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RAPIDS - Open GPU Data Science

What is RAPIDS?

The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

Visit rapids.ai for more information.

NOTE: Review our system requirements to ensure you have a compatible system!

Current Version - RAPIDS v26.06

RAPIDS Libraries included in the images:

  • cuDF
  • cuML
  • cuGraph
  • cuVS
  • RMM
  • RAFT
  • cuxfilter
  • cuCIM
  • xgboost

Image Types

The RAPIDS images are based on nvidia/cuda. The RAPIDS images provide amd64 & arm64 architectures where supported.

There are two types:

  • rapidsai/base - contains a RAPIDS environment ready for use.
    • TIP: Use this image if you want to use RAPIDS as a part of your pipeline.
  • rapidsai/notebooks - extends the rapidsai/base image by adding a jupyterlab server, example notebooks, and dependencies.
    • TIP: Use this image if you want to explore RAPIDS through notebooks and examples.

Image Tag Naming Scheme

The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:

26.06-cuda13-py3.14
^         ^    ^
|         |    Python version
|         |
|         CUDA major version
|
RAPIDS version

Note: Nightly builds of the images have the RAPIDS version appended with an a (ie 26.06a-cuda13-py3.14)

Note on CUDA versioning:

  • RAPIDS 25.12 and later: CUDA version tags are major-only (e.g., cuda12, cuda13).
  • RAPIDS 25.10: Both major.minor version tags (e.g., cuda12.9, cuda13.0) and major version tags (e.g., cuda12, cuda13). The major version tags are created by retagging the latest minor version builds.
  • RAPIDS 25.08 and older: CUDA version tags are major.minor (e.g., cuda12.9).

Usage

The rapidsai/base image starts with an ipython shell by default.

The rapidsai/notebooks image starts with the JupyterLab notebook server by default.

Container Ports

rapidsai/notebooks exposes port 8888 for the JupyterLab notebook server.

Environment Variables

The following environment variables can be passed to the docker run commands:

  • EXTRA_CONDA_PACKAGES - used to install additional conda packages in the container. Use a space separated list of values
  • CONDA_TIMEOUT - how long (in seconds) the conda command should wait before exiting
  • EXTRA_PIP_PACKAGES - used to install additional pip packages in the container. Use a space separated list of values
  • PIP_TIMEOUT - how long (in seconds) the pip command should wait before exiting

Example:

$ docker run \
    --rm \
    -it \
    --pull always \
    --gpus all \
    --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
    -e EXTRA_CONDA_PACKAGES="jq" \
    -e EXTRA_PIP_PACKAGES="beautifulsoup4" \
    -p 8888:8888 \
    rapidsai/notebooks:26.06-cuda13-py3.14

Bind Mounts

Mounting files/folders to the locations specified below provide additional functionality for the images.

  • /home/rapids/environment.yml - a YAML file that contains a list of dependencies that will be installed by conda. The file should look like:
dependencies:
  - beautifulsoup4
  - jq

Example:

$ docker run \
    --rm \
    -it \
    --pull always \
    --gpus all \
    --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
    -v $(pwd)/environment.yml:/home/rapids/environment.yml \
    rapidsai/base:26.06-cuda13-py3.14

Use JupyterLab to Explore the Notebooks

The rapidsai/notebooks container has notebooks for the RAPIDS libraries in /home/rapids/notebooks.

Extending RAPIDS Images

All RAPIDS images use conda as their package manager, and all RAPIDS packages are available in the base conda environment. These image run as the rapids user.

Access Documentation within Notebooks

You can check the documentation for RAPIDS APIs inside the JupyterLab notebook using a ? command, like this:

[1] ?cudf.read_csv

This prints the function signature and its usage documentation. If this is not enough, you can see the full code for the function using ??:

[1] ??cudf.read_csv

Check out the RAPIDS documentation for more detailed information.

More Information

Check out the RAPIDS User Guides and XGBoost API docs.

Where can I get help or file bugs/requests?

Please submit issues with the container to this GitHub repository: https://github.com/rapidsai/docker

For issues with RAPIDS libraries like cuDF, cuML, RMM, or others file an issue in the related GitHub project.

Additional help can be found on Stack Overflow.