def __init__()

in docker/build_artifacts/sagemaker/python_service.py [0:0]

• 23 lines of code
• 5 McCabe index (conditional complexity)

    def __init__(self):
        if SAGEMAKER_MULTI_MODEL_ENABLED:
            self._model_tfs_rest_port = {}
            self._model_tfs_grpc_port = {}
            self._model_tfs_pid = {}
            self._tfs_ports = self._parse_sagemaker_port_range_mme(SAGEMAKER_TFS_PORT_RANGE)
            # If Multi-Model mode is enabled, dependencies/handlers will be imported
            # during the _handle_load_model_post()
            self.model_handlers = {}
        else:
            self._tfs_grpc_ports = self._parse_concat_ports(TFS_GRPC_PORTS)
            self._tfs_rest_ports = self._parse_concat_ports(TFS_REST_PORTS)

            self._channels = {}
            for grpc_port in self._tfs_grpc_ports:
                # Initialize grpc channel here so gunicorn worker could have mapping
                # between each grpc port and channel
                self._setup_channel(grpc_port)

        if os.path.exists(INFERENCE_SCRIPT_PATH):
            # Single-Model Mode & Multi-Model Mode both use one inference.py
            self._handler, self._input_handler, self._output_handler = self._import_handlers()
            self._handlers = self._make_handler(self._handler,
                                                self._input_handler,
                                                self._output_handler)
        else:
            self._handlers = default_handler

        self._tfs_enable_batching = SAGEMAKER_BATCHING_ENABLED == "true"
        self._tfs_default_model_name = os.environ.get("TFS_DEFAULT_MODEL_NAME", "None")
        self._tfs_wait_time_seconds = int(os.environ.get("SAGEMAKER_TFS_WAIT_TIME_SECONDS", 300))