Getting Started

The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. Here’s a code snippet where we read in a CSV file and output some descriptive statistics:

import cudf
gdf = cudf.read_csv('path/to/file.csv')
for column in gdf.columns:

Try Now In Colaboratory

Jump right into a GPU powered RAPIDS notebook with Colabratory for free. Go to example notebook

GPU Powered Data Science

RAPIDS uses optimized NVIDIA CUDA® primitives and high-bandwidth GPU memory to accelerate data preparation and machine learning. The goal of RAPIDS is not only to accelerate the individual parts of the typical data science workflow, but to accelerate the complete end-to-end workflow.

We suggest that you take a look at the sample workflow in our Docker container (described below), which illustrates just how straightforward a basic XGBoost model training and testing workflow looks in RAPIDS.


NVIDIA Pascal™ GPU architecture or better

CUDA 9.2 with driver v396.37+ or CUDA 10.0 with driver v410.48+

Ubuntu 16.04 or 18.04

For Docker Images: Docker CE v18+ and NVIDIA-docker v2+

RAPIDS Release Selector

RAPIDS is available as conda packages, docker images, and from source builds. Use the tool below to select your preferred method, packages, and environment to install RAPIDS. Certain combinations may not be possible and are dimmed automatically. Be sure you’ve met the required prerequisites above and see the details blow.

Stable (0.7)
Nightly (0.8a)
cuDF & cuML
Ubuntu 16.04
Ubuntu 18.04
CentOS 7
Python 3.6
Python 3.7
CUDA 9.2
CUDA 10.0
# Javascript is needed for this tool to run, please make sure it is enabled

details below

Conda Install

You can get a minimal conda installation with Miniconda or get the full installation with Anaconda.

For instructions on how to build a development conda environment, see the cuDF README for more information. Also refer to the cuML README for conda install instructions for cuML.

Build From Source

Checkout the cuDF README or cuML README for from-source build instructions.

Where is Pip?

Refer to our blog post for details on why we no longer support PIP installs.

Docker Container

Run the following command within the Docker container started from the copied Docker command above to launch the notebook server.

NOTE: This will run JupyterLab on your host machine at port 8888.

(rapids) root@container:/rapids/notebooks# bash utils/

Use JupyterLab to Explore the Notebooks

Notebooks can be found in notebooks directory within the container:

/rapids/notebooks/cuml (cuML demos)

/rapids/notebooks/cugraph (cuGraph demos)

/rapids/notebooks/tutorials (DBSCAN tutorial)

/rapids/notebooks/xgboost (XGBoost demo)

Advanced Usage

See the RAPIDS Container README page for more information about using custom datasets. Docker Hub and NVIDIA GPU Cloud host RAPIDS containers with full list of available tags.