by Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, Carlo Manzo, Giovanni Volpe
No Starch Press, San Francisco (CA), 2025
ISBN-13: 9781718503922
https://nostarch.com/deep-learning-crash-course

A ready-to-run JupyterLab environment with all notebooks and dependencies baked in.
Works on Intel & Apple-Silicon Macs, Linux ×86_64 & ARM64; also provides an NVIDIA-CUDA-enabled variant for GPU hosts.

• Docker
  • macOS / Windows → Docker Desktop
  • Linux → Docker Engine + (optional) NVIDIA Container Toolkit
• (Optional) VS Code + Dev Containers extension

CPU-only (multi-arch)

docker pull ghcr.io/deeptrackai/deep-learning-crash-course:latest

GPU-enabled (amd64 + CUDA)

docker pull ghcr.io/deeptrackai/deep-learning-crash-course-gpu:latest

CPU-only (multi-arch)

docker run --rm -it \
  -p 8888:8888 \
  ghcr.io/deeptrackai/deep-learning-crash-course:latest

GPU-enabled (amd64 + CUDA)

docker run --rm -it --gpus all \
  -p 8888:8888 \
  ghcr.io/deeptrackai/deep-learning-crash-course-gpu:latest

After startup, copy the URL with token (e.g., http://127.0.0.1:8888/lab?token=…) into your browser to access JupyterLab.

1. In VS Code, open Command Palette (Ctrl+Shift+P).

2. Run Dev Containers: Attach to Running Container...

3. Select your CPU or GPU container from the list. A new VS Code window will pop up.

4. Install Python & Jupyter extensions when prompted.

5. Open Folder → /home/jovyan/work and Select Kernel → /opt/conda/bin/python (Python 3.11).

6. Open any .ipynb and run cells. If ipywidgets fails, Reload window.