vllm.entrypoints.cli.run_batch ¶

class RunBatchSubcommand(CLISubcommand):
    """The `run-batch` subcommand for vLLM CLI."""

    name = "run-batch"

    @staticmethod
    def cmd(args: argparse.Namespace) -> None:
        from vllm.entrypoints.openai.run_batch import main as run_batch_main

        logger.info(
            "vLLM batch processing API version %s", importlib.metadata.version("vllm")
        )
        logger.info("args: %s", args)

        # Start the Prometheus metrics server.
        # LLMEngine uses the Prometheus client
        # to publish metrics at the /metrics endpoint.
        if args.enable_metrics:
            from prometheus_client import start_http_server

            logger.info("Prometheus metrics enabled")
            start_http_server(port=args.port, addr=args.url)
        else:
            logger.info("Prometheus metrics disabled")

        asyncio.run(run_batch_main(args))

    def subparser_init(
        self, subparsers: argparse._SubParsersAction
    ) -> FlexibleArgumentParser:
        from vllm.entrypoints.openai.run_batch import make_arg_parser

        run_batch_parser = subparsers.add_parser(
            self.name,
            help="Run batch prompts and write results to file.",
            description=(
                "Run batch prompts using vLLM's OpenAI-compatible API.\n"
                "Supports local or HTTP input/output files."
            ),
            usage="vllm run-batch -i INPUT.jsonl -o OUTPUT.jsonl --model <model>",
        )
        run_batch_parser = make_arg_parser(run_batch_parser)
        run_batch_parser.epilog = VLLM_SUBCMD_PARSER_EPILOG.format(subcmd=self.name)
        return run_batch_parser