How to deal with it when I use my self training datasets?

1、I used windows10
2、I build datasets from LFW origin picture.

This error is blow:
(face) D:\facerecogntion\insightface\recognition\arcface_torch>python -m torch.distributed.launch --nproc_per_node=1 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train.py configs/ms1mv2_mbf
NOTE: Redirects are currently not supported in Windows or MacOs.
D:\pyproject\conda\envs\face\lib\site-packages\torch\distributed\launch.py:186: 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

FutureWarning,
[W C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\distributed\c10d\socket.cpp:558] [c10d] The client socket has failed to connect to [activate.navicat.com]:12581 (system error: 10049 - 在其上下文中，该请求的地址无效。).
[W C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\distributed\c10d\socket.cpp:558] [c10d] The client socket has failed to connect to [activate.navicat.com]:12581 (system error: 10049 - 在其上下文中，该请求的地址无效。).
[W C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\distributed\c10d\socket.cpp:558] [c10d] The client socket has failed to connect to [activate.navicat.com]:12581 (system error: 10049 - 在其上下文中，该请求的地址无效。).
[W C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\distributed\c10d\socket.cpp:558] [c10d] The client socket has failed to connect to [activate.navicat.com]:12581 (system error: 10049 - 在其上下文中，该请求的地址无效。).
Training: 2022-10-01 08:49:58,472-rank_id: 0
D:\pyproject\conda\envs\face\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
import imp
D:\pyproject\conda\envs\face\lib\site-packages\tensorflow\python\util\nest.py:1286: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
_pywrap_tensorflow.RegisterType("Mapping", _collections.Mapping)
D:\pyproject\conda\envs\face\lib\site-packages\tensorflow\python\util\nest.py:1287: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
_pywrap_tensorflow.RegisterType("Sequence", _collections.Sequence)
D:\pyproject\conda\envs\face\lib\site-packages\tensorflow\python\training\tracking\object_identity.py:61: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
class ObjectIdentityDictionary(collections.MutableMapping):
D:\pyproject\conda\envs\face\lib\site-packages\tensorflow\python\training\tracking\object_identity.py:112: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
class ObjectIdentitySet(collections.MutableSet):
D:\pyproject\conda\envs\face\lib\site-packages\tensorflow\python\training\tracking\data_structures.py:374: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
class _ListWrapper(List, collections.MutableSequence,
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:23: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:24: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:25: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:28: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
if hasattr(pil_image, 'HAMMING'):
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:29: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
_PIL_INTERPOLATION_METHODS['hamming'] = pil_image.HAMMING
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:30: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
if hasattr(pil_image, 'BOX'):
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:31: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
_PIL_INTERPOLATION_METHODS['box'] = pil_image.BOX
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:33: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
if hasattr(pil_image, 'LANCZOS'):
D:\pyproject\conda\envs\face\lib\site-packages\keras_preprocessing\image\utils.py:34: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
_PIL_INTERPOLATION_METHODS['lanczos'] = pil_image.LANCZOS
D:\pyproject\conda\envs\face\lib\site-packages\torch\nn\parallel\distributed.py:1737: UserWarning: You passed find_unused_parameters=true to DistributedDataParallel, `_set_static_graph` will detect unused parameters automatically, so you do not need to set find_unused_parameters=true, just be sure these unused parameters will not change during training loop while calling `_set_static_graph`.
"You passed find_unused_parameters=true to DistributedDataParallel, "
Training: 2022-10-01 08:50:01,847-: margin_list [1.0, 0.5, 0.0]
