def main(argv=None):
parser = argparse.ArgumentParser(description='SageMaker Training Job')
parser.add_argument('--region',
type=str,
help='The region where the cluster launches.')
parser.add_argument(
'--image',
type=str,
help=
'The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.'
)
parser.add_argument('--model_artifact_url',
type=str,
help='S3 model artifacts url')
parser.add_argument('--model_name',
type=str,
help='The name of the new model.')
parser.add_argument(
'--role',
type=str,
help=
'The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.'
)
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_client(args.region)
logging.info('Submitting model creation request to SageMaker...')
_utils.create_model(client, args.model_artifact_url, args.model_name,
args.image, args.role)
logging.info('Model creation completed.')
with open('/tmp/model_name.txt', 'w') as f:
f.write(args.model_name)
def test_sagemaker_exception_in_create_model(self):
mock_client = MagicMock()
mock_exception = ClientError({"Error": {"Message": "SageMaker broke"}}, "create_model")
mock_client.create_model.side_effect = mock_exception
mock_args = self.parser.parse_args(required_args)
with self.assertRaises(Exception):
_utils.create_model(mock_client, vars(mock_args))
def main(argv=None):
parser = create_parser()
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting model creation request to SageMaker...')
_utils.create_model(client, vars(args))
logging.info('Model creation completed.')
with open('/tmp/model_name.txt', 'w') as f:
f.write(args.model_name)
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting model creation request to SageMaker...')
_utils.create_model(client, vars(args))
logging.info('Model creation completed.')
_utils.write_output(args.model_name_output_path, args.model_name)
def test_create_model(self):
mock_client = MagicMock()
mock_args = self.parser.parse_args(required_args)
response = _utils.create_model(mock_client, vars(mock_args))
mock_client.create_model.assert_called_once_with(
EnableNetworkIsolation=True,
ExecutionRoleArn='arn:aws:iam::123456789012:user/Development/product_1234/*',
ModelName='model_test',
PrimaryContainer={'Image': 'test-image', 'ModelDataUrl': 's3://fake-bucket/model_artifact', 'Environment': {}},
Tags=[]
)
def main(argv=None):
parser = argparse.ArgumentParser(description='SageMaker Training Job')
parser.add_argument('--region',
type=str.strip,
required=True,
help='The region where the cluster launches.')
parser.add_argument('--model_name',
type=str.strip,
required=True,
help='The name of the new model.')
parser.add_argument(
'--role',
type=str.strip,
required=True,
help=
'The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.'
)
parser.add_argument(
'--container_host_name',
type=str.strip,
required=False,
help=
'When a ContainerDefinition is part of an inference pipeline, this value uniquely identifies the container for the purposes of logging and metrics.',
default='')
parser.add_argument(
'--image',
type=str.strip,
required=False,
help=
'The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.',
default='')
parser.add_argument(
'--model_artifact_url',
type=str.strip,
required=False,
help='S3 path where Amazon SageMaker to store the model artifacts.',
default='')
parser.add_argument(
'--environment',
type=_utils.str_to_json_dict,
required=False,
help=
'The dictionary of the environment variables to set in the Docker container. Up to 16 key-value entries in the map.',
default='{}')
parser.add_argument(
'--model_package',
type=str.strip,
required=False,
help=
'The name or Amazon Resource Name (ARN) of the model package to use to create the model.',
default='')
parser.add_argument(
'--secondary_containers',
type=_utils.str_to_json_list,
required=False,
help=
'A list of dicts that specifies the additional containers in the inference pipeline.',
default='{}')
parser.add_argument(
'--vpc_security_group_ids',
type=str.strip,
required=False,
help='The VPC security group IDs, in the form sg-xxxxxxxx.',
default='')
parser.add_argument(
'--vpc_subnets',
type=str.strip,
required=False,
help=
'The ID of the subnets in the VPC to which you want to connect your hpo job.',
default='')
parser.add_argument('--network_isolation',
type=_utils.str_to_bool,
required=False,
help='Isolates the training container.',
default=True)
parser.add_argument(
'--tags',
type=_utils.str_to_json_dict,
required=False,
help='An array of key-value pairs, to categorize AWS resources.',
default='{}')
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_client(args.region)
logging.info('Submitting model creation request to SageMaker...')
_utils.create_model(client, vars(args))
logging.info('Model creation completed.')
with open('/tmp/model_name.txt', 'w') as f:
f.write(args.model_name)
