Python

Learn how to run high-performance computing workloads managed by Slurm within a containerized OpenShift environment using the Slinky operator.

Learn how the Responses API in Llama Stack automates complex tool calling while maintaining granular control over conversation flow for AI agents. Discover the benefits and implementation details.

Discover how I used an AI assistant to develop a production-grade Ansible Playbook to audit RHEL versions across a fleet of servers, generating a clean report.

Learn how to estimate memory requirements for your LLM fine-tuning experiments using Red Hat Training Hub's memory_estimator.py API. This guide covers the memory components, adjusting training setups for specific GPU specifications, and using the memory estimator in your code. Streamline your model fine-tuning process with runtime estimates and automated hyperparameter suggestions.

Understand the PyTorch autograd engine internals to debug gradient flows. Learn about computational graphs, saved tensors, and performance optimization techniques.

In this blog post, we're looking at how the Tech Preview of Red Hat trusted

Learn about the Red Hat build of OpenTelemetry and its auto-instrumentation capabilities to achieve full-stack observability on OpenShift.

Explore big versus small prompting in AI agents. Learn how Red Hat's AI quickstart balances model capability, token costs, and task focus using LangGraph.

One conversation in Slack and email, real tickets in ServiceNow. Learn how the multichannel IT self-service agent ties them together with CloudEvents + Knative.

Learn how to fine-tune a RAG model using Feast and Kubeflow Trainer. This guide covers preprocessing and scaling training on Red Hat OpenShift AI.

Learn how to implement retrieval-augmented generation (RAG) with Feast on Red Hat OpenShift AI to create highly efficient and intelligent retrieval systems.

Learn about Fedora Rawhide testing a solution that embeds SBOM metadata directly into Python wheels, allowing scanners to recognize backported security fixes.

Learn how to migrate from Llama Stack’s deprecated Agent APIs to the modern, OpenAI-compatible Responses API without rebuilding from scratch.

What RHEL 8 and 9 users need to know about Python 3.9 reaching the end-of-life phase upstream.

Optimize AI scheduling. Discover 3 workflows to automate RayCluster lifecycles using KubeRay and Kueue on Red Hat OpenShift AI 3.

Use SDG Hub to generate high-quality synthetic data for your AI models. This guide provides a full, copy-pasteable Jupyter Notebook for practitioners.

Simplify LLM post-training with the Training Hub library, which provides a common, pythonic interface for running language model post-training algorithms.

Use Podman Desktop to create a bootable Flask-based application using image mode

Discover SDG Hub, an open framework for building, composing, and scaling synthetic data pipelines for large language models.

Use Podman Desktop to create a bootable Django-based application using image

Learn why changing the Python 3 interpreter in Red Hat Enterprise Linux 9 and later is unsupported and discover a safer alternative.

Learn how to install Python 3.13 on Red Hat Enterprise Linux and CentOS Stream using the Extra Packages for Enterprise Linux (EPEL) repository.

Discover the benefits of using Rust for building concurrent, scalable agentic systems, and learn how it addresses the GIL bottleneck in Python.

Enhance your Python AI applications with distributed tracing. Discover how to use Jaeger and OpenTelemetry for insights into Llama Stack interactions.

Learn how to optimize PyTorch code with minimal effort using torch.compile, a just-in-time compiler that generates optimized kernels automatically.