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)
