def __init_

def init()

in tensorflow/inference/docker/build_artifacts/sagemaker_neuron/serve.py [0:0]
55 lines of code
11 McCabe index (conditional complexity)

    def __init__(self):
        self._state = "initializing"
        self._nginx = None
        self._tfs = []
        self._gunicorn = None
        self._gunicorn_command = None
        self._enable_python_service = False
        self._tfs_version = os.environ.get("SAGEMAKER_TFS_VERSION", "1.13")
        self._nginx_http_port = os.environ.get("SAGEMAKER_BIND_TO_PORT", "8080")
        self._nginx_loglevel = os.environ.get("SAGEMAKER_TFS_NGINX_LOGLEVEL", "error")
        self._tfs_default_model_name = os.environ.get("SAGEMAKER_TFS_DEFAULT_MODEL_NAME", "None")
        self._sagemaker_port_range = os.environ.get("SAGEMAKER_SAFE_PORT_RANGE", None)
        self._gunicorn_workers = os.environ.get("SAGEMAKER_GUNICORN_WORKERS", None)
        self._gunicorn_threads = os.environ.get("SAGEMAKER_GUNICORN_THREADS", 1)
        self._tfs_config_path = "/sagemaker/model-config.cfg"
        self._tfs_batching_config_path = "/sagemaker/batching-config.cfg"
        self._user_ncgs =  os.environ.get('NEURONCORE_GROUP_SIZES', None)
        if self._user_ncgs is None:
            os.environ['NEURONCORE_GROUP_SIZES'] = "1"
            self._user_ncgs = 1
        if self._gunicorn_workers is None:
            num_host_cores = os.environ.get("NEURON_CORE_HOST_TOTAL")
            if num_host_cores is None:
                self._gunicorn_workers = 1
            else:
               self._gunicorn_workers = num_host_cores
        _enable_batching = os.environ.get("SAGEMAKER_TFS_ENABLE_BATCHING", "false").lower()
        _enable_multi_model_endpoint = os.environ.get("SAGEMAKER_MULTI_MODEL",
                                                      "false").lower()

        if _enable_multi_model_endpoint not in ["true", "false"]:
            raise ValueError("SAGEMAKER_MULTI_MODEL must be 'true' or 'false'")
        self._tfs_enable_multi_model_endpoint = _enable_multi_model_endpoint == "true"

        self._need_python_service()
        log.info("PYTHON SERVICE: {}".format(str(self._enable_python_service)))

        if _enable_batching not in ["true", "false"]:
            raise ValueError("SAGEMAKER_TFS_ENABLE_BATCHING must be 'true' or 'false'")
        self._tfs_enable_batching = _enable_batching == "true"

        if _enable_multi_model_endpoint not in ["true", "false"]:
            raise ValueError("SAGEMAKER_MULTI_MODEL must be 'true' or 'false'")
        self._tfs_enable_multi_model_endpoint = _enable_multi_model_endpoint == "true"

        self._use_gunicorn = self._enable_python_service or self._tfs_enable_multi_model_endpoint

        if self._sagemaker_port_range is not None:
            parts = self._sagemaker_port_range.split("-")
            low = int(parts[0])
            hi = int(parts[1])
            if low + 2 > hi:
                raise ValueError("not enough ports available in SAGEMAKER_SAFE_PORT_RANGE ({})"
                                 .format(self._sagemaker_port_range))
            self._tfs_grpc_port = str(low)
            self._tfs_rest_port = str(low + 1)
        else:
            # just use the standard default ports
            self._tfs_grpc_port = "9000"
            self._tfs_rest_port = "8501"

        # set environment variable for python service
        os.environ["TFS_GRPC_PORT"] = self._tfs_grpc_port
        os.environ["TFS_REST_PORT"] = self._tfs_rest_port
def __init__()

def init()
