Source code for ts.arg_parser

This module parses the arguments given through the torchserve command-line. This is used by model-server
at runtime.

import argparse

# noinspection PyTypeChecker
[docs]class ArgParser(object): """ Argument parser for torchserve and torchserve-export commands TODO : Add readme url """
[docs] @staticmethod def ts_parser(): """ Argument parser for torchserve start service """ parser = argparse.ArgumentParser(prog="torchserve", description="Torchserve") sub_parse = parser.add_mutually_exclusive_group(required=False) sub_parse.add_argument( "-v", "--version", action="store_true", help="Return TorchServe Version" ) sub_parse.add_argument( "--start", action="store_true", help="Start the model-server" ) sub_parse.add_argument( "--stop", action="store_true", help="Stop the model-server" ) parser.add_argument( "--ts-config", dest="ts_config", help="Configuration file for model server" ) parser.add_argument( "--model-store", required=False, dest="model_store", help="Model store location from where local or default models can be loaded", ) parser.add_argument( "--workflow-store", required=False, dest="workflow_store", help="Workflow store location from where local or default workflows can be loaded", ) parser.add_argument( "--models", metavar="MODEL_PATH1 MODEL_NAME=MODEL_PATH2...", nargs="+", help="Models to be loaded using [model_name=]model_location format. " "Location can be a HTTP URL or a model archive file in MODEL_STORE.", ) parser.add_argument( "--log-config", dest="log_config", help="Log4j configuration file for model server", ) parser.add_argument( "--foreground", help="Run the model server in foreground. If this option is disabled, the model server" " will run in the background.", action="store_true", ) parser.add_argument( "--no-config-snapshots", "--ncs", dest="no_config_snapshots", help="Prevents to server from storing config snapshot files.", action="store_true", ) parser.add_argument( "--plugins-path", "--ppath", dest="plugins_path", help="plugin jars to be included in torchserve class path", ) return parser
[docs] @staticmethod def model_service_worker_args(): """ ArgParser for backend worker. Takes the socket name and socket type. :return: """ parser = argparse.ArgumentParser( prog="model-server-worker", description="Model Server Worker" ) parser.add_argument( "--sock-type", required=True, dest="sock_type", type=str, choices=["unix", "tcp"], help="Socket type the model service worker would use. The options are\n" "unix: The model worker expects to unix domain-socket\n" "tcp: The model worker expects a host-name and port-number", ) parser.add_argument( "--sock-name", required=False, dest="sock_name", type=str, help="If 'sock-type' is 'unix', sock-name is expected to be a string. " 'Eg: --sock-name "test_sock"', ) parser.add_argument( "--host", type=str, help="If 'sock-type' is 'tcp' this is expected to have a host IP address", ) parser.add_argument( "--port", type=str, help="If 'sock-type' is 'tcp' this is expected to have the host port to bind on", ) parser.add_argument( "--metrics-config", dest="metrics_config", type=str, help="Metrics configuration file", ) return parser
[docs] @staticmethod def extract_args(args=None): parser = ArgParser.ts_parser() return parser.parse_args(args) if args else parser.parse_args()


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources