How to set up continuous deployment in your home project the easy way

Continuous deployment (CD) is a powerful technique that automatically deploys code changes to production as soon as they are ready. CD enables faster development cycles, lower risk releases, and frees developers from manual deployment work. While CD is frequently used by software companies and large open-source projects, it can be just as useful for individual developers working on personal projects.

In this guide, we‘ll walk through setting up a robust and easy-to-manage CD pipeline for your home projects using Docker. By leveraging containers and integrating with GitHub and Docker Hub, you can deploy changes to your own projects with a simple git push, while ensuring a consistent and reliable deployment process every time.

Whether you‘re building a web app, mobile backend, or data pipeline, this guide will show you how to spend less time on deployment and more time developing great software. Let‘s get started!

Why continuous deployment matters for your projects

Before we dive into implementation details, it‘s worth understanding what makes continuous deployment so powerful for personal projects as well as large software products.

CD automatically pushes every change that passes automated tests directly to production. This is in contrast to continuous delivery, in which releases are automatically prepared for deployment but may be delayed before pushing to production.

CD has several key benefits:

1. Faster development cycles: With CD, every code change can be released immediately once integration tests pass. This means new features and fixes reach end users much faster.

2. Less risk per release: Since changes are deployed in small batches, there is less surface area for potential issues compared to large releases. Faulty deploys can also be rolled back more easily.

3. More regular feedback: Users see updates more frequently, enabling quicker identification of bugs, UX problems, and opportunities for new features.

4. No manual deployment work: Developers can focus purely on writing code, without having to set aside time for manual testing and releases.

For personal projects, CD brings the added benefit of making projects easier to share and get feedback on. Whether you want to share your work with friends, solicit feedback from other developers, or start building a user base, CD makes your application available 24/7 with minimal overhead.

A primer on Docker and containers

Our CD pipeline will be built around Docker, a popular platform for developing and deploying applications in containers.

Containers are lightweight, standalone packages that include an application and all its dependencies. They are highly portable and can run in any environment that supports the container runtime. Docker is currently the most widely-used container platform.

For developers, Docker provides several key benefits:

1. Environment consistency: Containers ensure that your application runs the same everywhere, avoiding bugs and inconsistencies across development, testing and production.

2. Isolation: Containers isolate your application and its dependencies from the underlying system, improving security and reliability.

3. Resource efficiency: Containers are lightweight, allowing you to run many containers on a single host machine.

4. Modularity: Applications can be broken down into loosely coupled microservices, each running in its own container. This makes it easier to develop, test and deploy individual components.

5. Rapid deployment: New container instances can be started in milliseconds, making it possible to scale applications quickly in response to traffic.

Docker defines containers in a special file format called a Dockerfile. The Dockerfile specifies the base image for the container (typically a minimal Linux distribution or language runtime), along with build instructions like copying in code, installing dependencies, and specifying the startup command.

With this background in mind, let‘s start setting up our project for continuous deployment with Docker.

Setting up your project and Docker environment

To begin, we‘ll make sure we have a suitable application to deploy, and that our local environment is configured properly for working with Docker.

First, select the project you would like to set up continuous deployment for. This can be an existing application you‘ve been working on, or a new project. The application can be in any language or framework, as long as it can be run in a container.

Some good candidates for containerized CD include:

• Web applications (React app, Rails app, etc.)
• Backend APIs (RESTful API, GraphQL server, etc.)
• Static sites (Gatsby, Jekyll, etc.)
• Data pipelines and ETL jobs
• Machine learning models

For this example, let‘s assume we‘re working on a simple Python web application using the Flask framework.

With our application ready, we‘ll set up our local Docker environment. Install Docker Desktop from the official Docker site:

https://www.docker.com/products/docker-desktop

Docker Desktop includes the Docker runtime and CLI tools, as well as a GUI dashboard for managing containers and images.

Once installed, open a terminal and confirm that the Docker CLI is working properly by running:

docker --version

You should see the installed version of Docker, e.g:

Docker version 20.10.5, build 55c4c88

We now have a working Docker environment and a suitable application to set up our CD pipeline with. Next, we‘ll define our application runtime by creating a Dockerfile.

Creating a Dockerfile for your application

To run our application in Docker, we need to define its runtime environment and dependencies. We do this in a Dockerfile.

Create a new file named Dockerfile (without any extension) in the root directory of your project. Here‘s an example Dockerfile for our Python web app:

# Use the official Python image as the base
FROM python:3.9-slim-buster

# Set the working directory in the container
WORKDIR /app

# Copy the requirements file and install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy the application code into the container
COPY . .

# Specify the command to run the application
CMD ["python", "app.py"] 

Let‘s break this down:

• FROM specifies the base image for our container, in this case the official Python 3.9 image.
• WORKDIR sets the working directory inside the container to /app.
• The first COPY copies the requirements.txt file (listing our Python dependencies) into the container.
• RUN installs the Python dependencies using pip.
• The second COPY copies the rest of our application code into the /app directory in the container.
• CMD defines the command that will run when the container starts up, in this case running our Python app.

With the Dockerfile defined, we‘re ready to build the container image. From the same directory as the Dockerfile, run:

docker build -t myapp .

This command builds the container image based on the Dockerfile, and tags it with the name myapp. The . at the end specifies the build context (the current directory).

