Kubernetes

Introduction Training modern deep‑learning models often requires hundreds of gigabytes of data and billions of parameters. A single GPU can no longer finish the job in a reasonable time, so practitioners turn to distributed training. While data‑parallel synchronous training has become the de‑facto standard, asynchronous stochastic gradient descent (ASGD) offers compelling advantages in elasticity, fault tolerance, and hardware utilization—especially in heterogeneous or spot‑instance environments. At the same time, Kubernetes has emerged as the leading platform for orchestrating containerized workloads at scale. Its declarative API, built‑in service discovery, and robust auto‑scaling capabilities make it an ideal substrate for running large‑scale ML clusters. ...

Introduction Containerization has changed the way modern software is built, shipped, and run. While Docker made it easy to package an application with all its dependencies, the real challenge emerges when thousands of containers need to be orchestrated across a fleet of machines. That is where Kubernetes—the de‑facto standard for container orchestration—steps in. This guide is designed to take you from zero to hero: Zero – You’ll start with a clean slate, no prior Kubernetes knowledge required. Hero – You’ll finish with a solid mental model, hands‑on experience, and best‑practice patterns that let you design, deploy, and operate scalable, resilient workloads in production. Whether you are a solo developer, a team lead, or an SRE, the concepts, code snippets, and real‑world tips in this article will help you master Kubernetes for scalable container management. ...

Introduction Kubernetes has become the de‑facto platform for orchestrating containerized workloads, but its networking model is often perceived as a “black box.” Understanding how traffic moves inside a cluster is essential for developers who need to: Debug connectivity issues quickly. Design secure, multi‑tenant applications. Integrate service meshes, API gateways, or custom load balancers. Optimize performance and cost. This guide dives deep into the internal mechanics of Kubernetes networking. We’ll explore the underlying concepts, the role of the Container Network Interface (CNI), how pods talk to each other, how services are implemented, and how network policies enforce security. Real‑world YAML examples and code snippets illustrate each concept, and a mini‑project demonstrates the ideas in practice. ...

Kubernetes networking is often considered the “final boss” for system architects. While the platform abstracts away much of the complexity of container orchestration, the underlying networking model is a sophisticated web of IPAM, virtual interfaces, routing tables, and netfilter rules. Understanding how a packet travels from a user’s browser to a container deep within your cluster is essential for building scalable, secure, and resilient systems. In this guide, we will peel back the layers of the Kubernetes networking stack. ...

Large Language Models (LLMs) are moving from research labs into production systems at an incredible pace. As soon as organizations move beyond simple API calls to third‑party providers, a question appears: “How do we run LLMs ourselves, reliably, and at scale?” For many teams, the answer is: Kubernetes. This article dives into Kubernetes for LLMs—when it makes sense, how to design the architecture, common pitfalls, and concrete configuration examples. The focus is on inference (serving), with notes on fine‑tuning and training where relevant. ...