PyTorch Landscape(pytorch.landscape2.io)
53 pointsby salamo8 hours ago9 comments
  • lvncelot2 hours ago
    Interesting to see that PyTorch has become the de-facto standard after it seemed for a while that the dust had settled on Tensorflow.

    I also thought that Jax would in turn take over after PyTorch but it never seemed to quite take off (still in use though from what I can tell).

    • embedding-shapean hour ago
      > after it seemed for a while that the dust had settled on Tensorflow.

      Did it ever? PyTorch always seemed more popular to me, unless you were around Google people, then obviously everything was Tensorflow. But like many Google projects, I never got the impression it was as ubiquitous as PyTorch out in the community outside of Google. Maybe around 2018-2019 it felt like Tensorflow would become more popular than PyTorch eventually, but it never seemed to actually happen.

    • cold_harboran hour ago
      JAX is brilliant for research but the debugging story is still rough compared to PyTorch. eager mode + native Python exceptions win for most people.
    • porridgeraisinan hour ago
      The compiler based approach, with jax.cond, purity expectations, difficulty of making stateful abstractions, etc makes sense for large scale deployment, especially to purpose built accelerators like the systolic array ones (tpu, trainium) but not for being the primary framework used in the field. Imperative always wins. Side effects and state are necessary.
  • fouronnes35 hours ago
    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.

    • rhdunn4 hours ago
      It's useful to be able to run models/code on the CPU or split between CPU and GPU, especially for models that cannot fit into the GPU's VRAM, if you are running multiple models, or if you have training data that you need to move between CPU and GPU.
    • jamessb3 hours ago
      Rather than forking PyTorch (which has issues like continually needing updates), could you create a set of linter rules instead?
  • jeremyscanvic4 hours ago
    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

  • ternaus3 hours ago
    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.

  • kombine43 minutes ago
    Pytorch3D is flagged as archived even though it isn't on GitHub.
  • ViscountPenguin4 hours ago
    Pyro is really cool if you're into probabilistic programming languages, but the Numpyro is significantly faster iirc.
  • d3m0t3p5 hours ago
    Interesting to see clearml but not its bigger counterpart mlflow
  • yurimo5 hours ago
    Great stuff, thanks for posting.
  • reserve3 hours ago
    [dead]