Jupyter x Docker

Set up a local Jupyter base by creating a workspace, installing a Python 3.8 virtual environment with pipenv, and launching the notebook server within a Docker container workflow with authentication.

Configure the Jupiter notebook server for local use by creating a password protected setup, generating a hashed password with Python, and pointing to a project root and config path.

Learn how to package jupyter notebooks for ephemeral docker containers by zipping notebooks into a tar.gz file, then unloading and loading them for reliable deployment.

Create a minimal dockerfile using the python 3.8-slim image, copy project contents, install dependencies with pip, and set an entry point script to produce a small, reusable container.

Explore dockerfile cmd and entrypoint concepts by examining entry point scripts, permissions, and work directories, testing docker container setup and how configuration paths influence execution.

Build and run a docker container by creating a Dockerfile, tagging the image, and using docker run to launch a notebook server.

Learn to use the Hiroku CLI to create, push, and release a Jupyter Docker app on Heroku. The guide covers login, push, web process deployment, and opening the notebook server.

Discover how to upgrade a notebook environment with bonus installs using a dockerfile, adding numpy, pandas, OpenCV, TensorFlow, and scikit-learn for machine learning and data science projects.

Explore how Docker and Jupiter enable complete control over a notebook server, compare with Google CoLab, and consider persistent volume for production with Hiroku.