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Eugo for data scientists

Go from a single-machine notebook to distributed analysis over datasets that no longer fit in memory.

Who it's for

You already work in Python and pandas. You have hit the point where a dataset takes hours to process, or no longer fits on one machine, and you would rather not become an infrastructure engineer to fix it. This path takes you from your first session to distributing a real dataframe workload across a cluster.

At a glance

Duration
2h 29m
23 lessons
Level
Beginner
4 courses
Roles
Data scientist, Research scientist
Topics
Fundamentals, EugoIDE, Distributed compute, Data & I/O

Learning path

Work through these in order.

  1. Getting started with Eugo

    What Eugo is, what problem it solves, and how to run your first workload on a cluster.

    Beginner31m

  2. EugoIDE essentials

    Work fluently in the browser-based notebook environment where your Python actually runs.

    Beginner30m

  3. Distributed Python with eugo.hpc

    Turn ordinary Python into distributed work with a decorator, then scale it across a cluster.

    Intermediate50m

  4. Scaling a real workload

    Take a single-machine geospatial pipeline to a cluster, and find out where the time actually goes.

    Advanced38m

Good to know

What you need first

Working Python, and familiarity with pandas or NumPy. No prior distributed-computing experience is assumed. The path introduces tasks, futures, and actors from scratch.

Access

You need a workspace to follow along. If your organization has not set one up, the workspace admin path covers that side.

Other paths

All paths

Eugo for ML engineers

Train and serve models across GPU compute nodes, and understand what the runtime is doing on your behalf.

4 courses2h 16mIntermediate

Eugo for workspace admins

Stand up an organization, get a team productive inside it, and keep compute spend predictable.

3 courses1h 33mBeginner