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  1. 148
      blogs/python-development-with-guix.org
  2. 9
      blogs/python-large-deployment-with-guix.org

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blogs/python-development-with-guix.org

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* Python deployment is a solved problem
Time I wrote about using Python deployment in [[https://en.wikipedia.org/wiki/Deployment_environment][scenarios]] of
development, testing, staging and production, something we do on a
grand scale. In 2018 Python is the [[https://www.tiobe.com/tiobe-index/][4th]] largest programming language in
the world and may soon reach the top 3. Dealing with Python
deployment, however, is less than straightforward. If you got bitten
by Python dependencies in non-trivial scenarios, or if you find that
Docker deployment is terribly slow, or if you need to reproduce old
installations faithfully, or if you need to support multiple languages
and compilers or versions thereof: you may want to continue reading
because GNU Guix solves it all elegantly.
In this writeup I focus on isolation of components which is the basis
of a sane environment.
** Let's install some software for development
We are going to install software in Guix using python, python-numpy and python-sqlalchemy
: guix package -p ~/opt/python-dev -i python python-numpy python-sqlalchemy
here we use a secial directory ~/opt/python-dev that contains symlinks
to all Guix references. This is a convenience so we don't have to deal
with these funny looking HASH values ever (see below).
In a shell you can pull in a profile by running
: source ~/opt/python-dev/etc/profile
Now python3 should be in the PATH and modules are available
#+BEGIN_SRC python
python
import sqlalchemy
import numpy
--- do something
#+END_SRC
It just works. Now if you can create a second profile
: guix package -p ~/opt/python-dev -i python2 python2-numpy python2-sqlalchemy
Load that profile and you are set to run Python2. No interference with
the previous Python3 profile.
You can also use pip or pip3 to install software in your home directory:
: pip3 install xxx
Similarly you can use virtualenv and conda to install Python software
(and more). Guix does not stop you from doing that, though there is a
chance of deployments going pear shape because these tools do not give
you full control over the dependency graph and therefore are not
truely reproducible.
The Guix way is to install software with Guix and manage all versions
through profiles.
** Guix environment
** Multiple Python versions
First of all GNU Guix supports multiple Python interpreters out of the box:
'guix package -A python' lists 1,400 Python related packages including
the interpreters
#+BEGIN_SRC
python 2.7.14 out,tk gnu/packages/python.scm:143:2
python 3.6.3 out,tk gnu/packages/python.scm:343:2
#+END_SRC
This number reflects a module for each interpreter, so 'guix package -A numpy' lists
#+BEGIN_SRC
python-numpy 1.14.0 out gnu/packages/python.scm:2783:2
python2-numpy 1.8.2 out gnu/packages/python.scm:2865:2
python2-numpy 1.14.0 out gnu/packages/python.scm:2783:2
#+END_SRC
interestingly there is an older version for python2-numpy in there
because some other package (nmoldyn) requires it. This is already a
hint of how versions can be mixed in. To see the nmoldyn dependencies
you can run
: guix graph nmoldyn|grep label
and see that it has over 400 dependencies! You can draw the graph as an SVG using
: ???
Now we want to see the (computed) store paths with
: guix graph -t bag nmoldyn
which renders over 9,000 package links that this package depends on,
including
: /gnu/store/m3qrcmlrkx6ms2b5f4afidd7h2qrv994-python2-numpy-1.8.2.drv
You can also check that there is a dependency on
python2-cython.
: /gnu/store/9dqb0rfj0ammn321l9bai7bp6pq12xgy-python2-cython-0.27.drv
I mean, this is a really complex deployment! But if you check this way
you can see that other packages contain many many dependencies
too. Both python-scipy and python-matplotlib also have close to 10,000
nodes in the dependency graph. Note that all dependencies are uniquely
identified and tractable.
In other words: GNU Guix gives you full control over the dependency graph.
** These funny HASH values
So, what is this funny hash value you see in the path. When I do a
'ls /gnu/store/*python-3*/bin/python3' on my laptop I get something like:
#+BEGIN_SRC
/gnu/store/isc3rwn2picqha5m4yiqj4rj51la50pm-python-3.4.3/bin/python3
/gnu/store/3rpnwnzfyskkmp6yqdxfxz20gm8d62ki-python-3.5.2/bin/python3
/gnu/store/alk9r3rir93pjmv8im20f8xrvv90219z-python-3.5.2/bin/python3
/gnu/store/3aw9x28la9nh8fzkm665d7fywxzbl15j-python-3.5.3/bin/python3
/gnu/store/3lkypf5wnsnvkaidhw0pv7k3yjfh1r9g-python-3.6.3/bin/python3
/gnu/store/kw5v2fvsnvdfaihrrq73fddv0fwbj7fd-python-3.6.5/bin/python3
/gnu/store/gx7gjwr05gzbh7f2kwbwhbxrh27hvgk8-python-3.6.5/bin/python3
#+BEGIN_SRC
which implies I have multiple versions of python3 on my system and
they do not interfere with each other. The HASH value reflects the
unique source package that was used to compile this version (the
source changes, the HASH changes). Not only that if dependencies
change the HASH changes (so the last two versions may depend on
different versions of libraries, for example) and when the build
changes the HASH changes too (e.g., with and without SSL support, or
maybe an optimization switch).
If I install a module that depends on python-3.6.5 it will hard
reference, for example,
: /gnu/store/gx7gjwr05gzbh7f2kwbwhbxrh27hvgk8-python-3.6.5/bin/python3.
This way the exact Python gets called with the exact modules that you
specified when creating the dependency graph.

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** A complex deployment
In the context of a web service we deploy a large stack of tools that
invoke other tools. The web servers require multiple versions of
Python (2.4, 2.7 and 3.x series) mostly because of incompatibilities
between Python modules and their bindings. We also have tools in there
that are written in R (some with Python RPy2 bridges), C, C++, D,
Elixir, Fortran - you name it.
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