def deploy(
obj,
s3_model_location,
num_instances,
ec2_type,
aws_tags,
iam_role_arn,
external_id,
endpoint_name
):
"""
Command to deploy ML model(s) on SageMaker
"""
logger.info(ASCII_LOGO)
logger.info("Started deployment on SageMaker ...\n")
try:
endpoint_name = api_cloud.deploy(
dir=_config().sagify_module_dir,
s3_model_location=s3_model_location,
num_instances=num_instances,
ec2_type=ec2_type,
docker_tag=obj['docker_tag'],
aws_role=iam_role_arn,
external_id=external_id,
tags=aws_tags,
endpoint_name=endpoint_name
)
logger.info("Model deployed to SageMaker successfully")
logger.info("Endpoint name: {}".format(endpoint_name))
except ValueError as e:
logger.info("{}".format(e))
sys.exit(-1)
def deploy(obj, dir, s3_model_location, model_name, num_instances, ec2_type,
aws_tags, vpc_configs):
"""
Command to deploy ML model(s) on SageMaker
"""
logger.info(ASCII_LOGO)
logger.info("Started deployment on SageMaker ...\n")
try:
endpoint_name = api_cloud.deploy(dir=dir,
s3_model_location=s3_model_location,
model_name=model_name,
vpc_configs=vpc_configs,
num_instances=num_instances,
ec2_type=ec2_type,
docker_tag=obj['docker_tag'],
tags=aws_tags)
logger.info("Model deployed to SageMaker successfully")
logger.info("Endpoint name: {}".format(endpoint_name))
except ValueError as e:
logger.info("{}".format(e))
sys.exit(-1)