) # Handle the response for response in page_result: print(response)
def sample_export_tensorboard_time_series_data(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) request = aiplatform_v1.ExportTensorboardTimeSeriesDataRequest( tensorboard_time_series="tensorboard_time_series_value", ) # Make the request page_result = client.export_tensorboard_time_series_data(request=request) # Handle the response for response in page_result: print(response)
def sample_create_tensorboard_experiment(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) request = aiplatform_v1.CreateTensorboardExperimentRequest( parent="parent_value", tensorboard_experiment_id="tensorboard_experiment_id_value", ) # Make the request response = client.create_tensorboard_experiment(request=request) # Handle the response print(response)
def sample_delete_tensorboard_run(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) request = aiplatform_v1.DeleteTensorboardRunRequest(name="name_value", ) # Make the request operation = client.delete_tensorboard_run(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response)
def sample_update_tensorboard_run(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) tensorboard_run = aiplatform_v1.TensorboardRun() tensorboard_run.display_name = "display_name_value" request = aiplatform_v1.UpdateTensorboardRunRequest( tensorboard_run=tensorboard_run, ) # Make the request response = client.update_tensorboard_run(request=request) # Handle the response print(response)
def sample_update_tensorboard_time_series(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) tensorboard_time_series = aiplatform_v1.TensorboardTimeSeries() tensorboard_time_series.display_name = "display_name_value" tensorboard_time_series.value_type = "BLOB_SEQUENCE" request = aiplatform_v1.UpdateTensorboardTimeSeriesRequest( tensorboard_time_series=tensorboard_time_series, ) # Make the request response = client.update_tensorboard_time_series(request=request) # Handle the response print(response)
def sample_write_tensorboard_run_data(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) time_series_data = aiplatform_v1.TimeSeriesData() time_series_data.tensorboard_time_series_id = "tensorboard_time_series_id_value" time_series_data.value_type = "BLOB_SEQUENCE" request = aiplatform_v1.WriteTensorboardRunDataRequest( tensorboard_run="tensorboard_run_value", time_series_data=time_series_data, ) # Make the request response = client.write_tensorboard_run_data(request=request) # Handle the response print(response)
def sample_update_tensorboard(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) tensorboard = aiplatform_v1.Tensorboard() tensorboard.display_name = "display_name_value" request = aiplatform_v1.UpdateTensorboardRequest(tensorboard=tensorboard, ) # Make the request operation = client.update_tensorboard(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response)
def sample_batch_create_tensorboard_runs(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) requests = aiplatform_v1.CreateTensorboardRunRequest() requests.parent = "parent_value" requests.tensorboard_run.display_name = "display_name_value" requests.tensorboard_run_id = "tensorboard_run_id_value" request = aiplatform_v1.BatchCreateTensorboardRunsRequest( parent="parent_value", requests=requests, ) # Make the request response = client.batch_create_tensorboard_runs(request=request) # Handle the response print(response)
def sample_batch_create_tensorboard_time_series(): # Create a client client = aiplatform_v1.TensorboardServiceClient() # Initialize request argument(s) requests = aiplatform_v1.CreateTensorboardTimeSeriesRequest() requests.parent = "parent_value" requests.tensorboard_time_series.display_name = "display_name_value" requests.tensorboard_time_series.value_type = "BLOB_SEQUENCE" request = aiplatform_v1.BatchCreateTensorboardTimeSeriesRequest( parent="parent_value", requests=requests, ) # Make the request response = client.batch_create_tensorboard_time_series(request=request) # Handle the response print(response)