Torch.jl
oorb
Torch.jl | oorb | |
---|---|---|
6 | 3 | |
205 | 54 | |
2.0% | - | |
4.2 | 5.3 | |
13 days ago | about 2 months ago | |
Julia | Fortran | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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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.
oorb
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Show HN: OpenOrb, a curated search engine for Atom and RSS feeds
Imagine my surprise to see OpenOrb, the standard open source software package for orbit determination and minor planet propagation, on the front page of HackerNews. Its interesting software with a beautiful theoretical basis in Bayesian statistics, and a gnarly Fortran codebase - I can’t wait to see the discussion!
Oh.
It’s one thing to land near a name in use. It is quite another to take it directly!
https://github.com/oorb/oorb
- Julia 1.10 Released
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NASA’s Double Asteroid Redirection Test Is a Smashing Success
Mostly Python and Fortran. See for example https://github.com/oorb/oorb.
The hardest problems are always the social ones. How do you get uptake of a new method, how do you get funding, how do you politely tell a collaboration they are doing the wrong thing, etc.
But if you mean pure technical stuff - the hardest problem I had to solve was rethinking some of the inner loops of the THOR algorithm. The problem was essentially to speed up a Hough transform in 6D space. Lots of time spent profiling CPU cache timings to get that fast.
What are some alternatives?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
thor - Tracklet-less Heliocentric Orbit Recovery
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
threads - Threads for Lua and LuaJIT. Transparent exchange of data between threads is allowed thanks to torch serialization.
gluon-nlp - NLP made easy
Tidier.jl - Meta-package for data analysis in Julia, modeled after the R tidyverse.
SciPyDiffEq.jl - Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization
Lux.jl - Explicitly Parameterized Neural Networks in Julia
JuliaTorch - Using PyTorch in Julia Language
Transformers.jl - Julia Implementation of Transformer models
astropy - Astronomy and astrophysics core library