Module 8: GPU workflows, scale, and deployment

Module 8: GPU workflows, scale, and deployment#

Theme#

GPU workflows, scale, and deployment

Essential Question#

How do practitioners move from notebook experiments to reproducible GPU training pipelines?

Professional Scenario#

A prototype model is ready to leave a notebook, and the team needs a repeatable training job with accountable metrics and resource assumptions.

Learning Outcomes#

By the end of this module, students will be able to:

  • explain the core technical idea in precise neural-network vocabulary

  • connect architecture and training choices to the shape of the data and task

  • run or interpret the module lab as reproducible evidence

  • identify limitations, failure modes, and next steps for a defensible experiment

Module Components#

  • Book prose for conceptual framing and practitioner patterns.

  • Assignment notebook for the graded artifact: reproducible training workflow checklist.

  • Slides and narration for structured teaching.

  • Lab notebook for hands-on experimentation.

  • Rubric for grading technical correctness, evidence, tradeoffs, risk analysis, and communication.

Use This Module in Order#

  1. Read the learning chapter.

  2. Review the slide deck with the matching narration.

  3. In Populi, open the private student-repository link for this course and enter modules/module-8.

  4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

  5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.