Yggdrasil
BinaryBuilder.jl
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Yggdrasil | BinaryBuilder.jl | |
---|---|---|
4 | 5 | |
280 | 379 | |
2.2% | 1.6% | |
9.9 | 6.5 | |
about 3 hours ago | 17 days ago | |
Fortran | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
Yggdrasil
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Ann: Yggdrasil 1.0
Not to be confused with the Julia binary building platform of the same name (https://github.com/JuliaPackaging/Yggdrasil)
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jlrs v0.18: export types and functions written in Rust to Julia, improved version and platform support, and more!
For more information how to use this macro you can find the documentation here. I've been using this macro to write bindings for RustFFT; you can find the Rust bindings here, the build recipe for BinaryBuilder.jl in the Yggdrasil repo, and the RustFFT.jl package here.
- Homebrew 4.0.0 release
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Homebrew 3.0
Some of the Julia folks got tired with it and basically built a new binary builder and manager. The build scripts looks very similar to Homebrew recipes but the whole system is cross-plateform (works on windows, mac, linux, arm, ...) and the compilation itself happens in the cloud (github actions I think now), so it just downloads a small relocatable binary for you. It can seems a bit crazy to redo the whole thing from scratch but binary dependencies issues have basically disappeared from the ecosystem since the switch, so it seems to pays off big time.
BinaryBuilder.jl
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Is Julia suitable today as a scripting language?
There are some efforts and the startup times are getting better with every release and there's BinaryBuilder.jl.
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Because cross-compiling binaries for Windows is easier than building natively
There is the Julia package https://github.com/JuliaPackaging/BinaryBuilder.jl which creates an environment that fakes being another, but with the correct compilers and SDKs . It's used to build all the binary dependencies
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Discussion Thread
https://binarybuilder.org/. You can do it manually obviously, but this is easier.
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PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
> The main pain point is probably the lack of standard, multi-environment packaging solutions for natively compiled code.
Are you talking about something like BinaryBuilder.jl[1], which provides native binaries as julia-callable wrappers?
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[1] https://binarybuilder.org
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What to do about GPU packages on PyPI?
Julia did that for binary dependencies for a few years, with adapters for several linux platforms, homebrew, and for cross-compiled RPMs for Windows. It worked, to a degree -- less well on Windows -- but the combinatorial complexity led to many hiccups and significant maintenance effort. Each Julia package had to account for the peculiarities of each dependency across a range of dependency versions and packaging practices (linkage policies, bundling policies, naming variations, distro versions) -- and this is easier in Julia than in (C)Python because shared libraries are accessed via locally-JIT'd FFI, so there is no need to eg compile extensions for 4 different CPython ABIs (Julia also has syntactic macros which can be helpful here).
To provide a better experience for both package authors and users, as well as reducing the maintenance burden, the community has developed and migrated to a unified system called BinaryBuilder (https://binarybuilder.org) over the past 2-3 years. BinaryBuilder allows targeting all supported platforms with a single build script and also "audits" build products for common compatibility and linkage snafus (similar to some of the conda-build tooling and auditwheel). There was a nice talk at AlpineConf recently (https://alpinelinux.org/conf/) covering some of this history and detailing BinaryBuilder, although I'm not sure how to link into the video.
All that to say: it can work to an extent, but it has been tried various times before. The fact that conda and manylinux don't use system packages was not borne out of inexperience, either. The idea of "make binaries a distro packager's problem" sounds like a simplifying step, but that doesn't necessarily work out.
What are some alternatives?
Javis.jl - Julia Animations and Visualizations
functorch - functorch is JAX-like composable function transforms for PyTorch.