def test_tfs_model(region, boto_session, sagemaker_client,
sagemaker_runtime_client, model_name, tfs_model,
image_uri, instance_type, accelerator_type):
input_data = {'instances': [1.0, 2.0, 5.0]}
util.create_and_invoke_endpoint(region, boto_session, sagemaker_client,
sagemaker_runtime_client, model_name, tfs_model,
image_uri, instance_type, accelerator_type, input_data)
def test_invoke_endpoint(boto_session, sagemaker_client,
sagemaker_runtime_client, model_name, model_data,
image_uri, instance_type, accelerator_type,
input_data):
util.create_and_invoke_endpoint(boto_session, sagemaker_client,
sagemaker_runtime_client, model_name,
model_data, image_uri, instance_type,
accelerator_type, input_data)
def test_python_model_with_lib(region, boto_session, sagemaker_client,
sagemaker_runtime_client, model_name, python_model_with_lib,
image_uri, instance_type, accelerator_type):
if 'p3' in instance_type:
pytest.skip('skip for p3 instance')
# the python service needs to transform this to get a valid prediction
input_data = {'x': [1.0, 2.0, 5.0]}
output_data = util.create_and_invoke_endpoint(region, boto_session, sagemaker_client,
sagemaker_runtime_client, model_name, python_model_with_lib,
image_uri, instance_type, accelerator_type, input_data)
# python service adds this to tfs response
assert output_data['python'] is True
assert output_data['dummy_module'] == '0.1'
def test_python_model_with_lib(boto_session, sagemaker_client,
sagemaker_runtime_client, model_name,
python_model_with_lib, image_uri, instance_type,
accelerator_type):
if "p3" in instance_type:
pytest.skip("skip for p3 instance")
# the python service needs to transform this to get a valid prediction
input_data = {"x": [1.0, 2.0, 5.0]}
output_data = util.create_and_invoke_endpoint(
boto_session, sagemaker_client, sagemaker_runtime_client, model_name,
python_model_with_lib, image_uri, instance_type, accelerator_type,
input_data)
# python service adds this to tfs response
assert output_data["python"] is True
assert output_data["dummy_module"] == "0.1"