BinaryBuilder.jl
HTTP.jl
BinaryBuilder.jl | HTTP.jl | |
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5 | 7 | |
379 | 624 | |
1.1% | 0.8% | |
6.5 | 7.8 | |
9 days ago | 16 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
HTTP.jl
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
The req.url field contains the URL of the received request, the req.method field contains request method, like GET or POST, the req.body field contains the POST body of the request in binary format. HTTP request object contains much other information. All this you can find in HTTP.jl documentation. Our web application will only check the request method. If the received request is a POST request, it will parse req.body to JSON object and send the data from this object to the isSurvived function to make a prediction and return it to the client browser. For all other request types, it will just return the content of the index.html file, to display the web interface. This is how the whole source of titanic.jl web service looks:
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How can I use Julia to search on the web automatically?
If you want to just get the html of a website whose url you already have you can make requests from the http.jl package. https://juliaweb.github.io/HTTP.jl/stable/
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Automate the boring stuff with Julia?
HTTP.jl and Gumbo.jl for web-scraping
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Recommendations on how to start web scraping with julia for price updates? (if possible)
I haven't seen that tutorial, but I agree that HTTP.jl, Gumbo.jl, and Cascadia.jl are the way. I used them to export public wishlists from bookdepository, which has no API nor a built in exporting tool.
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Why not Julia?
I find some of the library documentation hard to understand. Compare http.jl with python's requests, for example. Something as core as HTTP requests should have clear docs with tonnes of examples. Part of this is also a personal dislike of documenter.jl styling. Idk why the contrast is so low – would prefer a standard readthedocs theme.
- Julia 1.6: what has changed since Julia 1.0?
What are some alternatives?
functorch - functorch is JAX-like composable function transforms for PyTorch.
geni-performance-benchmark
Yggdrasil - Collection of builder repositories for BinaryBuilder.jl
julia - The Julia Programming Language
dh-virtualenv - Python virtualenvs in Debian packages
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
RDKit - The official sources for the RDKit library
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
StarWarsArrays.jl - Arrays indexed as the order of Star Wars movies
PackageCompiler.jl - Compile your Julia Package
mxe - MXE (M cross environment)
Gumbo.jl - Julia wrapper around Google's gumbo C library for parsing HTML