GPU assumptions
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6 Docker Tricks for Data Science Reproducibility
Read Full Article: 6 Docker Tricks for Data Science Reproducibility
Reproducibility in data science can be compromised by issues such as dependency drift, non-deterministic builds, and hardware differences. Docker can mitigate these problems if containers are treated as reproducible artifacts. Key strategies include locking base images by digest to ensure deterministic rebuilds, installing OS packages in a single layer to avoid hidden cache states, and using lock files to pin dependencies. Additionally, encoding execution commands within the container and making hardware assumptions explicit can further enhance reproducibility. These practices help maintain a consistent and reliable environment, crucial for accurate and repeatable data science experiments.
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