gperdrizet/datascience-nvidia repository overview

⁠Data science NVIDIA GPU development container

Data science environment with NVIDIA GPU acceleration for XGBoost and CuPy (GPU-accelerated NumPy). Covers intro Python, data visualization, data cleaning, feature engineering, and traditional machine learning.

⁠1. Features

• Python 3.12
• JupyterLab
• scikit-learn, XGBoost (GPU), statsmodels
• pandas, numpy, scipy
• CuPy (GPU-accelerated NumPy-compatible arrays)
• matplotlib, seaborn, plotly
• optuna (hyperparameter optimization)
• CUDA 12.8, Pascal through Blackwell (sm_60-sm_100)

⁠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 \
  --gpus all \
  -v $(pwd):/workspace \
  gperdrizet/datascience-nvidia: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 \
  --gpus all \
  -p 8888:8888 \
  -v $(pwd):/workspace \
  gperdrizet/datascience-nvidia:latest \
  jupyter lab --ip=0.0.0.0 --no-browser --allow-root

⁠4. Requirements

• Docker with NVIDIA GPU support and host driver >= 570.x
• NVIDIA Container Toolkit

⁠5. License

MIT. See docker-images⁠.