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 = SKLearnModel(model_data=model_url,
source_dir=os.path.abspath(os.path.dirname(__file__)),
role=get_sm_execution_role(ON_SAGEMAKER_NOTEBOOK,
region),
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}')
# accept = 'text/csv')
model = SKLearnModel(
model_data=model_url,
source_dir=os.path.abspath(os.path.dirname(__file__)),
role=get_sm_execution_role(False, region),
entry_point='infer_preproc.py')
infer_mode = args.infer_mode
if 'bt' == infer_mode:
env = {'MODEL_SERVER_TIMEOUT':'120'}
transformer = model.transformer(
instance_count=1,
instance_type='ml.m4.xlarge',
output_path=args.output_dir,
assemble_with='Line',
max_payload=99,
accept='text/csv',
env=env,
max_concurrent_transforms=1,
tags=[{"Key": "Project", "Value": "SM Example"}],
)
transformer.transform(args.input_file, content_type="text/csv")
transformer.wait()
preprocessed_train = transformer.output_path
print(f'Preprocessed data at {preprocessed_train}')
elif 'ep' == infer_mode:
model.deploy(instance_type='ml.c5.xlarge', initial_instance_count=1)
else:
raise Exception(f'Unknown inference mode {infer_mode}')