in tensorflow/inference/docker/build_artifacts/sagemaker_neuron/serve.py [0:0]
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)
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
self._gunicorn_threads = os.environ.get("SAGEMAKER_GUNICORN_THREADS", 1)
self._gunicorn_loglevel = os.environ.get("SAGEMAKER_GUNICORN_LOGLEVEL", "info")
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
_enable_batching = os.environ.get("SAGEMAKER_TFS_ENABLE_BATCHING", "false").lower()
_enable_multi_model_endpoint = os.environ.get("SAGEMAKER_MULTI_MODEL", "false").lower()
self._tfs_wait_time_seconds = int(os.environ.get("SAGEMAKER_TFS_WAIT_TIME_SECONDS", 300))
self._tfs_inter_op_parallelism = os.environ.get("SAGEMAKER_TFS_INTER_OP_PARALLELISM", 0)
self._tfs_intra_op_parallelism = os.environ.get("SAGEMAKER_TFS_INTRA_OP_PARALLELISM", 0)
self._gunicorn_worker_class = os.environ.get("SAGEMAKER_GUNICORN_WORKER_CLASS", "gevent")
self._gunicorn_timeout_seconds = int(
os.environ.get("SAGEMAKER_GUNICORN_TIMEOUT_SECONDS", 30)
)
self._nginx_proxy_read_timeout_seconds = int(
os.environ.get("SAGEMAKER_NGINX_PROXY_READ_TIMEOUT_SECONDS", 60)
)
# Nginx proxy read timeout should not be less than the GUnicorn timeout. If it is, this
# can result in upstream time out errors.
if self._gunicorn_timeout_seconds > self._nginx_proxy_read_timeout_seconds:
log.info(
"GUnicorn timeout was higher than Nginx proxy read timeout."
" Setting Nginx proxy read timeout from {} seconds to {} seconds"
" to match GUnicorn timeout.".format(
self._nginx_proxy_read_timeout_seconds, self._gunicorn_timeout_seconds
)
)
self._nginx_proxy_read_timeout_seconds = self._gunicorn_timeout_seconds
if os.environ.get("OMP_NUM_THREADS") is None:
os.environ["OMP_NUM_THREADS"] = "1"
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])
self._tfs_grpc_ports = []
self._tfs_rest_ports = []
if low + 2 > hi:
raise ValueError(
"not enough ports available in SAGEMAKER_SAFE_PORT_RANGE ({})".format(
self._sagemaker_port_range
)
)
self._tfs_grpc_ports.append(str(low))
self._tfs_rest_ports.append(str(low + 1))
# concat selected ports respectively in order to pass them to python service
self._tfs_grpc_concat_ports = self._concat_ports(self._tfs_grpc_ports)
self._tfs_rest_concat_ports = self._concat_ports(self._tfs_rest_ports)
else:
# just use the standard default ports
self._tfs_grpc_ports = ["9000"]
self._tfs_rest_ports = ["8501"]
# provide single concat port here for default case
self._tfs_grpc_concat_ports = "9000"
self._tfs_rest_concat_ports = "8501"
# set environment variable for python service
os.environ["TFS_GRPC_PORTS"] = self._tfs_grpc_concat_ports
os.environ["TFS_REST_PORTS"] = self._tfs_rest_concat_ports