Labs

Reading a configuration is not enough to understand it. The site aims for every technical article to link to a lab that reproduces it on a workstation. That is not yet the case everywhere: here is the actual state.

Labs & Trainings forgeThe public code repository where the labs and course material published so far are freely available.

What is published and what is in preparation

Lab	Topic	State
labs/VMLLM	VictoriaMetrics as an LLM observability backend, material for the VictoriaMetrics course	published on the forge
labs/HPCOBS	HPC observability proof of concept, with a training simulator	published on the forge
labs/genaiotel	RAG application instrumented with OpenTelemetry, with an evaluation module	published on the forge
labs/cardinality-cost-lab	cardinality explosion and its cost, with load profiles	published on the forge
labs/llm-observability	kit for LLM observability: the labs (OpenTelemetry, VictoriaMetrics, Tempo, Grafana, Phoenix, Qdrant, Ollama)	being fixed, not yet published

Articles that do not have a lab yet say so, and a configuration published without having been run end to end carries a box that flags it.

Upcoming kits will ship a .devcontainer/ folder; the labs already published on the forge do not have one yet. Once the kit is published, the stack will open in VS Code (Dev Containers), or in GitHub Codespaces if the repository is also published on GitHub.

The HPCOBS lab on video

The real Grafana dashboard of the HPCOBS lab, filmed live (1 min 36). The data comes from the lab simulator: a 4-GPU training job, then two failures triggered on purpose. GPU utilization stays above 90% while the data loading wait rises and MFU drops; then the gradients explode while the infrastructure looks normal. The lab code is freely available on the forge (public code repository).Music: “Radar Focus”, Blue Saga (Epidemic Sound).

How a lab works

1. Clone the lab repository from the forge.

2. Start the stack with docker compose up. No account is required, but you need to download the container images and, for AI labs, the local models, which means several gigabytes on first start.

3. Follow the scenario in README.md, which has you trigger the incident, observe it and then fix it.

4. Check the result with the checks listed in README.md, when the lab provides them.