How to containerize your optimization model with Python and Docker

Want to make your optimization model transferable, replicable, and scalable? Containerization ensures consistency across environments, simplifies CI/CD integration, and enables scalable optimization services in modern architectures. Our colleague, Bruno Vieira, created this detailed video walkthrough of containerizing a facility location model in #Python with #Docker: https://lnkd.in/dj4qhfvk . You can read the full details in his blog post: https://lnkd.in/dvQKkQHB where he goes in detail of: - Writing a Dockerfile for an Xpress-based Python model - Handling licensing (static and web-floating) - Building and running containers - Using VS Code Dev Containers for streamlined development - Running pre-built images from DockerHub 📦 Whether you're deploying solvers in microservices or integrating with enterprise systems, this guide helps bridge the gap between PoC and production. 👉 Check out this GitHub repo for Xpress Python Dockerfiles: https://lnkd.in/dBa4YBiT #Optimization #DevOps #DecisionIntelligence #AI

𝗗𝗲𝗺𝘆𝘀𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗔𝘀𝘆𝗻𝗰𝗶𝗼: 𝗕𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗰𝗲𝗻𝗲𝘀 𝗼𝗳 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 & 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 Yesterday, I dove deep into 𝗣𝘆𝘁𝗵𝗼𝗻’𝘀 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 and truly got hands-on with 𝙚𝙫𝙚𝙣𝙩 𝙡𝙤𝙤𝙥𝙨, 𝙘𝙤𝙧𝙤𝙪𝙩𝙞𝙣𝙚 𝙨𝙘𝙝𝙚𝙙𝙪𝙡𝙞𝙣𝙜, 𝙖𝙣𝙙 𝙩𝙖𝙨𝙠 𝙤𝙥𝙩𝙞𝙢𝙞𝙯𝙖𝙩𝙞𝙤𝙣! For the first time, I saw clearly how Python manages I/O and CPU-bound operations, threading, and real concurrency strategies beyond just async and await. Here’s what my journey covered: - 𝗘𝘃𝗲𝗻𝘁 𝗹𝗼𝗼𝗽 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝘀: How Python coordinates non-blocking tasks and schedules execution under the hood - 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱 𝗮𝗻𝗱 𝗖𝗣𝗨-𝗯𝗼𝘂𝗻𝗱 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀: When to use async, threading, and multiprocessing for peak performance - 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: How asyncio’s task grouping enables effective parallelism and why production apps rely on it - 𝗖𝗼𝗿𝗼𝘂𝘁𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝘂𝘁𝗶𝗹𝗶𝘁𝗶𝗲𝘀: Building custom coroutines for efficient context switching Most developers only scratch the surface of asyncio – but unlocking these insights is essential for advanced, production-ready Python. I’ve documented code samples and exploration notes in my GitHub repo: https://lnkd.in/dTBW-wcM Ready for feedback, suggestions, or collaboration from anyone interested in advanced Python concurrency! #python #asyncio #concurrency #pythonprogramming #threading #multiprocessing #softwaredevelopment #coding #eventloop #pythoncode #performance #developers #opensource #github

Understanding how your code really works is key to building scalable systems. Python mode at www.newtongraph.com empowers you to do just that by making your source code visual and queryable, highlighting key concepts and topic clusters that shape your application. This visualization breaks down the flow of a simple hash map implementation, showing everything from initialization to successful data retrieval. It's a powerful reminder that good architecture starts with strong fundamentals and a clear vision!

v2 of our TIMRUN inference runtime and first release of our Python SDK are live! We're building the tools that our first customers wanted to improve the experience of using our platform for everyone.

𝗧𝗮𝗰𝗸𝗹𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻'𝘀 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗰𝗲𝗶𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝗖𝗣𝗨-𝗶𝗻𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝘁𝗮𝘀𝗸𝘀? You're hitting a common, frustrating bottleneck. But rewriting everything isn't the only solution. For anyone building high-performance agents, Karl Weinmeister's latest primer on polyglot agentic architectures is an essential read. It offers a strategic path forward, not just a temporary fix. It's an excellent starting point for a more robust approach. He expertly demonstrates how to leverage a hybrid architecture to get the best of both worlds. Key insights include: → Overcoming bottlenecks by offloading demanding operations, like Protocol Buffers (protobuf) serialization, to a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety (Rust). → Achieving seamless interoperability between Python and Rust using a set of Rust bindings for Python (PyO3). → Architecting high-performance servers, such as for a Model Context Protocol (MCP), that maintain Python's ease of use while harnessing Rust's raw speed. 💡 What makes his piece particularly powerful is that he closes by directing readers to a concrete, real-world implementation of these concepts. He bridges the gap between the why and the how. I've linked to that detailed walkthrough below. It's the practical application of the principles Karl outlines. #Python #Rust #PerformanceEngineering #SystemArchitecture #PolyglotProgramming https://lnkd.in/ef3MRTbb

