Overview

---

The Data Science/Machine Learning Platform (DSMLP) utilizes Docker containers to maintain consistent course/research environments for users. The following documentation is an overview of standard containers maintained by UC San Diego Educational Technology Services.

Standard Containers

---

IT Services supports and maintains 3 notebooks for courses and research which are all based off a stable version of jupyter/datascience-notebook.

The Datahub Docker Stack README describes the inheritance relationship among maintained containers. All child containers have the same features as their parent container.

The environments contain Anaconda environments and Jupyter kernels, which are described in the subsections. The conda environments may be used in 3 ways:

• By creating a new Jupyter notebook. Select "New > {ENVIRONMENT NAME}"
• By modifying the kernel inside a jupyter notebook. Select "Kernel > Change Kernel > {ENVIRONMENT_NAME}"
• At the command-line by using the command: conda activate {ENVIRONMENT_NAME}


Updated list of packages in each container

To see the current list of packages in the stable version of each container, please visit the IT Services datahub-docker-stacks repository Stable Tag wiki page.  Click on "Link" under "Manifest" next to your container of interest that has been labeled with the "stable" tag.  Scroll down to the "Conda packages" and/or the "System packages" link.  Click on "Details" to see package versions.  

Dockerfile for each image

To see the current dockerfile that was used when the container image was built, see the individual image directories under https://github.com/ucsd-ets/datahub-docker-stack/tree/main/images

Creating a Custom Container 

---

Please see Instructions on Building a Custom Image to create your own container from one of the examples above. 

If you are an instructor or TA, please see TA Support for Building and Testing Custom Course Containers on DSMLP.