#conda-forge — Public Fediverse posts

Totally normal workflow:

I work on documenting #Jinja syntax used in #CondaForge recipes.

https://github.com/conda-forge/conda-forge.github.io/pull/2782

I decide that the snippets would use Jinja syntax highlighting. However, #Prism doesn't have one. But Internets suggest Twig would work instead.

https://github.com/PrismJS/prism/issues/759

So I try Twig. Except that Twig highlighter crashes in #Docusaurus. But there's a workaround.

https://github.com/facebook/docusaurus/issues/8065

So I copy the code over to the project, fix it and while at it, rename it to "jinja" and adjust a bit.

But then, highlighting Jinja expressions alone looks pretty bleak, so let's combine it with YAML… Hmm, that actually doesn't work that well, needs some more adjustments. And before you know it, I have a pretty new Jinja highlighter, and a recipe highlighter that combines Jinja expressions, YAML, v0 recipe selectors, v1 if:/skip: conditions, and also highlighting shell / cmd variables for a good measure.

https://github.com/conda-forge/conda-forge.github.io/pull/2790

I'm looking at Repology, and I think most of the distributions and other downstreams have rightfully boycotted #Python #chardet #copywashing. Of course, there's the possibility that some of them are simply out-of-date, though.

So far chardet-7 is distributed by #Chromebrew, #CondaForge (not on Repology), #Homebrew, #KaOS, #OpenIndiana, #openmamba, #Ravenports, #Spack and #T2 SDE. Shame on you!

https://repology.org/project/chardet/versions
https://repology.org/project/python%3Achardet/versions

Another post on #Quansight PBC blog: "BLAS/LAPACK #packaging"

https://labs.quansight.org/blog/blas-lapack-packaging

"""
#BLAS and #LAPACK are the standard libraries for linear algebra. The original implementation, often called Netlib LAPACK, developed since the 1980s, nowadays serves primarily as the origin of the standard interface, the reference implementation and a conformance test suite. The end users usually use optimized implementations of the same interfaces. The choice ranges from generically tuned libraries such as OpenBLAS and BLIS, through libraries focused on specific hardware such as Intel® oneMKL, Arm Performance Libraries or the Accelerate framework on macOS, to ATLAS that aims to automatically optimize for a specific system.

The diversity of available libraries, developed in parallel with the standard interfaces, along with vendor-specific extensions and further downstream changes, adds quite a bit of complexity around using these libraries in software, and distributing such software afterwards. This problem entangles implementation authors, consumer software authors, build system maintainers and distribution maintainers. Software authors generally wish to distribute their packages built against a generically optimized BLAS/LAPACK implementation. Advanced users often wish to be able to use a different implementation, more suited to their particular needs. Distributions wish to be able to consistently build software against their system libraries, and ideally provide users the ability to switch between different implementations. Then, build systems need to provide the scaffolding for all of that.

I have recently taken up the work to provide such a scaffolding for the Meson build system; to add support for BLAS and LAPACK dependencies to Meson. While working on it, I had to learn a lot about BLAS/LAPACK packaging: not only how the different implementations differ from one another, but also what is changed by their respective downstream packaging. In this blog post, I would like to organize and share what I have learned.
"""

#CondaForge #Debian #Fedora #Gentoo

Naprawdę cenię w #CondaForge to, że kiedy pracuję nad potwornie paskudnym projektem, który ma mnóstwo zależności, ale wspiera wyłącznie stare wersje tych zależności, to #Conda zazwyczaj potrafi wykombinować jakieś rozwiązanie, dopasować odpowiednie paczki binarne i oszczędzić mi sporo czasu.

One thing I really love about #CondaForge is that if you're working on a very, very bad project, and it has a lot of dependencies, but builds only against outdated versions of them, #Conda can generally figure out a way to come up with a solution, provide a consistent environment using binary packages and save you lots of time.

🔄 Update: I’ve just expanded my #Miniforge migration guide with a dedicated troubleshooting section. It now covers common issues I’ve encountered over the past years — including #SSL certificate errors, channel configuration for conda-forge-only environments, and a workaround for situations where #mamba isn’t available in the base environment. If you have further tips, feel free to comment here or on the blog post. #conda #CondaForge

After recent license changes at #Anaconda, Inc., #Miniforge has become the recommended, fully open solution for institutional #Python environments. Now bundling both #conda and #mamba, it replaces #Miniconda/ #Mambaforge, ensuring high performance and broad package access via #CondaForge. Here I summarized the key changes and migration steps:

🌍 https://www.fabriziomusacchio.com/blog/2025-07-03-miniforge/

#Python #DataScience #OpenSource

🥳:qgis: @qgis 3.42 has landed in #condaforge

https://anaconda.org/conda-forge/qgis

My favorite way to install #QGIS

#conda #gischat #python #opensource

By default, the #Seqera #Containers web interface searches the #bioconda and #condaforge channels 📦

But did you know that you can prepend your search with *any* conda channel?

