训练文件：import os
import argparse
import random


def parse_args():
    """
    args for training.
    """
    parser = argparse.ArgumentParser(description='Parse args for training')
    # for train
    parser.add_argument('--script', type=str, help='training script name')
    parser.add_argument('--config', type=str, default='baseline', help='yaml configure file name')
    parser.add_argument('--save_dir', type=str, help='root directory to save checkpoints, logs, and tensorboard')
    parser.add_argument('--mode', type=str, choices=["single", "multiple", "multi_node"], default="multiple",
                        help="train on single gpu or multiple gpus")
    parser.add_argument('--nproc_per_node', type=int, help="number of GPUs per node")  # specify when mode is multiple
    parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0)  # whether datasets are in lmdb format
    parser.add_argument('--script_prv', type=str, help='training script name')
    parser.add_argument('--config_prv', type=str, default='baseline', help='yaml configure file name')
    parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0)  # whether to use wandb
    # for knowledge distillation
    parser.add_argument('--distill', type=int, choices=[0, 1], default=0)  # whether to use knowledge distillation
    parser.add_argument('--script_teacher', type=str, help='teacher script name')
    parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name')

    # for multiple machines
    parser.add_argument('--rank', type=int, help='Rank of the current process.')
    parser.add_argument('--world-size', type=int, help='Number of processes participating in the job.')
    parser.add_argument('--ip', type=str, default='127.0.0.1', help='IP of the current rank 0.')
    parser.add_argument('--port', type=int, default='20000', help='Port of the current rank 0.')

    args = parser.parse_args()

    return args


def main():
    args = parse_args()
    if args.mode == "single":
        train_cmd = "python lib/train/run_training.py --script %s --config %s --save_dir %s --use_lmdb %d " \
                    "--script_prv %s --config_prv %s --distill %d --script_teacher %s --config_teacher %s --use_wandb %d"\
                    % (args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv,
                       args.distill, args.script_teacher, args.config_teacher, args.use_wandb)
    elif args.mode == "multiple":
        train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_port %d lib/train/run_training.py " \
                    "--script %s --config %s --save_dir %s --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \
                    "--distill %d --script_teacher %s --config_teacher %s" \
                    % (args.nproc_per_node, random.randint(10000, 50000), args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb,
                       args.distill, args.script_teacher, args.config_teacher)
    elif args.mode == "multi_node":
        train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_addr %s --master_port %d --nnodes %d --node_rank %d lib/train/run_training.py " \
                    "--script %s --config %s --save_dir %s --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \
                    "--distill %d --script_teacher %s --config_teacher %s" \
                    % (args.nproc_per_node, args.ip, args.port, args.world_size, args.rank, args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb,
                       args.distill, args.script_teacher, args.config_teacher)
    else:
        raise ValueError("mode should be 'single' or 'multiple'.")
    os.system(train_cmd)


if __name__ == "__main__":
    main()
 ，run_trainning文件是：import os
import sys
import argparse
import importlib
import cv2 as cv
import torch.backends.cudnn
import torch.distributed as dist

import random
import numpy as np
torch.backends.cudnn.benchmark = False

import _init_paths
import lib.train.admin.settings as ws_settings


def init_seeds(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


def run_training(script_name, config_name, cudnn_benchmark=True, local_rank=-1, save_dir=None, base_seed=None,
                 use_lmdb=False, script_name_prv=None, config_name_prv=None, use_wandb=False,
                 distill=None, script_teacher=None, config_teacher=None):
    """Run the train script.
    args:
        script_name: Name of emperiment in the "experiments/" folder.
        config_name: Name of the yaml file in the "experiments/<script_name>".
        cudnn_benchmark: Use cudnn benchmark or not (default is True).
    """
    if save_dir is None:
        print("save_dir dir is not given. Use the default dir instead.")
    # This is needed to avoid strange crashes related to opencv
    cv.setNumThreads(0)

    torch.backends.cudnn.benchmark = cudnn_benchmark

    print('script_name: {}.py  config_name: {}.yaml'.format(script_name, config_name))

    '''2021.1.5 set seed for different process'''
    if base_seed is not None:
        if local_rank != -1:
            init_seeds(base_seed + local_rank)
        else:
            init_seeds(base_seed)

    settings = ws_settings.Settings()
    settings.script_name = script_name
    settings.config_name = config_name
    settings.project_path = 'train/{}/{}'.format(script_name, config_name)
    if script_name_prv is not None and config_name_prv is not None:
        settings.project_path_prv = 'train/{}/{}'.format(script_name_prv, config_name_prv)
    settings.local_rank = local_rank
    settings.save_dir = os.path.abspath(save_dir)
    settings.use_lmdb = use_lmdb
    prj_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
    settings.cfg_file = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_name, config_name))
    settings.use_wandb = use_wandb
    if distill:
        settings.distill = distill
        settings.script_teacher = script_teacher
        settings.config_teacher = config_teacher
        if script_teacher is not None and config_teacher is not None:
            settings.project_path_teacher = 'train/{}/{}'.format(script_teacher, config_teacher)
        settings.cfg_file_teacher = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_teacher, config_teacher))
        expr_module = importlib.import_module('lib.train.train_script_distill')
    else:
        expr_module = importlib.import_module('lib.train.train_script')
    expr_func = getattr(expr_module, 'run')

