GPU not found when using Tensorflow with older GPU on Fedora

I have a fresh install of Fedora 39 on a Laptop with GeForce GT 730M as the GPU.

I followed this guide to install the Nvidia drivers and Cuda packages with RPM Fusion: Nvidia Drivers
I also installed the cuDNN toolkit via this guide: Machine Learning Libraries

I chose this method of installation because of my older GPU. I do have to say that I have tried installing the drivers all through NVIDIA repos as well and it didn’t change anything.

After installing Tensorflow, tensorflow couldn’t find my GPU.
Because I installed the CUDA and cuDNN libraries through RPM Fusion, I don’t have the nvcc command. Not sure if it is related.

Here are the results for the following commands:

| NVIDIA-SMI 470.223.02   Driver Version: 470.223.02   CUDA Version: 11.4     |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 N/A |                  N/A |
| N/A   43C    P8    N/A /  N/A |      0MiB /   983MiB |     N/A      Default |
|                               |                      |                  N/A |
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|  No running processes found                                                 |
pip show tensorflow
Name: tensorflow
Version: 2.16.1
Summary: TensorFlow is an open source machine learning framework for everyone.
Author: Google Inc.
License: Apache 2.0
Location: /usr/local/lib64/python3.12/site-packages
Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, libclang, ml-dtypes, numpy, opt-einsum, packaging, protobuf, requests, setuptools, six, tensorboard, termcolor, typing-extensions, wrapt

pip show nvidia-cudnn-cu11
Name: nvidia-cudnn-cu11
Summary: cuDNN runtime libraries
Author: Nvidia CUDA Installer Team
License: NVIDIA Proprietary Software
Location: /usr/local/lib/python3.12/site-packages
Requires: nvidia-cublas-cu11, nvidia-cuda-nvrtc-cu11

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-03-13 20:25:24.253266: I external/local_tsl/tsl/cuda/] Could not find cuda drivers on your machine, GPU will not be used.
2024-03-13 20:25:24.256168: I external/local_tsl/tsl/cuda/] Could not find cuda drivers on your machine, GPU will not be used.
2024-03-13 20:25:24.297070: I tensorflow/core/platform/] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-13 20:25:25.192678: W tensorflow/compiler/tf2tensorrt/utils/] TF-TRT Warning: Could not find TensorRT
2024-03-13 20:25:25.843974: I external/local_xla/xla/stream_executor/cuda/] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at
2024-03-13 20:25:25.844650: W tensorflow/core/common_runtime/gpu/] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
dnf list installed | grep cuda
libcudnn8.x86_64                                       @nvidia-machine-learning  
libcudnn8-devel.x86_64                                 @nvidia-machine-learning  
libnccl.x86_64                                    2.8.3-1+cuda11.2                  @nvidia-machine-learning  
libnccl-devel.x86_64                              2.8.3-1+cuda11.2                  @nvidia-machine-learning  
xorg-x11-drv-nvidia-470xx-cuda.x86_64             3:470.223.02-1.fc39               @rpmfusion-nonfree        
xorg-x11-drv-nvidia-470xx-cuda-libs.x86_64        3:470.223.02-1.fc39               @rpmfusion-nonfree        
find /usr/lib* -name "libcuda*"

Also it is not a SELinunx issue since I set it to permissive - and there is no error log from SELinux.

Hi @Soheil_Sadeghi

Welcome to the TensorFlow Forum!

There is no effect of OS upgrade in installing TensorFlow. You may need to follow the correct steps to install TensorFlow along with the correct compatible version of CUDA,cuDNN and Python to have GPU support enabled in your system. For this, Please check the TensorFlow’s tested build configuration and install the supportive CUDA and CuDNN version as per installed TensorFlow version.

Please refer to this same issue and let us know if the issue still persists. Thank you.