Why containerize ML training + serving?

medium

Answer

$(1) Reproducibility: exact Python + CUDA + system libs pinned.$
$(2) Portability: same image on laptop / cluster / prod.$
$(3) Isolation: no dependency conflicts.$
$(4) Scalability: Kubernetes / batch schedulers.$
$(5) Version pinning: image tag = exact env.$
$Dockerfile pins: base image (nvidia/cuda), Python version, pip requirements with hashes, model artifacts.$
$Anti-pattern: latest tag (non-reproducible).$
$Use immutable digest for prod.$

Check yourself — multiple choice

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Reproducibility (pin Python + CUDA + libs) + portability + isolation + scale on K8s + version pinning; Dockerfile pins base + versions + hashes + artifacts; use digest not latest for prod
Not needed
Just Python

Containerize: reproduce + portable + isolate + scale + version pin.

#infrastructure#reproducibility

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