Understanding GPU Memory Fragmentation in Fast.ai

What Is Memory Fragmentation?

GPU memory fragmentation occurs when memory allocations and deallocations create non-contiguous free memory blocks, making it impossible to allocate large tensors even if total free memory is sufficient. This is particularly problematic in Fast.ai where dynamic learning workflows and model callbacks may cache intermediate states.

Why It Happens in Fast.ai

  • Frequent model training restarts in the same kernel (e.g., Jupyter notebook).
  • Uncleared memory from callbacks, learners, or data loaders.
  • Layer-wise unfreezing in transfer learning causing partial reallocation.
  • Multiple experiments run sequentially in an interactive session without cleanup.

Diagnosing the Fragmentation Problem

Using nvidia-smi

Start with:

nvidia-smi --query-compute-apps=pid,used_memory --format=csv

This shows whether your process is still consuming memory even after you've stopped training.

Python Memory Check

import torch
print(torch.cuda.memory_summary())

This gives a breakdown of allocated, reserved, and active memory blocks.

Fast.ai Resource Audit

After a model training loop:

learn = None
gc.collect()
torch.cuda.empty_cache()

Manually freeing references and clearing cache helps release memory to prevent future fragmentation.

Common Pitfalls

1. Reusing Learner Objects Without Cleanup

Fast.ai Learner objects hold onto memory buffers across training sessions. Re-instantiating a new Learner without disposing the old one compounds fragmentation.

2. Forgetting to Clear DataLoaders

DataLoaders use pinned memory for speed, which can remain active unless explicitly deleted. This becomes a hidden consumer in longer sessions.

3. Not Restarting Kernel in Jupyter

Unlike standalone scripts, notebooks maintain object state. Even if you delete variables, Python's reference counting may delay actual deallocation.

Step-by-Step Fix Strategy

1. Proactive Cleanup

Before running a new training session:

del learn
gc.collect()
torch.cuda.empty_cache()

This ensures all associated memory buffers are de-referenced.

2. Recreate Learner Every Run

Always define a fresh instance of the Learner:

learn = vision_learner(dls, resnet34, metrics=accuracy)

Do not reuse the object across different training iterations.

3. Monitor Memory After Training

Track GPU usage at the end of training:

torch.cuda.synchronize()
print(torch.cuda.memory_allocated())

This helps validate whether memory was released appropriately.

4. Use IPython Magics to Reset GPU

%reset -f
import os
os.system("kill -9 $(nvidia-smi | awk '/ python / { print $5 }')")

Use this cautiously to forcefully release hung memory in development environments.

Best Practices

  • Modularize Training Code: Run training inside a function to ensure objects are garbage-collected post-execution.
  • Restart Jupyter Kernels Periodically: Especially after multiple model runs or tuning iterations.
  • Log Memory Metrics: Integrate memory logs into TensorBoard or wandb for tracking long-term fragmentation patterns.
  • Use torch.no_grad() for Inference: Prevents unnecessary memory accumulation when not training.
  • Use batch_size wisely: Adjust according to model size and GPU limits to prevent mid-epoch OOM errors.

Conclusion

GPU memory fragmentation is a subtle but severe problem when working with Fast.ai in iterative or long-running training workflows. While Fast.ai simplifies model experimentation, it also abstracts some of the memory management complexities. By adopting a disciplined memory handling strategy—cleaning up Learner instances, resetting environments, and tracking allocations—developers can ensure consistent training performance and avoid costly runtime interruptions.

FAQs

1. Why do OOM errors occur even when GPU memory usage is low?

Because of fragmentation—large tensors require contiguous memory blocks, and non-contiguous free memory can still lead to allocation failure.

2. Is it better to use standalone scripts instead of Jupyter notebooks?

Yes, for memory-sensitive training, standalone scripts offer better memory lifecycle control compared to interactive notebook kernels.

3. Can PyTorch automatically handle memory fragmentation?

PyTorch tries to optimize memory reuse, but it cannot always manage fragmentation introduced by improper deallocation or repeated training sessions.

4. How can I debug memory growth over time?

Log memory usage at each epoch or training loop and visualize the trend using monitoring tools like wandb, TensorBoard, or NVML APIs.

5. Should I always call torch.cuda.empty_cache()?

It's useful for freeing cached memory for other applications, but doesn't release memory held by active tensors—use in combination with gc.collect().