def predict(app_name, api_name):
try:
payload = request.get_json()
except Exception as e:
return "Malformed JSON", status.HTTP_400_BAD_REQUEST
model = local_cache["model"]
api = local_cache["api"]
response = {}
if not util.is_dict(payload) or "samples" not in payload:
util.log_pretty(payload, logging_func=logger.error)
return prediction_failed(payload, "top level `samples` key not found in request")
logger.info("Predicting " + util.pluralize(len(payload["samples"]), "sample", "samples"))
predictions = []
samples = payload["samples"]
if not util.is_list(samples):
util.log_pretty(samples, logging_func=logger.error)
return prediction_failed(
payload, "expected the value of key `samples` to be a list of json objects"
)
for i, sample in enumerate(payload["samples"]):
util.log_indent("sample {}".format(i + 1), 2)
is_valid, reason = is_valid_sample(sample)
if not is_valid:
return prediction_failed(sample, reason)
for column in local_cache["required_inputs"]:
sample[column["name"]] = util.upcast(sample[column["name"]], column["type"])
try:
result = run_predict(sample)
except CortexException as e:
e.wrap("error", "sample {}".format(i + 1))
logger.error(str(e))
logger.exception(
"An error occurred, see `cx logs api {}` for more details.".format(api["name"])
)
return prediction_failed(sample, str(e))
except Exception as e:
logger.exception(
"An error occurred, see `cx logs api {}` for more details.".format(api["name"])
)
return prediction_failed(sample, str(e))
predictions.append(result)
if model["type"] == "regression":
response["regression_predictions"] = predictions
if model["type"] == "classification":
response["classification_predictions"] = predictions
response["resource_id"] = api["id"]
return jsonify(response)
def run_predict(sample):
request_handler = local_cache.get("request_handler")
prepared_sample = sample
if request_handler is not None and util.has_function(
request_handler, "pre_inference"):
prepared_sample = request_handler.pre_inference(
sample, local_cache["metadata"]["signatureDef"])
if util.is_resource_ref(local_cache["api"]["model"]):
transformed_sample = transform_sample(prepared_sample)
prediction_request = create_prediction_request(transformed_sample)
response_proto = local_cache["stub"].Predict(prediction_request,
timeout=10.0)
result = parse_response_proto(response_proto)
util.log_indent("Raw sample:", indent=4)
util.log_pretty(sample, indent=6)
util.log_indent("Transformed sample:", indent=4)
util.log_pretty(transformed_sample, indent=6)
util.log_indent("Prediction:", indent=4)
util.log_pretty(result, indent=6)
result["transformed_sample"] = transformed_sample
else:
prediction_request = create_raw_prediction_request(prepared_sample)
response_proto = local_cache["stub"].Predict(prediction_request,
timeout=10.0)
result = parse_response_proto_raw(response_proto)
util.log_indent("Sample:", indent=4)
util.log_pretty(sample, indent=6)
util.log_indent("Prediction:", indent=4)
util.log_pretty(result, indent=6)
if request_handler is not None and util.has_function(
request_handler, "post_inference"):
result = request_handler.post_inference(
result, local_cache["metadata"]["signatureDef"])
return result
def run_predict(sample):
transformed_sample = transform_sample(sample)
prediction_request = create_prediction_request(transformed_sample)
response_proto = local_cache["stub"].Predict(prediction_request, timeout=10.0)
result = parse_response_proto(response_proto)
util.log_indent("Raw sample:", indent=4)
util.log_pretty(sample, indent=6)
util.log_indent("Transformed sample:", indent=4)
util.log_pretty(transformed_sample, indent=6)
util.log_indent("Prediction:", indent=4)
util.log_pretty(result, indent=6)
return result
def run_predict(raw_features):
transformed_features = transform_features(raw_features)
prediction_request = create_prediction_request(transformed_features)
response_proto = local_cache["stub"].Predict(prediction_request,
timeout=10.0)
result = parse_response_proto(response_proto)
util.log_indent("Raw features:", indent=4)
util.log_pretty(raw_features, indent=6)
util.log_indent("Transformed features:", indent=4)
util.log_pretty(transformed_features, indent=6)
util.log_indent("Prediction:", indent=4)
util.log_pretty(result, indent=6)
return result
def predict(deployment_name, api_name):
try:
payload = request.get_json()
