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Eugo for ML engineers

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

Who it's for

You build and ship models, and your bottleneck is training throughput or inference cost. This path focuses on GPU acceleration, resource requests, and the automatic optimizations that run whether or not you ask for them. Knowing what they do is what lets you work with them rather than against them.

At a glance

Duration
2h 16m
21 lessons
Level
Intermediate
4 courses
Roles
ML engineer, HPC engineer
Topics
GPU & acceleration, AI/ML, Performance & optimization, Distributed compute

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. Distributed Python with eugo.hpc

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

    Intermediate50m

  3. GPU acceleration on Eugo

    How work reaches the GPU, which operations benefit, and how to confirm it actually happened.

    Intermediate26m

  4. How automatic optimizations work

    Eugo rewrites and tunes your code as it runs. Here is what it does, and how to help it.

    Intermediate29m

Good to know

Supported frameworks

Eugo does not support TensorFlow, spaCy, or JAX. Plan around PyTorch and the scientific Python stack.

GPU availability

GPU compute nodes depend on your plan. Check the dashboard before designing a workload that assumes them.

Other paths

All paths

Eugo for data scientists

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

4 courses2h 29mBeginner

Eugo for workspace admins

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

3 courses1h 33mBeginner