StaticTools.jl
BinaryBuilder.jl
StaticTools.jl | BinaryBuilder.jl | |
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6 | 5 | |
161 | 380 | |
- | 1.3% | |
6.4 | 6.5 | |
12 days ago | 9 days ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
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StaticTools.jl
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Is Julia suitable today as a scripting language?
It's not beta. I mean PackageCompiler.jl (used in production, by e.g. PumasAI company, a huge success) which makes though non-small binaries. Other tools for tiny binaries (and limited subset of Julia), are yes "experimental" but work: https://github.com/brenhinkeller/StaticTools.jl
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My Journey from R to Julia
We already have some forward prototypes of being able to run Julia ahead-of-time compiled native code from the command line.
https://github.com/brenhinkeller/StaticTools.jl
I think what we'll end up with is a language that can be used in both a fully static mode and in a dynamic mode along with some possible mixing. We may yet get the benefits of a statically compiled language as the tooling continues to develop. I do not see anything inherent in the language that would prevent that from happening.
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Size of a "hello world" application
https://github.com/brenhinkeller/StaticTools.jl is meant to facilitate this.
- Statictools.jl: Compilation of (some) Julia code to standalone native binaries
- We Use Julia, 10 Years Later
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?
ProtoStructs.jl - Easy prototyping of structs
functorch - functorch is JAX-like composable function transforms for PyTorch.
www.julialang.org - Julia Project website
Yggdrasil - Collection of builder repositories for BinaryBuilder.jl
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
HTTP.jl - HTTP for Julia
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
dh-virtualenv - Python virtualenvs in Debian packages
julia - The Julia Programming Language
RDKit - The official sources for the RDKit library
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
StarWarsArrays.jl - Arrays indexed as the order of Star Wars movies