This codebase is designed for training large-scale vision models on Cloud TPU VMs. It is based on Jax/Flax libraries, and uses tf.data and TensorFlow Datasets for scalable input pipelines in the Cloud.

The open-sourcing of this codebase has two main purposes:

1. Publishing the code of research projects developed in this codebase (see a list below).
2. Providing a strong starting point for running large-scale vision experiments on Google Cloud TPUs, which should scale seamlessly and out-of-the box from a single TPU core to a distributed setup with up to 2048 TPU cores.

Note, that despite being TPU-centric, our codebase should in general support CPU, GPU and single-host multi-GPU training, thanks to JAX' well-executed and transparent support for multiple backends.

big_vision aims to support research projects at Google. We are unlikely to work on feature requests or accept external contributions, unless they were pre-approved (ask in an issue first). For a well-supported transfer-only codebase, see also vision_transformer.

The following research projects were originally conducted in the big_vision codebase:

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, by Alexey Dosovitskiy*, Lucas Beyer*, Alexander Kolesnikov*, Dirk Weissenborn*, Xiaohua Zhai*, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby*
• Scaling Vision Transformers, by Xiaohua Zhai*, Alexander Kolesnikov*, Neil Houlsby, and Lucas Beyer*
• How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers, by Andreas Steiner*, Alexander Kolesnikov*, Xiaohua Zhai*, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer*
• MLP-Mixer: An all-MLP Architecture for Vision, by Ilya Tolstikhin*, Neil Houlsby*, Alexander Kolesnikov*, Lucas Beyer*, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy

• LiT: Zero-Shot Transfer with Locked-image Text Tuning, by Xiaohua Zhai*, Xiao Wang*, Basil Mustafa*, Andreas Steiner*, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer*

• Knowledge distillation: A good teacher is patient and consistent, by Lucas Beyer*, Xiaohua Zhai*, Amélie Royer*, Larisa Markeeva*, Rohan Anil, and Alexander Kolesnikov*

• Are we done with ImageNet?, by Lucas Beyer*, Olivier J. Hénaff*, Alexander Kolesnikov*, Xiaohua Zhai*, and Aäron van den Oord*

The main entry point is a trainer module, which typically does all the boilerplate related to creating a model and an optimizer, loading the data, checkpointing and training/evaluating the model inside a loop. We provide the canonical trainer train.py in the root folder. Normally, individual projects within big_vision fork and customize this trainer.

All models, evaluators and preprocessing operations live in the corresponding subdirectories and can often be reused between different projects. We encourage compatible APIs within these directories to facilitate reusability, but it is not strictly enforced, as individual projects may need to introduce their custom APIs.

We have a powerful configuration system, with the configs living in the configs/ directory. Custom trainers and modules can seamlessly extend/modify the configuration options.

Training jobs are robust to interruptions and will resume seamlessly from the last saved checkpoint (assuming user provides the correct --workdir path).

Each configuration file contains a comment at the top with a COMMAND snippet to run it, and some hint of expected runtime and results. See below for more details, but generally speaking, running on a GPU machine involves calling python -m COMMAND while running on TPUs, including multi-host, involves

gcloud alpha compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all
  --command "bash big_vision/run_tpu.sh COMMAND"

See instructions below for more details on how to use Google Cloud TPUs.

The first release contains the core part of pre-training, transferring, and evaluating classification models at scale on Cloud TPU VMs.

Features and projects we plan to release in the near future, in no particular order:

• ImageNet-21k in TFDS.
• MLP-Mixer.
• Loading misc public models used in our publications (NFNet, MoCov3, DINO).
• Contrastive Image-Text model training and evaluation as in LiT and CLIP.
• "Patient and consistent" distillation.
• Memory-efficient Polyak-averaging implementation.
• Advanced JAX compute and memory profiling. We are using internal tools for this, but may eventually add support for the publicly available ones.

We will continue releasing code of our future publications developed within big_vision here.

