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| NVIDIA-SMI 440.36       Driver Version: 440.36       CUDA Version: 10.2     |
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| 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 108…  On   | 00000000:83:00.0 Off |                  N/A |
| 21%   34C    P8    10W / 250W |      1MiB / 11178MiB |      0%      Default |
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0
gpu03
/home/cwzhou/.conda/envs/pytorch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:118: 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
warnings.warn("Detected call of lr_scheduler.step() before optimizer.step(). "
/home/cwzhou/.conda/envs/pytorch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:143: UserWarning: The epoch parameter in scheduler.step() was not necessary and is being deprecated where possible. Please use scheduler.step() to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
/home/cwzhou/.conda/envs/pytorch/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:395: UserWarning: To get the last learning rate computed by the scheduler, please use get_last_lr().
warnings.warn("To get the last learning rate computed by the scheduler, "
/home/cwzhou/.conda/envs/pytorch/lib/python3.8/site-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn(“nn.functional.tanh is deprecated. Use torch.tanh instead.”)
[epoch 0 step 0 loss 2588.6765]
[epoch 0 step 1 loss 2246.1255]
[epoch 0 step 2 loss 1857.5001]
[epoch 0 step 3 loss 1608.0941]
[epoch 0 step 4 loss 1188.3115]
[epoch 0 step 5 loss 1005.5057]
[epoch 0 step 6 loss 950.6890]
[epoch 0 step 7 loss 861.3840]
[epoch 0 step 8 loss 711.6000]
[epoch 0 step 9 loss 639.1685]
[epoch 0 step 10 loss 689.5997]
[epoch 0 step 11 loss 668.2221]
[epoch 0 step 12 loss 672.7594]
Traceback (most recent call last):
File “main.py”, line 146, in 
y_val = model(x_test)
File “/home/cwzhou/.conda/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 550, in call
result = self.forward(*input, **kwargs)
File “/home/cwzhou/20200729_H2B_tocaiwei/PReNet.py”, line 96, in forward
x = F.relu(self.res_conv4(x)+resx)
RuntimeError: CUDA out of memory. Tried to allocate 114.00 MiB (GPU 0; 10.92 GiB total capacity; 10.33 GiB already allocated; 59.06 MiB free; 10.34 GiB reserved in total by PyTorch)
what should I do?I was confused