Data science development environment

A ready-to-use data science environment for VS Code, designed for data science and ML bootcamp students. Covers data visualization, data cleaning, feature engineering, and traditional machine learning.

Requirements

All users

• Docker Desktop (Windows / Mac) or Docker Engine (Linux)
• VS Code with the Dev Containers extension

NVIDIA GPU users (also required)

• NVIDIA driver ≥570 (download)
• NVIDIA Container Toolkit (Linux only, not needed on Windows)

Mac users: GPU acceleration (Metal/MPS) does not pass through to Docker containers. The Mac configuration uses native ARM64 CPU, no extra setup needed beyond Docker Desktop.

Quick start

1. Fork this repository (click Fork at the top of this page)

2. Clone your fork:

   git clone https://github.com/<your-username>/datascience-devcontainer.git

3. Open the folder in VS Code, then open the Command Palette (Ctrl+Shift+P / Cmd+Shift+P) and run Dev Containers: Open Folder in Container

   VS Code will ask which configuration to use, pick the one that matches your machine (see table below).

4. Verify your setup by running notebooks/environment_test.ipynb

Which configuration should I use?

If you have...	Choose this
NVIDIA GPU (GTX 10xx / RTX / Quadro / Tesla)	DataScience NVIDIA
Windows or Linux machine, no NVIDIA GPU	DataScience CPU
Apple Silicon Mac (M1 / M2 / M3 / M4)	DataScience Mac

Not sure if your GPU is compatible? Check: NVIDIA CUDA GPUs (need compute capability ≥6.0).

Using as a template for new projects

Fork this repo once, then use it as a GitHub template to spin up new projects instantly.

One-time setup

1. Go to your fork on GitHub
2. Click Settings → scroll to Template repository → enable it

Creating a new project

1. Go to your fork and click Use this template → Create a new repository

2. Name your new repo and click Create repository

3. Clone it and start working:

   git clone https://github.com/<your-username>/my-new-project.git

4. Clean it up - remove anything that doesn't belong to your project:

   • Update README.md to describe your project
   • Delete unused devcontainer configs (e.g. if you only use CPU, remove nvidia/ and mac/)
   • Remove or replace notebooks/environment_test.ipynb with your own notebooks
   • Delete test data from data/

   git add -A && git commit -m "Initial project setup" && git push

Adding Python packages

Temporary (lost on container rebuild)

pip install <package-name>

Permanent (recommended)

1. Create a requirements.txt in the repository root:

   lightgbm
   shap
   

2. Add a postCreateCommand to the relevant .devcontainer/*/devcontainer.json:

   "postCreateCommand": "pip install -r requirements.txt"

3. Rebuild the container (Ctrl+Shift+P → Dev Containers: Rebuild Container)

Keeping your fork updated

# Add upstream once
git remote add upstream https://github.com/gperdrizet/datascience-devcontainer.git

# Pull in updates
git fetch upstream && git merge upstream/main

What's included

Package	Purpose
numpy, pandas, scipy	Core data science stack
scikit-learn, xgboost, statsmodels	Machine learning and statistics
matplotlib, seaborn, plotly	Visualization
optuna	Hyperparameter optimization
jupyterlab	Interactive notebooks
cupy-cuda12x	GPU-accelerated arrays (NVIDIA only)
python-dotenv	Environment variable management

GPU compatibility (NVIDIA)

Requires compute capability ≥6.0 (Pascal / GTX 10xx or newer):

Architecture	Example GPUs	Compute Capability
Pascal	GTX 1050–1080, Tesla P100	6.0–6.1
Volta	Tesla V100, Titan V	7.0
Turing	RTX 2060–2080, GTX 1660	7.5
Ampere	RTX 3060–3090, A100	8.0–8.6
Ada Lovelace	RTX 4060–4090	8.9
Hopper	H100, H200	9.0
Blackwell	RTX 5070–5090, B100, B200	10.0

Project structure

datascience-devcontainer/
├── .devcontainer/
│   ├── nvidia/
│   │   └── devcontainer.json   # NVIDIA GPU configuration
│   ├── cpu/
│   │   └── devcontainer.json   # CPU configuration
│   └── mac/
│       └── devcontainer.json   # Mac (ARM64) configuration
├── data/                       # Store datasets here
├── notebooks/
│   └── environment_test.ipynb  # Verify your setup
├── .gitignore
├── LICENSE
└── README.md

Troubleshooting

Problem	Solution
Docker won't start	Enable virtualization in BIOS / enable WSL2 on Windows
Permission denied (Linux)	Add your user to the docker group, then log out and back in
GPU not detected	Update NVIDIA drivers (≥570); Linux: install NVIDIA Container Toolkit
Container build fails	Check your internet connection
Module not found	Add the package to requirements.txt and rebuild the container