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ffcv ffcv g leclerc a ilyas l engstrom s m park h salman a madry ffcv accelerating training by removing data bottlenecks 2022 cvpr 2023 train machine learning models fast conda create n ffcv python 3 9 cupy pkg config libjpeg turbo opencv pytorch torchvision cudatoolkit 11 6 numba c conda forge c pytorch conda activate ffcv conda update ffmpeg pip install ffcv copy see the code read the docs get support keep your training code intact drop in replacement for existing loaders quickstart examples import torch from torchvision import datasets transforms from torch utils data import dataloader train_ds datasets imagefolder pth to data transform transforms compose transforms totensor transforms randomresizedcrop transforms randomhorizontalflip p 0 5 transforms normalize mean stdev train_loader dataloader train_ds shuffle true batch_size 512 num_workers 8 for ims labs in train_loader ims ims half cuda non_blocking true to memory_format ch channels_last model training from ffcv loader import loader orderoption from ffcv fields decoders import randomresizedcroprgbimagedecoder from ffcv transforms import import torchvision as tv train_loader loader pth to data beton batch_size 512 num_workers 8 order orderoption random pipelines image randomresizedcroprgbimagedecoder 224 224 totensor move to gpu asynchronously as uint8 todevice ch device cuda 0 automatically channels last totorchimage convert ch float16 standard torchvision transforms still work tv transforms normalize mean stdev prefetching caching move to gpu all handled for ims labs in train_loader model training fast train imagenet in minutes not days ffcv cuts training times and comes with simple optimized code for standard datasets see our benchmarks optimized for speed and usability drop in speed ffcv doesn t require you to change any training code make training faster by just replacing the data loading and augmenattion pipeline more models per gpu thanks to fully asynchronous thread based data loading you can now interleave training multiple models on the same gpu efficiently without any data overhead remove bottlenecks ffcv allows you to shift compute load between gpu cpu disk and memory to eliminate bottlenecks under almost any resource constraint custom fast pipelines this isn t just about fast data loading ffcv automatically fuses and compiles the data processing pipeline into machine code users can build their own compiled data transformations through a simple python api or just continue using standard pytorch data transformations hyper optimized everything about ffcv is optimized it carefully handles the caching preloading threading scheduling compilation etc so that you don t have to the numbers speak for themselves docs and support ffcv comes with continually updating documentation that includes a variety of example use cases the projects maintainers can also be reached through an ffcv slack workspace ffcv 2022 cite ffcv contributors license apache v2 maintained with by guillaume leclerc andrew ilyas and logan engstrom