UC San Diego Jupyterhub (Data Science) Platform

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Student Resources

Instructor Resources

Request Datahub/DSMLP - Instructional Technology Request (CINFO)
Scope of Support & Guidelines for Usage
Instructor Guidance for Datahub/DSMLP
Educational Technology Services Instructional Github
Blink Documentation
Datahub Grading Tools

nbgrader
Sync assignments and grades with your Canvas course: nb2canvas

Overview

UC San Diego's Data Science/Machine Learning Platform (DSMLP) provides undergraduate and graduate students with access to research-class CPU/GPU resources for coursework, formal independent study, and student projects.

Instructors may request Datahub/DSMLP for their courses via an Instructional Technology Request (CINFO), with students automatically receiving access 1-2 days prior to start of each term. Access to Datahub/DSMLP is described in the ITS Instructional Technology Guides.

Jupyter Notebooks

Web-based Jupyter notebooks allow students to combine live code, equations, visualizations and narrative text for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and more. DSMLP's Jupyter notebooks offer straightforward interactive access to popular languages and GPU-enabled frameworks such as Python, R, Pandas, PyTorch, TensorFlow, Keras, NLTK, and AllenNLP.

Machine Learning

Complex ML workflows are supported through terminal/SSH logins, background batch jobs, and a full Linux/Ubuntu CUDA development suite. Users may install additional library packages (e.g. conda/pip, CRAN) as needed, or can opt to replace the default environment entirely by launching their own custom Docker containers.

High-speed cluster-local storage houses student workspaces, course files, and common training corpora (e.g. CIFAR, ImageNet).