How to Fix the CUDA Out of Memory?

CUDA Out of Memory Issue

CUDA, short for Compute Unified Device Architecture, is a software and hardware integration technology launched by NVIDIA.

Through this technology, users can use NVIDIA’s GPU to perform operations other than image processing and can use the GPU as a development environment for C-compilers. Therefore, some program developers will use the GPU for machine learning, etc.

However, you may encounter the CUDA out of memory error when using PyTorch, Stable Diffusion, or other machine learning libraries.

While training the model, I encountered the following problem: RuntimeError: CUDA out of memory. Tried to allocate 304.00 MiB (GPU 0; 8.00 GiB total capacity; 142.76 MiB already allocated; 6.32 GiB free; 158.00 MiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation… https://stackoverflow.com/questions/71498324/pytorch-runtimeerror-cuda-out-of-memory-with-a-huge-amount-of-free-memory

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How to Fix the CUDA Out of Memory

How to solve the Stable Diffusion CUDA out of memory or CUDA out of memory PyTorch issue? You can refer to the following content.

Stable Diffusion CUDA Out of Memory

1. Restart the computer. This is the simplest method.
2. Reduce your image to 256 x 256 resolution by making an input of -W 256 -H 256 in the command line.
3. Increase the memory that the CUDA device has access to. You do this by modifying your system’s GPU settings.
4. Buy a new GPU with more memory to replace the existing GPU if VRAM is consistently causing runtime problems.
5. Divide the data into smaller batches. Processing smaller sets of data may be needed to avoid memory overload.

CUDA Out of Memory PyTorch

1. Do what the error says.
2. Decrease the batch size used for the PyTorch model. A smaller batch size would require less memory on the GPU and may help avoid the out of memory error.
3. Try to Set max_split_size_mb to a smaller value to avoid fragmentation.
4. There is a DataParallel module in PyTorch, which allows you to distribute the model across multiple GPUs. This would help in running the PyTorch model on multiple GPUs in parallel.
5. Clear cache.

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Bottom Line

This post tells you what to do if you encounter the CUDA out of memory error when training an AI model in Stable Diffusion or PyTorch. If you have other ways to solve the issue, leave a comment in the following zone for sharing. I will appreciate that very much.

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