RDKit
BinaryBuilder.jl
RDKit | BinaryBuilder.jl | |
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4 | 5 | |
2,452 | 380 | |
2.8% | 1.3% | |
9.5 | 6.5 | |
7 days ago | 18 days ago | |
HTML | Julia | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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RDKit
- rdkit: RDKit is a collection of cheminformatics and machine-learning software written in C++ and Python.
- Free Solvent Accessible Surface Area
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What to do about GPU packages on PyPI?
I agree. So many times I have had to reinstall rdkit using homebrew and link it again and again. Although, you can now install rdkit via pip: pip install rdkit-pypi
GitHub Issue: https://github.com/rdkit/rdkit/issues/1812#issuecomment-8088...
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?
Biopython - Official git repository for Biopython (originally converted from CVS)
functorch - functorch is JAX-like composable function transforms for PyTorch.
NetworkX - Network Analysis in Python
Yggdrasil - Collection of builder repositories for BinaryBuilder.jl
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
HTTP.jl - HTTP for Julia
Numba - NumPy aware dynamic Python compiler using LLVM
dh-virtualenv - Python virtualenvs in Debian packages
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
StarWarsArrays.jl - Arrays indexed as the order of Star Wars movies
Dask - Parallel computing with task scheduling
mxe - MXE (M cross environment)