    expr_func(settings)


def main():
    parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.')
    parser.add_argument('--script', type=str, required=True, help='Name of the train script.')
    parser.add_argument('--config', type=str, required=True, help="Name of the config file.")
    parser.add_argument('--cudnn_benchmark', type=bool, default=True, help='Set cudnn benchmark on (1) or off (0) (default is on).')
    # parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training')
    parser.add_argument('--save_dir', type=str, help='the directory to save checkpoints and logs')
    parser.add_argument('--seed', type=int, default=3407, help='seed for random numbers, default=42')
    parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0)  # whether datasets are in lmdb format
    parser.add_argument('--script_prv', type=str, default=None, help='Name of the train script of previous model.')
    parser.add_argument('--config_prv', type=str, default=None, help="Name of the config file of previous model.")
    parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0)  # whether to use wandb
    # for knowledge distillation
    parser.add_argument('--distill', type=int, choices=[0, 1], default=0)  # whether to use knowledge distillation
    parser.add_argument('--script_teacher', type=str, help='teacher script name')
    parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name')

    args = parser.parse_args()
    ##############
    if 'LOCAL_RANK' in os.environ:
        args.local_rank = int(os.environ['LOCAL_RANK'])
    ####################3
    if args.local_rank != -1:
        dist.init_process_group(backend='nccl')
        torch.cuda.set_device(args.local_rank)
    else:
        torch.cuda.set_device(0)
    run_training(args.script, args.config, cudnn_benchmark=args.cudnn_benchmark,
                 local_rank=args.local_rank, save_dir=args.save_dir, base_seed=args.seed,
                 use_lmdb=args.use_lmdb, script_name_prv=args.script_prv, config_name_prv=args.config_prv,
                 use_wandb=args.use_wandb,
                 distill=args.distill, script_teacher=args.script_teacher, config_teacher=args.config_teacher)


if __name__ == '__main__':
    # os.environ['CUDA_VISIBLE_DEVICES'] = "4"
    main()
，运行训练命令：python -m torch.distributed.launch --nproc_per_node=2 tracking/train.py --script ceutrack --config ceutrack_coesot --save_dir ./output --use_wandb 0 现在报错：/home/heyuting/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/distributed/launch.py:181: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use-env is set by default in torchrun.
If your script expects `--local-rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See 
https://pytorch.org/docs/stable/distributed.html#launch-utility for 
further instructions

  warnings.warn(
[2024-06-14 21:57:17,732] torch.distributed.run: [WARNING] 
[2024-06-14 21:57:17,732] torch.distributed.run: [WARNING] *****************************************
[2024-06-14 21:57:17,732] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
[2024-06-14 21:57:17,732] torch.distributed.run: [WARNING] *****************************************
usage: train.py [-h] [--script SCRIPT] [--config CONFIG] [--save_dir SAVE_DIR]
                [--mode {single,multiple,multi_node}] [--nproc_per_node NPROC_PER_NODE]
                [--use_lmdb {0,1}] [--script_prv SCRIPT_PRV] [--config_prv CONFIG_PRV]
                [--use_wandb {0,1}] [--distill {0,1}] [--script_teacher SCRIPT_TEACHER]
                [--config_teacher CONFIG_TEACHER] [--rank RANK] [--world-size WORLD_SIZE] [--ip IP]
                [--port PORT]
usage: train.py [-h] [--script SCRIPT] [--config CONFIG] [--save_dir SAVE_DIR]
                [--mode {single,multiple,multi_node}] [--nproc_per_node NPROC_PER_NODE]
                [--use_lmdb {0,1}] [--script_prv SCRIPT_PRV] [--config_prv CONFIG_PRV]
                [--use_wandb {0,1}] [--distill {0,1}] [--script_teacher SCRIPT_TEACHER]
                [--config_teacher CONFIG_TEACHER] [--rank RANK] [--world-size WORLD_SIZE] [--ip IP]
                [--port PORT]
train.py: error: unrecognized arguments: --local-rank=0
train.py: error: unrecognized arguments: --local-rank=1
[2024-06-14 21:57:22,742] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 2) local_rank: 0 (pid: 454403) of binary: /home/heyuting/anaconda3/envs/mamba/bin/python
Traceback (most recent call last):
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/distributed/launch.py", line 196, in <module>
    main()
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/distributed/launch.py", line 192, in main
    launch(args)
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/distributed/launch.py", line 177, in launch
    run(args)
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/distributed/run.py", line 797, in run
    elastic_launch(
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/heyuting/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 264, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
tracking/train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-06-14_21:57:22
  host      : server12
  rank      : 1 (local_rank: 1)
  exitcode  : 2 (pid: 454404)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-06-14_21:57:22
  host      : server12
  rank      : 0 (local_rank: 0)
  exitcode  : 2 (pid: 454403)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html