Reproducible development environment

Last updated on 2024-09-17 | Edit this page

Overview

Questions

What are virtual environments in software development and why use them?
How can we manage Python virtual coding environments and external (third-party) libraries on our machines?

Objectives

After completing this episode, participants should be able to:

Set up a Python virtual coding environment for a software project using venv and pip.

So far we have created a local Git repository to track changes in our software project and pushed it to GitHub to enable others to see and contribute to it.

We now want to start developing the code further. If we have a look at our script, we may notice a few import lines like the following:

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import json

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import csv

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import datetime as dt

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import matplotlib.pyplot as plt

This means that our code requires several external libraries (also called third-party packages or dependencies) - json, csv, datetime and matplotlib.

Python applications often use external libraries that do not come as part of the standard Python distribution. This means that you will have to use a package manager tool to install them on your system. Applications will also sometimes need a specific version of an external library (e.g. because they were written to work with feature, class, or function that may have been updated in more recent versions), or a specific version of Python interpreter. This means that each Python application you work with may require a different setup and a set of dependencies so it is useful to be able to keep these configurations separate to avoid confusion between projects. The solution for this problem is to create a self-contained virtual environment per project, which contains a particular version of Python installation plus a number of additional external libraries.

Virtual development environments

So what exactly are virtual environments, and why use them?

A Python virtual environment helps us create an isolated working copy of a software project that uses a specific version of Python interpreter together with specific versions of a number of external libraries installed into that virtual environment. Python virtual environments are implemented as directories with a particular structure within software projects, containing links to specified dependencies allowing isolation from other software projects on your machine that may require different versions of Python or external libraries.

It is recommended to create a separate virtual environment for each project. Then you do not have to worry about changes to the environment of the current project you are working on affecting other projects - you can use different Python versions and different versions of the same third party dependency by different projects on your machine independently from one another.

Another big motivator for using virtual environments is that they make sharing your code with others much easier - as we will see shortly you can record your virtual environment in a special file and share it with your collaborators who can then recreate the same development environment on their machines.

You do not have to worry too much about specific versions of external libraries that your project depends on most of the time. Virtual environments also enable you to always use the latest available version without specifying it explicitly. They also enable you to use a specific older version of a package for your project, should you need to.

Managing virtual environments

There are several command line tools used for managing Python virtual environments - we will use venv, available by default from the standard Python distribution since Python 3.3.

Part of managing your (virtual) working environment involves installing, updating and removing external packages on your system. The Python package manager tool pip is most commonly used for this - it interacts and obtains the packages from the central repository called Python Package Index (PyPI).

So, we will use venv and pip in combination to help us create and share our virtual development environments.

Creating virtual environments

Creating a virtual environment with venv is done by executing the following command:

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$ python -m venv /path/to/new/virtual/environment

where /path/to/new/virtual/environment is a path to a directory where you want to place it - conventionally within your software project so they are co-located. This will create the target directory for the virtual environment.

For our project let’s create a virtual environment called “venv_spacewalks” from our project’s root directory.

Firstly, ensure you are located within the project’s root directory:

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$ cd /path/to/spacewalks

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$ python -m venv venv_spacewalks

If you list the contents of the newly created directory “venv_spacewalks”, on a Mac or Linux system (slightly different on Windows as explained below) you should see something like:

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$ ls -l venv_spacewalks

OUTPUT

total 8
drwxr-xr-x  12 alex  staff  384  5 Oct 11:47 bin
drwxr-xr-x   2 alex  staff   64  5 Oct 11:47 include
drwxr-xr-x   3 alex  staff   96  5 Oct 11:47 lib
-rw-r--r--   1 alex  staff   90  5 Oct 11:47 pyvenv.cfg

So, running the python -m venv venv_spacewalks command created the target directory called “venv_spacewalks” containing:

pyvenv.cfg configuration file with a home key pointing to the Python installation from which the command was run,
bin subdirectory (called Scripts on Windows) containing a symlink of the Python interpreter binary used to create the environment and the standard Python library,
lib/pythonX.Y/site-packages subdirectory (called Lib\site-packages on Windows) to contain its own independent set of installed Python packages isolated from other projects, and
various other configuration and supporting files and subdirectories.

Once you’ve created a virtual environment, you will need to activate it.

On Mac or Linux, it is done as:

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$ source venv_spacewalks/bin/activate
(venv_spacewalks) $

On Windows, recall that we have Scripts directory instead of bin and activating a virtual environment is done as:

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$ source venv_spacewalks/Scripts/activate
(venv_spacewalks) $

Activating the virtual environment will change your command line’s prompt to show what virtual environment you are currently using (indicated by its name in round brackets at the start of the prompt), and modify the environment so that running Python will get you the particular version of Python configured in your virtual environment.

You can verify you are using your virtual environment’s version of Python by checking the path using the command which:

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(venv_spacewalks) $ which python

When you’re done working on your project, you can exit the environment with:

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(venv_spacewalks) $ deactivate

If you’ve just done the deactivate, ensure you reactivate the environment ready for the next part:

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$ source venv_spacewalks/bin/activate
(venv_spacewalks) $

Note that, since our software project is being tracked by Git, the newly created virtual environment will show up in version control - we will see how to handle it using Git in one of the subsequent episodes.

