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Installation of CUDA 12.3 and CuDNN 8.9 on Opensuse Leap 15.5 for Machine Learning

Machine Learning on a Linux system is no fun without a GPU and its parallel processing capabilities. On a system with a Nvidia card you need basic Nvidia drivers and additional libraries for optimal support of Deep Neural Networks and Linear Algebra operations on the GPU sub-processors. E.g., Keras and Tensorflow 2 [TF2] use CUDA and cuDNN-libraries on your Nvidia GPU. Basic information can be found here:

This means that you must not only perform an installation of (proprietary) Nvidia drivers, but also of CUDA and cuDNN on your Linux system. As I have started to work with ResNet-110v2 and ResNet-164v2 variants lately I was interested whether I could get a combination of

  • TF 2.15 with Keras
  • the latest of Nvidia GPU drivers 545.29.06 – but see the Addendum at the end of the post and the warnings therein.
  • the latest CUDA-toolkit version 12.3
  • and cuDNN version 8.9.7

to work on an Opensuse Leap 15.5 system. This experiment ended successfully, although the present compatibility matrices on the Nvidia web pages do not yet include the named combination. While system wide installations of the CUDA-toolkit and cuDNN are no major problems, some additional settings of environment variables are required to make the libraries available for Python notebooks in Jupyterlab or classic Jupyter Notebooks (i.e. IPython based environments). These settings are not self-evident.

This post summarizes the most important steps of a standard system-wide installation of CUDA and cuDNN on an Opensuse Leap 15.5 system. I do not install TensorRT in this post. As long as you do not work with (pre-trained) LLMs you do not really need TensorRT.

Level of his post: Active ML user – advanced. You should know how RPM and tar-based installations work on a Leap system. You should also have a working Python3 installation (in a virtual environment) and a Jupyter Notebook or (better) a Jupyterlab-installation on your system to be able to perform ML-tests based on Keras. I do not discuss a Jupyter and Python installation in this post.

Limitations and requirements

GPU capabilities: You need a fairly new Nvidia graphics card to make optimal use of the latest CUDA features. In my case I tested with a Nvidia 4060 TI. Normally the drivers and libraries should detect the capabilities of older cards and adapt to them. But I have not tested with older graphics cards.

Disk space: CUDA and cuDNN require a substantial amount of disk space (almost 7 GiB) when you install the full CUDA-toolkit as it is recommended by NVIDIA.

Remark regarding warnings: Installing CUDA 12.3 and using it with Tensorflow 2.15 will presently (Jan. 2024) lead to warnings in your Python 3 notebooks. However, in my experience these warnings have no impact on the performance. My 4060 TI did its job in test calculations with convolutional Autoencoders and ResNets as expected. Regarding ResNets even 5% faster than with CUDA 11.2.

Alternative installation methods: You may find information about a pure Python based installations including CUDA via pip. See e.g. here: While this potentially makes local user-specific installations easier, the disadvantage for multiple virtual Python environments is the resulting consumption of disk space. So, I still prefer a system wide installation. It also seems to be such that one should not mix both ways of installation – system-wide and virtual-environment specific. I have e.g. tried to install TensorRT via pip after a systemwide standard CUDA installation. The latter itself had worked. But after the additional TensorRT installation with pip my GPU could no longer used by Keras/TF2 based ML code started from Jupyterlab notebooks.

Installation of basic Nvidia drivers

The Nvidia graphics card must already be supported for regular X or Wayland services on a Linux system. CUDA and cuDNN come on top.

Note: You need a fairly new Nvidia driver for CUDA-12.3 ! To get the latest drivers for an Opensuse system I install the proprietary Nvidia drivers from the Opensuse’s Nvidia repository:

Nvidia Repository address for Leap 15.5:

Note that presently YaST2 has a bug (see here). You may need to use zypper on the command-line to this repository to your package manager. See the man pages for zypper for the right syntax. IN the end you should see the Nvidia repository in YAST2:

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