When contributing packages to conda-forge, Grayskull can make your life much easier. Grayskull generates recipes for Python packages hosted on PyPI.

Conda-forge is participating in the upcoming round of Outreachy i.e May 2021 to August 2021. The goal of this program is to increase participation from under-represented groups in free and open-source software. Outreachy is organized by Software Freedom Conservancy.

As 2020 winds down, the Core team thought it'd be fun to review some of the big accomplishments our community has made this year.

Various members of the community have raised questions publicly and privately about the implications of Anaconda's new Terms of Service (TOS) on anaconda.com. First of all, we understand your concerns. We would like to explain a bit how conda-forge works, how the TOS change affects us and conda-forge users, and what our plans as a community are for the future.

A new platform osx-arm64 has been added to the build matrix of conda-forge. osx-arm64 packages are built to run on upcoming macOS arm64 processors marketed as Apple Silicon. An installer for this platform can be found here.

tl;dr Depending on specific version numbers of underlying libraries may be too inaccurate and cause headaches as upstream libraries evolve and change. A more detailed approach is needed. In this post I outline current and potential work on a path towards a more complete inspection of requirements based on APIs and dynamic pinning of libraries.

While the R 4.0 migration has been functionally complete for quite a while, the recent migration of r-java and its dependents gives a good opportunity to write a retrospective on the technical issues with large-scale migrations in conda-forge and how we solved them.

Abstract:​

Have some thoughts about conda-forge and how it can be expanded in a way that is sustainable? Join us in this virtual Birds of a Feather discussion where we'll discuss maintenance, pain points, opportunities within conda-forge. Any and all are welcome, and we especially are seeking new viewpoints and opinions!

Recently I've been thinking about operational risk (op. risk). Operational risks arise from failures of processes, for instance a missing email, or an automated software system not running properly. Many commercial institutions are interested in minimizing op. risk, since it is risk that produces no value, as opposed to risks associated with investing. This is also something I think about in my job at Lab49, where I'm a software engineering consultant focusing on financial institutions. I think there is also a good analogy for Conda-Forge, even though we are not a commercial outfit. In this case the risk we incur isn't the potential for lost earnings but frustration for our users and maintainers in the form of bugs and lackluster user experience. In this post I explore three main sources of operational risk for Conda-Forge: Automation, Top-Down Control, and Self-Service Structure.

conda-forge now supports PyPy3.6 as the python interpreter in a conda environment

Supported platforms are,

• Linux-x86_64 (glibc 2.12 or newer)
• OSX-x86_64 (OSX 10.9 or newer)
• Linux-aarch64 (glibc 2.17 or newer)
• Linux-ppc64le (glibc 2.17 or newer)