FFCV: Accelerating Training by Removing Data Bottlenecks
This addresses the issue of inefficient data loading and transfer for machine learning practitioners, enabling faster model training with competitive accuracy, though it is incremental as it builds on existing techniques.
The authors tackled the problem of slow machine learning training due to data bottlenecks by introducing FFCV, a library that accelerates training through efficient data handling, achieving a 75% accuracy on ImageNet with ResNet-50 in just 20 minutes on a single machine.
We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process. In particular, we combine techniques such as an efficient file storage format, caching, data pre-loading, asynchronous data transfer, and just-in-time compilation to (a) make data loading and transfer significantly more efficient, ensuring that GPUs can reach full utilization; and (b) offload as much data processing as possible to the CPU asynchronously, freeing GPU cycles for training. Using FFCV, we train ResNet-18 and ResNet-50 on the ImageNet dataset with competitive tradeoff between accuracy and training time. For example, we are able to train an ImageNet ResNet-50 model to 75\% in only 20 mins on a single machine. We demonstrate FFCV's performance, ease-of-use, extensibility, and ability to adapt to resource constraints through several case studies. Detailed installation instructions, documentation, and Slack support channel are available at https://ffcv.io/ .