Closed YingjieQuBio closed 1 month ago
Maybe it's a problem with Windows and switching to Linux system can solve it. Of note, using GPU is not too much accelerating because it's just an inference process.
Thank you for the suggestion. The Windows system is not suitable, so I switched to the Linux system and the warning of running the Run inference with only receptor
was still there:
fused_multi_tensor is not installed corrected
fused_rounding is not installed corrected
fused_layer_norm is not installed corrected
fused_softmax is not installed corrected
Loading pretrained weights from /raid/quyj/DeepGlycanSite-main/src/unimol_tools/weights/mol_pre_all_h_220816.pt
GPU available: True (cuda), used: False
......
but the Retraining DeepGlycanSite
could use the GPU without the warning above using Linux system.
Hello, I make sure the environment has the correct pytorch, pytorch-geometric, cuda versions: Python 3.9.16
Name Version Build Channel pytorch-lightning 1.9.3 pypi_0 pypi torch 2.0.0+cu118 pypi_0 pypi torch-cluster 1.6.3+pt20cu118 pypi_0 pypi torch-geometric 2.5.3 pypi_0 pypi torch-scatter 2.1.1+pt20cu118 pypi_0 pypi torch-sparse 0.6.18+pt20cu118 pypi_0 pypi torch-spline-conv 1.2.2+pt20cu118 pypi_0 pypi torchmetrics 1.4.0.post0 pypi_0 pypi
======================================================================
But I am running the
Run inference with only receptor
instance with the following warning: fused_multi_tensor is not installed corrected fused_rounding is not installed corrected fused_layer_norm is not installed corrected fused_softmax is not installed corrected Loading pretrained weights from D:\Desktop\code\DeepGlycanSite-main\src\unimol_tools\weights\mol_pre_all_h_220816.pt GPU available: True (cuda), used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs Testing: 0it [00:00, ?it/s] ...... Testing DataLoader 0: 100%|██████████| 1/1 [00:01<00:00, 1.92s/it] ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Test metric DataLoader 0 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── test_loss 0.0 ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────======================================================================
I changed 593 lines of code from
torch.device('cpu')
totorch.device('cuda'),
and the problem still exists, even thoughtorch.cuda.is_available()
returns TrueI would like to know if there is any impact on the normal use of DeepGlycanSite, and how to use GPU acceleration, Thank you!