Optimizing Python Production Deployments with Gunicorn, Uvicorn, and Supervisor

💻 Docker Practice: Using Environment Variables Today I practiced making my Docker containers more flexible by using environment variables to control application behavior. 💠 Dynamic Configuration: Used the ENV instruction in the Dockerfile to set a variable (APP_MODE=Production). 💠 Code Integration: Updated the Python script to read the variable using os.environ.get(), allowing the app to adapt to its environment. 💠 Build & Verification: Built the productionapp image and confirmed that the container correctly identified its mode during execution. 💠 Execution Success: Verified the output: "application mode: Production" without having to change a single line of Python code. #Docker #DevOps #Backend #PythonDevelopment #Automation #Configuration #SoftwareEngineering

🎨 The Art of Refactoring Software evolution often looks like a Studio Ghibli film: a bit messy in the middle, but magical in the end. Transitioning from a legacy PHP monolith to a modern Python-Django stack is more than a tech shift—it's a complete architectural evolution. The stages of the journey: The Foundation: Reliable but heavy legacy systems. The Integration: The "awkward" hybrid phase where systems merge. The Synthesis: Achieving a scalable, clean, and "high-level" architecture. Refactoring isn't just about replacing code; it's about growing into a better system. Is your team currently evolving its stack, or have you reached the final form? 👇 #WebDev #Python #Django #SoftwareEngineering #TechStack

🚀Deploying my Algorithm to #Docker stack so it can run fully automated, 24/7. Tech stack: -Python for core logic -Flask API to receive and process signals -MongoDB for logging and state -Docker for reliable, always‑on deployment -Upstox / Dhan broker webhooks for execution Once deployed, I’ll be able to use the strategy directly via broker webhooks, without manually sitting on the charts. More details and architecture breakdown coming soon.

Q1. 🚀 Production Failure Due to Missing Dependencies Today I worked on a DevOps scenario where a Python application deployed inside a Docker container failed to start in production due to missing libraries. 🔍 Issue: The container failed during startup with errors like: ModuleNotFoundError ImportError 💡 Root Cause: Required Python dependencies were not installed inside the container requirements.txt was missing or not used in Dockerfile Environment mismatch between local setup and container 🛠️ Solution: Added a proper requirements.txt with all dependencies Updated Dockerfile to install dependencies: pip install -r requirements.txt Ensured correct Dockerfile structure: Copy requirements first (for caching) Install dependencies Copy application code Rebuilt Docker image: docker build -t my-app . Ran container with restart policy: docker run -d --restart=always my-app ✅ Result: Container started successfully without errors Application ran smoothly in production No manual intervention required after deployment 📌 Key Learning: *Always package dependencies inside the Docker image *Never rely on local environment *Use requirements.txt and proper Docker layering for production-ready builds 🚀 #Linuxworld #devops

Task Manager REST API built with Django and Django REST Framework. This project includes: • CRUD operations for tasks • Owner-based permissions (only owners can modify tasks) • Pagination for task lists • Filtering by status and due date • Clean RESTful API structure This project helped me strengthen my understanding of API development, serializers, permissions, and backend architecture. #Python #Django #DjangoRESTFramework #BackendDevelopment #RESTAPI #WebDevelopment #GitHub

Deploying to production teaches you a thing or two. I ran into a few subtle issues that only showed up after deployment: silent login failures, memory crashes during startup, and uncertainty about whether the backend was even running. 🔵 A hardcoded localhost API URL meant the frontend was still pointing to my laptop. Updating it fixed everything instantly 🔵 Loading heavy dependencies at startup exceeded memory limits. Switching to lazy loading stabilized the service 🔵 A simple /health endpoint confirmed the backend was running and removed debugging guesswork Small things, but critical in production. #buildinpublic #fastapi #deployment #devops #python

The latest update for #ScoutAPM includes "Best #Python #APM Tools in 2026: A Developer's Guide" and "How a Singleton Pattern Broke Our #Django #Logging". #monitoring #devops #observability https://lnkd.in/dZjpXF2

🚀 Milestone Update! My open-source developer tools have crossed 1400+ installs on PyPI. It's exciting to see developers trying tools I built to simplify everyday Git workflows. 🔧 Tools: • Run-Git → Simplifies Git push workflow • Ollama-Git-Commit → Generates AI-powered commit messages locally using Ollama Building small tools that automate repetitive developer tasks has been a great learning experience. Thanks to everyone who tried the tools and shared feedback with me 🙌 GitHub: https://lnkd.in/gJbJgC2Y #python #opensource #developers #git #buildinpublic

💻 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