There have been so many blog posts and presentations about Python packaging that I’m reluctant to write what could be interpreted as yet another one, but I’ll give it a go. Why? Because up until recently, I was working on several interconnected libraries, each of which had a more ridiculous build process than the next.
Needless to say, each had its own deployment procedure. Some were documented, others were left to the author’s interpretation. Dozens of commands to execute. Some had to be built locally because the CI was apparently “too low on resources”. All of this for pure Python packages. No C extensions. It’s like saying “our machine doesn’t have the resources to create a ZIP file”.
Not to mention libraries that had themselves listed as build dependencies, or
setup.py reading the virtual environment’s environment variables to determine which version of a particular dependency should be installed. Or git branching workflows so complicated that made resolving merge conflicts a routine, with the changelog being the worst of it.
If you’re a pragmatic developer who has better things to do with its time than watching broken processes in action, you would have searched for a better way. And you would have found one after a quick search and a few mouse clicks away because these issues have already been addressed and solved by the community.
When I started looking for a solution, I wanted to achieve three things:
- Have a single command for performing all the package validity checks: build, test suite, linting, documentation, etc.
- Have a single command for deployment where the developer specifies the next version and it magically appears on PyPI;
- Make the aforementioned processes easy to debug, modify and replace if a better option arises.
This led me to
tox, an automation tool primarily used for running test suites against multiple version of dependencies. However, it’s also useful for automating all sorts of different tasks, even the ones requiring Python 3 when you’re still using Python 2. To give you a taste of where this leads, the following things were achieved:
- The test suite was executed against the installed distribution and multiple versions of dependencies, thus ensuring the distribution is valid and you support multiple versions of your dependencies. This is achieved by executing
toxin the command line to run the whole test suite, or
tox -e <env>if you want to test a specific use case.
- When one wants to create a new release, one has to execute
tox -e releaseto create a feature release, or
tox -e release -- patchto create a patch release when semantic versioning is used. The release process comprises several other
toxprocesses, such as
manifest, each of which can be executed individually.
- The deployment was executed in the CI environment, but it could also be executed locally. This ensured the distributions are created in a controlled and stable environment, not on someone’s machine with some random settings.
This process was propagated into other packages and into the internal
cookiecutter project to make it available to new packages as well. This means that developers had a unified, standardized workflow which enabled everyone to initiate a new release almost instantly, only to have it appear on PyPI in a matter of minutes.
This post will summarize my experiences and point you to other useful posts covering this subject matter so you don’t waste time on the same issues. I’ll spare you the intricate details of producing the package as there is already enough material on that subject online.
However, if you need a reference where everything is already confgured, take a look at the
centerline package. I’ve created this package during college as an exercise, but nowadays I use it primarily for testing packaging and distribution. The main files you should be focusing on are
.travis.yml, and last but not least,
A bit of history
Python has a rather complicated build and distribution mechanism when it comes to third party libraries. It’s not rocket science, just a large amount of legacy that you have to navigate. The initial idea was to have a batteries included approach where the Python’s core had all the things you would ever need.
However, that process inhibited reuse of code that was considered useful, but not useful enough to be included into Python’s core. And so PyPI was born, a place where people could upload their Python code and share it with the world. Now you have the best of both worlds, right?
Well, yes and no. In hindsight, its easy to criticize the decisions that were made at the time that led us to a point where we have multiple ways to build and distribute libraries and call Python’s standard library a dead end. The point is, although things are not ideal, there is no point complaining about it if you’re not going to do anything about it.
To correct the course of the entire Python ecosystem, a large amount of time, energy and money is needed. That’s something the open-source community is traditionally scarce at, but things are improving.
On the other hand, if you are experiencing issues with it, and you want to start somewhere, you can start in your backyard (so to speak). Review the tooling choices that are currently made available to you, standardize your libraries’ maintenance process, obfuscate the internals by providing simple high level APIs or access points to get the job done.
During this process you’ll familiarize yourself with the ins and outs of the build system, the terminology, and you’ll be able to make informed decisions based on your needs, and spare your colleagues who are less interested in the package distribution.
