rapidsai/notebooks repository overview

⁠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.

Tag summary