Founded in 2009, MaGe Education is a top Chinese high‑end IT training brand. Its graduates earn 12K+ RMB salaries, and the school has trained tens of thousands of students. It offers high‑pay courses in Linux cloud operations, Python full‑stack, automation, data analysis, AI, and Go high‑concurrency architecture. Thanks to quality courses and a solid reputation, it has talent partnerships with numerous internet firms.
This guide walks through verifying the Nginx environment, configuring a reliable upstream block, setting proper proxy headers and timeouts, validating backend health, handling failures, exposing observability, and performing controlled rollouts to ensure traffic is correctly balanced without service disruption.
This guide explains how to schedule large‑model inference pods onto GPU nodes that meet exact hardware requirements—such as A100 80 GB cards, specific node pools, and zones—by converting those needs into Kubernetes node‑affinity, taint, and topology constraints, verifying the deployment, monitoring its health, and safely rolling out or rolling back changes.
The article explains how to replace CPU‑only autoscaling for large‑model inference services with a Kubernetes HPA that scales pods according to a custom queue‑depth metric exported to Prometheus, covering metric definition, deployment configuration, Prometheus‑Adapter setup, HPA creation, capacity calculation, validation, troubleshooting, and rollback procedures.
This article presents a step‑by‑step Nginx log‑analysis workflow that helps operators identify slow requests, 5xx spikes, CC attacks, scanners and SQL‑injection attempts by parsing access_log and error_log fields, aggregating by IP, URL, UA and time windows, and then applying rate‑limiting, map‑based blocking, geo‑blocking and firewall rules to mitigate the threats while ensuring proper log rotation and verification.
This article provides a step‑by‑step, Linux‑focused workflow for recording driver and GPU versions, interpreting utilization versus memory metrics, locating memory‑consuming processes, handling container and Kubernetes mappings, checking temperature, power, ECC, MIG, driver health, OOM conditions, and setting up reliable monitoring and alert thresholds for data‑center GPUs.
This guide walks through deploying vLLM on Linux with NVIDIA GPUs and Docker Compose, covering service boundaries, host and container checks, model directory validation, image and parameter verification, minimal startup, Compose configuration, API testing, concurrency tuning, multi‑GPU troubleshooting, Nginx exposure, upgrade/rollback procedures, and daily health checks.
This guide explains how to set up Nginx as a load balancer for several OpenAI‑compatible large language model instances, covering health checks, upstream configuration, algorithm selection, streaming vs non‑streaming proxy settings, logging, rate limiting, graceful reloads, and troubleshooting techniques.
This guide walks through deploying SGLang on Linux with NVIDIA GPUs and Docker Compose, covering environment checks, image versioning, minimal foreground launch, Docker Compose configuration, health checks, troubleshooting, performance testing, security hardening, and upgrade/rollback procedures to reliably expose an OpenAI‑compatible large model API in production.
This article provides a step‑by‑step, safety‑focused guide for expanding LVM logical volumes and ext4 or XFS filesystems online, covering device‑mapping verification, free‑space assessment, metadata backup, three expansion paths (VG free space, underlying disk growth, adding a new disk), and thorough post‑expansion validation.
This guide explains how to configure Fail2ban on Linux servers to detect repeated SSH authentication failures, extract source IPs from logs, and automatically apply firewall rules via nftables, iptables, or firewalld, while covering verification, safe deployment, parameter tuning, whitelist management, and integration with broader security practices.
