CODEWITHSUNDEEP
cudapytorchcudnn
aerin
aerin

Reputation: 22624

pytorch Rnn.py RuntimeError: CUDNN_STATUS_INTERNAL_ERROR

I'm getting an CUDNN_STATUS_INTERNAL_ERROR error like below.

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

What should I try to resolve this issue? I tried deleting .nv but no success.

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 |
+-----------------------------------------------------------------------------+

Update:

The same code runs without error using Nvidia Driver Version: 396.26 (cuda V9.1.85. torch.backends.cudnn.version(): 7102). I'm getting an error using Driver Version: 390.67 (cuda V9.1.85. torch.backends.cudnn.version(): 7102)

Upvotes: 0

Views: 4225

Answers (3)

Khawar Islam
Khawar Islam

Reputation: 2914

Go to https://pytorch.org/ copy a command which is in "Run this Command:" box. Do not select anything, just select copy command and paste in your editor you are using. I hope it works. For me, it works fine.

For RTX 2070

Tip 1

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Tip 2

conda install pytorch-nightly cudatoolkit=10.0 -c pytorch

Upvotes: 0

張介騰
張介騰

Reputation: 1

Go to pytorch website, and choose the version which satisfies your cuda version 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

Upvotes: 0

aerin
aerin

Reputation: 22624

solved by below steps.

  1. export LD_LIBRARY_PATH= "/usr/local/cuda-9.1/lib64"

  2. Due to nfs issue, have pytoch cache not in nfs. For example:

    $ rm ~/.nv -rf

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

    $ ln -s /tmp/$USER/.nv ~/.nv

Upvotes: 2

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