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(