With that in mind, I would like to present to you one of the ways that helped us resolve our differences and enabled us to get past the semi-automated phase. It’s important to note that this is not the way. If it doesn’t suit you or your team’s needs, fine. This also won’t cast blame on the packaging ecosystem, but show you an approach that improved the collaboration on various libraries by adopting the following changes:
- Switching to the
- Declarative configuration
- Trunk based development
- Changelog and version management
- Release automation
When dealing with Python libraries that are sometimes called packages (i.e. a directory with the
__init__.py file) because they mostly consist of packages that need to be distributed, there are two most-common ways of organizing your codebase:
- Namespace layout (I’m not sure it’s the correct term, but let’s go with it for now):
├── mypackage │ ├── __init__.py │ └── mod1.py ├── tests ├── setup.py └── setup.cfg
├── src │ └── mypackage │ ├── __init__.py │ └── mod1.py ├── tests ├── setup.py └── setup.cfg
You may be wondering what’s the difference? The difference is how the Python’s
import system treats these files during development and testing, and that has an effect on the build and distribution processes.
Before we get into details, please bear in mind that some people use symlinks to connect packages to projects where they are being used, whereas others use development mode enabled by
editable installs. I personally prefer the latter which goes something along the lines of this:
$ mkproject centerline # provided by `virtualenvwrapper` $ git clone https://github.com/fitodic/centerline . $ pip install -e .[dev,test,docs]
Why do I prefer this? Because from my experience, it’s the most stable and reliable development approach when developing and distributing packages. It also drastically simplifies the package’s setup, both in testing and production environments.
On the other hand, I found
symlinks impractical because not all packages are meant to be used as dependencies in other projects, for example Django packages. And I prefer to have them tested immediately as a standalone codebase. Which brings us to the crux of the problem.
When using namespace layouts, i.e. the package is in the project’s root directory where
setup.py is located, Python will implicitly include the current directory in
sys.path. This means that when you run your test suite, the tests will be ran against the code you have in your current working directory, not the installed distribution.
If you don’t think thats a big deal, let me give you an example. Let’s say you have a library that has a test suite that is executed in the CI (Continuous Integration) environment. Someone introduces a change to the library’s configuration (e.g.
setup.py) or forgets to include some static files in the
MANIFEST.in file (they are not picked up by default). All the tests pass and you create a new release.
The release is successfully deployed to PyPI and installed by your users who start getting
ImportErrors. The library is clearly installed, but its empty. It has its metadata so everything looks OK from
pip’s point of view, but the code is missing (or parts of it).
How can this scenario be avoided? By placing your code in the
src/ directory and configuring
setuptools (or whichever build system you are using) to search for packages in the
src/ directory. This forces you to install the distribution that would be shipped to your users and running the test suite against it. That way, if there are any configuration errors, you’ll catch them immediately.
You may have also noticed that in the
src/ layout pictured above, the
tests directory is located on the same level as is the
src/ directory. The
pytest documentation has a chapter on good integration practices which I recommend you read. The approach described above is in line with these practices for exactly these reasons, to improve the reliability of the entire library development process.
If you are interested in more details, I highly recommend the following blog posts:
To make the package usable, you have to configure it before you deploy it. This configuration comprises package metadata, the list of dependencies that need to be installed, what to include into the distribution, the build system, etc. Unfortunately, at this moment, there are a number of files you have to configure to achieve this.
Why is this important? Long story short, when
pip installs the package from an
sdist (source distribution), it executes the
setup.py file in order to build a
wheel (binary distribution). When executing
setup.py, it assumes its only dependencies are
wheel, and it needs
setuptools to do that. What if this is deployed in a restrained environment without
setuptools installed beforehand? How do you specify which package to download in order to install your own package? Especially when you need
setuptools to read the configuration in the first place.
pyproject.toml solves this issue by allowing you to specify the build dependencies, and you only need
pip. You could have specified the build dependencies in
setup.py before that, but then again, you had to have
setuptools installed to read it.
There is also another thing that is problematic with
setup.py. You could find all manner of customized “stuff” in them, some even requiring dependencies that were yet to be installed. For instance, the
setup.py imported the library’s version from
myproject/__init__.py, which also contained imports to dependencies. But how did we come to that? I suppose package developers treated packages as regular projects and kept using
requirements.txt files, custom build scripts and various other procedures to “make it work”.
