Python on Wheels

The python packaging infrastructure has long received criticism from both Python developers as well as system administrators. For a long time even the Python community in itself could not agree on what exactly the tools to use were. We had distutils, setuptools, distribute, distutils2 as basic distribution mechanisms and then virtualenv, buildout, easy_install and pip as high level tools to deal with this mess.

As distribution formats before setuptools we had source files and for Windows there were some binary distributions in form of MSIs. On Linux we had bdist_dumb which was historically broken and bdist_rpm which only worked on Red Hat based systems. But even bdist_rpm did not actually work good enough that people were actually using it.

A few years ago PJE stepped up and tried to fix the initial distribution problems by providing the mix of setuptools + pkg_resources to improve distutils and to provide metadata for Python packages. In addition to that he wrote the easy_install tool to install packages. In lack of a distribution format that supported the required metadata, the egg format was invented.

Python eggs are basically zip packages that include the python package plus the metadata that is required. Even though many people probably never built eggs intentionally, the egg metadata is still alive and kicking and everybody deploys things through setuptools now.

Now unfortunately a few years ago the community split in half and part of the community declared the death to binary distributions and eggs. When that happened the replacement for easy_install (pip) stopped accepting eggs altogether.

Fast forward a few years later. The removal of binary distributions has become noticed very painfully as people started more and more cloud deployment and having to recompile C libraries on every single machine is no fun. Because eggs at that point were poorly understood I assume, they were reimplemented on top of newer PEPs and called wheels.

As a general information before we dive in: I'm assuming that you are in all cases operating out of a virtualenv.

What are Wheels So let's start simple. What exactly are wheels and what's the difference to eggs? Both eggs and wheels are basically just zip files. The main difference is that you could import eggs without having to unpack them. Wheels on the other hand are just distribution archives that you need to unpack upon installation. While there are technically no reasons for wheels not to be importable, that was never the plan to begin with and there is currently no support for importing wheels directly. The other main difference is that eggs included compiled python bytecode whereas wheels do not. The biggest advantage of this is that you don't need to make wheels for different Python versions for as long as you don't ship binary modules that link against libpython. On newer Python 3 versions you can actually even safely link against libpython for as long as you only use the stable ABI. There are a few problems with wheels however. One of the problems is that wheels inherit some of the problems that egg already had. For instance Linux binary distributions are still not an option for most people because of two basic problems: Python itself being compiled in different forms on Linux and modules being linked against different system libraries. The first problem is caused by Python 2 coming in two flavours that are both incompatible to each other: UCS2 Pythons and UCS4 Pythons. Depending on which mode Python is compiled with the ABI looks different. Presently the wheel format (from what I can tell) does not annotate for which Python unicode mode a library is linked. A separate problem is that Linux distributions are less compatible to each other as you would wish and concerns have been brought up that wheels compiled on one distribution will not work on others. The end effect of this is that you presently cannot upload binary wheels to PyPI on concerns of incompatibility with different setups. In addition to that wheel currently only knows two extremes: binary packages and pure Python packages. When something is a binary package it's specific to a Python version on 2.x. Right now that's actually not the worst thing in the world because Python 2.x is end of life and we really only need to build packages for 2.7 for a long time to come. If however we would start considering Python 2.8 then it would be interesting to have a way to say: this package is Python version independent but it ships binaries so it needs to be architecture specific. The reason why you might have a package like this are packages that ship shared libraries loaded with ctypes of CFFI. These libraries do not link against libpython and as such would work cross Python (even cross Python implementation which means you can use them with pypy). On the bright side: nothing stops yourself from using binary wheels for your own homogenous infrastructure.

Building Wheels So now that you know what a wheel is, how do you make one? Building a wheel out of your own libraries is a very straightforward process. All you need to do is using a recent version of setuptools and the wheel library. Once you have both installed you can build a wheel out of your package by running this command: $ python setup.py bdist_wheel This will throw a wheel into your distribution folder. There are however one extra things you should be aware of and that's what happens if you ship binaries. By default the wheel you build (assuming you don't use any binary build steps as part of your setup.py) is to produce a pure Python wheel. This means that even if you ship a .so , .dylib or .dll as part of your package data the wheel spit out will look like it's platform independent. The solution for this problem is to manually subclass the setuptools distribution to flip the purity flag to false: import os from setuptools import setup from setuptools.dist import Distribution class BinaryDistribution ( Distribution ): def is_pure ( self ): return False setup ( ... , include_package_data = True , distclass = BinaryDistribution , )

Installing Wheels Now you have a wheel, how do you install it? On a recent pip version you can install it this way: $ pip install package-1.0-cp27-none-macosx_10_7_intel.whl But what about your dependencies? This is what it gets a bit tricker. Generally what you would want is to install a package without ever connecting to the internet. Pip thankfully supports that by disabling downloading from an index and by providing a path to a folder for all the things it needs to install. So assuming you have all the wheels for all your dependencies in just the right version available, you can do this: $ pip install --no-index --find-links=path/to/wheels package==1.0 This will then install the 1.0 version of package into your virtualenv.

