What is sad is that: - many projects are arrived. - It is unclear who is responsible for the updates.

I work on one of the projects in the list, need to update a link to the project, as old one is not actual anymore. And unclear how to do it => at least with respect to my project Albumentations, the landscape is outdated :(

--- Also, added the project to the Pytorch Ecosystem many years back, but if you ask me about practical value of being the part of the Ecosystem, I would not be able to tell you anything useful.

I work on one of the projects featured in the PyTorch Ecosystem [1] and I really recommend it to anyone working on a PyTorch library. Their team is really responsive and they even offer promotion on their blog & social media.

[1] https://github.com/deepinv/deepinv

I work on differentiable geometric optics with PyTorch. Seeing a list like this is really illustrative of the power that PyTorch provides when you start considering it like a general purpose GPU-enabled state of the art numerical optimization framework.

One thing I wonder is why no one has made a fork of PyTorch yet that removes all the API surface that doesn't produce GPU friendly code. Make dtype and device arg mandatory without defaults, remove in place operations that trigger a CPU sync, etc. This would increase confidence that written code will run on the GPU and pass torch.export() on the first try.

Pyro is really cool if you're into probabilistic programming languages, but the Numpyro is significantly faster iirc.

Interesting to see clearml but not its bigger counterpart mlflow

Great stuff, thanks for posting.