Blazingly Fast Computer Vision Training with the Mosaic ResNet and Composer
Match benchmark accuracy on ImageNet (He et al., 2015) in 27 minutes, a 7x speedup (ResNet-50 on 8xA100s). Reach higher levels of accuracy up to 3.8x faster than existing state of the art (Wightman et al., 2021). Try it out in Composer, our open-source library for efficient neural network training. It’s written in standard, easy-to-use PyTorch, so modify it to suit your needs and build on it!
Composer + FFCV: Faster Together
Composer is pushing the envelope on speed and efficiency in model training. Integrating Composer with FFCV, a fast dataloading library from Aleks Madry’s lab at MIT, unlocks new speedup methods by eliminating the dataloader bottleneck often experienced when using CPU-intensive operations in the training loop. The FFCV dataloader is one of the ingredients of our Mosaic ResNet recipe, which demonstrates how algorithmic efficiency can dramatically speed up model training.
We have even more exciting things in the works. Get early access to our technology preview
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