Once the build completes, you can see the new image listed by running:

docker images

Our application image is now ready to deploy. However, we want to automate the process of building and deploying the image whenever our code changes. To enable this, we‘ll push our code to GitHub and configure Docker Hub to automatically build images from our repo.

Setting up continuous integration with GitHub

To automatically build and deploy our application when code changes, we first need to store our code in a version control repository. We‘ll use GitHub, but the same principles apply for other Git hosts like GitLab or Bitbucket.

If you haven‘t already, create a new repository on GitHub for your project. Make sure to initialize the repo without a README or license file, as we‘ll import an existing repository.

Next, initialize a Git repository in your local project directory:

git init

Add all the project files to the repo:

git add .

Commit the initial version:

git commit -m "Initial commit"

Add the GitHub repository as a remote:

git remote add origin https://github.com/yourusername/yourproject.git

Finally, push your local commits to GitHub:

git push -u origin main

Your code is now safely versioned on GitHub. Next, we‘ll set up the connection to automatically build Docker images when you push changes to GitHub.

Configuring automated builds on Docker Hub

To complete our CD pipeline, we‘ll configure Docker Hub to automatically build container images from our GitHub repository. This way, every time we push code changes, a new image will be built and ready to deploy without any manual steps.

First, create a free account on Docker Hub if you don‘t already have one:

https://hub.docker.com/

In your Docker Hub account settings, connect your GitHub account in the "Linked Accounts" section. This will allow Docker Hub to access your GitHub repositories.

Next, create a new repository on Docker Hub. Make sure to link it to your GitHub repository by selecting it from the list.

In the "Build Settings" for the new repository, specify the following:

• Source type: Branch
• Branch: main (or whichever branch you want to build from)
• Dockerfile location: / (or the path to your Dockerfile if not in the repo root)
• Autobuild: On

Save the changes. Now, every time you push to the main branch of your GitHub repository, Docker Hub will automatically build a new container image and push it to the Docker Hub registry with the :latest tag.

With your images building automatically, there‘s just one step left to complete the continuous deployment process.

Automatic deployment with Watchtower

The final step in our CD pipeline is to automatically pull and run the latest version of our image whenever a new build is pushed to Docker Hub. We‘ll use a handy open-source utility called Watchtower to enable this.

Watchtower runs as a lightweight container that monitors your running containers and watches for changes to their images in a remote registry like Docker Hub. When Watchtower detects an updated version of an image, it gracefully shuts down the existing container and starts a new one with the latest image.

To run Watchtower, use the following Docker command:

docker run -d \
    --name watchtower \
    -v /var/run/docker.sock:/var/run/docker.sock \
    containrrr/watchtower

This starts Watchtower in the background and mounts the Docker daemon socket so Watchtower can communicate with Docker to monitor and start/stop containers.

Now start your application container with the --name flag:

docker run -d --name myapp myusername/myapp:latest 

The myusername/myapp portion should match the name of your Docker Hub repository.

With your application container running, Watchtower will immediately begin monitoring it on the schedule defined in its configuration (every few minutes by default).

Whenever you push updates to GitHub, Docker Hub will build and push an updated image to your registry within a few minutes. Watchtower will detect the change, gracefully stop your existing myapp container and restart it with the :latest image.

That‘s it – you now have a fully automated continuous deployment pipeline for your project! Whenever you make changes and push them to GitHub, they will be live in production shortly after with no manual steps required.

Taking CD even further with automated testing

Our continuous deployment pipeline is already a massive improvement over manual deployment. However, we can make our process even more robust by adding automated testing.

On top of building our application image, we can configure Docker Hub (or another CI service like CircleCI or Travis CI) to run our application‘s test suite, and only deploy the new image if the tests pass. This way, we catch potential bugs before they ever make it to production.

For example, using Docker Hub‘s automated tests feature, you can add a docker-compose.test.yml file to your repo that defines a service to run your tests. Here‘s an example:

sut:
  build: .
  command: python -m pytest tests/

This Compose file defines a service that builds our application and runs the Python pytest command to execute tests in the tests/ directory.

To enable automated tests on Docker Hub:

1. In your Docker Hub repository, go to "Builds" and click "Configure automated tests"
2. Select "Internal pull requests" to run tests against pull requests before merging
3. Specify the Compose file as docker-compose.test.yml

Now before building the production image, Docker Hub will run your test suite. The image will only be built and pushed to the registry if the tests pass. If a test fails, the build will be marked as failed and the committer notified to fix the issue before merging.

With automated testing enabled, we can have even greater confidence in the quality of the code that gets deployed to production.

Conclusion

In this guide, we‘ve seen how easy it is to set up a powerful continuous deployment pipeline for your personal projects using Docker. By adopting a fully automated build-test-deploy process, you can accelerate development, reduce the risk of bugs in production, and forget about manual deployment chores.

The workflow we‘ve defined is just a starting point. As your project grows, you may also want to explore:

• Running multiple containers and scaling your application with Docker Compose or Kubernetes
• Performing database migrations and other administrative tasks as part of your CD process
• Implementing feature flags and controlling the rollout of changes to a subset of users
• Sending notifications to team members or external stakeholders when a deployment succeeds or fails

Continuous deployment can be a major boost to your development productivity and confidence. By investing a small amount of time in setting up CD for your personal projects, you can reap the benefits of effortless, low-risk deployments in your future work. Happy deploying!