def test_transform_pytorch_vpc_custom_model_bucket(
sagemaker_session,
pytorch_inference_latest_version,
pytorch_inference_latest_py_version,
cpu_instance_type,
custom_bucket_name,
):
data_dir = os.path.join(DATA_DIR, "pytorch_mnist")
ec2_client = sagemaker_session.boto_session.client("ec2")
subnet_ids, security_group_id = get_or_create_vpc_resources(ec2_client)
model_data = sagemaker_session.upload_data(
path=os.path.join(data_dir, "model.tar.gz"),
bucket=custom_bucket_name,
key_prefix="integ-test-data/pytorch_mnist/model",
)
model = PyTorchModel(
model_data=model_data,
entry_point=os.path.join(data_dir, "mnist.py"),
role="SageMakerRole",
framework_version=pytorch_inference_latest_version,
py_version=pytorch_inference_latest_py_version,
sagemaker_session=sagemaker_session,
vpc_config={
"Subnets": subnet_ids,
"SecurityGroupIds": [security_group_id]
},
code_location="s3://{}".format(custom_bucket_name),
)
transform_input = sagemaker_session.upload_data(
path=os.path.join(data_dir, "transform", "data.npy"),
key_prefix="integ-test-data/pytorch_mnist/transform",
)
transformer = model.transformer(1, cpu_instance_type)
transformer.transform(
transform_input,
content_type="application/x-npy",
job_name=unique_name_from_base("test-transform-vpc"),
)
with timeout_and_delete_model_with_transformer(
transformer,
sagemaker_session,
minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
transformer.wait()
model_desc = sagemaker_session.sagemaker_client.describe_model(
ModelName=transformer.model_name)
assert set(subnet_ids) == set(model_desc["VpcConfig"]["Subnets"])
assert [security_group_id
] == model_desc["VpcConfig"]["SecurityGroupIds"]
model_bucket, _ = s3.parse_s3_url(
model_desc["PrimaryContainer"]["ModelDataUrl"])
assert custom_bucket_name == model_bucket
local_input = Path('data/test_AM_image/source_tiles')
for group_path in local_input.iterdir():
for file_path in (group_path / 'source').iterdir():
s3_file_path = input_prefix / group_path.name / file_path.name
print(f'Uploading {file_path} to {s3_file_path}')
s3.upload_file(str(file_path), bucket, str(s3_file_path))
session = Session()
s3_input = f's3://{bucket}/{input_prefix}'
s3_output = f's3://{bucket}/{output_prefix}'
pytorch_model = PyTorchModel(model_data='s3://am-segm/unet.tar.gz',
image='236062312728.dkr.ecr.eu-west-1.amazonaws.com/intsco/am-segm',
role='AM-SegmSageMakerRole',
entry_point='sagemaker/main.py',
sagemaker_session=session)
transformer = pytorch_model.transformer(instance_count=3,
instance_type='ml.c4.xlarge',
# instance_type='ml.p2.xlarge',
output_path=s3_output,
accept='application/x-image',
strategy='SingleRecord',
env={'MODEL_SERVER_TIMEOUT': '180'})
start = time()
transformer.transform(data=s3_input,
data_type='S3Prefix',
content_type='application/x-image')
transformer.wait()
print('{} min {} sec'.format(*divmod(int(time() - start), 60)))
