Linux / amd64
Visit rapids.ai for more information.
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.
NOTE: Review our system requirements to ensure you have a compatible system!
RAPIDS Libraries included in the images:
There are two types:
rapidsai/base- contains a RAPIDS environment ready for use.
rapidsai/notebooks- extends the
rapidsai/baseimage by adding a
jupyterlabserver, example notebooks, and dependencies.
The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
23.08-cuda11.8-py3.10 ^ ^ ^ | | Python version | | | CUDA version | RAPIDS version
Note: Nightly builds of the images have the RAPIDS version appended with an
rapidsai/base image starts with an
ipython shell by default.
rapidsai/notebooks image starts with the JupyterLab notebook server by default.
rapidsai/notebooks exposes port
8888 for the JupyterLab notebook server.
The following environment variables can be passed to the
docker run commands:
EXTRA_CONDA_PACKAGES- used to install additional
condapackages in the container. Use a space separated list of values
CONDA_TIMEOUT- how long (in seconds) the
condacommand should wait before exiting
EXTRA_PIP_PACKAGES- used to install additional
pippackages in the container. Use a space separated list of values
PIP_TIMEOUT- how long (in seconds) the
pipcommand should wait before exiting
$ 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:23.08-cuda11.8-py3.10
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
$ 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:23.08-cuda11.8-py3.10
rapidsai/notebooks container has notebooks for the RAPIDS libraries in
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
You can check the documentation for RAPIDS APIs inside the JupyterLab notebook using a
? command, like this:
This prints the function signature and its usage documentation. If this is not enough, you can see the full code for the function using
Check out the RAPIDS documentation for more detailed information.
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.