def invocations():
payload = flask.request.data
if len(payload) == 0:
return flask.Response(response="", status=http.client.NO_CONTENT)
try:
dtest, content_type = serve_utils.parse_content_data(
payload, flask.request.content_type)
except Exception as e:
logging.exception(e)
return flask.Response(response=str(e),
status=http.client.UNSUPPORTED_MEDIA_TYPE)
try:
format = ScoringService.load_model()
except Exception as e:
logging.exception(e)
return flask.Response(response="Unable to load model: %s" % e,
status=http.client.INTERNAL_SERVER_ERROR)
try:
preds = ScoringService.predict(data=dtest,
content_type=content_type,
model_format=format)
except Exception as e:
logging.exception(e)
return flask.Response(
response="Unable to evaluate payload provided: %s" % e,
status=http.client.BAD_REQUEST)
if serve_utils.is_selectable_inference_output():
try:
accept = _parse_accept(flask.request)
except Exception as e:
logging.exception(e)
return flask.Response(response=str(e),
status=http.client.NOT_ACCEPTABLE)
return _handle_selectable_inference_response(preds, accept)
if SAGEMAKER_BATCH:
return_data = "\n".join(map(str, preds.tolist())) + '\n'
else:
return_data = ",".join(map(str, preds.tolist()))
return flask.Response(response=return_data,
status=http.client.OK,
mimetype="text/csv")
def default_input_fn(self, input_data, input_content_type):
"""Take request data and de-serializes the data into an object for prediction.
When an InvokeEndpoint operation is made against an Endpoint running SageMaker model server,
the model server receives two pieces of information:
- The request Content-Type, for example "application/json"
- The request data, which is at most 5 MB (5 * 1024 * 1024 bytes) in size.
The input_fn is responsible to take the request data and pre-process it before prediction.
Args:
input_data (obj): the request data.
input_content_type (str): the request Content-Type. XGBoost accepts CSV, LIBSVM, and RECORDIO-PROTOBUF.
Returns:
(obj): data ready for prediction. For XGBoost, this defaults to DMatrix.
"""
if len(input_data) == 0:
raise NoContentInferenceError()
dtest, content_type = serve_utils.parse_content_data(input_data, input_content_type)
return dtest, content_type
def test_parse_libsvm_data(libsvm_content_type):
data_payload = b'0:1'
parsed_payload, parsed_content_type = serve_utils.parse_content_data(data_payload, libsvm_content_type)
assert parsed_content_type == data_utils.LIBSVM
def test_incorrect_content_type(incorrect_content_type):
data_payload = '0'
with pytest.raises(exc.UserError):
serve_utils.parse_content_data(data_payload, incorrect_content_type)
def test_parse_csv_data(csv_content_type):
data_payload = b'1,1'
parsed_payload, parsed_content_type = serve_utils.parse_content_data(data_payload, csv_content_type)
assert parsed_content_type == data_utils.CSV