Training: 2022-10-01 08:50:01,848-: network mbf
Training: 2022-10-01 08:50:01,849-: resume False
Training: 2022-10-01 08:50:01,853-: save_all_states False
Training: 2022-10-01 08:50:01,854-: output work_dirs\ms1mv2_mbf
Training: 2022-10-01 08:50:01,854-: embedding_size 128
Training: 2022-10-01 08:50:01,855-: sample_rate 1.0
Training: 2022-10-01 08:50:01,856-: interclass_filtering_threshold0
Training: 2022-10-01 08:50:01,863-: fp16 True
Training: 2022-10-01 08:50:01,863-: batch_size 2
Training: 2022-10-01 08:50:01,863-: optimizer sgd
Training: 2022-10-01 08:50:01,864-: lr 0.1
Training: 2022-10-01 08:50:01,864-: momentum 0.9
Training: 2022-10-01 08:50:01,865-: weight_decay 0.0005
Training: 2022-10-01 08:50:01,873-: verbose 2000
Training: 2022-10-01 08:50:01,873-: frequent 10
Training: 2022-10-01 08:50:01,874-: dali False
Training: 2022-10-01 08:50:01,874-: gradient_acc 1
Training: 2022-10-01 08:50:01,875-: seed 2048
Training: 2022-10-01 08:50:01,875-: num_workers 0
Training: 2022-10-01 08:50:01,875-: rec ../_datasets_/faces_lfw
Training: 2022-10-01 08:50:01,876-: num_classes 6
Training: 2022-10-01 08:50:01,885-: num_image 58
Training: 2022-10-01 08:50:01,885-: num_epoch 5
Training: 2022-10-01 08:50:01,886-: warmup_epoch 0
Training: 2022-10-01 08:50:01,887-: val_targets ['lfw', 'cfp_fp', 'agedb_30']
Training: 2022-10-01 08:50:01,894-: total_batch_size 2
Training: 2022-10-01 08:50:01,894-: warmup_step 0
Training: 2022-10-01 08:50:01,895-: total_step 145
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torch.Size([12000, 3, 112, 112])
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torch.Size([14000, 3, 112, 112])
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torch.Size([12000, 3, 112, 112])
D:\pyproject\conda\envs\face\lib\site-packages\torch\optim\lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
Training: 2022-10-01 08:50:45,541-Reducer buckets have been rebuilt in this iteration.
Training: 2022-10-01 08:50:46,859-Speed 27.43 samples/sec Loss 38.3868 LearningRate 0.075510 Epoch: 0 Global Step: 20 Fp16 Grad Scale: 64 Required: 0 hours
Training: 2022-10-01 08:50:47,678-Speed 24.54 samples/sec Loss 34.1343 LearningRate 0.064000 Epoch: 1 Global Step: 30 Fp16 Grad Scale: 64 Required: 0 hours
Training: 2022-10-01 08:50:48,408-Speed 27.43 samples/sec Loss 34.6079 LearningRate 0.053441 Epoch: 1 Global Step: 40 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:49,147-Speed 27.06 samples/sec Loss 35.2048 LearningRate 0.043834 Epoch: 1 Global Step: 50 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:49,936-Speed 25.35 samples/sec Loss 34.2611 LearningRate 0.035177 Epoch: 2 Global Step: 60 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:50,683-Speed 26.77 samples/sec Loss 34.6517 LearningRate 0.027472 Epoch: 2 Global Step: 70 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:51,426-Speed 26.92 samples/sec Loss 32.0162 LearningRate 0.020718 Epoch: 2 Global Step: 80 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:52,204-Speed 25.74 samples/sec Loss 31.9312 LearningRate 0.014916 Epoch: 3 Global Step: 90 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:52,952-Speed 26.77 samples/sec Loss 31.7183 LearningRate 0.010064 Epoch: 3 Global Step: 100 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:53,692-Speed 27.03 samples/sec Loss 35.9666 LearningRate 0.006164 Epoch: 3 Global Step: 110 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:54,470-Speed 25.74 samples/sec Loss 30.7639 LearningRate 0.003215 Epoch: 4 Global Step: 120 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:55,206-Speed 27.21 samples/sec Loss 29.2269 LearningRate 0.001218 Epoch: 4 Global Step: 130 Fp16 Grad Scale: 32 Required: 0 hours
Training: 2022-10-01 08:50:55,939-Speed 27.32 samples/sec Loss 35.6505 LearningRate 0.000171 Epoch: 4 Global Step: 140 Fp16 Grad Scale: 32 Required: 0 hours
Exception ignored in:
Traceback (most recent call last):
File "D:\pyproject\conda\envs\face\lib\site-packages\mxnet\recordio.py", line 84, in __del__
File "D:\pyproject\conda\envs\face\lib\site-packages\mxnet\recordio.py", line 217, in close
TypeError: super() argument 1 must be type, not None

3、How to deal with it?
This is building datasets having error or others?

Best regards,
Star