def gen_example_client(model_name): client_type = request.args.get("language", default="bash", type=str) inference_service = self.manager.model_name_service_map[model_name] example_client_string = gen_client.gen_tensorflow_client( inference_service, client_type, model_name) return example_client_string
elif args.model_platform == "mxnet": inference_service = MxnetInferenceService(args.model_name, args.model_base_path, args.verbose) elif args.model_platform == "onnx": inference_service = OnnxInferenceService(args.model_name, args.model_base_path, args.verbose) model_name_service_map[args.model_name] = inference_service # Generate client code and exit or not if args.gen_client != "": if args.model_platform == "tensorflow": inference_service = model_name_service_map[args.model_name] gen_client.gen_tensorflow_client(inference_service, args.gen_client, args.model_name) exit(0) # Start thread to periodically reload models or not if args.reload_models == "True" or args.reload_models == "true": for model_name, inference_service in model_name_service_map.items(): if inference_service.platform == "tensorflow": inference_service.dynmaically_reload_models() # The API to render the dashboard page @application.route("/", methods=["GET"]) @requires_auth def index(): return render_template("index.html",
def gen_example_json(model_name): inference_service = self.manager.model_name_service_map[model_name] data_json_dict = gen_client.gen_tensorflow_client( inference_service, "json", model_name) return json.dumps(data_json_dict)
return f(*decorator_args, **decorator_kwargs) return decorated # Initialize flask application application = Flask(__name__, template_folder='templates') # Initialize TensorFlow inference service to load models inferenceService = TensorFlowInferenceService(args.model_base_path, args.custom_op_paths, args.verbose) # Generate client code and exit or not if args.gen_client != "": gen_client.gen_tensorflow_client(inferenceService, args.gen_client) exit(0) # Start thread to periodically reload models or not if args.reload_models == True: inferenceService.dynmaically_reload_models() # The API to render the dashboard page @application.route("/", methods=["GET"]) @requires_auth def index(): return render_template( "index.html", model_versions=inferenceService.version_session_map.keys(), model_graph_signature=str(inferenceService.model_graph_signature))
inference_service = MxnetInferenceService( args.model_name, args.model_base_path, args.verbose) elif args.model_platform == "h2o": inference_service = H2oInferenceService(args.model_name, args.model_base_path, args.verbose) elif args.model_platform == "onnx": inference_service = OnnxInferenceService( args.model_name, args.model_base_path, args.verbose) model_name_service_map[args.model_name] = inference_service # Generate client code and exit or not if args.gen_client != "": if args.model_platform == "tensorflow": inference_service = model_name_service_map[args.model_name] gen_client.gen_tensorflow_client(inference_service, args.gen_client, args.model_name) sys.exit(0) # Start thread to periodically reload models or not if args.reload_models == "True" or args.reload_models == "true": for model_name, inference_service in model_name_service_map.items(): if inference_service.platform == "tensorflow": inference_service.dynmaically_reload_models() # The API to render the dashboard page @application.route("/", methods=["GET"]) @requires_auth def index(): return render_template(