Adding HPO unit test (#3791)

* Adding HPO unit test

* Adding best training job

* Addressing comment

components/aws/sagemaker/common/_utils.py

@@ -592,7 +592,7 @@ def create_hyperparameter_tuning_job(client, args):
     """Create a Sagemaker HPO job"""
     request = create_hyperparameter_tuning_job_request(args)
     try:
-        job_arn = client.create_hyper_parameter_tuning_job(**request)
+        client.create_hyper_parameter_tuning_job(**request)
         hpo_job_name = request['HyperParameterTuningJobName']
         logging.info("Created Hyperparameter Training Job with name: " + hpo_job_name)
         logging.info("HPO job in SageMaker: https://{}.console.aws.amazon.com/sagemaker/home?region={}#/hyper-tuning-jobs/{}"
@@ -604,7 +604,7 @@ def create_hyperparameter_tuning_job(client, args):
         raise Exception(e.response['Error']['Message'])
 
 
-def wait_for_hyperparameter_training_job(client, hpo_job_name):
+def wait_for_hyperparameter_training_job(client, hpo_job_name, poll_interval=30):
     ### Wait until the job finishes
     while(True):
         response = client.describe_hyper_parameter_tuning_job(HyperParameterTuningJobName=hpo_job_name)
@@ -617,7 +617,7 @@ def wait_for_hyperparameter_training_job(client, hpo_job_name):
             logging.error('Hyperparameter tuning failed with the following error: {}'.format(message))
             raise Exception('Hyperparameter tuning job failed')
         logging.info("Hyperparameter tuning job is still in status: " + status)
-        time.sleep(30)
+        time.sleep(poll_interval)
 
 
 def get_best_training_job_and_hyperparameters(client, hpo_job_name):
@@ -880,4 +880,4 @@ def yaml_or_json_str(str):
 def str_to_bool(str):
     # This distutils function returns an integer representation of the boolean
     # rather than a True/False value. This simply hard casts it.
-    return bool(strtobool(str))
+    return bool(strtobool(str))

components/aws/sagemaker/hyperparameter_tuning/src/hyperparameter_tuning.py

@@ -22,7 +22,7 @@ def create_parser():
 
   parser.add_argument('--job_name', type=str, required=False, help='The name of the tuning job. Must be unique within the same AWS account and AWS region.')
   parser.add_argument('--role', type=str, required=True, help='The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.')
-  parser.add_argument('--image', type=str, required=True, help='The registry path of the Docker image that contains the training algorithm.', default='')
+  parser.add_argument('--image', type=str, required=False, help='The registry path of the Docker image that contains the training algorithm.', default='')
   parser.add_argument('--algorithm_name', type=str, required=False, help='The name of the resource algorithm to use for the hyperparameter tuning job.', default='')
   parser.add_argument('--training_input_mode', choices=['File', 'Pipe'], type=str, required=False, help='The input mode that the algorithm supports. File or Pipe.', default='File')
   parser.add_argument('--metric_definitions', type=_utils.yaml_or_json_str, required=False, help='The dictionary of name-regex pairs specify the metrics that the algorithm emits.', default={})
@@ -63,7 +63,7 @@ def create_parser():
 
 def main(argv=None):
   parser = create_parser()
-  args = parser.parse_args()
+  args = parser.parse_args(argv)
 
   logging.getLogger().setLevel(logging.INFO)
   client = _utils.get_sagemaker_client(args.region)
@@ -90,4 +90,4 @@ def main(argv=None):
 
 
 if __name__== "__main__":
-  main()
+  main(sys.argv[1:])