Choosing your next step by role
You can now open a session, launch a cluster, dispatch parallel work, and collect results. That is the core loop.
Where to go next depends on what you are actually trying to accomplish.
If you analyze data
You want Distributed Python with eugo.hpc. It covers
fan-out properly, per-call resource requests, actors for stateful work, and a realistic parallel
pandas workload.
Then scaling a real workload for the case where the dataset is large enough that I/O becomes the constraint.
The data scientist path sequences these for you.
If you train or serve models
Go to GPU acceleration on Eugo. It covers which operations reach the GPU, how to request one, and how to confirm it happened.
Pair it with how automatic optimizations work. Knowing what the runtime does on your behalf is what lets you write code it can help.
The ML engineer path covers both.
Remember: PyTorch, not TensorFlow. TensorFlow, spaCy, and JAX are not supported.
If you are setting up for a team
Workspaces & organizations is the one you need. It covers organizations, workspaces, members, roles, plans, and the activity trail.
The workspace admin path adds the platform context around it.
If you want the full tour first
Eugo 101 covers the whole platform in order rather than by role. Worth it if you would rather have the complete map before specializing.
If you just want to see the numbers
The interactive demos let you explore scaling curves, a CPU-versus-GPU comparison including the cases where a machine's GPU loses to its own CPU, and a cluster sizer that estimates worker count from the shape of your workload. The chart figures there are illustrative rather than measured, and the page says so — they are for reasoning about a regime, not for quoting.
Two things worth reading regardless
- Workload readiness checklist, to verify a workload is worth distributing before spending cluster time on it.
- Glossary, the vocabulary used consistently across these courses and the platform docs.