Julia: faster than Fortran, cleaner than Numpy

• Flux.jl

  22 4,393 8.7 Julia

  Relax! Flux is the ML library that doesn't make you tensor

  

My advice would be to use Python for deep learning for now but keep watching the development of deep learning in Julia. For instance, there is an effort to achieve feature parity with PyTorch in Flux.jl. I believe Julia will be more than a viable language for deep learning in near future.

• Pytorch

  340 78,016 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

PyTorch for example is a C++ library with a Python user interface, see e.g. the language shares in GitHub (https://github.com/pytorch/pytorch ). There is also a Julia binding for Torch (https://github.com/FluxML/Torch.jl), but I do not know how up-to-date it is.

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• Torch.jl

  6 204 4.2 Julia

  Sensible extensions for exposing torch in Julia.

  

PyTorch for example is a C++ library with a Python user interface, see e.g. the language shares in GitHub (https://github.com/pytorch/pytorch ). There is also a Julia binding for Torch (https://github.com/FluxML/Torch.jl), but I do not know how up-to-date it is.

• JuliaInterpreter.jl

  5 157 7.6 Julia

  Interpreter for Julia code

  

It could, but that is a lot more work than it sounds. It might be easier to make it possible to swap out the compiler for one that is much faster (LLVM is slow but does good optimisations, other compilers like cranelift are faster but produce slower code). There is a Julia interpreter but it was written in Julia itself (it was written to support debuggers), so it doesn't really solve the latency issues.

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