gperdrizet/deeplearning-cpu repository overview

⁠Deep learning CPU development environment

A ready-to-use deep learning environment for VS Code. Includes both PyTorch and TensorFlow frameworks. Designed for CPU-only systems or development without GPU dependencies.

⁠1. Features

• Dual framework support: Both TensorFlow 2.17 and PyTorch (CPU) pre-installed
• Lightweight and portable: No GPU or CUDA dependencies required
• VS Code Dev Container ready: Pre-configured vscode user for seamless devcontainer integration

⁠2. Included software

⁠2.1. Additional Python packages

• jupyterlab - Notebook interface
• pandas - Data manipulation
• scikit-learn - Machine learning utilities
• scipy - Scientific computing
• matplotlib - Data visualization
• seaborn - Statistical data visualization
• tensorboard - Training visualization
• keras_tuner - Hyperparameter tuning for Keras
• optuna - Hyperparameter optimization framework
• ipywidgets - Interactive widgets for Jupyter

⁠3. Usage with VS Code Dev Containers

This image is designed for use with the deeplearning-devcontainer⁠ repository, which provides ready-to-use VS Code Dev Container configurations. The vscode user (UID 1000) is pre-configured with sudo access for a seamless development experience.

⁠4. Running directly with Docker

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

⁠5. Requirements

• Docker

⁠6. License

See the GitHub repository⁠ for license information.