Continuous Integration \u0026 Continuous Delivery in Python
Continuous Integration \u0026 Continuous Delivery in Python


Integrating CI/CD into Your Python Development

I’m probably not the only one guilty of a Pull and Pray. You know, when you clone your application using git, install the dependencies with pip, and pray it all works out.

Obviously, this is not a good way to build and deploy Python applications.

Not to mention all the problems it causes like bloated deployment time; errors in the build; and no guarantee the app is the same server-to-server.

What you need – what we all want – is good up-time and scale for everything. Especially for large deployments.

You want CI/CD for Python.

The two aren’t often discussed together, but CI/CD principles and best practices can 100% apply to Python development.

What is CI/CD and Why Should You Use it?

Inedo is all about CI/CD, so before I really get into the meat of Python CI/CD, let’s quickly cover what it is in general.

It’s basically the automation of building committed code and deploying it after testing.

The principles of CI (continuous integration) help fight problems that occur with rapid release cadences like bad quality control on new features or performance issues for end users. With CI, developers can implement changes into their code and make sure it integrates with the main codebase with automatic testing.

On the other end, CD (continuous deployment/delivery) is about producing and delivering software. Every organization has a different CD process, but essentially boils down to changes made that are automatically bug tested, uploaded, and deployed to production. The results are three noticeable benefits: better quality software; faster delivery of ideas to market; and cheaper implementation.

CI/CD together is a super powerful tool, and the benefits are worth the human resources to implement it into your development process.

The Many Benefits of CI/CD for Python

If you’re in the Pull and Pray club, you’re probably familiar with the problems.

As I mentioned before, there are bloated deployment times because pip installs the build from the source. Deploying with pip doesn’t guarantee the application will be the same server-to-server. There may be errors in the build process, resulting in inconsistencies that are hard to debug.

These problems will be especially noticeable if you have a business-critical app run on many servers. Plus, Pull and Pray doesn’t scale well since it’s inconsistent and unreliable.

Enter CI/CD.

It’s all about automatically building committed code, so you can use it with Python to avoid breaking your builds. You can create and maintain a stable base, clone and commit frequently, and test rapidly!

You instantly get faster deployments thanks to the eliminated pip install bloat. The process eliminates having to manually watch an execution, so Python users can take that saved time and put it towards more development.

In fact, a CI/CD development pipeline is entirely customizable to your organization’s needs or your team’s preferences. A pipeline could include multiple automated checks and run much faster and more accurately than any human.

A proper CI/CD tool, like BuildMaster, will include multiple features that a manual or patched-together pipeline may not have. For example, you obviously want good security with increased speed, so you could set your pipeline to have automated gates and in-tool approves to help managers increase efficiency in reviews. BuildMaster has built-in audit trails, so every action is recorded in an access log making review even easier!

Prior to CI/CD, software releases were usually delayed by unpredictable changes, bugs, defects, poor code quality – the list goes on. Python users can seriously up their development by implementing even a basic CI/CD process like “[input] -> [testing] -> [production].”

Setting up a Python CICD Pipeline

CI/CD is all about automating the tedious parts of deployment, so you’ll want a good tool that has all the features and benefits I mentioned above.

We at Inedo use BuildMaster (we’re a little biased), but there are others out there. Some, like Jenkins, aren’t ideal, but you can scrap together a CD pipeline if you put in the effort.

To get started, you’ll have to configure your tool for Python applications. Most tools will offer a variety of application formats or templates.

Of course, you’ll connect to GitHub to source your code from your source repository, and then use pip to install any dependent packages. Using the pip freeze command will help with this since it’ll list all your installed modules and their version. Or, you could check via the requirements.txt file.

BuildMaster cuts a lot of the work for you, so following the user interface will automatically install Python packages and execute PyUnit.

Once a build is complete, an artifact will be created. You can now deploy! Again, your tool will do a lot of the work for you. BuildMaster stops hosting services, deploys, and restarts services.

BuildMaster allows users to point team members and/or end users to the build face and download the artifact from there, but you could also create a Universal Package and send it via file, or distribute the build as an executable windows program.

After you configure it, Python and CI/CD are easy to do and maintain! It’ll help your team rapidly increase release cadence, and overall improve the quality of your code. Plus, it helps with the tricky means of deploying/distributing an application for end users on different set-ups.

Leave Pull & Pray in the Past: Try CI/CD

There are better ways to set up your development environment than cloning from GitHub and installing dependencies via pip. I hope I’ve made that very clear.

CI/CD is one of the best ways to develop and distribute your Python Applications.

You will instantly improve uptimes, you’ll have fast releases without sacrificing security, and managers can set up multiple checkpoints (automatic or manual) for testing and approvals.

I highly recommend moving to a CI/CD process if you haven’t already, and doing so with BuildMaster is a good start 😉.

Or you could learn about other aspects of Python development that may have been under your radar like license detection or Python package versioning. Sign up for our upcoming Python guide:

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