Demystify RAM Usage in Multi-Process Data Loaders
A typical PyTorch training program on 8 GPUs with 4 dataloader workers per GPU would create at least
8 * (4+1) = 40 processes.
A naive use of torch dataset and dataloader can easily replicate your dataset's RAM usage by 40 times.
This issue has probably affected everyone who has done anything nontrivial with PyTorch.
This blog post explains why it happens, and how to avoid the 40x RAM usage.
This github repo contains code and results for the above article.
Dependencies
- Python >= 3.7
- Linux
- PyTorch >= 1.10
python -m pip install psutil tabulate msgpack- Detectron2 and GPUs needed only for
main-multigpu*.py: installation instructions