Question

我遇到了CUDNN_STATUS_INTERNAL_ERROR错误，如下所示。

python train_v2.py

Traceback (most recent call last):
  File "train_v2.py", line 113, in <module>
    main()
  File "train_v2.py", line 74, in main
    model.cuda()
  File "/home/ahkim/Desktop/squad_vteam/src/model.py", line 234, in cuda
    self.network.cuda()
  File "/home/ahkim/anaconda3/envs/san/lib/python3.6/site-packages/torch/nn/modules/module.py", line 249, in cuda
    return self._apply(lambda t: t.cuda(device))
  File "/home/ahkim/anaconda3/envs/san/lib/python3.6/site-packages/torch/nn/modules/module.py", line 176, in _apply
    module._apply(fn)
  File "/home/ahkim/anaconda3/envs/san/lib/python3.6/site-packages/torch/nn/modules/module.py", line 176, in _apply
    module._apply(fn)
  File "/home/ahkim/anaconda3/envs/san/lib/python3.6/site-packages/torch/nn/modules/module.py", line 176, in _apply
    module._apply(fn)
  File "/home/ahkim/anaconda3/envs/san/lib/python3.6/site-packages/torch/nn/modules/rnn.py", line 112, in _apply
    self.flatten_parameters()
  File "/home/ahkim/anaconda3/envs/san/lib/python3.6/site-packages/torch/nn/modules/rnn.py", line 105, in flatten_parameters
    self.batch_first, bool(self.bidirectional))
RuntimeError: CUDNN_STATUS_INTERNAL_ERROR

我应该尝试解决什么问题？我尝试删除.nv，但没有成功。

nvidia-smi

Wed Aug  8 10:56:29 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.67                 Driver Version: 390.67                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX TIT...  Off  | 00000000:04:00.0 Off |                  N/A |
| 22%   21C    P8    15W / 250W |    125MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  GeForce GTX TIT...  Off  | 00000000:05:00.0 Off |                  N/A |
| 22%   24C    P8    14W / 250W |     11MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   2  GeForce GTX TIT...  Off  | 00000000:08:00.0 Off |                  N/A |
| 22%   23C    P8    14W / 250W |     11MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   3  GeForce GTX TIT...  Off  | 00000000:09:00.0 Off |                  N/A |
| 22%   23C    P8    15W / 250W |     11MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   4  GeForce GTX TIT...  Off  | 00000000:85:00.0 Off |                  N/A |
| 22%   24C    P8    14W / 250W |     11MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   5  GeForce GTX TIT...  Off  | 00000000:86:00.0 Off |                  N/A |
| 22%   23C    P8    15W / 250W |     11MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   6  GeForce GTX TIT...  Off  | 00000000:89:00.0 Off |                  N/A |
| 22%   21C    P8    15W / 250W |     11MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   7  GeForce GTX TIT...  Off  | 00000000:8A:00.0 Off |                  N/A |
| 22%   23C    P8    15W / 250W |     11MiB / 12212MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1603      C   /usr/bin/python                              114MiB |
+-----------------------------------------------------------------------------+

更新：

使用Nvidia，相同的代码运行没有错误 Driver Version: 396.26（CUDAV9.1.85。torch.backends.cudnn.version（）： 7102）。我在使用Driver Version: 390.67时遇到了错误（CUDA V9.1.85。torch.backends.cudnn.version（）：7102）

Answer 1

通过以下步骤解决。

export LD_LIBRARY_PATH= "/usr/local/cuda-9.1/lib64"
由于nfs问题，因此pytoch缓存不在nfs中。例如：

$ rm〜/ .nv -rf

$ mkdir -p /tmp/$USER/.nv

$ ln -s /tmp/$USER/.nv〜/ .nv

Answer 2

转到pytorch网站，然后选择满足您的cuda版本的版本 https://pytorch.org/

cu100 = cuda 10.0

pip3 uninstall torch
pip3 install https://download.pytorch.org/whl/cu100/torch-1.0.1.post2-cp36-cp36m-linux_x86_64.whl

Answer 3

转到https://pytorch.org/复制“运行此命令：”框中的命令。不要选择任何内容，只需选择复制命令并粘贴到您使用的编辑器中即可。我希望它能起作用。对我来说，它很好用。

对于RTX 2070

提示1

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

提示2

conda install pytorch-nightly cudatoolkit=10.0 -c pytorch

pytorch Rnn.py RuntimeError：CUDNN_STATUS_INTERNAL_ERROR

更新：

3 个答案: