The Python development community concurrently supports multiple version of the Python programming language. These are in various states such as "prerelease", "bugfix", "security", or "end-of-life". For example version 3.12 is currently in "bugfix" while 3.8 is the oldest still receiving security updates. The cadence of future release is currently governed by PEP 602 – Annual Release Cycle for Python

Espresso AI started as a primarily Linux shop, but as we've added more developers on OS X we've needed a way to build docker containers on OS X that are compatible with our Linux production environment.

Pants has supported running portions of the build in docker containers for a while, but the exact configuration is a little tricky, particularly if you want to build x86_64 containers on Apple ARM processors.

Today we are excited to announce that Klaviyo has become the first Platinum sponsor of the Pants project via the corporate sponsorship program! This is one of the first such sponsorships through our new Open Source Sponsorship program, through which we are trying to support the open source projects we depend on. While the core development and maintenance of Pants is volunteer driven, there are costs involved with maintaining and distributing such a large piece of software, costs which far too often are covered by individual volunteers. We benefit enormously from this project and want to help contribute to its ongoing success.

We are pleased to announce Pants 2.21.0, the latest release of Pantsbuild, the scalable and ergonomic build system. To update, set pants_version = "2.21.0" in your pants.toml. If you're not using Pants yet, get started now.

Highlights in 2.21 include:

• 🧪 All backends now support [test].attempts_default
• ⚡ Pants internal manipulation of Python dependencies by way of Pex is now substantially faster in many cases.
• 📜 The __defaults__ symbol will now set default values for generated targets too. For instance, __defaults__({python_source: dict(skip_black=True)}) will now apply to the python_source targets generated by a python_sources target, without having to write __defaults__({(python_source, python_sources): ...})
• ♻ When running in CI, Pants will output a command to rerun only the failed tests.
• 🐍 All built in lockfiles are now Python 3.12 ready.
• 🚡 As part of regenerating lockfiles for Python 3.12, the default version of most builtin Python tools has been updated.
• 🐳 The cache_from field on docker_image now supports multiple values.
• 🏜 The experimental-deploy goal now has built-in support for the --dry-run option.
• 🪜 Scalafix can now be run by Pants using the new pants.backend.experimental.scala.lint.scalafix backend.
• 📈 And lots of smaller features, update, bugfixes, and general improvements

A frequently asked question on Pants Slack is how to set a version - such as for a docker image, helm chart or Python distribution - based on git state.

We are pleased to announce Pants 2.20.0, the latest release of Pantsbuild, the scalable and ergonomic build system. To update, set pants_version = "2.20.0" in your pants.toml. If you're not using Pants yet, get started now.

Highlights in 2.20 include:

• ⚡ Support for the Ruff formatter, for both BUILD files and normal Python files.
• 🔒 Built-in support for Terraform lockfiles.
• 📁 Support for more remote-caching providers: file system and GitHub Actions Cache.
• 🪥 New helpers for defining "adhoc" code-quality tools, without requiring a full plugin.
• ☕ JVM third-party artifacts can now be read from pom.xml files, and other related improvements.
• 🐿 Go 1.22 can now be used.
• 🥚 Several features of the Pex tool are now exposed by Pants including sh_boot executable, and check.
• ⚠ Pex's emit_warnings now works as advertised and comes with a global option.
• 🐋 Experimental support for Podman with pants.backend.experimental.docker.podman.
• 📈 And lots of smaller features, bugfixes, and general improvements

An important distinguishing feature of Pants v2 is its ability to automatically infer your code's internal and external dependencies. The information about those inferences is available via the dependencies and peek goals, so that metadata about what modules import from other modules is always readily accessible to you.

We're pleased to announce Pants 2.19.0, the latest release of Pantsbuild, the scalable and ergonomic build system. To update, set pants_version = "2.19.0" in your pants.toml. If you're not using Pants yet, get started now.