except Exception as e:
return "Malformed JSON", status.HTTP_400_BAD_REQUEST
ctx = local_cache["ctx"]
api = local_cache["api"]
response = {}
if not util.is_dict(payload) or "samples" not in payload:
util.log_pretty(payload, logging_func=logger.error)
return prediction_failed(
payload, "top level `samples` key not found in request")
logger.info("Predicting " +
util.pluralize(len(payload["samples"]), "sample", "samples"))
predictions = []
samples = payload["samples"]
if not util.is_list(samples):
util.log_pretty(samples, logging_func=logger.error)
return prediction_failed(
payload,
"expected the value of key `samples` to be a list of json objects")
for i, sample in enumerate(payload["samples"]):
util.log_indent("sample {}".format(i + 1), 2)
if util.is_resource_ref(api["model"]):
is_valid, reason = is_valid_sample(sample)
if not is_valid:
return prediction_failed(sample, reason)
for column in local_cache["required_inputs"]:
column_type = ctx.get_inferred_column_type(column["name"])
sample[column["name"]] = util.upcast(sample[column["name"]],
column_type)
try:
result = run_predict(sample)
except CortexException as e:
e.wrap("error", "sample {}".format(i + 1))
logger.error(str(e))
logger.exception(
"An error occurred, see `cortex logs -v api {}` for more details."
.format(api["name"]))
return prediction_failed(sample, str(e))
except Exception as e:
logger.exception(
"An error occurred, see `cortex logs -v api {}` for more details."
.format(api["name"]))
# Show signature def for external models (since we don't validate input)
schemaStr = ""
signature_def = local_cache["metadata"]["signatureDef"]
if (not util.is_resource_ref(api["model"]) and
signature_def.get("predict") is not None # Just to be safe
and signature_def["predict"].get("inputs") is
not None # Just to be safe
):
schemaStr = "\n\nExpected shema:\n" + util.pp_str(
signature_def["predict"]["inputs"])
return prediction_failed(sample, str(e) + schemaStr)
predictions.append(result)
response["predictions"] = predictions
response["resource_id"] = api["id"]
return jsonify(response)
def predict(app_name, api_name):
try:
payload = request.get_json()
except Exception as e:
return "Malformed JSON", status.HTTP_400_BAD_REQUEST
sess = local_cache["sess"]
api = local_cache["api"]
request_handler = local_cache.get("request_handler")
input_metadata = local_cache["input_metadata"]
output_metadata = local_cache["output_metadata"]
response = {}
if not util.is_dict(payload) or "samples" not in payload:
util.log_pretty(payload, logging_func=logger.error)
return prediction_failed(
payload, "top level `samples` key not found in request")
logger.info("Predicting " +
util.pluralize(len(payload["samples"]), "sample", "samples"))
predictions = []
samples = payload["samples"]
if not util.is_list(samples):
util.log_pretty(samples, logging_func=logger.error)
return prediction_failed(
payload,
"expected the value of key `samples` to be a list of json objects")
for i, sample in enumerate(payload["samples"]):
util.log_indent("sample {}".format(i + 1), 2)
try:
util.log_indent("Raw sample:", indent=4)
util.log_pretty(sample, indent=6)
if request_handler is not None and util.has_function(
request_handler, "pre_inference"):
sample = request_handler.pre_inference(sample, input_metadata)
inference_input = convert_to_onnx_input(sample, input_metadata)
model_outputs = sess.run([], inference_input)
result = []
for model_output in model_outputs:
if type(model_output) is np.ndarray:
result.append(model_output.tolist())
else:
result.append(model_output)
if request_handler is not None and util.has_function(
request_handler, "post_inference"):
result = request_handler.post_inference(
result, output_metadata)
util.log_indent("Prediction:", indent=4)
util.log_pretty(result, indent=6)
prediction = {"prediction": result}
except CortexException as e:
e.wrap("error", "sample {}".format(i + 1))
logger.error(str(e))
logger.exception(
"An error occurred, see `cx logs -v api {}` for more details.".
format(api["name"]))
return prediction_failed(sample, str(e))
except Exception as e:
logger.exception(
"An error occurred, see `cx logs -v api {}` for more details.".
format(api["name"]))
return prediction_failed(sample, str(e))
predictions.append(prediction)
response["predictions"] = predictions
response["resource_id"] = api["id"]
return jsonify(response)
