Containers & Reproducible ML Environments
Docker fundamentals, dependency pinning, GPU images, and reproducibility — for ML and beyond
Learn how Docker images and layers actually work, why ML images balloon to multiple gigabytes, how dependency pinning and lockfiles prevent 'works on my machine' failures that are worse in ML because of native and CUDA dependencies, how to reason about GPU base images and multi-stage builds, and what true reproducibility requires beyond a container: seeding across every layer of randomness and treating data as a versioned pointer, not a copy.
Practice questions (5)
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Diagnose a CUDA Driver/Toolkit Mismatch After a Deploy
Intermediate · Free -
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Shrink a 9 GB Serving Image Without Losing Functionality
Intermediate -
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Explain a Silent Metric Regression Traced to an Unpinned Dependency
Intermediate -
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A 'Reproducible' Training Run That Isn't Fully Reproducible
Intermediate -
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Design a Data-Versioning Scheme for a Retraining Pipeline
Intermediate