Torch.jl
erfa
| Torch.jl | erfa | |
|---|---|---|
| 6 | 2 | |
| 205 | 125 | |
| 2.0% | 0.0% | |
| 4.2 | 4.0 | |
| 13 days ago | 13 days ago | |
| Julia | C | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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Torch.jl
- Julia 1.10 Released
- Julia 1.9: A New Era of Performance and Flexibility
- How usable is Julia for Natural Language Processing Machine learning?
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Does Julia Have a Chance to Overthrown Python in the Machine Learning Industry?
For frontends Python has quite some head-start. In principle it would be possible to write Julia frond-ends to existing ML libraries (written e.g. in C), for example https://github.com/FluxML/Torch.jl , but the advantages over Python frontends would be very limited. Only a front-to-back Julia implementation leverages most of the language advantages like composibility and flexibility.
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Julia: faster than Fortran, cleaner than Numpy
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.
erfa
- Julia 1.10 Released
-
License Adherence Help
I'm working on a pure Rust approximation of astropy. Up til now, I was able to recreate the intent by looking at an external API, but I'm moving on to functionality that I don't understand enough to implement without basically copying the code. Astropy uses the BSD-3 license, and it wraps the ERFA library which uses a custom license. My project currently uses the MIT license. My PR is here - my question is have I attributed everything correctly, or is there anything I need to change for everything to be above-board?
What are some alternatives?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
astro-rs - Astronomy utils written in Rust
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
astropy - Astronomy and astrophysics core library
gluon-nlp - NLP made easy
assist - ASSIST is a software package for ephemeris-quality integrations of test particles.
SciPyDiffEq.jl - Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization
PyCallChainRules.jl - Differentiate python calls from Julia
JuliaTorch - Using PyTorch in Julia Language
threads - Threads for Lua and LuaJIT. Transparent exchange of data between threads is allowed thanks to torch serialization.
Lux.jl - Explicitly Parameterized Neural Networks in Julia