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@@ -210,8 +210,11 @@ You shouldn't use 🤗 Accelerate if you don't want to write a training loop you
 
 If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around your training loop, some frameworks that are built on top of 🤗 Accelerate are listed below:
 
-* [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) is a lightweight training library, with a streamlined feature set centred around a general-purpose [Trainer](https://pytorch-accelerated.readthedocs.io/en/latest/trainer.html), that places a huge emphasis on simplicity and transparency; enabling users to understand exactly what is going on under the hood, but without having to write and maintain the boilerplate themselves!
+* [Animus](https://github.com/Scitator/animus) is a minimalistic framework to run machine learning experiments. Animus highlights common "breakpoints" in ML experiments and provides a unified interface for them within [IExperiment](https://github.com/Scitator/animus/blob/main/animus/core.py#L76).
+* [Catalyst](https://github.com/catalyst-team/catalyst#getting-started) is a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. Catalyst provides a [Runner](https://catalyst-team.github.io/catalyst/api/core.html#runner) to connect all parts of the experiment: hardware backend, data transformations, model train, and inference logic.
 * [Kornia](https://kornia.readthedocs.io/en/latest/get-started/introduction.html) is a differentiable library that allows classical computer vision to be integrated into deep learning models. Kornia provides a [Trainer](https://kornia.readthedocs.io/en/latest/x.html#kornia.x.Trainer) with the specific purpose to train and fine-tune the supported deep learning algorithms within the library.
+* [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) is a lightweight training library, with a streamlined feature set centred around a general-purpose [Trainer](https://pytorch-accelerated.readthedocs.io/en/latest/trainer.html), that places a huge emphasis on simplicity and transparency; enabling users to understand exactly what is going on under the hood, but without having to write and maintain the boilerplate themselves!
+
 
 ## Installation
 



Discussion: body: btw, it would be great if you could comment on my "Accelerator mixin" - [Engine](https://github.com/Scitator/animus/blob/main/animus/torch/engine.py) in terms of "a bit more user-friendly Accelerator API". I found it useful for me to run pipelines within notebooks or python scripts without CLI.
body: Ah thanks! I am wrapping up some things in Transformers and will go back to Accelerate pretty soon (haven't forgotten #215 !)

You mixins look great! Having one per kind of distributed setup is a bit against the philosophy of Accelerate (where the same exact code runs on any setup) but I like the abstraction.

To run things in notebooks, we also have `notebook_launcher`, I don't know if you tried it or not.
body: yeah, I found `notebook_launcher` an interesting functional alternative... nevertheless, from my previous experience I knew a few limitations of pure functional way, so... a small class-based API was created ;)