Highlights in 2.19 include:

• New backends: semgrep and openapi-format
• Test retries for flaky Python tests
• buildx support in the Docker backend
• Using parametrize to set multiple fields in tandem
• And lots of smaller features, bugfixes, and general improvements

If you need to make some functionality available to all engineers in your company, you can author and release a Pants plugin (either internally or via a public PyPI index) independently of the rest of the codebase...

Pants 2.16 introduces visibility features. These can be vital for keeping a monorepo's repository structure under control as the codebase grows. You'll benefit from having cleaner architecture and a dependency graph that is easier to reason about.

Welcome to our new chat mirror, with all six of the current Slack public channels being mirrored to the web.

Make builds more reliable and save time doing so. The upcoming Pants 2.16 introduces a couple of exciting changes to make Pants safer, faster, and more user-friendly. Here we preview a pair of changes which increase hermeticity.

Now you can cross-test or cross-build your code on multiple different platforms concurrently, using Environments. Pants uses its precise knowledge of your build's deps to run exactly the relevant processes inside reusable Docker containers (or evenly remotely on a cluster of workers)…

We embrace that open source is not code alone. We truly value a wide spectrum of contributions to Pants project and to the thriving community that underlies it. Here are some of the many ways anyone can potentially contribute to the vitality of Pants...

Now that Pants 2.15 is out, let's whet your appetite for 2.16: lint your Python monorepo faster than ever with Pants and Ruff, two projects that share a passion for combining the raw power of Rust with the elegance of Python.

Alexey reviews the Python packaging landscape. You'll learn more about the options you have, their pros and cons, and how to find the best approach to distribute your Python applications.

The 2.15 series represents the biggest change to Pants since version 2.0, and we're excited to share how it can let you complete more workflows, more easily, in more places. Including cross-platform builds, containerized builds with Docker, and easier configuration for local builds…

Every year, we do a survey to identify community members' wishlist for the year ahead, and get a sense of what's highest priority for you. Please take the survey now!

A Pants maintainer's personal and professional retrospective on nearly two years of involvement in the project, starting as a curious newcomer.

At Tweag we have a lot of experience with Bazel, as we maintain the Haskell rules. However, I had feedback that Bazel's Python support was not ideal. In contrast, Python is Pants' strong point. My client's fear of boilerplate also made Bazel unappealing. Whereas, Pants reduces boilerplate…

Pants balances release velocity and end-user stability via judicious use of deprecation cycles. Experimental backends are a way to get quick feedback on new functionality, before "graduating" it to the formal deprecation policy. Experimental features are still well-supported, and not to be feared!

A year in, builds take only a few minutes (~150K LOC, ~1500 tests), PRs have become smaller as devs no longer try to squeeze as much as code as possible into a single change, code reviews get completed much faster (and are more likely to provide useful feedback), development velocity has increased…

Pants 2's design incorporated many lessons learned from the first version of Pants, and many of its contemporary build systems. On Pants 2's 2nd anniversary, we return to some of the earliest decisions we made when rewriting Pants and look back at how they've helped us evolve at a remarkable pace…

Unlike earlier build systems, Pants v2 automatically infers your code's internal and external dependencies. And it does this at the file level, so that you get optimal invalidation, caching, and concurrency performance without having to manually create and maintain mountains of BUILD file metadata.

Highlights include: less boilerplate via hierarchical defaults for target field values, better Golang monorepo support, with multiple go.mods, do more of your workflows in Pants with the experimental deploy goal (with initial support for Helm), and much more…

How we worked around some quirks and limitations of GitHub Actions to skip CI jobs that aren't necessary in certain scenarios, without breaking branch protection rules that normally require those CI jobs to succeed.

I was born in the early 80's in a small fishing town in the Spanish Atlantic Coast, far away from the big urban sprawls and common traits of miles-long beaches, year-round summertime and chill out vibes normally identified with life in Spain. This is the autonomous region named Galicia…

We're pleased to announce Pants 2.13.0. Highlights include better command line arguments for file sets, improved JVM support, easier access to parallel execution in CI, and lower barriers to adoption for Python projects that currently use existing distribution and build tools.

