Reproducible Python Environments with Conda

By default, Python doesn’t handle binary dependencies very well. There have been several occasions when I’ve tried to pip install library and it just choked on me because it was trying to compile something and I didn’t have the magic combination of compiler versions and build tools needed. At the same time, it’s absolutely worth fiddling with many of these libraries because they can be tremendously powerful. Matplotlib, Paramiko, and Neovim are Python libraries I depend on that have binary dependencies. Luckily, this hard problem has been alleviated by conda, a “package, dependency and environment manager for any language”. It isn’t perfect, but it lives up to the name and also solves a related problem: creating lightweight, cross-platform, easily-reproducible environments for Python code. Here’s an example of how I use conda with my code, including common problems I run into and how I solve them. The docs have been very helpful.

Install miniconda3.

I usually do this on Linux, so I’m only going to put those instructions here. Follow the link above to find instructions for other platforms.

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Follow the instructions, and make sure you prepend Miniconda’s directory to the PATH when it gives you the option, then start a new terminal instance.

Create a new project

I like to create my project in it’s own directory with a README.md to explain what it does. This is usually the point when I decide what libraries I’m going to need for this code. For the sake of this blog post, let’s say I want to write a script that SSHes into a Linux box, runs the command uptime, and prints that. I’m going to call it remote-uptime, and I know from experience that a script like that needs a library named paramiko to run and paramiko needs an binary crytography implementation (which makes it difficult to install via pip). Furthermore I want to use the netmiko wrapper on top of paramiko so I can also use this for network devices. So those are my goals, create an environment with paramiko and netmiko, then save those dependencies so the environment can be easily reproduced elsewhere.

Create Environment and install libraries

First things first- find out out which libraries are in conda’s repos, and which will have to be installed via pip. The output of conda search paramiko indicates that that it is in conda’s repos. That’s really good because it means that we can let conda deal with paramiko’s binary dependencies. However, conda search netmiko comes up empty, so the next step is to search for any binary dependencies it has, and try to deal with those independently of the environment. I normally deal with this situation by probing the website for installation instructions, and linking those in the README for my project. However, in this case, netmiko’s dependencies are pure Python except for paramiko, which is already being dealt with by conda. Finally, let’s actually create the darn thing:

conda create --name multi-uptime python=3 paramiko

This command creates an environment with a completely separate copy of Python and some libraries for us.

Notice I’m only specifying the libraries that conda can install in the command above- We’ll deal with the ones that need pip… right after we activate the environment.

source activate multi-uptime

This command makes sure that the multi-uptime version of Python is the first one found on our PATH- this means that whenever we do any more python things with this (like installing netmiko), it will only affect this copy of Python, leaving our system installation of Python unentangled with the copy dedicated to multi-uptime. The prompt also changes to tell us we’re using a project specific Python version.

21:36:29 [bbkane@bbkane-Latitude-E7440 Code]
$ source activate multi-uptime
(multi-uptime) 21:36:39 [bbkane@bbkane-Latitude-E7440 Code]
$

Now let’s install netmiko:

python -m pip install netmiko

You’ll notice that it does install some helper libraries, but the binary dependent one is already installed:

Requirement already satisfied: paramiko>=1.13.0 in /home/bbkane/anaconda3/envs/multi-uptime/lib/python3.6/site-packages (from netmiko)

So conda has helped us successfully sidestep netmiko’s binary dependencies without having to install packages at the system level!

The last step on our setup now is to save this mix of libraries to a text file so other contributors can use it without going through the same dance we’ve had to:

conda env export > environment.yaml

environment.yaml looks like this:

name: multi-uptime
channels:
- defaults
dependencies:
- asn1crypto=0.22.0=py36_0
- cffi=1.10.0=py36_0
- cryptography=1.8.1=py36_0
- idna=2.5=py36_0
- libffi=3.2.1=1
- openssl=1.0.2l=0
- packaging=16.8=py36_0
- paramiko=2.1.2=py36_0
- pip=9.0.1=py36_1
- pyasn1=0.2.3=py36_0
- pycparser=2.18=py36_0
- pyparsing=2.2.0=py36_0
- python=3.6.2=0
- readline=6.2=2
- setuptools=27.2.0=py36_0
- six=1.10.0=py36_0
- sqlite=3.13.0=0
- tk=8.5.18=0
- wheel=0.29.0=py36_0
- xz=5.2.2=1
- zlib=1.2.8=3
- pip:
  - netmiko==1.4.2
  - pyyaml==3.12
  - scp==0.10.2
prefix: /home/bbkane/anaconda3/envs/multi-uptime

Notice that dependencies is a list of items- mostly Python libraries, but also other things, like Python itself, binary libraries, like OpenSSL, and pip, which has its own list of libraries. This is basically a superset of what pip freeze gives. The last thing to note is that last line there- prefix: /path/to/env. To be honest, I’m not sure why that line is there. It doesn’t need to be, and keeping actually hampers using this environment.yaml on another machine. Delete it (so in this example, - scp==0.10.2 would be the last line), and save this file in your repository.

Use it on another machine

When someone else wants to use your environment, they only have to use the following command:

conda env create -f environment.yaml

Which will do all the work we just did without them having to do much of anything.

Delete the environment

When you need more space, or you screw something up and you want to delete the environment, use

conda remove --name <name> --all

Because conda stores environments separately from your code, you don’t have to worry about it deleting anything you created, and if you need it the environment back, you can just recreate it with your environment.yaml.