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GPU DataFrames with cuDF

Run pandas-style DataFrame operations on a GPU with cuDF, and see what changes when your data is measured in gigabytes instead of megabytes.

Updated Sep 14, 2026

About this course

Most data workflows run on CPUs, and that's fine until your dataset gets large enough that you spend more time waiting than thinking. cuDF gives you a DataFrame interface that looks almost identical to pandas but runs on a GPU, and on large datasets the speed difference is not incremental. This course starts with what cuDF actually is: where it fits in the RAPIDS ecosystem, how it relates to pandas, and why GPU memory and parallelism produce the gains they do. Then you move into the practical side. You'll see which pandas patterns carry over directly, where they don't (dtypes, host-device transfers, operations that don't parallelize cleanly), and how to avoid the gotchas that trip people up when they first switch. By the end, you'll know how to load data onto a GPU, run real transformations with cuDF, and decide when GPU DataFrames are the right tool and when they're not.

Details

Updated Sep 14, 2026
1 unit, 1 lesson
1 project
1 assessment

Skills you'll learn

GPU DataFrame Operations

Load, filter, transform, group, and join data with cuDF on a real GPU.

cuDF vs. pandas Reasoning

Spot which pandas patterns transfer directly to cuDF and which need a different approach.

GPU Memory Management

Reason about GPU memory limits and host-device transfer costs, and avoid the most common pitfalls.

RAPIDS Ecosystem Orientation

Place cuDF within the broader RAPIDS stack and connect it to neighboring GPU-accelerated libraries.

Syllabus

1 unit • 1 lesson • 1 project • 1 assessment

Learning activities

Every lesson gives you several ways to learn.

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Frequently asked questions

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