LLMs in Production: Serving Open-Source Models Efficiently with Python You don't need a massive GPU cluster to run LLMs in production! 🔥 With tools like Ollama and LangChain, you can deploy powerful open-source models efficiently. Here's how to build a reliable Python service around them. Deployment Strategy: 1: Run Ollama in a Docker container 2: Use this Python service as a management layer 3: Implement Redis for caching frequent prompts 4: Add health checks and circuit breakers 5: Use GPU-enabled nodes in Kubernetes This approach gives you control over your AI infrastructure without vendor lock-in! Have you deployed open-source LLMs? What challenges did you face? #LLM #AI #GenerativeAI #MLOps #DevOps #Python #Ollama #Kubernetes #Docker

DVC is git for Data Scientists/ML Engineers. I cannot my imagine my workflow without it. DVC -> allows one to track changes to their files and get DAG for defining the workflow in the yaml file so that the next person who you uses your code knows which commands to run and what are the dependencies and outputs of each Python file. dvclive - allows one to turn their vscode into an ML Experiment where you can track metrics and artifacts . Just great. documentation : https://dvc.org/doc

𝗜 𝗕𝘂𝗶𝗹𝘁 𝗠𝘆 𝗢𝘄𝗻 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 — 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝘆 𝗬𝗼𝘂 𝗠𝗶𝗴𝗵𝘁 𝗪𝗮𝗻𝘁 𝗧𝗼, 𝗧𝗼𝗼 Over the past week, I’ve been exploring how Model Context Protocol (MCP) is quietly changing the way AI tools like GitHub Copilot Chat interact with real-world systems. We already saw what’s possible with Playwright’s MCP (if you haven’t read my breakdown yet, check this : https://lnkd.in/g_Wa9nCC) — it’s a brilliant example of how a product MCP exposes tool-specific capabilities. Just like Playwright, even Postgres now has its own MCP extension (https://lnkd.in/gYwHH7rF). And MCPs are popping up everywhere — from monitoring tools to testing frameworks. So I started thinking…  • What happens when your enterprise wants to bring its own data model, its own rules, and its own workflows into this ecosystem?  • What if your AI assistant could reason directly over your database, APIs, or CI/CD pipelines — securely and contextually? That’s where Custom MCP Servers come in. I built one in Python using FastMCP and Postgres — complete with schema discovery, sample queries, and contextual data exploration — all accessible right inside VS Code Copilot Chat. 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/gYjzhFgy 𝗙𝘂𝗹𝗹 𝗕𝗹𝗼𝗴: https://lnkd.in/gvzhpeKE 𝗬𝗼𝘂𝘁𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼: https://lnkd.in/gNCJYkvS 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Product MCPs (like Playwright, Postgres) expose tool capabilities. Custom MCPs expose business capabilities. Together, they create a bridge between AI assistants and enterprise data ecosystems — safely, contextually, and locally. If you’re exploring AI-native DevOps, data, or testing workflows — this space is worth watching closely. The MCP era is just beginning. #MCP #AI #GitHubCopilot #FastMCP #Python #Postgres #DeveloperTools #VSCode #AIEngineering #OpenAI #SlayItCoder

GitHub - allmonday/fastapi-voyager: Explore your API interactively, https://lnkd.in/ee_tV6_8 IA Summary: Visualize your FastAPI endpoints and their dependencies interactively with `fastapi-voyager`, transforming your API architecture into a clear, explorable graph. This powerful tool helps identify design issues, understand data relationships, and streamline internal API integrations through comprehensive visualization and code navigation. #dev #python #api #fastapi #documentation #exploration #design #datastructures #fastapivoyager

Python is reshaping how businesses automate, analyze, and operate. Manual data pulling, messy spreadsheets, and scattered workflows used to slow teams down. At Neksogix, we build Python-powered automation that turns daily tasks into smart, scalable systems. Key Highlights • Web scraping & automated reporting • Data cleaning with real-time alerts • Workflow orchestration across tools • Hours saved weekly through automation Smarter operations. Faster decisions. Scalable growth. #Neksogix #Automation #Python #BusinessAutomation