pip install torch torchvision tqdm numpy compressai sklearn git+https://github.com/openai/CLIP.git

Using pytorch>1.7.1 : CLIP forces pytorch version 1.7.1, this is because it needs this version to use JIT. If you don't need JIT (no JIT by default) you can alctually use more recent versions of torch and torchvision pip install -U torch torchvision. Make sure to update after having isntalled CLIP.

1. pip install git+https://github.com/openai/CLIP.git
2. pip uninstall -y torchtext (probably not necessary but can cause issues if got installed as wrong pytorch version)
3. pip install scikit-learn==0.24.2 lightning-bolts==0.3.4 compressai==1.1.5 pytorch-lightning==1.3.8

Using pytorch>1.7.1 : CLIP forces pytorch version 1.7.1 you should be able to use a more recent versions. E.g.:

1. pip install git+https://github.com/openai/CLIP.git
2. pip install -U torch torchvision scikit-learn lightning-bolts compressai pytorch-lightning

1. Clone repository
2. Install PyTorch >= 1.7
3. pip install -r requirements.txt

Other installation

• For the bare minimum packages: use pip install -r requirements_mini.txt instead.
• For conda: use conda env update --file requirements/environment.yaml.
• For docker: we provide a dockerfile at requirements/Dockerfile.

Notes

• CLIP forces pytorch version 1.7.1, this is because it needs this version to use JIT. We don't use JIT so you can alctually use more recent versions of torch and torchvision pip install -U torch torchvision.
• For better logging: hydra and pytorch lightning logging don't work great together, to have a better logging experience you should comment out the folowing lines in pytorch_lightning/__init__.py :

if not _root_logger.hasHandlers():
     _logger.addHandler(logging.StreamHandler())
     _logger.propagate = False

Test installation

To test your installation and that everything works as desired you can run bin/test.sh, which will run an epoch of BICNE and VIC on MNIST.

All scripts can be found in bin and run using the command bin/*/<experiment>.sh. This will save all results, checkpoints, logs... The most important results (including summary resutls and figures) will be saved at results/exp_<experiment>. Most important are the summarized metrics results/exp_<experiment>*/summarized_metrics_merged.csv and any figures results/exp_<experiment>*/*.png.

The key experiments that that do not require very large compute are:

• VIC/VAE on rotation invariant Banana distribution: bin/banana/banana_viz_VIC.sh
• VIC/VAE on augmentation invariant MNIST: bin/mnist/augmist_viz_VIC.sh
• CLIP experiments: bin/clip/main_linear.sh

By default all scripts will log results on weights and biases. If you have an account (or make one) you should set your username in conf/user.yaml after wandb_entity:, the passwod should be set directly in your environment variables. If you prefer not logging, you can use the command bin/*/<experiment>.sh -a logger=csv which changes (-a is for append) the default wandb logger to a csv logger.

Generally speaking you can change any of the parameters either directly in conf/**/<file>.yaml or by adding -a to the script. We are using Hydra to manage our configurations, refer to their documentation if something is unclear.

If you are using Slurm you can submit directly the script on servers by adding a config file under conf/slurm/<myserver>.yaml, and then running the script as bin/*/<experiment>.sh -s <myserver>. For example configurations files for slurm see conf/slurm/vector.yaml or conf/slurm/learnfair.yaml. For more information check the documentation from submitit's plugin which we are using.