def run_as_local_main():
args = parse_infer_args()
sm_boto3 = boto3.client('sagemaker')
sess = sagemaker.Session()
region = sess.boto_session.region_name
model_url = args.model_file
model = PyTorchModel(model_data=model_url,
source_dir=os.path.abspath(os.path.dirname(__file__)),
role=get_sm_execution_role(ON_SAGEMAKER_NOTEBOOK,
region),
framework_version='1.0.0',
entry_point='inference.py')
infer_mode = args.infer_mode
if 'bt' == infer_mode:
env = {'MODEL_SERVER_TIMEOUT': '120'}
transformer = model.transformer(
instance_count=1,
instance_type='ml.c5.xlarge',
output_path=args.output_dir,
max_payload=99,
env=env,
max_concurrent_transforms=1,
tags=[{
"Key": "Project",
"Value": "SM Example"
}],
)
transformer.transform(args.input_file, content_type="text/csv")
transformer.wait()
elif 'ep' == infer_mode:
model.deploy(instance_type='ml.c5.xlarge', initial_instance_count=1)
else:
raise Exception(f'Unknown inference mode {infer_mode}')
def batch_inference(session, client, model_name, setting, pytorch):
sagemaker_session = sagemaker.Session(boto_session=session,
sagemaker_client=client)
conf = yaml.load(open(setting))
# check the target model exists
if _model_exists(client, model_name):
logger.info('use the registered model.')
deploy_args = conf['deploy']
deploy_args['model_name'] = model_name
deploy_args['base_transform_job_name'] = model_name
deploy_args['sagemaker_session'] = sagemaker_session
transformer = Transformer(**deploy_args)
else:
# [TODO] updateing case (delete and create).
# Basically, models have dependencies on multiple endpoints and inference jobs,
# so it is not easy to delete it.
logger.info('register the new model.')
model_args = conf['model']
model_args['sagemaker_session'] = sagemaker_session
model_args['name'] = model_name
if pytorch:
model = PyTorchModel(**model_args)
else:
model = ChainerModel(**model_args)
deploy_args = conf['deploy']
transformer = model.transformer(**deploy_args) # register model
transform_args = conf['transform']
# use default job_name (model_name + datetime.now())
transformer.transform(**transform_args)