python-feedstock VS Pytorch

It's already there:

https://anaconda.org/conda-forge/python

https://github.com/conda-forge/python-feedstock/pull/577

Using mamba to create a new encoding called py311 with python 3.11:

    mamba create -n py311 python=3.11

https://news.ycombinator.com/item?id=18040664

Today, conda-forge compiles CPython to relocatable platform+architecture-specific binaries with LLVM. https://github.com/conda-forge/python-feedstock/blob/master/...

Pyodide (JupyterLite) compiles CPython to WASM (or LLVM IR?) with LLVM/emscripten IIRC. Hopefully there's a clear way to implement the new GIL-less multithreading support with Web Workers in WASM, too?

The https://rapids.ai/ org has a bunch a fast Python for HPC; with Dask and pick a scheduler. Less process overhead and less need for interprocess locking of memory handles that transgress contexts due to a new GIL removal approach would be even faster than debuggable one process per core Python.

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():