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 prevent AMP from working correctly:

1. PyTorch Version: Ensure you are using a compatible PyTorch version. While you mentioned using PyTorch 2.0, it's always good to check the compatibility with the specific YOLOv5 version you are using.

2. CUDA Version: CUDA 11.8 should generally be fine, but compatibility between PyTorch, CUDA, and your GPU drivers can sometimes cause issues. Make sure all components are compatible.

3. Graphics Hardware: Your 4070Ti should support AMP, but ensure your NVIDIA drivers are up to date.

4. Software Dependencies: Sometimes, other dependencies or libraries might interfere. Ensure all dependencies are up to date by running:

   pip install -U torch torchvision torchaudio

5. YOLOv5 Version: Ensure you are using the latest version of YOLOv5. You can update it by pulling the latest changes from the repository:

   git pull

6. Environment Configuration: Sometimes, the environment configuration might cause issues. Try running your training script in a clean virtual environment.

If you have verified all the above and the issue persists, you can try running a minimal example to isolate the problem. Here's a simple script to test AMP functionality:

import torch
from torch.cuda.amp import autocast, GradScaler

# Check if AMP is available
if not torch.cuda.is_available():
    print("CUDA is not available.")
else:
    scaler = GradScaler()
    model = torch.nn.Linear(10, 10).cuda()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
    data = torch.randn(16, 10).cuda()
    target = torch.randn(16, 10).cuda()

    for epoch in range(10):
        optimizer.zero_grad()
        with autocast():
            output = model(data)
            loss = torch.nn.functional.mse_loss(output, target)
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
        print(f"Epoch {epoch}, Loss: {loss.item()}")

If the above script runs without issues, the problem might be specific to your YOLOv5 setup. If you still encounter issues, please provide any error messages or logs, and we can further investigate.

Thanks again for your support and patience! 😊