Installing external packages

We noticed earlier that our code depends on four external packages/libraries - json, csv, datetime and matplotlib. As of Python 3.5, Python comes with in-built JSON and CSV libraries - this means there is no need to install these additional packages (if you are using a fairly recent version of Python), but you still need to import them in any script that uses them. However, we still need to install packages datetime and matplotlib as they do not come as standard with Python distribution.

To install the latest version of packages datetime and matplotlib with pip you use pip’s install command and specify the package’s name, e.g.:

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(venv_spacewalks) $ python -m pip install datetime
(venv_spacewalks) $ python -m pip install matplotlib

or like this to install multiple packages at once for short:

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(venv_spacewalks) $ python -m pip install datetime matplotlib

The above commands have installed packages datetime and matplotlib in our currently active venv_spacewalks environment and will not affect any other Python projects we may have on our machines.

If you run the python -m pip install command on a package that is already installed, pip will notice this and do nothing.

To install a specific version of a Python package give the package name followed by == and the version number, e.g. python -m pip install matplotlib==3.5.3.

To specify a minimum version of a Python package, you can do python -m pip install matplotlib>=3.5.1.

To upgrade a package to the latest version, e.g. python -m pip install --upgrade matplotlib.

To display information about a particular installed package do:

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(venv_spacewalks) $ python -m pip show matplotlib

OUTPUT

Name: matplotlib
Version: 3.9.0
Summary: Python plotting package
Home-page:
Author: John D. Hunter, Michael Droettboom
Author-email: Unknown <matplotlib-users@python.org>
License: License agreement for matplotlib versions 1.3.0 and later
=========================================================
...
Location: /opt/homebrew/lib/python3.11/site-packages
Requires: contourpy, cycler, fonttools, kiwisolver, numpy, packaging, pillow, pyparsing, python-dateutil
Required-by:

To list all packages installed with pip (in your current virtual environment):

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(venv_spacewalks) $ python -m pip list

OUTPUT

Package         Version
--------------- -----------
contourpy       1.2.1
cycler          0.12.1
DateTime        5.5
fonttools       4.53.1
kiwisolver      1.4.5
matplotlib      3.9.2
numpy           2.0.1
packaging       24.1
pillow          10.4.0
pip             23.3.1
pyparsing       3.1.2
python-dateutil 2.9.0.post0
pytz            2024.1
setuptools      69.0.2
six             1.16.0
zope.interface  7.0.1

To uninstall a package installed in the virtual environment do: python -m pip uninstall <package-name>. You can also supply a list of packages to uninstall at the same time.

You are collaborating on a project with a team so, naturally, you will want to share your environment with your collaborators so they can easily ‘clone’ your software project with all of its dependencies and everyone can replicate equivalent virtual environments on their machines. pip has a handy way of exporting, saving and sharing virtual environments.

To export your active environment - use python -m pip freeze command to produce a list of packages installed in the virtual environment. A common convention is to put this list in a requirements.txt file in your project’s root directory:

BASH

(venv_spacewalks) $ python -m pip freeze > requirements.txt
(venv_spacewalks) $ cat requirements.txt

OUTPUT

contourpy==1.2.1
cycler==0.12.1
DateTime==5.5
fonttools==4.53.1
kiwisolver==1.4.5
matplotlib==3.9.2
numpy==2.0.1
packaging==24.1
pillow==10.4.0
pyparsing==3.1.2
python-dateutil==2.9.0.post0
pytz==2024.1
six==1.16.0
zope.interface==7.0.1

The first of the above commands will create a requirements.txt file in your current directory. Yours may look a little different, depending on the version of the packages you have installed, as well as any differences in the packages that they themselves use.

The requirements.txt file can then be committed to a version control system (we will see how to do this using Git in a moment) and get shipped as part of your software and shared with collaborators and/or users.

Note that you only need to share the small requirements.txt file with your collaborators - and not the entire
venv_spacewalks directory with packages contained in your virtual environment. We need to tell Git to ignore that directory, so it is not tracked and shared - we do this by creating a file .gitignore in the root directory of our project and adding a line venv_spacewalks to it.

Let’s now put requirements.txt under version control and share it along with our code.

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(venv_spacewalks) $ git add requirements.txt
(venv_spacewalks) $ git commit -m "Initial commit of requirements.txt."
(venv_spacewalks) $ git push origin main

Your collaborators or users of your software can now download your software’s source code and replicate the same virtual software environment for running your code on their machines using requirements.txt to install all the necessary depending packages.

To recreate a virtual environment from requirements.txt, from the project root one can do the following:

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(venv_spacewalks) $ python -m pip install -r requirements.txt

As your project grows - you may need to update your environment for a variety of reasons, e.g.:

one of your project’s dependencies has just released a new version (dependency version number update),
you need an additional package for data analysis (adding a new dependency), or
you have found a better package and no longer need the older package (adding a new and removing an old dependency).

What you need to do in this case (apart from installing the new and removing the packages that are no longer needed from your virtual environment) is update the contents of the requirements.txt file accordingly by re-issuing pip freeze command and propagate the updated requirements.txt file to your collaborators via your code sharing platform.

Reproducible development environment

Overview

Questions

Objectives

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Virtual development environments

Managing virtual environments

Creating virtual environments

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OUTPUT

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Installing external packages

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OUTPUT

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Further reading

Key Points

Reproducible development environment

Overview

Questions

Objectives

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Virtual development environments

Managing virtual environments

Creating virtual environments

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OUTPUT

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Installing external packages

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OUTPUT

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Sharing virtual environments

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OUTPUT

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Further reading

Key Points