RAPIDS
Suite of libraries for data analytics and machine learning on GPUs.
News
5.10.2022 Due to Puhti's update to Red Hat Enterprise Linux 8 (RHEL8), the number of supported RAPIDS versions has been reduced. Please contact our servicedesk if you really need access to older versions.
5.5.2022 Due to Mahti's update to Red Hat Enterprise Linux 8 (RHEL8), older versions of RAPIDS are no longer fully supported. Please contact our servicedesk if you really need access to older versions.
4.2.2022 All old RAPIDS versions which were based on direct Conda installations have been deprecated, and we encourage users to move to newer versions. Read more on our separate Conda deprecation page.
Available
RAPIDS is available on both Puhti and Mahti. Currently supported RAPIDS versions:
- 22.04, based on RAPIDS official Docker images:
22.04
Contains the RAPIDS suite (including cuDF, cuML, cuGraph, and XGBoost) for Python with GPU support via CUDA.
If you find that some package is missing, you can often install it yourself with
pip install --user. See our Python
documentation for
more information on how to install packages yourself. If you think that some
important RAPIDS-related package should be included in the module provided by
CSC, please contact our servicedesk.
All modules are based on containers using Apptainer (previously known
as Singularity). Wrapper scripts have been provided so that common
commands such as python, python3, pip and pip3 should work as
normal. For other commands, you need to prefix them with
apptainer_wrapper exec. For more information, see CSC's general
instructions on how to run Apptainer
containers.
License
RAPIDS is licensed under Apache License 2.0
Usage
To use this software, initialize it with:
module load rapids
to access the default version.
This will show all available versions:
module avail rapids
To check the exact packages and versions included in the loaded module you can run:
list-packages
Note
Note that login nodes are not intended for heavy computing, please use slurm batch jobs instead. See our instructions on how to use the batch job system.
Local storage
The GPU nodes have fast local storage which is useful for IO-intensive applications. See our general instructions on how to take the fast local storage into use.