Pytorch Alternatives

Pytorch reviews and mentions

• Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
  4 projects | dev.to | 10 Dec 2023

  This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

• Apple releases MLX for Apple Silicon
  4 projects | /r/LocalLLaMA | 8 Dec 2023

  The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

• MLX: An array framework for Apple Silicon
  3 projects | news.ycombinator.com | 5 Dec 2023

  Exactly right that this targets a narrower surface to enable many deep learning models. I wonder how uncommon it is to hit some operation that is not included, though? It seems pretty common from a PyTorch MPS tracking issue:

  https://github.com/pytorch/pytorch/issues/77764

  NVIDIA's moat is not just in providing BLAS++ operations, but extending this to a wider range of cuSPARSE, cuSOLVE, cuTENSOR, etc. Without these, it feels like Apple is just trying to play catch up with whatever is popular and unsupported...

• 🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
  17 projects | dev.to | 6 Nov 2023

  #3 PyTorch

  17 projects | dev.to | 6 Nov 2023

  Definitely check it out and get started with it! Support PyTorch on GitHub ⭐

• Are there people out there who still like Sam atlman - AI IS AT DANGER
  3 projects | /r/ChatGPT | 31 Oct 2023
• What happens when you vectorize wide PyTorch expressions?
  3 projects | news.ycombinator.com | 26 Oct 2023

  > My so-called CPU “active” time is actually an inferred value; CUDA spins the CPU 100% constantly, even when the CPU is just waiting for the GPU

  The CUDA Runtime and Driver APIs allow you to use“blocking synchronization” where the CPU will go to sleep while waiting for synchronization with the device. However, it seems that PyTorch doesn’t expose this functionality in any of its Python APIs:

  https://github.com/pytorch/pytorch/issues/28224

  What happens when you try using ctypes to call into libcudart.so to set the device flags as described in the above issue? You’ll have to call torch.cuda.init() for it to work, and unfortunately it won’t work if PyTorch is launching kernels from other threads.

• Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
  3 projects | /r/learnpython | 25 Oct 2023

  I was looking at all the big projects like numpy, pytorch, flask, etc.

• From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
  4 projects | dev.to | 15 Oct 2023

  Sample config files are available in the repo, and it lets you set the paths of the folders containing your pictures for training and testing. Then, once your model is trained and validated, you can use the inference script to test it under simulated conditions on a single image or a folder of images. For example, with PyTorch, you can run the inference script as follows:

• Samsung expected to report 80% profit plunge as losses mount at chip business
  3 projects | news.ycombinator.com | 10 Oct 2023

  > there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).

  There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.

  I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.

  And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.

  [0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...

  [1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...

  [2] https://github.com/pytorch/pytorch/tree/main/aten/src

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