A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

A minimal, single-file PyTorch ImageNet training script designed for hackability. Run train_imagenet.py to get…

• …high accuracies on ImageNet
• …with as many lines of code as the PyTorch ImageNet example
• …in 1/10th the time.

Results

Train models more efficiently, either with 8 GPUs in parallel or by training 8 ResNet-18’s at once.

See benchmark setup here: https://docs.ffcv.io/benchmarks.html.

Citation

If you use this setup in your research, cite:

@misc{leclerc2022ffcv,
    author = {Guillaume Leclerc and Andrew Ilyas and Logan Engstrom and Sung Min Park and Hadi Salman and Aleksander Madry},
    title = {ffcv},
    year = {2022},
    howpublished = {url{https://github.com/libffcv/ffcv/}},
    note = {commit xxxxxxx}
}

Configurations

The configuration files corresponding to the above results are: