Error executing command : python -u train.py --network r100 --loss arcface --dataset emore

Me when running execute the command
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r100 --loss arcface --dataset emore.
Error:
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python -u train.py --network r100 --loss arcface --dataset emore
gpu num: 4
prefix ./models\r100-arcface-emore\model
image_size [112, 112]
num_classes 85742
Called with argument: Namespace(batch_size=512, ckpt=3, ctx_num=4, dataset='emore', frequent=20, image_channel=3, kvstore='device', loss='arcface', lr=0.1, lr_steps='100000,160000,220000', models_root='./models', mom=0.9, network='r100', per_batch_size=128, pretrained='', pretrained_epoch=1, rescale_threshold=0,
verbose=2000, wd=0.0005) {'bn_mom': 0.9, 'workspace': 256, 'emb_size': 512, 'ckpt_embedding': True, 'net_se': 0, 'net_act': 'prelu', 'net_unit': 3, 'net_input': 1, 'net_blocks': [1, 4, 6, 2], 'net_output': 'E', 'net_multiplier': 1.0, 'val_targets': ['lfw', 'cfp_fp', 'agedb_30'], 'ce_loss': True, 'fc7_lr_mult': 1.0, 'fc7_wd_mult': 1.0, 'fc7_no_bias': False, 'max_steps': 0, 'data_rand_mirror': True, 'data_cutoff': False, 'data_color': 0, 'data_images_filter': 0, 'count_flops': True, 'memonger': False, 'is_shuffled_rec': False, 'fp16': False, 'loss_name': 'margin_softmax', 'loss_s': 64.0, 'loss_m1': 1.0, 'loss_m2': 0.5, 'loss_m3': 0.0, 'net_name': 'fresnet', 'num_layers': 100, 'dataset': 'emore', 'dataset_path': '../datasets/faces_emore', 'num_classes': 85742, 'image_shape': [112, 112, 3], 'loss': 'arcface', 'network': 'r100', 'num_workers': 1, 'batch_size': 512, 'per_batch_size': 128}
0 1 E 3 prelu False
Network FLOPs: 24.2G
loading: ../datasets/faces_emore\train.rec False
INFO:root:loading recordio ../datasets/faces_emore\train.rec...
header0 label [5822654. 5908396.]
id2range 85742
5822653
rand_mirror True
call reset()
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(12000, 3, 112, 112)
ver lfw
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(14000, 3, 112, 112)
ver cfp_fp
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(12000, 3, 112, 112)
ver agedb_30
lr_steps [100000, 160000, 220000]
Traceback (most recent call last):
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\symbol\symbol.py", line 1832, in simple_bind
ctypes.byref(exe_handle)))
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\base.py", line 246, in check_call
raise get_last_ffi_error()
mxnet.base.MXNetError: Traceback (most recent call last):
File "C:\Jenkins\workspace\mxnet-tag\mxnet\src\storage\storage.cc", line 119
MXNetError: Compile with USE_CUDA=1 to enable GPU usage

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "train.py", line 485, in
main()
File "train.py", line 481, in main
train_net(args)
File "train.py", line 475, in train_net
epoch_end_callback=epoch_cb)
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\module\base_module.py", line 498, in fit
for_training=True, force_rebind=force_rebind)
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\module\module.py", line 429, in bind
state_names=self._state_names)
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\module\executor_group.py", line 280, in __init__
self.bind_exec(data_shapes, label_shapes, shared_group)
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\module\executor_group.py", line 384, in bind_exec
shared_group))
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\module\executor_group.py", line 678, in _bind_ith_exec
shared_buffer=shared_data_arrays, **input_shapes)
File "C:\Users\nhatd\AppData\Local\Programs\Python\Python36\lib\site-packages\mxnet\symbol\symbol.py", line 1838, in simple_bind
raise RuntimeError(error_msg)
RuntimeError: simple_bind error. Arguments:
data: (128, 3, 112, 112)
softmax_label: (128,)
Traceback (most recent call last):
File "C:\Jenkins\workspace\mxnet-tag\mxnet\src\storage\storage.cc", line 119
MXNetError: Compile with USE_CUDA=1 to enable GPU usage
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Can you help me see what's wrong with it?. Thank you

Start with train.py and the reported training command, then inspect the Python environment and MXNet installation referenced by the traceback. Done means the command can bind the shown input shapes and proceed without the reported USE_CUDA error.

Written by the indexing model from the issue text.