Question

我需要在不同端口上部署ml模型。这是请求预测值的代码的一部分。

第一个模型：

def main():
    p = argparse.ArgumentParser(description="Searcher - For sudrf.ru")
    p.add_argument('-c', '--config', metavar='<config.yaml>', dest='config_file', required=True,
               help='Config file.')
    p.add_argument('--loglevel', metavar='<LOG_LEVEL>', dest='log_level', choices=['INFO', 'info', 'DEBUG', 'debug'], default='info',
               help='Logger level: info|debug')
    args = p.parse_args()

    print('Config file: "%s"' % args.config_file)

    config = Config(args.config_file)
    if not config.is_loaded():
    print(config.get_errmsg())
    return 1

    # create pid file
    pidlock = PidLock('courts_search_httpservice.pid')
    pid = pidlock.getpid()
    if pid:
    print('Service is already running with pid %s' % pid)
    return 1
    pidlock.lock()
    print('PID file: %s' % pidlock.get_pidfile())

    logging.basicConfig(level=args.log_level.upper(), format='%(asctime)s:courts_search:%(levelname)s\t%(message)s',
                    filemode='w', filename=config.fields.get('log_file', 'court_search.log'))
    # bind on host-port
    host = config.fields.get('host', '0.0.0.0')
    port = config.fields.get('port', 8080)

    logging.info('Binding on address %s:%s' % (host, port))
    http_handler_type = HttpHandler
    http_handler_type.error_content_type = CONTENT_TYPE
    server = ThreadedHTTPServer((host, port), http_handler_type)

    # init indexer and query parser
    # TODO: fixup path if it's not absolute
    logging.info('Loading model' + config.fields['graph_file'])
    server.model = Model(config.fields['checkpoint_path'], config.fields['graph_file'], config.fields['data_file'], config.fields['threshold_l'], config.fields['threshold_h'])
    logging.info('Done.')

    # start serving
    print('Starting server, use <Ctrl-C> to stop')
    logging.info('Processing incoming requests...')
    try:
    server.serve_forever()
    except KeyboardInterrupt:
    logging.info('[STOP] KeyboardInterrupt received')
    server.socket.close()

    return 0

我尝试将port更改为8000，但它返回

Service is already running with pid 19931

我应该更改那里的以并行运行2个模型吗？我不能只更改端口以并行使用它吗？

Python：部署ml模型

0 个答案: