Using Pip on Debian / Ubuntu

In the old days, it was hard and error-prone to use Python tools rather than your Debian / Ubuntu package manager to install Python packages.

One of the big problems was the easy_install program, that installed packages in a way that could make them particularly difficult to uninstall (see Clean out packages installed with easy_install).

Things started to improve as Pip took over from easy_install as the standard Python package installer.

They got better still when Pip got a binary installer format – wheels.

The combination of virtualenv, Pip and wheels makes it much easier to maintain a set of Python environments to develop and test code.

This page is a recipe for setting up these environments on your Debian or Ubuntu machine.

Do you really want to do this?

Debian and Ubuntu package maintainers put a lot of effort into maintaining binary .deb installers for common Python packages like numpy, scipy and matplotlib. For example, to get the standard versions of these for your Debian / Ubuntu distribution, you can do this:

sudo apt-get install python-numpy python-scipy python-matplotlib

Standard apt-get installation may well be all you need. The versions of numpy, scipy, matplotlib for your distribution can be a little behind the latest version available from pypi (the Python package index). If you want a more recent version of common Python packages, you might also consider installing Debian / Ubuntu packages from NeuroDebian. Again, you can use the standard Debian tools like apt-get to do your installs.

The advantage of always using standard Debian / NeuroDebian packages, is that the packages are carefully tested to be compatible with each other. The Debian packages record dependencies with other libraries so you will always get the libraries you need as part of the install.

If you use Pip to install packages, then you don’t get these guarantees. If you use Pip and run into problems with your Python installation, it will be harder for you to get support from the Debian / Ubuntu community, because you are using an installation method that they do not support, and that is more fragile.

So, consider whether you can get away with the package versions in your distribution, maybe by using the most recent packages from NeuroDebian. If you can use these, then you probably should not use the Pip installs I’m describing below.

Why you might want to use Pip

Although Pip installs are more fragile than Debian / Ubuntu package installs, they do have several advantages. With Pip you can:

  • get the latest version of the package;
  • install specific packages into virtualenvs;
  • install packages that have not yet been built for your distribution.

If you do want Pip installs on your Debian / Ubuntu system

The recipe I propose is this:

  • if you have any easy_install installations, remove them;
  • install Pip and virtualenvwrapper into your user directories (rather than the system directories);
  • use pip install --user to install packages into your day-to-day default Python environment;
  • install Python packages via Pip, and let Pip wheel caching take care of keeping a binary wheel ready for the next time you install this package;
  • have a very low threshold for using virtualenvs, via virtualenvwrapper.

I suggest you never use Pip to change your system-wide packages – so you never use Pip with sudo. This makes sure your Pip-installed packages do not break your system. To avoid sudo you should always install into your user directories (via pip install --user) or within virtualenvs (see below).

Use Pip --user installs for your default environment

The --user flag to pip install tells Pip to install packages in some specific directories within your home directory. This is a good way to have your own default Python environment that adds to the packages within your system directories, and therefore, does not affect the system Python installation.

So, if you install a package like this:

pip install --user mypackage

then mypackage will be installed into a special user-specific directory, that, by default, is on your Python module search path. For example, outside any virtualenv, here is what I get for the Python module search path (sys.path) (after I have done a --user install as above):

$ python
Python 2.7.9 (default, Mar  1 2015, 12:57:24)
[GCC 4.9.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> print('\n'.join(sys.path))

/usr/lib/python2.7
/usr/lib/python2.7/plat-x86_64-linux-gnu
/usr/lib/python2.7/lib-tk
/usr/lib/python2.7/lib-old
/usr/lib/python2.7/lib-dynload
/home/vagrant/.local/lib/python2.7/site-packages
/usr/local/lib/python2.7/dist-packages
/usr/lib/python2.7/dist-packages
/usr/lib/python2.7/dist-packages/PILcompat
/usr/lib/python2.7/dist-packages/gtk-2.0
/usr/lib/pymodules/python2.7
/usr/lib/python2.7/dist-packages/wx-3.0-gtk2

(For an explanation of the dist-packages entries, see Debian and Ubuntu Python package paths).

Notice the line /home/vagrant/.local/lib/python2.7/site-packages. This is the path containing packages that have been installed with the Pip --user option.

Python packages often install scripts (executables) as well as Python modules. To get full use of --user installed packages, you may also want to put the matching executable path onto your system. I do this with the following lines in my ~/.bashrc file:

export PY_USER_BIN=$(python -c 'import site; print(site.USER_BASE + "/bin")')
export PATH=$PY_USER_BIN:$PATH

These lines work on Linux or OSX.

If you do this, then you can use the command line scripts installed by packages like ipython. When using virtualenvs, you may want to make sure you aren’t getting the --user installed scripts, by taking this directory off the path. If you are using virtualenvwrapper (see below) you can do this automatically, with something like this in a ~/.virtualenvs/postactivate file:

# Clear user Python binary path when using virtualenvs
export PATH=$(echo $PATH | sed "s|${PY_USER_BIN}:\{0,1\}||")

Install, update Pip using pip install --user

For these steps to work, you will need the Pip --user install binary directory on your path. See above for how to do this. Check that your --user binary directory is on the path with:

echo $PATH

The output should contain something like /home/your-user/.local/bin.

