How to Fix RuntimeError: CUDA out of memory in PyTorch

Encountering the `CUDA out of memory` error while using PyTorch? This guide breaks down effective troubleshooting steps and the ultimate solution to resolve the issue efficiently.
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How to Fix RuntimeError: CUDA out of memory in PyTorch

If you've recently encountered the frustrating RuntimeError: CUDA out of memory message while running your PyTorch code, you're not alone. This error can be perplexing, especially when it appears that you have enough GPU memory available. This post will walk you through the reasons behind this error and how to effectively resolve it.

Understanding the Error

The error typically surfaces when your model tries to allocate more VRAM (Video RAM) than is available on your GPU. In your case, the output hinted at a total capacity of 1.95 GiB with 1.23 GiB already allocated and approximately 26.94 MiB free. Despite what seems like enough memory, PyTorch can reserve memory that may affect the results.

Common Sources of the Error:

High Batch Size: Large batch sizes amplify memory usage. While you tried adjusting your batch size, it's worth revisiting this aspect.

Misconfigured CUDA Versions: The version of CUDA you use can impact how memory is managed in PyTorch.

Memory Fragmentation: Previously running processes may not have freed up the memory while you assumed it was available.

Steps to Resolve the Error

1. Reduce Batch Size

Start by reducing the batch size further. While you changed it to 1, examine the possibility of running even smaller batches as a temporary fix while troubleshooting.

2. Clear the GPU Memory

Sometimes leftover processes can cause memory allocation issues. You can manually clear the memory by restarting your runtime or using the following code in your script:

[[See Video to Reveal this Text or Code Snippet]]

3. Monitor GPU Memory Usage

Utilize tools like nvidia-smi in your terminal to monitor your GPU's memory usage in real-time. This can give you insights into which processes are utilizing memory.

4. Reconfigure CUDA Version

This might be the key to resolving your issue. In this specific case, moving from CUDA 11.2 to CUDA 10.2 solved the memory allocation problems. Follow these steps to revert to the stable version:

Uninstall the existing CUDA version.

Install CUDA 10.2 from the official NVIDIA website.

5. Verify Compatibility of Other Libraries

Make sure that other libraries you depend on (like some drivers or frameworks) are compatible with the CUDA version you are installing. Compatibility can sometimes affect performance and stability.

6. Utilize Efficient Memory Management in PyTorch

Conclusion

Encountering the CUDA out of memory error can be a significant hurdle in your deep learning workflow. By following these troubleshooting steps and particularly by carefully managing your CUDA versions and batch sizes, you can resolve the issue efficiently. Don't hesitate to dive into memory specifications and configurations, as often these can be the underlying causes of such problems. Happy coding!