Workspaces and persistent storage
A workspace is an isolated compute environment. It owns three things that matter to you daily: persistent storage, installed libraries, and a resource allocation.
What isolation means
Two workspaces in the same organization do not share storage or installed packages. A library installed in one is absent from the other; a file written in one is invisible to the other.
That isolation is what lets one organization run separate projects, or separate production and experimental environments, without either interfering with the other.
Persistent storage
Files in the workspace tree survive between sessions. This is where your work belongs.
# Persists: the workspace tree
df.to_parquet("/workspace/results/run-2026-08-30.parquet")
# Does NOT persist: session-local
df.to_parquet("/tmp/results.parquet")
The failure mode is an afternoon of output written to /tmp, gone after a restart. It happens to
everyone once.
Workspace storage is visible to your session. Data that many workers need should be in object storage, which every compute node can reach. A path that works in your notebook is not automatically a path a worker can read.
Where data should live
| Data | Where | Why |
|---|---|---|
| Notebooks, scripts | Workspace tree | Persists, and only your session needs it |
| Small reference files | Workspace tree | Convenient, and cheap to copy into tasks |
| Large inputs read by workers | Object storage | Every node can reach it in parallel |
| Run outputs | Object storage | Durable, and readable outside the workspace |
Libraries
The scientific Python stack comes preinstalled: NumPy, pandas, SciPy, scikit-learn, PyTorch, plus common geospatial and imaging libraries.
For anything else:
%pip install some-package
Install into the workspace for something the whole team needs. Installing into each person's session means every new session pays the setup cost again, and environments quietly diverge.
TensorFlow, spaCy, and JAX are not supported on Eugo. Plan around PyTorch.
Resource allocation
A workspace's limits come from its pricing plan. The plan governs concurrency as well as capacity: how much can run at once, not only how much total.
That distinction bites as a team grows. Ten people on a plan sized for two will queue, and the symptom looks like slowness rather than a limit.
Choosing the boundary
Workspaces get split per team, per project, or per environment. Decide which before you create things, because storage and limits follow the boundary, and moving later means moving data. The workspace setup checklist walks through the decision in order.