eScience 2026 Tutorial
Accelerated Data Science with RAPIDS
This site hosts the web materials for the eScience 2026 tutorial on GPU-accelerated data science. It combines the current tutorial proposal, the hands-on notebook links, and the practical information attendees need before and during the session.
Event details
- Event: eScience 2026
- Tutorial: Accelerated Data Science with RAPIDS
- Presenter: Didier Barradas-Bautista
- Affiliation: KAUST Visualization Core Lab
- Date: Tuesday September 29, 2026 , 14:00 - 17:00
- Location: Room: AWS
What this tutorial covers
Participants will work through GPU-accelerated data science workflows with the NVIDIA RAPIDS ecosystem, including:
- Data processing and feature engineering with cuDF
- Machine learning workflows with cuML and GPU-enabled XGBoost
- Graph analytics with cuGraph
- A compact advanced extension on distributed hyperparameter optimization with Ray Tune and selected Skorch/PyTorch patterns
- Practical guidance for reproducible, performance-aware data science in cloud-ready notebooks
Site navigation
- Setup instructions
- Syllabus and schedule
- Part 1: Foundations, data engineering, scale
- Part 2: Graph analytics
- Part 3: Machine learning
- Part 4: Advanced extension
- Resources
- FAQ
Tutorial materials
The hands-on materials are based on the Google Colab notebooks used in the tutorial:
- Part 1: RAPIDS Core I: DataFrames at Scale
- Part 2: RAPIDS Core III: Graph Analytics
- Part 3: RAPIDS Core II: Machine Learning on GPUs
- Part 4: Advanced Extension: Distributed Hyperparameter Optimization
The source repository for the materials is available at D-Barradas/Accelerated-Data-Science-with-RAPIDS.