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/ .

FFCV是一个用于简单快捷地进行机器学习模型训练的库，通过消除训练过程中的数据瓶颈来加速模型训练，结合有效的文件存储格式、缓存、数据预处理、异步数据传输和即时编译等技术，以使数据加载和传输更加高效，确保GPU可以达到完全利用率，并异步地将尽可能多的数据处理卸载到CPU，从而释放GPU周期进行训练。

FFCV：排除数据瓶颈以加速培训