Linux / arm64
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:
cuDF
cuML
cuGraph
cuVS
RMM
RAFT
cuSpatial
cuxfilter
cuCIM
xgboost
The RAPIDS images are based on nvidia/cuda
and rapidsai/miniforge-cuda
. The RAPIDS images provide amd64
& arm64
architectures where supported.
There are two types:
rapidsai/base
- contains a RAPIDS environment ready for use.rapidsai/notebooks
- extends the rapidsai/base
image by adding a jupyterlab
server, example notebooks, and dependencies.The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
24.10-cuda12.5-py3.12
^ ^ ^
| | Python version
| |
| CUDA version
|
RAPIDS version
Note: Nightly builds of the images have the RAPIDS version appended with an a
(ie 24.10a-cuda12.5-py3.12
)
The rapidsai/base
image starts with an ipython
shell by default.
The 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 conda
packages in the container. Use a space separated list of valuesCONDA_TIMEOUT
- how long (in seconds) the conda
command should wait before exitingEXTRA_PIP_PACKAGES
- used to install additional pip
packages in the container. Use a space separated list of valuesPIP_TIMEOUT
- how long (in seconds) the pip
command should wait before exitingExample:
$ 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:24.10-cuda12.5-py3.12
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:24.10-cuda12.5-py3.12
The rapidsai/notebooks
container has notebooks for the RAPIDS libraries in /home/rapids/notebooks
.
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.
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.
Check out the RAPIDS User Guides and XGBoost API docs.
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.