The following exist in the internal variant of this codebase, and there is no plan for their release:

• Regular regression tests for both quality and speed. They rely heavily on internal infrastructure.
• Advanced logging, monitoring, and plotting of experiments. This also relies heavily on internal infrastructure. However, we are open to ideas on this and may add some in the future, especially if implemented in a self-contained manner.
• Not yet published, ongoing research projects.

To create a single machine with 8 TPU cores, follow the following Cloud TPU JAX document: https://cloud.google.com/tpu/docs/run-calculation-jax

To support large-scale vision research, more cores with multiple hosts are recommended. Below we provide instructions on how to do it.

First, create some useful variables, which we be reused:

export NAME="a name of the TPU deployment, e.g. my-tpu-machine"
export ZONE="GCP geographical zone, e.g. europe-west4-a"
export GS_BUCKET_NAME="Name of the storage bucket, e.g. my_bucket"

The following command line will create TPU VMs with 32 cores, 4 hosts.

gcloud alpha compute tpus tpu-vm create $NAME --zone $ZONE --accelerator-type v3-32 --version tpu-vm-tf-2.8.0

Fetch the big_vision repository, copy it to all TPU VM hosts, and install dependencies.

git clone --branch=master https://github.com/google-research/big_vision
gcloud alpha compute tpus tpu-vm scp --recurse big_vision/big_vision $NAME: --worker=all --zone=$ZONE
gcloud alpha compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "bash big_vision/run_tpu.sh"

Everything in this section you need to do only once, and, alternatively, you can also do it on your local machine and copy the result to the cloud bucket. For convenience, we provide instructions on how to prepare data using Cloud TPUs.

Download and prepare TFDS datasets using a single worker. Seven TFDS datasets used during evaluations will be generated under ~/tensorflow_datasets/ (should take 10-15 minutes in total).

gcloud alpha compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=0 --command "bash big_vision/run_tpu.sh big_vision.tools.download_tfds_datasets cifar10 cifar100 oxford_iiit_pet oxford_flowers102 cars196 dtd uc_merced"

Copy the datasets to GS bucket, to make them accessible to all TPU workers.

gcloud alpha compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=0 --command "rm -r ~/tensorflow_datasets/downloads && gsutil cp -r ~/tensorflow_datasets gs://$GS_BUCKET_NAME"

If you want to integrate other public or custom datasets, i.e. imagenet2012, please follow the official guideline.

For the full list of pre-trained models check out the load function defined in the same module as the model code. And for example config on how to use these models, see configs/transfer.py.

The following command line fine-tunes a pre-trained vit-i21k-augreg-b/32 model on cifar10 dataset.

gcloud alpha compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "TFDS_DATA_DIR=gs://$GS_BUCKET_NAME/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.train --config big_vision/configs/transfer.py:model=vit-i21k-augreg-b/32,dataset=cifar10,crop=resmall_crop --workdir gs://$GS_BUCKET_NAME/big_vision/workdir/`date '+%m-%d_%H%M'` --config.lr=0.03"

To train your own big_vision models on a large dataset, e.g. imagenet2012 (prepare the TFDS dataset), run the following command line.

gcloud alpha compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "TFDS_DATA_DIR=gs://$GS_BUCKET_NAME/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.train --config big_vision/configs/bit_i1k.py  --workdir gs://$GS_BUCKET_NAME/big_vision/workdir/`date '+%m-%d_%H%M'`"

We provide a well-tuned ViT-S/16 baseline in the config file named vit_s16_i1k.py. It achieves 76.5% accuracy on ImageNet validation split in 90 epochs of training, being a strong and simple starting point for research on the ViT models.

Please see our arXiv note for more details and if this baseline happens to by useful for your research, consider citing

@article{vit_baseline,
  url = {https://arxiv.org/abs/2205.01580},
  author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
  title = {Better plain ViT baselines for ImageNet-1k},
  journal={arXiv preprint arXiv:2205.01580},
  year = {2022},
}

If you found this codebase useful for your research, please consider using the following BibTEX to cite it:

@misc{big_vision,
  author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
  title = {Big Vision},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/google-research/big_vision}}
}

This is not an official Google Product.