Python

Learn how to implement Llama Stack's built-in guardrails with Python, helping to improve the safety and performance of your LLM applications.

Build a simple agentic AI server with the Model Context Protocol (MCP). This guide shows you how to create a Python-based weather tool.

Discover how Redis and PostgreSQL work together to provide caching for improved SQL query performance in this demo.

This tutorial shows you how to use the Llama Stack API to implement retrieval-augmented generation for an AI application built with Python.

Learn about the advantages of prompt chaining and the ReAct framework compared to simpler agent architectures for complex tasks.

Harness Llama Stack with Python for LLM development. Explore tool calling, agents, and Model Context Protocol (MCP) for versatile integrations.

Integrate Red Hat AI Inference Server with LangChain to build agentic document processing workflows. This article presents a use case and Python code.

Learn how to run vLLM on CPUs with OpenShift using Kubernetes APIs and dive into performance experiments for LLM benchmarking in this beginner-friendly guide.

Deploy AI at the edge with Red Hat OpenShift AI. Learn to set up OpenShift AI, configure storage, train models, and serve using KServe's RawDeployment.

Explore the complete machine learning operations (MLOps) pipeline utilizing Red

Discover how you can use the Podman AI Lab extension for Podman Desktop to work

Jupyter Notebook works with OpenShift AI to interactively classify images. In

Explore how the new pyproject RPM macros simplify packaging modern Python projects by supporting diverse build backends and reusing upstream metadata.

Explore how Red Hat Developer Hub and OpenShift AI work together with OpenShift to build workbenches and accelerate AI/ML development.

Discover how Red Hat Enterprise Linux (RHEL) provides security-compliant Python streams with long life cycles.

Cloud native technologies to develop, deploy, and manage responsive and scalable applications anywhere.

Learn how to build a ModelCar container image and deploy it with OpenShift AI.

Learn how to effectively manage Python content for producing Ansible execution environments.

The RamaLama project simplifies AI model management for developers by using OCI containers to automatically configure and run AI models.

This article details new Python performance optimizations in RHEL 9.5.

Find out what's new in Red Hat Enterprise Linux (RHEL) 9.5, including enhancements for workloads, container management and security, and Identity Management.

If you're a Python developer who relies on the Eventlet library, it's time to think about migrating your projects to Asyncio. This article helps you get started.

Red Hat® OpenShift® is a trusted, comprehensive, and consistent platform to develop, modernize, and deploy applications at scale, including today’s AI-enabled apps. Innovate faster with a complete set of services for bringing apps to market on your choice of infrastructure.

This short guide explains how to choose a GPU framework and library (e.g., CUDA vs. OpenCL), as well as how to design accurate benchmarks.

Learn how to write a GPU-accelerated quicksort procedure using the algorithm for prefix sum/scan and explore other GPU algorithms, such as Reduce and Game of Life.