def TransformInput(): request = extract_message() logger.debug("Request: %s", request) sanity_check_request(request) if hasattr(user_model, "transform_input_rest"): return jsonify(user_model.transform_input_rest(request)) else: features = get_data_from_json(request) names = request.get("data", {}).get("names") transformed = transform_input(user_model, features, names) logger.debug("Transformed: %s", transformed) # If predictions is an numpy array or we used the default data then return as numpy array if isinstance(transformed, np.ndarray) or "data" in request: new_feature_names = get_feature_names(user_model, names) transformed = np.array(transformed) data = array_to_rest_datadef(transformed, new_feature_names, request.get("data", {})) response = {"data": data, "meta": {}} else: response = {"binData": transformed, "meta": {}} tags = get_custom_tags(user_model) if tags: response["meta"]["tags"] = tags metrics = get_custom_metrics(user_model) if metrics: response["meta"]["metrics"] = metrics return jsonify(response)
def TransformOutput(): request = extract_message() logger.debug("Request: %s", request) sanity_check_request(request) if hasattr(user_model, "transform_output_rest"): return jsonify(user_model.transform_output_rest(request)) else: features = get_data_from_json(request) names = request.get("data", {}).get("names") meta = get_meta_from_json(request) transformed = transform_output(user_model, features, names, meta=meta) logger.debug("Transformed: %s", transformed) if isinstance(transformed, np.ndarray) or "data" in request: new_class_names = get_class_names(user_model, names) data = array_to_rest_datadef(transformed, new_class_names, request.get("data", {})) response = {"data": data, "meta": {}} else: response = {"binData": transformed, "meta": {}} tags = get_custom_tags(user_model) if tags: response["meta"]["tags"] = tags metrics = get_custom_metrics(user_model) if metrics: response["meta"]["metrics"] = metrics return jsonify(response)
def TransformOutput(self, request, context): if hasattr(self.user_model, "transform_output_grpc"): return self.user_model.transform_output_grpc(request) else: features = get_data_from_proto(request) datadef = request.data data_type = request.WhichOneof("data_oneof") # Construct meta data meta = prediction_pb2.Meta() metaJson = {} tags = get_custom_tags(self.user_model) if tags: metaJson["tags"] = tags metrics = get_custom_metrics(self.user_model) if metrics: metaJson["metrics"] = metrics json_format.ParseDict(metaJson, meta) transformed = transform_output(self.user_model, features, datadef.names) if isinstance(transformed, np.ndarray) or data_type == "data": transformed = np.array(transformed) class_names = get_class_names(self.user_model, datadef.names) if data_type == "data": default_data_type = request.data.WhichOneof("data_oneof") else: default_data_type = "tensor" data = array_to_grpc_datadef(transformed, class_names, default_data_type) return prediction_pb2.SeldonMessage(data=data, meta=meta) else: return prediction_pb2.SeldonMessage(binData=transformed, meta=meta)
def Route(): request = extract_message() logger.debug("Request: %s", request) sanity_check_request(request) if hasattr(user_router, "route_rest"): return jsonify(user_router.route_rest(request)) else: datadef = request.get("data") features = rest_datadef_to_array(datadef) routing = np.array( [[route(user_router, features, datadef.get("names"))]]) # TODO: check that predictions is 2 dimensional class_names = [] data = array_to_rest_datadef(routing, class_names, datadef) response = {"data": data, "meta": {}} tags = get_custom_tags(user_router) if tags: response["meta"]["tags"] = tags metrics = get_custom_metrics(user_router) if metrics: response["meta"]["metrics"] = metrics return jsonify(response)
def Route(self, request, context): if hasattr(self.user_model, "route_grpc"): return self.user_model.route_grpc(request) else: datadef = request.data features = grpc_datadef_to_array(datadef) routing = np.array([[route(self.user_model, features, datadef.names)]]) # TODO: check that predictions is 2 dimensional class_names = [] data = array_to_grpc_datadef( routing, class_names, request.data.WhichOneof("data_oneof")) # Construct meta data meta = prediction_pb2.Meta() metaJson = {} tags = get_custom_tags(self.user_model) if tags: metaJson["tags"] = tags metrics = get_custom_metrics(self.user_model) if metrics: metaJson["metrics"] = metrics json_format.ParseDict(metaJson, meta) return prediction_pb2.SeldonMessage(data=data, meta=meta)
