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)