You will need Pip version >= 6.0 in order to get Pip wheel caching. This is a killer Pip feature, that means that you only build wheels from source once, the first time you install a package. Pip then caches the wheel so you use the cached version next time you do an install.

First install the latest version of Pip into your user account by following the instructions at install Pip with get-pip.py:

curl -LO https://bootstrap.pypa.io/get-pip.py
python get-pip.py --user

If you are using both python 2 and python 3 versions, do the installation for both versions, installing last for the Python version that you want to own the Pip command, e.g:

# Intall pip for Python 2 installs
python get-pip.py --user
# Upgrade Pip for Python 3 installs (this one owns "pip" now)
python3 get-pip.py --user

Now check the Pip version is >= 6.0:

pip --version

If you installed for both Python 2 and Python 3:

pip2 --version
pip3 --version

Check you are picking up the --user Pip by looking at the output of:

which pip
which pip2
which pip3

This should give you outputs like /home/your-user/.local/bin/pip.

Install, update virtualenvwrapper

virtualenvwrapper is a very useful – er – wrapper around – er – virtualenv, that makes it easier and neater to have a library of virtual Python environments. First install the Debian packaged version to your system directories. This sets up bash shell integration:

sudo apt-get install virtualenvwrapper

Now upgrade your user installation to the latest virtualenvwrapper:

pip install --user --upgrade virtualenvwrapper

The --upgrade in the installation is important because virtualenv (installed by virtualenvwrapper) contains its own copy of Pip. We need the latest version of virtualenv to make sure we will get a recent version of Pip in our virtualenvs.

Check you are getting your new --user installed version, with:

which virtualenv

This should you something like /home/your-user/.local/bin/virtualenv.

The virtualenvwrapper apt package puts useful aliases into the default bash shell environment. To get these aliases loaded up in your current shell, this one time you should do:

source ~/.bashrc

Check you have the virtualenvwrapper aliases loaded with:

mkvirtualenv

This should give you the help for the mkvirtualenv virtualenvwrapper command.

Set up your system to build binary Python packages

This will install the tools that Python needs to build any binary package:

# For Python 2
sudo apt-get install -y python-dev

# For Python 3
sudo apt-get install -y python3-dev

Pip will need these tools when installing Python packages that do not already have binary packages for your platform (see below).

Build or install wheels by installing with Pip

Many standard Python packages have binary manylinux wheels. These binary installers will work for almost any Intel-based Linux, including Debian / Ubuntu. If your platform is compatible, Pip will download and install the binary package when you do a simple:

pip install --user numpy

where numpy is the package to install.

Start up a new virtualenv for Python:

mkvirtualenv venv

Install numpy and cython. If you are on an Intel platform, this will download binary wheels for the latest numpy and cython. If you are not on Intel, Pip will download the source packages, then build and cache the wheels [1]:

pip install numpy cython

Now you can install (and, if not on Intel, build and cache) other wheels you might need:

pip install scipy matplotlib h5py

Finish up by deactivating the virtualenv:

deactivate

This is the same sequence using Python 3:

mkvirtualenv --python=/usr/bin/python3 venv-py3
pip install numpy cython
pip install scipy matplotlib h5py
deactivate

Now you are in virtualenv nirvana

It’s often good to use virtualenvs to start a development session. Doing so means that you can install exactly the requirements that you need, without causing changes to your other virtualenvs.

You can now make virtualenvs for your testing development quickly. Say you want to test something out for Python 3:

# Make clean virtual environment
mkvirtualenv --python=/usr/bin/python3 testing-something
pip install numpy scipy matplotlib h5py
# install anything else you want
# run your tests
deactivate

Nice.

Even if you are offline, you can always install things you have already built and cached, by adding the --no-index flag to Pip:

# Make another clean virtual environment
mkvirtualenv --python=/usr/bin/python3 testing-offline
pip install numpy scipy matplotlib h5py --no-index
# install anything else you want
# run your tests
deactivate

Adding new packages and wheels

Adding new wheels is usually as simple as:

# Switch to relevant virtualenv to build, cache, install wheel
workon venv
pip install my-package

Sometimes the Python package you are installing has nasty binary dependencies. In this case, usually your easiest path is to install the build dependencies for the corresponding Debian / Ubuntu package, and then continue as before:

sudo apt-get build-dep pillow
workon venv
pip install pillow

Sometimes, it’s a package too far

There are some Python packages that have heavy binary dependencies, or use complicated build systems, so that it is not practical to build a wheel with Pip. Examples I know of are VTK and ITK. For those cases, your best option is to install the Python package using apt-get, and then make your virtualenv with the --system-site-packages flag, so that it will pick up the installed packages:

sudo apt-get install python-vtk
mkvirtualenv --system-site-packages an-env-including-vtk

Doesn’t work for you? Help improve this page

If you try the instructions here, and you can’t get a particular package or set-up to work, then why not make an issue for the repository hosting these pages, and I’ll see if I can work the fix into this page somewhere.

Footnotes

[1]

If you need to build common packages such as Numpy (for example, on platforms like ARM), you should first install the Debian / Ubuntu packages with the build dependencies for these packages. For example you might want to run something like this:

sudo apt-get build-dep python-numpy python-scipy matplotlib h5py

This will take a fairly long time. See Adding new packages and wheels.