UC San Diego Jupyterhub (Data Science) Platform
- 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
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.
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).