Eugo University
Eugo University is our learning site: courses, videos, guides, and interactive demos for running real workloads on Eugo.
These docs and the University do different jobs. This documentation is reference — what a feature is, what a setting does, what the API accepts. The University is teaching — working through a task end to end, including the failures you will hit along the way and how to get past them.
When you want to know what something does, stay here. When you want to learn how to do something, start there.
Where to start
| If you want to | Go to |
|---|---|
| Run your first workload on a cluster | Getting started with Eugo |
| Understand the whole platform in order | Eugo 101 |
| Distribute an existing pandas or NumPy workload | Distributed Python with eugo.hpc |
| Use GPUs, and confirm the work reached one | GPU acceleration on Eugo |
| Set up a workspace for a team | Workspaces & organizations |
| See measured scaling and benchmark data | Interactive demos |
What is there
- Courses — sequenced material, from a first cluster to a seven-terabyte pipeline.
- Learning paths — curated course sequences by role: data scientist, ML engineer, workspace admin.
- Videos — short walkthroughs of the mechanics and the common mistakes.
- Interactive demos — measured scaling curves, a CPU versus GPU benchmark, and a cluster sizer, including the cases where the answer is "don't".
- Resources — checklists for workload readiness, performance tuning, workspace setup, and cost.
- Glossary — the vocabulary used across the platform, these docs, and the courses.
- Certifications — credentials in platform fundamentals and distributed computing.