Codvo's Guide to Deploying Python Microservices on Kubernetes

-By Divyang Bissa


In today's fast-paced tech landscape, microservices have emerged as a game-changer, and Kubernetes (K8s) stands tall as the go-to platform for their deployment. But how do you navigate this intricate world? Welcome to the definitive Codvo guide – a beacon to lead you through the intricacies of deploying Python microservices on Kubernetes. Whether you're a seasoned developer or just venturing into the microservices domain, our guide promises clarity, precision, and a streamlined pathway to elevate your projects. Dive in to uncover the secrets of Kubernetes, leverage Python's capabilities, and enhance your deployment expertise.


Before we dive in, ensure you have the necessary tools installed, especially if you're on macOS:


Install using the command

/bin/bash -c "$(curl -fsSL"


Installation instructions here


Get started with minikube





Official Helm Installation Guide

To install using a script:

$ curl -fsSL -oget_helm.sh

$ chmod 700

$ ./


Alternatively, using brew: brew install helm

kubectl: For macOS M1 chip, install using:

curl -LO "$(curl-L -s"

Setting Up the Python Virtual Environment

Virtualenv enables Python environment setup. From Python 3.3onwards, a version of it, 'venv', is integrated into the standard library. To use venv:

  1. Install virtualenv with pip install virtualenv.
  2. Navigate to your project directory and initialize the virtual environment with python<version> -m venv<virtual-environment-name>.

Activating the Virtual Environment

After creation, activate the virtual environment. For macOS:

            source env/bin/activate

For Windows, refer to the official documentation.

Checking Your Virtual Environment

To ensure isolation from the host Python, activate the environment and run pip list. Only two default packages, pip and setup tools, should appear.

Setting Up the Fast API Project

Here's a step-by-step guide:

Helm Chart Creation

  1. Inside the application's directory, run helm create ml_model_api.
  2. Install the chart on your minikube instance: helm install api-svc-ml . -napi-service.
  3. Upgrade the deployment with: helm upgrade api-svc-ml . -napi-service.
  4. Test your service by port-forwarding.

Deploying Python-based microservices on Kubernetes is streamlined and efficient. By following these steps, you'll have your service up and running in no time!


Mastering Kubernetes deployments for Python-based microservices is the future of scalable and resilient infrastructure. With this Codvo guide, the journey from virtual environment setup to harnessing the power of Helm charts has been streamlined. As the tech landscape evolves, adopting containerized solutions becomes paramount. Being adept in Kubernetes means optimizing operational efficiency and setting the stage for exponential application growth. Dive deep, experiment with Codvo's guidelines, and witness the transformation in your deployment strategies.

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