juliaup
vectorflow
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| juliaup | vectorflow | |
|---|---|---|
| 10 | 12 | |
| 889 | 1,289 | |
| 3.6% | 0.3% | |
| 9.2 | 0.0 | |
| 2 days ago | 10 months ago | |
| Rust | D | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
juliaup
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How to tell Quarto where the julia-1.8 kernel is?
There is juliaup, available in the Microsoft store, for managing julia versions: https://github.com/JuliaLang/juliaup
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Is it better to install Julia with its own executable or to install it in an Anaconda environment?
Better use the official binaries from https://julialang.org/downloads/. For Windows, best use the Julia app in the MS Store (https://github.com/JuliaLang/juliaup ).
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Neovim can't find executable path for program
Thanks - fwiw, I currently use juliaup, which basically does the same thing. Unfortunately, the problem isn't that Julia isn't on the path (it is), it's that Neovim's exepath can't find it, for some reason. :)
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Julia 1.8 released
But it’s considered prerelease for Mac and Linux according to its git repository. So presumably it won’t become the default until it’s not experimental any more.
- appropriate way to run two versions of Julia on linux
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Créer simplement un cluster k8s dans PhoenixNAP avec Rancher en quelques clics …
GitHub - JuliaLang/juliaup: Julia installer and version multiplexer GitHub - JuliaPluto/PlutoUI.jl GitHub - fonsp/Pluto.jl: 🎈 Simple reactive notebooks for Julia
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I don't want to abandon Rust for Julia
Use the right tool for the job! I would never write a system utility or OS or user application or compiler in Julia, for example. juliaup is a Julia utility being written in Rust for this exact reason!
- Small Neural networks in Julia 5x faster than PyTorch
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What's the standard procedure for package requests?
And yes, I know about jill.py and have considered using it as well. It seems fairly straightforwad to use. A similar project that I found interesting is juliaup. Still, I would definitely prefer just using zypper instead of relying on third party tools. That's why I figured I might as well ask on here.
- Get first n element from an array
vectorflow
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Programming languages endorsed for server-side use at Meta
>> Mozilla (of course)
Mozilla is a c++ and javascript shop. What do they ship in Rust? How much of Firefox is written in rust for example?
>> Microsoft, Meta, Google/Acrobat, Amazon
Large firms have lots of devs and consequently lots of toy projects. Is their usage of rust more significant than their use of D? I mean Meta was churning out projects in D a while back (warp, flint, etc) and looked like it might be going all in at one point (they even hired one of the leads on D lang).
>> That's practically all of FAANG
Who were we missing? Netflix, they’ve dabbled with D too: https://github.com/Netflix/vectorflow
Don’t misunderstand my point - it’s not that D is more popular than rust, it’s that rust is not used for real work in any significant capacity yet.
Where’s the big project written in rust? Servo and the rust compiler are the only two large rust projects on github.
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Cloud TPU VMs are generally available
Thanks Zak, already applied.
Just wondering does TPU VM support Vectorflow?
https://github.com/Netflix/vectorflow
- Vectorflow is a minimalist neural network library optimized for sparse data and single machine environments open sourced by Netflix (r/MachineLearning)
- [P] Vectorflow is a minimalist neural network library optimized for sparse data and single machine environments open sourced by Netflix
- Vectorflow is a minimalist neural network library optimized for sparse data and single machine environments open sourced by Netflix
- Vectorflow: Minimalist neural network library faster than TensorFlow in D
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Small Neural networks in Julia 5x faster than PyTorch
A library I designed a few years ago (https://github.com/Netflix/vectorflow) is also much faster than pytorch/tensorflow in these cases.
In "small" or "very sparse" setups, you're memory bound, not compute bound. TF and Pytorch are bad at that because they assume memory movements are worth it and do very little in-place operations.
Different tools for different jobs.
What are some alternatives?
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
jill.py - A cross-platform installer for the Julia programming language
dcompute - DCompute: Native execution of D on GPUs and other Accelerators
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
jill - Command line installer of the Julia Language.
LeNetTorch - PyTorch implementation of LeNet for fitting MNIST for benchmarking.
Pluto.jl - 🎈 Simple reactive notebooks for Julia
blis - BLAS-like Library Instantiation Software Framework
n - Node version management
ugrep - NEW ugrep 5.1: an ultra fast, user-friendly, compatible grep. Ugrep combines the best features of other grep, adds new features, and searches fast. Includes a TUI and adds Google-like search, fuzzy search, hexdumps, searches nested archives (zip, 7z, tar, pax, cpio), compressed files (gz, Z, bz2, lzma, xz, lz4, zstd, brotli), pdfs, docs, and more