Reproducible environments, faster builds, and shareable note pads– without yak-shaving.
A sensible 5 -step Docker roadmap for data science: clean images, quick caching, compose for notebooks, tidy quantities, and one-command keep up genuine code.
Let’s be actual: “service my maker” is not an attribute. Docker transforms your laptop into a reliable lab– very same Python, exact same libs, exact same results– every single time. Right here’s the lean, five-step playbook I make use of to make it painless.
1 Beginning with a tidy, reproducible picture
Select a tiny base, pin dependencies, decline root, and disregard junk in your context.
# Dockerfile
# syntax=docker/dockerfile: 1 6
FROM python: 3 11 -slim AS base
ENV PIP_DISABLE_PIP_VERSION_CHECK= 1 PIP_NO_CACHE_DIR= 1 PYTHONDONTWRITEBYTECODE= 1
# system deps first (cached)
RUN-- place=kind=cache, target=/ var/cache/apt \
apt-get update && & apt-get install -y-- no-install-recommends \
build-essential git && & rm -rf/ var/lib/apt/ lists/ *
# lockfile prior to resource for topmost cache hits
WORKDIR/ application
Duplicate requirements.txt./
RUN-- place=kind=cache, target=/ origin/. cache/pip \
pip set up -r requirements.txt
# include a non-root individual
RUN useradd -m -u 1000 ds
Individual ds
COPY.
CMD ...