Strange Cuda Out of Memory Behavior in Pytorch: Understanding the Root Cause and Solution

Описание: Struggling with `Cuda out of memory` errors in PyTorch? Discover the common causes and learn how to resolve them, including the impact of worker numbers on memory allocation.
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Strange Cuda Out of Memory Behavior in PyTorch: Understanding the Root Cause and Solution

If you're deep into the world of PyTorch and GPU computing, you might have encountered the frustrating CUDA out of memory error. This is especially true when working with large datasets and intensive models like U-Net for image segmentation. Even on powerful hardware, like the 24GB Titan RTX, it's not uncommon to run into challenges that seem paradoxical. In this guide, we will delve into a specific case where reducing batch sizes seemed to exacerbate the out of memory issue, leading to confusion. More importantly, we'll uncover the solution that resolved the problem.

The Problem: Baffling Memory Allocation Issues

In the reported case, an experiment with a 24GB Titan RTX card led to repeated CUDA out of memory errors, despite having seemingly ample free memory. Here’s a summary of the observations:

Different configurations showed increasing memory usage when reducing batch sizes.

Statements from PyTorch indicated it tried to allocate more memory despite the available free space.

The context involved working with images of varying sizes, from 448 pixels to 224 pixels, with batch sizes ranging from 1 to 8.

For instance, at an image size of 448 and a batch size of 6, PyTorch attempted to allocate 3.12 GiB while 19.66 GiB was reported as free. This contradiction left many scratching their heads.

The Solution: Worker Count Impact on Memory

After extensive trials and investigations, the culprit behind the memory allocation issue turned out to be the number of workers used when loading data. It's commonly overlooked, yet it plays a crucial role in memory management in PyTorch.

Steps Taken to Resolve the Issue

Reduce the Number of Workers: By lowering the number of workers in the DataLoader, the memory utilization was optimized. This adjustment allows for a smoother process in how data is fetched and processed.

Testing Configurations: After modifying the worker count, the model was retried with various configurations (image sizes and batch sizes). The errors that initially plagued the training process were no longer present after this adjustment.

Key Takeaways

Monitor Worker Count: When dealing with CUDA memory issues, always check how many workers you are utilizing. Too many workers can lead to unnecessary memory strain.

Understand Memory Utilization: Know how memory allocation works in PyTorch, including how data loaders interact with processes.

Iterate Configuration Changes: Changes in parameters should be tested one at a time to understand their impact, which aids in targeted troubleshooting.

Conclusion

In conclusion, the CUDA out of memory error in PyTorch can be perplexing, especially when the math doesn't seem to add up. In this case, simply lowering the number of workers mitigated the issue entirely. Always remember that memory behavior can be influenced by multiple factors when working with models and GPUs. Keep experimenting, and don’t hesitate to make changes to your configuration; the solution might be simpler than you think!