JuliaTorch
Torch.jl
| JuliaTorch | Torch.jl | |
|---|---|---|
| 1 | 6 | |
| 70 | 204 | |
| - | 1.5% | |
| 10.0 | 4.2 | |
| over 4 years ago | 7 days ago | |
| Julia | Julia | |
| - | GNU General Public License v3.0 or later |
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JuliaTorch
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.
What are some alternatives?
gluon-nlp - NLP made easy
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
SciPyDiffEq.jl - Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization
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
oorb - An open-source orbit-computation package for Solar System objects.
PyCallChainRules.jl - Differentiate python calls from Julia
LibTIFF.jl - Clang.jl generated wrapper around Libtiff_jll.jl
Oceananigans.jl - 🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
assist - ASSIST is a software package for ephemeris-quality integrations of test particles.