def test_transformer_implementation():
test.create_resource_config()
test.create_input_data_config()
test.create_hyperparameters_config({"sagemaker_program": "user_script.py"})
model_path = os.path.join(env.model_dir, "fake_ml_model")
fake_ml_framework.Model(weights=[6, 9, 42]).save(model_path)
transform = transformer.Transformer(model_fn=model_fn,
predict_fn=predict_fn)
transform.initialize()
with worker.Worker(transform_fn=transform.transform,
module_name="fake_ml_model").test_client() as client:
payload = [6, 9, 42.0]
response = post(client, payload, content_types.NPY, content_types.JSON)
assert response.status_code == http_client.OK
assert response.get_data(as_text=True) == "[36.0, 81.0, 1764.0]"
response = post(client, payload, content_types.JSON, content_types.CSV)
assert response.status_code == http_client.OK
assert response.get_data(as_text=True) == "36.0\n81.0\n1764.0\n"
response = post(client, payload, content_types.CSV, content_types.NPY)
assert response.status_code == http_client.OK
response_data = encoders.npy_to_numpy(response.get_data())
np.testing.assert_array_almost_equal(response_data,
np.asarray([36.0, 81.0, 1764.0]))
def _user_module_transformer(user_module):
model_fn = getattr(user_module, 'model_fn', default_model_fn)
input_fn = getattr(user_module, 'input_fn', default_input_fn)
predict_fn = getattr(user_module, 'predict_fn', default_predict_fn)
output_fn = getattr(user_module, 'output_fn', default_output_fn)
return transformer.Transformer(model_fn=model_fn, input_fn=input_fn, predict_fn=predict_fn,
output_fn=output_fn)
def _user_module_transformer(user_module):
model_fn = getattr(user_module, "model_fn", default_model_fn)
input_fn = getattr(user_module, "input_fn", None)
predict_fn = getattr(user_module, "predict_fn", None)
output_fn = getattr(user_module, "output_fn", None)
transform_fn = getattr(user_module, "transform_fn", None)
if transform_fn and (input_fn or predict_fn or output_fn):
raise exc.UserError(
"Cannot use transform_fn implementation with input_fn, predict_fn, and/or output_fn"
)
if transform_fn is not None:
return transformer.Transformer(model_fn=model_fn, transform_fn=transform_fn)
else:
return transformer.Transformer(
model_fn=model_fn,
input_fn=input_fn or default_input_fn,
predict_fn=default_predict_fn,
output_fn=output_fn or default_output_fn,
)
def _transformer_with_transform_fn(model_fn, transform_fn):
user_transformer = transformer.Transformer(model_fn=model_fn,
transform_fn=transform_fn)
user_transformer.initialize()
return user_transformer