Wheels for Dependencies Alright, but what if you don't have the wheels for your dependencies? Pip in theory supports doing that through the wheel command. In theory this is supposed to work: pip wheel --wheel-dir=path/to/wheels package==1.0 In this case wheel will throw all packages that package depends on into the given folder. There are two problems with this. The first one is that the command currently has a bug and does not actually throw dependencies into the wheel folder if the dependencies are already wheels. What the command is supposed to do is to collect all the dependencies and the convert them into wheels if necessary and then places them in the wheel folder. What's actually happening though is that it only places wheels there for things that were not wheels to begin with. So if a dependency is already available as a wheel on PyPI then pip will skip it and not actually put it there. The workaround is a shell script that goes through the download cache and manually moves downloaded wheels into the wheel directory: #!/bin/sh WHEEL_DIR=path/to/wheels DOWNLOAD_CACHE_DIR=path/to/cache rm -rf $DOWNLOAD_CACHE_DIR mkdir -p $DOWNLOAD_CACHE_DIR pip wheel --use-wheel -w "$WHEEL_DIR" -f "$WHEEL_DIR" \ --download-cache "$DOWNLOAD_CACHE_DIR" package==1.0 for x in "$DOWNLOAD_CACHE_DIR/"*.whl; do mv "$x" "$WHEEL_DIR/${x##*%2F}" done The second problem is more severe. How can pip wheel find your own package if it's not on PyPI? The answer is: it cannot. So what the documentation generally recommends is to not run pip wheel package but to run pip wheel -r requirements.txt where requirements.txt includes all the dependencies of the package. Once that is done, manually copy your own package's wheel in there and distribute the final wheel folder.

DevPI Based Package Building That workaround with depending on the requirements certainly works in simple situations, but what do you do if you have multiple in-house Python packages that depend on each other? It quickly falls apart. Thankfully Holker Krekel sat down last year and build a solution for this problem called devpi. DevPI is essentially a practical hack around how pip interacts with PyPI. Once you have DevPI installed on your own computer it acts as a transparent proxy in front of PyPI and you can point pip to install from your local DevPI server instead of the public PyPI. Not only that, it also automatically caches all packages downloaded from PyPI locally so even if you kill your network connection you can continue downloading those packages as if PyPI was still running. In addition to being a proxy you can also upload your own packages into that local server so once you point pip to that server it will both find public packages as well as your own ones. In order to use DevPI I recommend making a local virtualenv and installing it into that and then linking devpi-server and devpi into your search path (in my case ~/.local/bin is on my PATH ): $ virtualenv devpi-venv $ devpi-venv/bin/pip install --ugprade pip wheel setuptools devpi $ ln -s `pwd`/devpi-venv/bin/devpi ~/.local/bin $ ln -s `pwd`/devpi-venv/bin/devpi-server ~/.local/bin Afterwards all you need to do is to start devpi-server and it will continue running until you shut it down or reboot your computer: $ devpi-server --start Once it's running you need to initialize it once: $ devpi use http://localhost:3141 $ devpi user -c $USER password= $ devpi login $USER --password= $ devpi index -c yourproject In this case because I use DevPI locally for myself only I use the same name for the DevPI user as I use for my system. As the last step I create an index named after my project. You can have multiple indexes next to each other to separate your work. To point pip to your DevPI you can export an environment variable: $ export PIP_INDEX_URL=http://localhost:3141/$USER/yourproject/+simple/ Personally I place this in the postactivate script of my virtualenv to not accidentally download from the wrong DevPI index. To place your own wheels on your local DevPI you can use the devpi binary: $ devpi use yourproject $ devpi upload --no-vcs --formats=bdist_wheel The --no-vcs flag disables some magic in DevPI which tries to detect your version control system and moves some files off first. Personally this does not work for me because I ship files in my projects that I do not want to put into version control (like binaries). Lastly I would strongly recommend breaking your setup.py files in a way that PyPI will reject them but DevPI will accept them to not accidentally release your code with setup.py release . The easiest way to accomplish this is to add an invalid PyPI trove classifier to your setup.py: setup ( ... classifier = [ 'Private :: Do Not Upload' ], )

Wrapping it Up Now with all that done you can start inter depending on your own private packages and build out wheels in one go. Once you have that, you can zip them up and upload them to another server and install them into a separate virtualenv. All in all this whole process will get a bit simpler when the pip wheel command stops ignoring already existing wheels. Until then, a shell script is not the worst workaround.