Catalyst extensions

Catalyst has a wide variety of framework extensions availabe if you need them. The base Catalyst package made as light as possible to extend only the PyTorch. Nevertheless, there are much more availabe:

pip install catalyst[comet] # + comet_ml
pip install catalyst[cv] # + imageio, opencv, scikit-image, torchvision, Pillow
pip install catalyst[deepspeed] # + deepspeed
pip install catalyst[dev] # used for Catalyst development and documentaiton rendering
pip install catalyst[ml] # + scipy, matplotlib, pandas, scikit-learn
pip install catalyst[mlflow] # + mlflow
pip install catalyst[neptune] # + neptune-client
pip install catalyst[onnx-gpu] # + onnx, onnxruntime-gpu
pip install catalyst[onnx] # + onnx, onnxruntime
pip install catalyst[optuna] # + optuna
pip install catalyst[profiler] # + profiler
pip install catalyst[wandb] # + wandb
pip install catalyst[all] # + catalyst[cv], catalyst[ml], catalyst[optuna]

As far as you can see, Catalyst has a lot of extensions, and not all of them are strictly required for everyday PyTorch use, so they are extras. Please see the documentaiton for notes about extra requirements.


To make your workflow reproducible, you could create a .catalyst file under your project or root directory so that Catalyst could understand all needed requirements during framework initialization. For example:

cv_required = false
mlflow_required = false
ml_required = true
neptune_required = false
optuna_required = false

With such a configuration file, Catalyst will raise you an error if there are now required catalyst[ml] dependencies were found.

If you haven’t found the answer for your question, feel free to join our slack for the discussion.