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In this blog post, we will explore the process of deploying a Python API created using FastAPI on an Azure Virtual Machine using Continuous Integration and Continuous Deployment (CI/CD) pipelines. The focus will be on setting up a CI pipeline to create an artifact, and subsequently, a CD pipeline to automate the deployment process.
Step 1: Creating a CI Pipeline on Azure DevOps To get started, you’ve already created a CI pipeline on Azure DevOps to generate an artifact. This CI pipeline is responsible for building and testing the Python API codebase and producing a deployable artifact, typically a Docker image or a package. By doing this, you ensure that your code is stable and error-free before proceeding to the deployment phase.
Step 2: Setting Up the CD Pipeline Now that the CI pipeline has successfully created the artifact, it’s time to create the CD pipeline to automate the deployment process. Rather than manually copying the artifact to the Azure Virtual Machine and running the Python API through a bash command, we’ll leverage Azure DevOps’ capabilities to automate this process efficiently.
Step 3: Creating Jobs in the CD Pipeline In the CD pipeline, you can create two distinct jobs. The first job is responsible for copying the artifact (i.e., the Docker image or package) from the CI pipeline’s output to the Azure Virtual Machine. Azure DevOps provides various deployment tasks to facilitate this, such as Azure CLI, Azure PowerShell, or Azure Copy File.
Step 4: Running the Python API on the Azure Virtual Machine Once the artifact is successfully copied to the Azure Virtual Machine, the second job comes into play. In this job, you can use a bash command line (or any other appropriate method) to run the Python API on the VM. Depending on how you’ve packaged the API, you may need to install the required dependencies and start the server using the appropriate command.
Step 5: Configuring Triggers and Artifacts To ensure that the CD pipeline automatically runs whenever there’s a new artifact available, you can set up a trigger that monitors the output of the CI pipeline for changes. This way, each time a new version of the API is created, it will trigger the CD pipeline to deploy the latest changes to the Azure Virtual Machine.
By creating a CI/CD pipeline to deploy your Python API on an Azure Virtual Machine, you streamline the deployment process, reduce the chances of manual errors, and ensure faster and consistent deployments. With automation in place, you can focus more on improving your application rather than managing deployment logistics.
Remember, this is just a high-level overview of the process, and depending on your specific use case, there might be additional steps or considerations required. Nevertheless, by following the general guidelines outlined in this blog post, you’ll be well on your way to achieving an efficient and automated deployment workflow for your FastAPI-based Python API on Azure. Happy coding!
