A Docker DevBox for Jupyter Notebook's with a focus on Computer Vision, Machine Learning, Finance, Statistics and Visualization.
MIT License
A Docker development box for Jupyter Notebooks with a focus on Computer Vision, Machine Learning, Finance, Statistics and Visualization.
The following sections describe the container images, including their packages, how to build or download them, and how to set them up.
This is a Docker container supporting multiple architectures based on Debian Linux (amd64 & arm64).
It sets up an Jupyter Notebook development environment for interactive Python programming for Visual Studio Code.
It has preinstalled scientific computing packages (including OpenCV, Tensorflow, Keras, Numpy, Pandas, DuckDB, Sklearn, Scipy, Matplotlib, Seaborn, Imutils, SqlAlchemy).
The images of this repository are available on Github Container Registry (GHCR).
Base: Debian 12 - Bookworm
On top of the base image the following tools are installed:
These programming languages are included:
The installed Python libraries are:
You can find a list of all installed packages in the notebooks/check_devbox.ipynb Notebook.
You need the following things to run this:
There are two ways of setting the container up.
Either by building the container image locally or by fetching the prebuilt container image from the Github container registry.
Step 1. Get the source: clone this repository using git or download the zip
Step 2. (optional) The repository contains multiple images.
You select an image by modifying the dockerFile
to use in ./devcontainer/devcontainer.json
:
By default "dockerFile": "amd64/Dockerfile"
is set.
For an image with architecture:
amd64/Dockerfile
arm64v8/Dockerfile
Step 3. In VSCode open the folder in a container (Remote Containers: Open Folder in Container
):
This will build the container image (Starting Dev Container (show log): Building image..
)
Which takes a while...
Then, finally...
Step 4. Open the file notebooks\test.ipynb
Step 5. You might get a warning message for "untrusted" Notebook content.
Click Trust
to allow running the content of the Notebook.
Step 6. You are now able to edit cells and run their content interactively in VSCode.
You might also run your scripts inside your browser at http://localhost:8888/
Enjoy! 😎
This container image is published to the Github Container Registry (GHCR).
You may find the package here: https://github.com/jakoch/jupyter-devbox/pkgs/container/jupyter-devbox.
You can install the container image from the command line:
docker pull ghcr.io/jakoch/jupyter-devbox:latest
You might also use this container image as a base image in your own Dockerfile:
FROM ghcr.io/jakoch/jupyter-devbox:latest
x86_64 - linux/amd64
aarch64 - linux/aarch64, linux/arm64/v8, linux/arm64v8
not supported:
You can check your platform and available features with
dpkg --print-architecture
cat /proc/cpuinfo