The declarative configuration is a way to limit the scope of abuse. By declaring everything in
setup.cfg, or one day
pyproject.toml if it supports it, there aren’t many ways to “hack” your way around it. I hope.
Even though I prefer
setup.cfg, that doesn’t mean you cannot put all of this in
setup.py to achieve the same result. It’s a matter of preference, although more and more tools have started adding support for reading their configuration from
pyproject.toml. In my opinion, one file would ideally hold the entire project’s configuration, but then again, it’s a matter of preference.
Libraries declare, or should declare, their dependencies in spans to support multiple versions simultaneously, unlike projects like Web applications, where you would want to specify the exact version that’s being deployed. Furthermore, you would also want to list you extra dependencies that users may or may not install, depending on their needs. You can use the same extra dependencies for setting up your development and testing environment as well:
install_requires = Fiona>=1.7.0 Shapely>=1.5.13 numpy>=1.10.4 scipy>=0.16.1 Click>=7.0 [options.extras_require] dev = tox gdal = GDAL>=2.3.3 lint = flake8 isort black test = pytest>=4.0.0 pytest-cov pytest-sugar pytest-runner docs = sphinx
Trunk based development
Once the library’s configuration is in place, other developers will most likely join in and couldn’t care less about the package’s structure and deployment, as long as it works. But collaboration on a package isn’t just run the test suite -> code review -> merge changes. Packages often provide functionality that builds on its dependencies.
As with all dependencies, there are deprecation periods and sometimes, its just not possible to continue supporting all versions. The Python 2 to 3 migration is one such example where packages simultaneously supported both version of Python, and then dropped Python 2. These types of changes impact their users the most, so they should be executed with care.
The difficult part is supporting multiple incompatible versions at the same time. A
compat.py module, or the module where all the compatibility edge-cases are hidden, can only get you so far. At some point in time, you’ll want to create a release that will only continue receiving security patches, while most of the development and innovation carries on.
This is where release management from the version control system’s perspective kicks in. There are several available options, such as Git Flow or Trunk based development (TBD). I’ve used both of them when working on libraries and can safely say that TBD produces more satisfying results.
In a nutshell, TBD requires developers to create short-lived branches from the
master branch that will be merged back into the
master branch. Every once in a while, a release branch is created from the
master branch that carries a version designation, such as
release_1_11. That branch is the source for creating 1.11.X releases until the version or branch is deprecated. Meanwhile, all the security patches that are merged into the
master branch are
cherry-picked into the
release branch. If you’re lucky and the developers working on the project make sensible commits and commit messages, it’s even easier to manage releases, especially around bugfixes and security patches.
This approach enables the continuation of feature development according to the project’s roadmap, without worrying too much about backwards compatibility. After all, that’s what the
release branches are for.
Changelog and version management
One of the reasons why TBD was adopted in the first place was changelog and version management. My team inherited a customized GitFlow workflow that introduced more frustration when someone introduced an ill-advised method for handling multiple releases simultaneously. There were practically two codebases whose functionality was more or less the same, apart from the code that handled compatibility issues between two versions of a certain dependency.
This brought even more frustration when a new release had to be made. Various merge requests were issued, slow pipelines executed, merge conflicts resolved (mostly around the changelog), and so forth. There had to be a better way.
After some research, I stumbled upon TBD and everything just clicked as described in the previous chapter. Furthermore, I dropped the custom built changelog and versioning script in favor of
bumpversion. I don’t think there is a need to go into too many details as their documentations are preety straightforward. After some minor issues, these two libraries were successfully integrated into
The last step to set up was the release process. This is also a
tox environment, named
release that built the changelog using
towncrier, bumped the package version using
bumpversion, tagged it and pushed the changes to
From there on, the CI would run the test suite one more time, again using
tox so developers can easily reproduce issues if they arise, build the standard (
sdist) and binary (
wheel) distributions, and upload them to PyPI using
twine. If you are using Travis CI, you can use its own mechanism for uploading distributions.
As mentioned earlier, this is one approach that may or may not suit you and your team. Each library used here has an alternative and it also comes down to a matter of preference.
If you are starting out or need to create a new library, I would advise you to use one fo the existing Python cookiecutters, such as
cookiecutter-pylibrary. You can also build your own as I did. Beats copy/pasting coded from an existing project or documentation.