RAPIDS Tutorial - PyData VA 2025

This repository contains materials for the tutorial: Getting Started with RAPIDS: GPU-Accelerated Data Science for PyData Users

Running the tutorial

Google Colab

You can run this tutorial on Google Colab. With a basic free account, you'll have access to:

• An interactive Python environment with GPU support
• Pre-installed RAPIDS libraries (cuDF and cuML)

To run each notebook:

1. Click on the corresponding link below to open it in Google Colab
2. Change the runtime type to T4 GPU
3. Save your changes

Notebooks

Notebook	Link
0 Welcome and Setup
1 Intro to cuDF
2 cudf.pandas
3 cudf polars engine
4 Intro to cuML
5 cuml.accel

Local

If you have access to a GPU you can run this locally:

Get Notebooks and Setup Environment

In a terminal:

git clone https://github.com/rapidsai-community/tutorial

Once inside the repository:

conda env create -f local-env.yaml
conda activate rapids-tutorial

Get the data

During this tutorial we will use different datasets, you can get them all by running the cell below.

python data_setup.py --all