async def sample_create_hyperparameter_tuning_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) hyperparameter_tuning_job = aiplatform_v1beta1.HyperparameterTuningJob() hyperparameter_tuning_job.display_name = "display_name_value" hyperparameter_tuning_job.study_spec.metrics.metric_id = "metric_id_value" hyperparameter_tuning_job.study_spec.metrics.goal = "MINIMIZE" hyperparameter_tuning_job.study_spec.parameters.double_value_spec.min_value = 0.96 hyperparameter_tuning_job.study_spec.parameters.double_value_spec.max_value = 0.962 hyperparameter_tuning_job.study_spec.parameters.parameter_id = "parameter_id_value" hyperparameter_tuning_job.max_trial_count = 1609 hyperparameter_tuning_job.parallel_trial_count = 2128 hyperparameter_tuning_job.trial_job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value" request = aiplatform_v1beta1.CreateHyperparameterTuningJobRequest( parent="parent_value", hyperparameter_tuning_job=hyperparameter_tuning_job, ) # Make the request response = await client.create_hyperparameter_tuning_job(request=request) # Handle the response print(response)
async def sample_cancel_hyperparameter_tuning_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.CancelHyperparameterTuningJobRequest( name="name_value", ) # Make the request await client.cancel_hyperparameter_tuning_job(request=request)
async def sample_get_data_labeling_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.GetDataLabelingJobRequest(name="name_value", ) # Make the request response = await client.get_data_labeling_job(request=request) # Handle the response print(response)
async def sample_list_custom_jobs(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.ListCustomJobsRequest(parent="parent_value", ) # Make the request page_result = client.list_custom_jobs(request=request) # Handle the response async for response in page_result: print(response)
async def sample_get_batch_prediction_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.GetBatchPredictionJobRequest( name="name_value", ) # Make the request response = await client.get_batch_prediction_job(request=request) # Handle the response print(response)
async def sample_get_model_deployment_monitoring_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.GetModelDeploymentMonitoringJobRequest( name="name_value", ) # Make the request response = await client.get_model_deployment_monitoring_job(request=request ) # Handle the response print(response)
async def sample_delete_custom_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1beta1.DeleteCustomJobRequest(name="name_value", ) # Make the request operation = client.delete_custom_job(request=request) print("Waiting for operation to complete...") response = await operation.result() # Handle the response print(response)
async def sample_create_custom_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) custom_job = aiplatform_v1beta1.CustomJob() custom_job.display_name = "display_name_value" custom_job.job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value" request = aiplatform_v1beta1.CreateCustomJobRequest( parent="parent_value", custom_job=custom_job, ) # Make the request response = await client.create_custom_job(request=request) # Handle the response print(response)
async def sample_create_model_deployment_monitoring_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) model_deployment_monitoring_job = aiplatform_v1beta1.ModelDeploymentMonitoringJob() model_deployment_monitoring_job.display_name = "display_name_value" model_deployment_monitoring_job.endpoint = "endpoint_value" request = aiplatform_v1beta1.CreateModelDeploymentMonitoringJobRequest( parent="parent_value", model_deployment_monitoring_job=model_deployment_monitoring_job, ) # Make the request response = await client.create_model_deployment_monitoring_job(request=request) # Handle the response print(response)
async def sample_update_model_deployment_monitoring_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) model_deployment_monitoring_job = aiplatform_v1beta1.ModelDeploymentMonitoringJob( ) model_deployment_monitoring_job.display_name = "display_name_value" model_deployment_monitoring_job.endpoint = "endpoint_value" request = aiplatform_v1beta1.UpdateModelDeploymentMonitoringJobRequest( model_deployment_monitoring_job=model_deployment_monitoring_job, ) # Make the request operation = client.update_model_deployment_monitoring_job(request=request) print("Waiting for operation to complete...") response = await operation.result() # Handle the response print(response)
async def sample_create_data_labeling_job(): # Create a client client = aiplatform_v1beta1.JobServiceAsyncClient() # Initialize request argument(s) data_labeling_job = aiplatform_v1beta1.DataLabelingJob() data_labeling_job.display_name = "display_name_value" data_labeling_job.datasets = ['datasets_value_1', 'datasets_value_2'] data_labeling_job.labeler_count = 1375 data_labeling_job.instruction_uri = "instruction_uri_value" data_labeling_job.inputs_schema_uri = "inputs_schema_uri_value" data_labeling_job.inputs.null_value = "NULL_VALUE" request = aiplatform_v1beta1.CreateDataLabelingJobRequest( parent="parent_value", data_labeling_job=data_labeling_job, ) # Make the request response = await client.create_data_labeling_job(request=request) # Handle the response print(response)