gperdrizet/datascience-cpu repository overview

⁠Data science CPU development container

Lightweight data science environment for intro Python, data visualization, data cleaning, feature engineering, and traditional machine learning. No deep learning frameworks; keeps the image small and fast.

⁠1. Features

• Python 3.12
• JupyterLab
• scikit-learn, XGBoost, statsmodels
• pandas, numpy, scipy
• matplotlib, seaborn, plotly
• optuna (hyperparameter optimization)
• CPU only, linux/amd64; works on any machine

⁠2. Usage with VS Code

Start the container, then use VS Code's Dev Containers: Attach to Running Container command to connect:

docker run --rm -it \
  -v $(pwd):/workspace \
  gperdrizet/datascience-cpu:latest \
  /bin/bash

Open the Command Palette (Ctrl+Shift+P), run Dev Containers: Attach to Running Container, and select the container.

⁠3. Running directly with Docker

docker run --rm -it \
  -p 8888:8888 \
  -v $(pwd):/workspace \
  gperdrizet/datascience-cpu:latest \
  jupyter lab --ip=0.0.0.0 --no-browser --allow-root

⁠4. Requirements

• Docker

⁠5. License

MIT. See docker-images⁠.