What prevents me from using the AMP function？

[thgpddl]

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Question

Thank you very much for your work. I would like to be able to use the AMP function, but when training on my device it says AMP checks failed ❌, disabling Automatic Mixed Precision. My device situation is as follows:

pytorch=2.0
CUDA=11.8
4070Ti

I would like to know what are the factors that prevent AMP from working? Like CUDA version, graphics hardware, or other factors, because I really want to use the AMP feature!

Additional

No response

[glenn-jocher]

@thgpddl hi there!

Thank you for your kind words and for providing detailed information about your setup. The AMP (Automatic Mixed Precision) feature can indeed be very beneficial for speeding up training and reducing memory usage. Here are a few factors that might be preventing AMP from working on your device:

1. PyTorch Version: Ensure that you are using a compatible version of PyTorch. While you mentioned using PyTorch 2.0, it might be worth checking if there are any known issues with this version and AMP.

2. CUDA Version: Your CUDA version (11.8) should generally be compatible with AMP, but it's always good to verify that your CUDA toolkit and drivers are up to date.

3. Graphics Hardware: The NVIDIA 4070Ti should support AMP, but ensure that you have the latest drivers installed.

4. Software Dependencies: Sometimes, other dependencies or libraries might interfere with AMP. Make sure all your packages are up to date.

5. Code Implementation: Ensure that your code is correctly set up to use AMP. Here’s a small example of how to enable AMP in your training loop:

   scaler = torch.cuda.amp.GradScaler()
   
   for epoch in range(num_epochs):
       for data, target in train_loader:
           optimizer.zero_grad()
           with torch.cuda.amp.autocast():
               output = model(data)
               loss = criterion(output, target)
           scaler.scale(loss).backward()
           scaler.step(optimizer)
           scaler.update()

6. YOLOv5 Configuration: Ensure that the --amp flag is correctly set when running your training script.

If you have verified all the above and the issue persists, it might be helpful to check the YOLOv5 issues and discussions for any similar reports or updates. Additionally, you can try running the latest version of YOLOv5 to see if the issue has been resolved in a newer release.

Feel free to share any additional logs or error messages you encounter, and we can further investigate the issue together. 😊