Junaid Bilal’s Post

💻 Docker Practice: Managing External Dependencies Today I practiced building a Docker image that includes external Python libraries using a requirements file. 💠 Dependency Management: Created a requirements.txt file to list necessary packages (like requests). 💠 Automated Installation: Used the RUN pip install command in the Dockerfile to install dependencies during the build phase. 💠 Layered Build: Verified the build process as Docker copied the requirements, installed the packages, and then added the application code. 💠 Successful Execution: Ran the container and confirmed the output: "external dependency loaded successfully." #Docker #Python #SoftwareDevelopment #DevOps #Pip #Automation #BackendEngineering

💻 Docker Practice: Building & Running Custom Images Today I practiced containerizing a Python application using a custom Dockerfile. 💠 Defined the Environment: Created a Dockerfile using python:3.11-slim as the base image. 💠 Built the Image: Used docker build -t mydockerapp to package the app.py script into a portable image. 💠 Execution: Ran the container and successfully received the "welcome to my dockerized application" output. 💠 Image Management: Verified the new image in the local repository and checked its disk usage. #Docker #DevOps #Python #Containerization #LearningJourney #BackendDevelopment

💻 Docker Practice: Optimizing Image Size Today I practiced reducing the footprint of my Docker images by switching to a lightweight base. 💠 Base Image Swap: Switched the Dockerfile from a standard slim image to python:3.12-alpine. 💠 Efficient Packaging: Rebuilt the application (optimizedapp) to see the impact on storage. 💠 Result Analysis: Successfully shrunk the image size from 183MB down to just 72.5MB. 💠 Verification: Ran the container to ensure the Alpine-based environment still executes the Python script perfectly. #Docker #Optimization #AlpineLinux #DevOps #CloudComputing #Efficiency #Backend

Day 41 of #90DaysOfDevOps 🚀 Today I explored GitHub Actions Triggers & Matrix Builds. 🔹 Trigger workflows on Pull Requests 🔹 Schedule workflows using cron 🔹 Run workflows manually with workflow_dispatch 🔹 Execute jobs across multiple environments using matrix builds I also learned how fail-fast and exclude help control matrix behavior when running jobs across different Python versions and operating systems. Git Repo: https://lnkd.in/gERs7E2e #90DaysOfDevOps #DevOpsKaJosh #TrainWithShubham #GitHubActions #DevOps

Day 6 of my 90 Days DRF Challenge 🚀 Today I learned how to use Mixins in Django REST Framework to simplify CRUD operations like create, update, delete, and list. I built APIs using these mixins and tested them on Postman to ensure everything worked smoothly. Mixins make API development faster, cleaner, and more reusable a powerful tool for real-world product building. #Django #DRF #Mixins #Python #API #Postman #LearningJourney #90DaysChallenge

CI/CD has improves code quality - and GitHub Actions makes it easier than ever to get started. I just built my first CI/CD pipeline with GitHub Actions! Check out my documentation for the step-by-step process 👇 ✅ Created a Python project with pytest tests ✅ Set up GitHub Actions workflow ✅ Configured automated testing on every push ✅ Watched my first green build pass Huge thanks to @NextWork for this awesome project guide. https://lnkd.in/gqvEJrP9 #CICD #GitHubActions #Python #DevOps #NextWork

🚀 Hands-on with Docker: Building a Lightweight Production Container Image As part of strengthening my containerization fundamentals, I worked on creating a lightweight Docker image for a Python application to optimize performance and efficiency in production environments. 🔹 Project Overview Created a simple Python application (app.py) Built a Docker image using a lightweight base image (python:alpine) Structured the container with a proper working directory Built the image named optimizedapp Successfully ran the container and verified the application output 🔹 Key Learnings Using minimal base images significantly reduces container size Smaller images improve deployment speed and resource efficiency #Docker #DevOps #Containers #CloudComputing #Python#LinuxWorld

Just finished building a Dockerized 2FA-Lite Microservice (Python/Flask). Minimal footprint, headless API structure, QR codes and tokens in RAM without writing to disk at any point. Anyone can integrate it. Check the logic here: https://lnkd.in/dBKjNWhY #Python #Docker #DevOps #Security #Backend #Microservices

As a #Python developer, there’s a lot more beyond writing code i.e. Isolation -> packaging -> publishing -> distribution. Still using PYTHONPATH and sys.path and calling it a package…? Is virtual environments your only packaging strategy…? Still zipping files and calling it distribution…? You are not alone — most of us have been there. #Python #PythonPackaging #PyPI #PreCommit #DevOps #SoftwareEngineering #CodeQuality #Docker #CI #CD #Ruff #uv

YAML is a data serialization format that's designed to be easy to read and write. And if you've worked with config files, Docker Compose, Kubernetes, or CI/CD pipelines, you've likely used it. In this guide, Bala teaches you how to work with YAML in Python with code examples. https://lnkd.in/gkuXgZEX