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PackagingCon 2023 will bring together different software packaging ecosystems to share best practices and discuss common challenges in software packaging. From Python’s pip to Rust ’s cargo to Julia’s Pkg, from Debian apt over Nix, to Homebrew, conda and mamba, and from vcpkg to Spack. The 2021 conference drew 350+ participants and featured over 60 presentations.
Over a year ago, the conda team at Anaconda, together with conda developers at Quansight and QuantStack, announced the release of a new solver for conda based on the libraries that the mamba project had developed on top of the venerable libsolv project.
The default solver change for conda will be occurring in a special 23.10.0 release (not in 23.9.0). See the special announcement here.
conda-libmamba-solver will be part of the various installers. The default conda solver is still classic. Look out for the release of miniconda, Anaconda Distribution and miniforge soon.libmamba. classic will be available via --solver=classic, if needed.
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Welcome to the first ever conda community newsletter! The newsletter will highlight recent and upcoming activity that is relevant to the conda community. This first issue highlights community gathering spaces, upcoming events, the new rattler library, channel updates from conda-forge and Bioconda, a summary or recent conda-related web content, and finally releases and announcements.
We are hoping these will become a semi-regular feature in the conda landscape going forward. If you have an item to include in a future newsletter, please let us know by posting it to the Conda Communications Matrix Channel or by creating an issue in the Conda Communications GitHub Repo.
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Welcome to conda Community Spaces!, Your gateway to a vibrant community where connections are forged, knowledge is shared, and inspiration flourishes. At conda, we are passionate about fostering open collaboration, embracing diverse perspectives, and creating a supportive environment for everyone. Join us on this exciting journey as we delve into the multitude of conda spaces designed to bring our community closer, empower your learning, and ignite your creativity.
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The newest conda doctor command will be available with conda version 23.5.0, enabling users to quickly detect corrupted packages (packages with files missing) in their conda environment.
One way packages in an environment may become corrupt is when one or more files in that package are accidentally deleted. Even a single missing file leads to a corrupted package.
Until now there was no way to learn of these missing files except manually checking for their existence. With conda doctor, users can learn about all such packages in their conda environment with a single short command.
May releases are here! 🎊 Conda 23.5.0, conda-build 3.25.0, and conda-libmamba-solver 23.5.0 are now available on both main and conda-forge:
To update conda, run:
conda install -n base conda=23.5.0
The long-awaited conda doctor subcommand plugin has been implemented! The related conda issue is over nine years old and has been a regularly requested feature. The conda doctor command enables conda users to detect any packages with files missing (i.e., corrupt packages) in their conda environment.
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conda-build is an application that automates the process of building and distributing Python packages. It is a powerful tool that has the added advantage of handling dependencies that require C/C++ or other languages. This is particularly useful for scientific computing, where many Python packages have complex dependencies and require specialized libraries and tools.
A recipe outlines the steps needed to build a package from source code. We can create this "recipe" using grayskull. A recipe includes all the necessary information, from downloading and installing dependencies to compiling the source code and creating the final package. Conda-build then renders the recipe to build the package. A recipe typically includes:
In this article, we will guide you through the process of building your Python package with conda-build! conda-build can help us handle even the most complex dependencies for our package, making the process smoother and more efficient. Let's get ready to build an amazing Python package!
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The new conda 23.3.1 release from March, 2023 includes an
--experimental=jlapflag orexperimental: ["jlap"].condarcsetting that can reduce repdata.json fetch bandwidth by orders of magnitude. This is how we developed conda's new incremental repodata feature.
Conda is a cross-platform, language-agnostic binary package manager that includes a constraint solver to choose compatible sets of packages. Before conda can install a package, it downloads information about all available packages. This allows the solver to make global decisions about which packages to install. The time and bandwidth spent downloading this metadata can be significant, but we have improved this in conda 23.3.1. By enabling the experimental: ["jlap"] feature in .condarc, conda users can see more than a 99% reduction in index fetch bandwidth.
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Welcome to conda.org! This website was created by and for the conda community and aims to support the greater conda ecosystem. As a site visitor, these are just some of the things you can expect to find here going forward:
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Anaconda surveyed the conda community in late 2022. This post reviews what we learned from that survey and how it is impacting the future directions of conda.
Around the same time, the Python Software Foundation published the results of their (much, much bigger) Python Packaging Survey. The two surveys asked some similar questions and some distinct ones. We include insights from the PSF survey when they are particularly relevant to the conda community.