当前采用多GPU训练报错：/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 21:36:21,506] torch.distributed.run: [WARNING]
[2024-06-14 21:36:21,506] torch.distributed.run: [WARNING] *****************************************
[2024-06-14 21:36:21,506] 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 21:36:21,506] torch.distributed.run: [WARNING] *****************************************
2024-06-14 21:36:23.475564: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-14 21:36:23.503752: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-14 21:36:24.309452: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-06-14 21:36:24.358850: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
usage: run_training.py [-h] --script SCRIPT --config CONFIG [–cudnn_benchmark CUDNN_BENCHMARK]
[–local_rank LOCAL_RANK] [–save_dir SAVE_DIR] [–seed SEED]
[–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]
run_training.py: error: unrecognized arguments: --local-rank=1
usage: run_training.py [-h] --script SCRIPT --config CONFIG [–cudnn_benchmark CUDNN_BENCHMARK]
[–local_rank LOCAL_RANK] [–save_dir SAVE_DIR] [–seed SEED]
[–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]
run_training.py: error: unrecognized arguments: --local-rank=0
[2024-06-14 21:36:26,516] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 2) local_rank: 0 (pid: 451104) 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:
============================================================
lib/train/run_training.py FAILED
------------------------------------------------------------
Failures:
[1]:
time : 2024-06-14_21:36:26
host : server12
rank : 1 (local_rank: 1)
exitcode : 2 (pid: 451105)
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_21:36:26
host : server12
rank : 0 (local_rank: 0)
exitcode : 2 (pid: 451104)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================，run_trainning.py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 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()