PyTorch 1.8 release with AMD ROCm support

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • jetson-inference

    Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

    > They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference

    I was aware of that repository but from taking a cursory look at it I had thought dusty was just converting models from PyTorch to TensorRT, like here[0, 1]. Am I missing something?

    > I get 140 fps on a Xavier NX

    That really is impressive. Holy shit.

    [0]: https://github.com/dusty-nv/jetson-inference/blob/master/doc...

    [1]: https://github.com/dusty-nv/jetson-inference/issues/896#issu...

  • Pytorch

    Tensors and Dynamic neural networks in Python with strong GPU acceleration

  • WorkOS

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  • tensorflow

    An Open Source Machine Learning Framework for Everyone

    Tensorflow does officially support ROCm. The project was started by AMD and later upstreamed.

    https://github.com/tensorflow/tensorflow/tree/master/tensorf...

    https://github.com/tensorflow/tensorflow/blob/master/tensorf...

    It is true that it is not Google who are distributing binaries compiled with ROCm support through PyPI (tensorflow and tensorflow-gpu is uploaded by Google, but tensorflow-rocm is uploaded by AMD). Is this what you meant by "not officially supporting"?

  • Enzyme

    High-performance automatic differentiation of LLVM and MLIR. (by EnzymeAD)

    Have you tried using Enzyme[0] on Numba IR?

    [0] https://enzyme.mit.edu

  • syntaxdot

    Neural syntax annotator, supporting sequence labeling, lemmatization, and dependency parsing.

    What I like about PyTorch is that most of the functionality is actually available through the C++ API as well, which has 'beta API stability' as they call it. So, there are good bindings for some other languages as well. E.g., I have been using the Rust bindings in a larger project [1], and they have been awesome. A precursor to the project was implemented using Tensorflow, which was a world of pain.

    Even things like mixed-precision training are fairly easy to do through the API.

    [1] https://github.com/tensordot/syntaxdot

  • tensorrt_demos

    TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet

    > I'll also add a caveat that toolage for Jetson boards is extremely incomplete.

    A hundred times this. I was about to write another rant here but I already did that[0] a while ago, so I'll save my breath this time. :)

    Another fun fact regarding toolage: Today I discovered that many USB cameras work poorly on Jetsons (at least when using OpenCV), probably due to different drivers and/or the fact that OpenCV doesn't support ARM64 as well as it does x86_64. :(

    > They supply you with a bunch of sorely outdated models for TensorRT like Inceptionv3 and SSD-MobileNetv2 and VGG-16.

    They supply you with such models? That's news to me. AFAIK converting something like SSD-MobileNetv2 from TensorFlow to TensorRT still requires substantial manual work and magic, as this code[1] attests to. There are countless (countless!) posts on the Nvidia forums by people complaining that they're not able to convert their models.

    [0]: https://news.ycombinator.com/item?id=26004235

    [1]: https://github.com/jkjung-avt/tensorrt_demos/blob/master/ssd... (In fact, this is the only piece of code I've found on the entire internet that managed to successfully convert my SSD-MobileNetV2.)

  • Zygote.jl

    21st century AD

    > There's sadly no performant autodiff system for general purpose Python.

    Like there is for general purpose Julia? (https://github.com/FluxML/Zygote.jl)

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