# Why is Python, not Julia, still used for most state-of-the-art AI research?

**URL:** <https://discourse.julialang.org/t/why-is-python-not-julia-still-used-for-most-state-of-the-art-ai-research/45896>\
**Category:** Offtopic\
**Tags:** knet, flux, machine-learning, mlj, sciml\
**Created:** [September 1, 2020, 1:49pm UTC](https://discourse.julialang.org/t/why-is-python-not-julia-still-used-for-most-state-of-the-art-ai-research/45896 "2020-09-01T13:49:21Z")\
**Posts on this page:** 1\
**Showing post:** 37

<div class="post-metadata">

**Author:** ![Palli](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/palli/32/3380_2.png) [@Palli](https://discourse.julialang.org/u/Palli)\
**Post date:** [September 2, 2020, 10:41am UTC](https://discourse.julialang.org/t/why-is-python-not-julia-still-used-for-most-state-of-the-art-ai-research/45896/37 "2020-09-02T10:41:47Z")

</div>

I don’t know, maybe you can get all the important (speed) benefits of PyTorch with (without giving up any Julia benefits?):

> **[GitHub - cambridge-mlg/ThArrays.jl: A Julia interface for PyTorch's C++...](https://github.com/cambridge-mlg/ThArrays.jl)**
>
> A Julia interface for PyTorch's C++ backend, focusing on Tensor, AD, and JIT - GitHub - cambridge-mlg/ThArrays.jl: A Julia interface for PyTorch's C++ backend, focusing on Tensor, AD, and JIT

EDIT: There’s already (available since at least April):

> **[GitHub - FluxML/Torch.jl: Sensible extensions for exposing torch in Julia.](https://github.com/FluxML/Torch.jl)**
>
> Sensible extensions for exposing torch in Julia. Contribute to FluxML/Torch.jl development by creating an account on GitHub.

The short answer to the question below was “Yes and Flux”, I also link directly to a more detailed answer:

> [@Is it a good time for a PyTorch developer to move to Julia? If so, Flux? Knet?](https://discourse.julialang.org/t/is-it-a-good-time-for-a-pytorch-developer-to-move-to-julia-if-so-flux-knet/38453/8):
>
> I have also switched from Pytorch. Within a few years I think the strengths of Julia will place it far ahead of Pytorch and others: Pytorch requires underlying code to be written in c++/cuda to get the needed performance, 10x as much code to write. With Flux in particular, native data types can be used. This means that you can potentially take the gradient through some existing code (say a statistics routine) that was never intended for use with Flux. To do this with Pytorch would requir…

I’m not sure how good [GitHub - boathit/JuliaTorch: Using PyTorch in Julia Language](https://github.com/boathit/JuliaTorch)  
is. It’s a wrapper, but I tried to install in, and I see now it’s not yet a proper package (so, neither registered), so you have to git clone or download.

I guess it is/would be nice to have it (with easy installation), while I’m not so sure you would use Julia to its full potential (nor sure you could mix with Julia’s frameworks), so I think a migration to a Julia-only solution (e.g. maybe with other registered package above?) should be on people’s radar.

---

_[View the full topic](https://discourse.julialang.org/t/why-is-python-not-julia-still-used-for-most-state-of-the-art-ai-research/45896)._