Illustration by Mahe Iram Khan

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:

• A script that contains metadata.
• The script that installs the files for the package on macOS and Linux.
• The build script that installs the files for the package on Windows.
• Any additional setup files, depending on the complexity of the package.

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!

The newest conda doctor command will be available with the latest conda 23.5.0, enabling conda 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.

How Does It Work?​

Every conda environment has a conda-meta directory associated with it, which keeps a log of all the packages (and all the files in them) ever installed into that environment. conda doctor parses these metadata files, checks for the existence of each of the listed files, and reports the missing ones. This is extremely helpful, as it is otherwise very difficult to know that a package has missing files and to detect these missing files in the package.

The Plugin Mechanism​

conda doctor is implemented using conda’s new plugins mechanism, utilizing the "subcommand plugin hook", and comes bundled with conda itself.

Often the issues that conda users run into are issues in their conda environment. The idea behind implementing a conda doctor command is to "diagnose" the inconsistencies in a conda environment and report to the user, therefore empowering the user to better understand the cause of the issues they are experiencing.

A Nine Year Old Issue​

What’s interesting is that this feature was requested by Peter Wang, the CEO of Anaconda, nine years ago in the early days of conda. A number of features requested via this conda doctor issue back then ended up being implemented elsewhere in conda over the years. conda doctor was eventually boiled down to “a command that will diagnose the health of the environment and report inconsistencies to the user”.

Health Checks​

We designed the conda doctor functionality to have various “health checks” for the user’s environment. The first health check implemented and made available to users in this release is “detect missing files in packages”. More health checks will be made available in conda doctor in future releases. Go check out the new conda doctor command and do let us know of any “health checks” you would like conda doctor to have that would improve your conda experience. You can do so by opening a feature request on the conda GitHub repository.

Photo by Jason Leung on Unsplash

The new conda 23.3.1 release from March, 2023 includes an --experimental=jlap flag or experimental: ["jlap"] .condarc setting 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 bandwith.

Photo by Nico Smit on Unsplash

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:

• News and updates for many projects within the conda ecosystem; from organizations such as conda, mamba, and conda-forge
• Information on how to get involved with our projects (check out the Community page)
• Emerging trends and technologies within the ecosystem; this site will be a platform to promote cutting edge work going on in the community
• Plus more!

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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.

We are pleased to announce that the conda community has migrated our chat rooms to the Matrix/Element platform. For the first time since the conda ecosystem was young, every conda-related chat room is now easily accessible on one platform.

Matrix and Element have been around for a couple of years, and are used widely by communities like ours. Let's dive in:

Conda Announce is a new mailing list for announcements and news relevant to the conda ecosystem and community.

If you want to stay informed about software releases, new features, upcoming events, and other community news then this is the list for you.

The mailing list is moderated and low-volume: Most news will go out in monthly summary posts. (Our goal is to keep the list volume low enough, and the content relevant enough, that you don’t feel a need to filter it out of your inbox.)

See:

• Conda Announce mailing list home
• Subscribe
• List Archive

We hope to see you online!

The conda Organization is delighted to announce that we are now fiscally sponsored by NumFOCUS. This will enable conda to apply for grants, accept and distribute funds, manage trademarks and own web domains with a distinct legal standing.

This milestone demonstrates how far the organization has come in creating an effective open source community with capable governance processes on par with other established projects. With this sponsorship comes the promise of further development - such as transitioning into full openness within GitHub – so stay tuned!

The latest conda release is now available. To update run:

conda install -n base conda=22.11.1

This release includes a number of improvements including:

• 🚅 Making the conda-libmamba-solver generally available. See the Getting Started Guide .
• 🏎️ Parallel package downloads.
• 🧩 A new plugin framework. See the conda Plugin Docs and the Plugin Templates for examples.

Check out the 🎥 release video (!) and the changelog for the complete list of improvements.

conda-build is a conda package for building your own packages for conda and other package managers.

To install conda-build run

conda install conda-build

If you already have it installed, you can upgrade to the newest version with

conda upgrade conda-build

The 3.23.0 and 3.23.1 releases have several updates:

• Outputs now support both script and files arguments. When both script and an explicit file list are given, the script is run first and then the files given in the explicit file list are packaged. (#4281)
• Add overlinking_ignore_patterns build parameter to speed up recipes where it is not helpful. (#4576)
• Add win-arm64 as a recognized platform (subdir). (#4579)
• Add opt-in environment variable to run conda in isolated mode (python -I -m conda) when invoked from conda-build. This is necessary to fix an issue when packaging conda itself. Alternative solutions (see #4628) are under investigation, so the current implementation will likely change. (#4604, #4625)
• Refactored conda_build.convert.update_lib_contents to use pathlib.Path. Mark test_cli.test_convert as xfail on Windows (something with the GitHub Windows Runner makes this particularly flaky). (#4619, #4626)

See the conda-build documentation for more on how to use it to create packages.