def Aggregate(): request = extract_message() logger.debug("Request: %s", request) sanity_check_seldon_message_list(request) if hasattr(user_model, "aggregate_rest"): return jsonify(user_model.aggregate_rest(request)) else: features_list = [] names_list = [] for msg in request["seldonMessages"]: features = get_data_from_json(msg) names = msg.get("data", {}).get("names") features_list.append(features) names_list.append(names) aggregated = aggregate(user_model, features_list, names_list) logger.debug("Aggregated: %s", aggregated) # If predictions is a numpy array or we used the default data then return as numpy array if isinstance( aggregated, np.ndarray) or "data" in request["seldonMessages"][0]: new_feature_names = get_feature_names(user_model, names_list[0]) aggregated = np.array(aggregated) data = array_to_rest_datadef( aggregated, new_feature_names, request["seldonMessages"][0].get("data", {})) response = {"data": data, "meta": {}} else: response = {"binData": aggregated, "meta": {}} tags = get_custom_tags(user_model) if tags: response["meta"]["tags"] = tags metrics = get_custom_metrics(user_model) if metrics: response["meta"]["metrics"] = metrics return jsonify(response)
def Predict(self, request, context): if hasattr(self.user_model, "predict_grpc"): return self.user_model.predict_grpc(request) else: features = get_data_from_proto(request) meta = get_meta_from_proto(request) datadef = request.data data_type = request.WhichOneof("data_oneof") predictions = predict(self.user_model, features, datadef.names, meta=meta) # Construct meta data meta = prediction_pb2.Meta() metaJson = {} tags = get_custom_tags(self.user_model) if tags: metaJson["tags"] = tags metrics = get_custom_metrics(self.user_model) if metrics: metaJson["metrics"] = metrics json_format.ParseDict(metaJson, meta) if isinstance(predictions, np.ndarray) or data_type == "data": predictions = np.array(predictions) if len(predictions.shape) > 1: class_names = get_class_names(self.user_model, predictions.shape[1]) else: class_names = [] if data_type == "data": default_data_type = request.data.WhichOneof("data_oneof") else: default_data_type = "tensor" data = array_to_grpc_datadef(predictions, class_names, default_data_type) return prediction_pb2.SeldonMessage(data=data, meta=meta) else: return prediction_pb2.SeldonMessage(binData=predictions, meta=meta)
def Aggregate(self, request, context): if hasattr(self.user_model, "aggregate_grpc"): return self.user_model.aggregate_grpc(request) else: features_list = [] names_list = [] for msg in request.seldonMessages: features = get_data_from_proto(msg) features_list.append(features) names_list.append(msg.data.names) data_type = request.seldonMessages[0].WhichOneof("data_oneof") aggregated = aggregate(self.user_model, features_list, names_list) # Construct meta data meta = prediction_pb2.Meta() metaJson = {} tags = get_custom_tags(self.user_model) if tags: metaJson["tags"] = tags metrics = get_custom_metrics(self.user_model) if metrics: metaJson["metrics"] = metrics json_format.ParseDict(metaJson, meta) if isinstance(aggregated, np.ndarray) or data_type == "data": aggregated = np.array(aggregated) feature_names = get_feature_names(self.user_model, []) if data_type == "data": default_data_type = request.seldonMessages[ 0].data.WhichOneof("data_oneof") else: default_data_type = "tensor" data = array_to_grpc_datadef(aggregated, feature_names, default_data_type) return prediction_pb2.SeldonMessage(data=data, meta=meta) else: return prediction_pb2.SeldonMessage(binData=aggregated, meta=meta)
def Predict(): request = extract_message() logger.debug("Request: %s", request) sanity_check_request(request) if hasattr(user_model, "predict_rest"): return jsonify(user_model.predict_rest(request)) else: features = get_data_from_json(request) names = request.get("data", {}).get("names") meta = get_meta_from_json(request) predictions = predict(user_model, features, names, meta=meta) logger.debug("Predictions: %s", predictions) # If predictions is an numpy array or we used the default data then return as numpy array if isinstance(predictions, np.ndarray) or "data" in request: predictions = np.array(predictions) if len(predictions.shape) > 1: class_names = get_class_names(user_model, predictions.shape[1]) else: class_names = [] data = array_to_rest_datadef(predictions, class_names, request.get("data", {})) response = {"data": data, "meta": {}} else: response = {"binData": predictions, "meta": {}} tags = get_custom_tags(user_model) if tags: response["meta"]["tags"] = tags metrics = get_custom_metrics(user_model) if metrics: response["meta"]["metrics"] = metrics return jsonify(response)