Try it out: https://seqera.io/containers/

Okej, mnie już skończyły się pomysły, więc czas poprosić innych o uwagi. Rozpocząłem pracę nad "dyskusją" na packaging.python.org, która ma za zadanie wyjaśnić autorom paczek Pythona, jak wygląda praca z ich paczkami w dystrybucjach, i jak mogą nam pomóc. Oparłem się na moje doświadczenie w #Gentoo, więc uwagi od innych dystrybucji się bardzo przydadzą, wżdy ich doświadczenie może różnić się od naszego.

https://github.com/pypa/packaging.python.org/pull/1791

#ArchLinux #CondaForge #Debian #Fedora #Homebrew #Python

Okay, I'm mostly done with the ideas I have, so time to request feedback from others. I've started preparing a "discussion" for packaging.python.org that aims to explain to #Python package upstreams how downstream #packaging works, and what they could do to help us. I've based it on my #Gentoo experience, and I'd appreciate feedback from other distributions, since their experience may be different from mine.

https://github.com/pypa/packaging.python.org/pull/1791

#ArchLinux #CondaForge #Debian #Fedora #Homebrew

#Biopython 1.85 released, supports Python 3.9 through 3.13 and PyPI. https://biopython.org/

Pre-compiled wheels available on PyPI https://pypi.org/project/biopython/1.85/ with #CondaForge to follow soon https://anaconda.org/conda-forge/biopython

So there's a separate conda-forge repo for every single Python and R package, meaning there are almost 24,000 repos within the organisation! https://github.com/orgs/conda-forge/repositories

#conda #condaforge

Edit: check out @jornfranke's reply below for a possibly even better idea (using mamba)

Using Miniconda with Conda-Forge to Avoid Anaconda Licensing Issues
"Using miniconda with conda-forge is a powerful combination that allows you to avoid the licensing issues associated with Anaconda while still leveraging the full power of the #conda package and environment management system. By following the steps outlined in this blog post, you can set up a flexible and open-source #Python development environment tailored to your specific needs."
https://dev.to/kaamisan/using-miniconda-with-conda-forge-to-avoid-anaconda-licensing-issues-5hkj #condaforge #anaconda

#Pixi pack: una opción para resolver el problema de distribución de paquetes multiplataforma y de lenguaje agnóstico.
Pixi es un proyecto de @prefix que moderniza la gestión de dependencias basado en paquetes #conda, que por defecto uso el canal de #condaforge (luego, no es susceptible a los cambios de licencia de #anaconda).

#python #rlang #data #fortran

@prefix https://hachyderm.io/@prefix/113151718824543158

@ashwinvis I have heard that #Anaconda is starting to shakedown larger academic institutes (most easily exceed the 200 employee threshold) using the default channel - at least one is looking at blocking use via their firewall as part of avoiding this!

Time to switch from #MiniConda etc to #MiniForge https://github.com/conda-forge/miniforge where the #CondaForge channel is set as the default (and only) channel.

@ocefpaf I don’t know if repodata patching is *the* best feature of #CondaForge, but it sure is a contender.

@ogi Thanks. It was indeed a lot of work, even though we built on the efforts of @conda / #condaforge and especially @napari. Many mistakes were made and discovered on the way and it still remains to be seen how it will pan out in the wild when we release Spyder 6, but we are hopeful. 🙏

Hot on the heels of my last #GDAL post, here's a blog post I never tooted when I wrote it late last year: How to get GeoParquet support in GDAL when installing from conda-forge - https://blog.rtwilson.com/how-to-get-geoparquet-support-in-gdal-ogr-from-conda-forge/

#GeoParquet #Parquet is the new cool data format, and getting conda-forge GDAL to work with it is just a package installation away...

#GIS #Geospatial #OGR #conda #condaforge

Starting today, you can install iosacal with conda. This adds to the existing installation procedure with pip. Conda is a good fit for complex projects and has better tooling for reproducibility.

Installing iosacal can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge conda config --set channel_priority strict

Once the conda-forge channel has been enabled, iosacal can be installed with conda:

conda install iosacal

or with mamba:

mamba install iosacal

https://steko.iosa.it/2024/03/install-iosacal-with-conda/

#conda #condaForge #iosacal #python #radiocarbon

@johnm I have similar concerns about #CondaForge #BusFactor again from the perspective of a minor contributor to/of a few recipes.

I wonder what #condaforge’s plan is to handle the transition to #Numpy 2.0 (expected to land about a month from now). My guess is that all build-time dependent packages will probably need to be rebuilt ? I don’t really know who/where to ask about this, so any help would be welcome !

(If Numpy 2.0 is news to you and makes you nervous I suggest reading https://github.com/numpy/numpy/issues/24300#issue-1828940995)

#Python #packagemanagement #maintenance