"Code development at Astranis has been migrating towards a monorepo structure. Astranis has grown rapidly since this migration started and, along with it, so has the volume of Python hosted in our monorepo. We went looking for new tooling to bring us into the future."

Pantsbuild provides a common interface to run all code quality tools in parallel. This post explores how Pants augments excellent linters such as bandit, flake8, shellcheck, etc. by offering its own linting mechanisms, too, including regex matches, dependency analysis, and metadata checks.

Pants can build a PEX file, an executable zip file containing your Python code and all transitive dependencies. Deploying your application is as simple as copying the file. This post elaborates on how to get best performance out of the powerful combination of Pants+PEX+Docker.

We ended up setting up a self-hosted GitHub Actions runner, on a hosted Mac M1. Getting the machine itself up and running was easy, thanks to MacStadium's simple, effective UX. But setting up the GHA runner on it was a little trickier, for a couple of reasons...

Rather than forcing global or per-project lockfiles, Pants uses a hybrid approach...This allows a repo to operate with the minimum number of lockfiles required to support their conflicting library versions, without necessarily going to the costly extreme of per-project lockfiles.

Working effectively in a monorepo requires appropriate tooling. While Pants can be a really useful system in repos of all sizes and architectures, it has some features that make it particularly appealing in a monorepo setting…

If you're responsible for your organization's codebase architecture, then at some point you have to make some emphatic choices about how to manage this growth in a scalable way. There are two common architectural alternatives to choose from...

Pants's codegen support solves one of the biggest problems with code generation: how to make sure that local developers, CI, and production are using the same generated code?

Alliterations aside, this post discusses the new PyOxidizer integration coming in Pants 2.10 for packaging Python applications.

Choosing a compatible Python interpreter carefully is important not only for developers, but for the Pants repository administrators as well. Developers using Pants build system in their project may see various errors and have different behaviors depending on the Python interpreter...

We're pleased to announce Pants 2.9.0, the latest release of Pants: the scalable and ergonomic build system!

Many considerations go into evaluating and adopting a new build system: performance, scalability language and framework support, ease of adoption and use, extensibility, compatibility with existing practices, and more.…

Pants 2.8 adds Autoflake & Pyupgrade, Docker publishing, Golang, PEP 517 publishing, and Google Cloud Functions

Why Pants for Golang? A consistent interface for all languages & tools, integration with Git + advanced project introspection, and remote caching and execution. All with minimal boilerplate.

Andreas lives in Visby, the main city of Gotland, which in turn is the biggest island in Sweden, and home to world-famous rauks. He is mostly self-taught as a developer, and has experience working with embedded systems and hardware engineering.

Pants 2.6 can now understand Poetry's pyproject.toml configuration for third-party dependency management, addressing one of our most requested features in the last year!

Pants Contributor Liam Wilson delves into this new feature as well as his experiences developing the macro as a Toolchain intern.

Successful open source projects are full of tradeoffs between purity vs. pragmatism. We often remind ourselves "Do not let perfect be the enemy of good".

As of Pants 2.2, Pants now knows how to use dependency inference with Protobuf! This includes:

1. Protobuf imports of other Protobuf files
2. Python imports of generated Protobuf code, including gRPC.

Fast incremental re-builds are critical in large codebases and monorepos. Thanks to deep support for cancellation and a side-effect free execution model, Pants is able to further reduce re-build latency by speculatively re-executing work.

How the Pants build tool empowers incremental migrations by:

1. giving fine-grained insights into your migration with minimal boilerplate, and
2. running all your tests and linters, in parallel, with the correct interpreter for each part of your code.

Scalable build tools have historically meant a significant boilerplate burden.

But it doesn't have to be that way! Pants v2 supports all of the caching, concurrency, and introspection you need to scale your repository, with significantly less boilerplate, thanks to… Dependency inference!

There are so many tools in the Python development ecosystem. Installing, configuring and orchestrating them—all while not re-executing work unnecessarily—is a hard problem, especially as your codebase grows.

Fortunately, there is now a tailor-made (pun intended) solution: Pants v2!