Could not load dynamic library 'libcudart.so.11.0'; - tensorflow

Question

I am trying to use Tensorflow 2.7.0 with GPU, but I am constantly running into the same issue:
2022-02-03 08:32:31.822484: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/username/.cache/pypoetry/virtualenvs/poetry_env/lib/python3.7/site-packages/cv2/../../lib64:/home/username/miniconda3/envs/project/lib/
2022-02-03 08:32:31.822528: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
This issue has already appeared multiple times here & on github. However, the solutions usually proposed to a) download the missing CUDA files, b) downgrade/upgrade to the correct CUDA version, c) set the correct LD_LIBRARY_PATH.
I have been already using my PC with CUDA-enabled PyTorch, and I did not have a single issue there. My nvidia-smi returns 11.0 version, which is exactly the only I want to have. Also, if I try to run:
import os
LD_LIBRARY_PATH = '/home/username/miniconda3/envs/project/lib/'
print(os.path.exists(os.path.join(LD_LIBRARY_PATH, "libcudart.so.11.0")))
it returns True. This is exactly the part of LD_LIBRARY_PATH from the error message, where Tensorflow, apparently, cannot see the libcudart.so.11.0 (which IS there).
Is there something really obvious that I am missing?
nvidia-smi output:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.156.00 Driver Version: 450.156.00 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
nvcc:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

Answer 1

Firstly:
Can you find out where the "libcudart.so.11.0" is
If you lost it at error stack, you can replace the "libcudart.so.11.0" by your word in below:
sudo find / -name 'libcudart.so.11.0'
Outputs in my system. This result shows where the "libcudart.so.11.0" is in the system:
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudart.so.11.0
If the result shows nothing, please make sure you have install cuda or other staff that must install in your system.
Second, add the path to environment file.
# edit /etc/profile
sudo vim /etc/profile
# append path to "LD_LIBRARY_PATH" in profile file
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.1/targets/x86_64-linux/lib
# make environment file work
source /etc/profile
You may also refer to this link
Third thing you may try is:
conda install cudatoolkit

Answer 2

Installing the correct version of cuda 11.3 and cudnn 8.2.1 for tf2.8. Based on this blog https://www.tensorflow.org/install/source#gpu using following commands.
conda uninstall cudatoolkit
conda install cudnn
Then exporting LD path - dynamic link loader path after finding location by
this sudo find / -name 'libcudnn' System was able to find required libraries and use GPU for training.
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/usr/miniconda3/envs/tf2/lib/
Hope it helped.

Answer 3

Faced the same issue with tensorflow 2.9 and cuda 11.7 on arch linux x86_64 with 2 nvidia gpus (1080ti / titan rtx) and solved it:
It is not absolutely necessary to respect the compatibility matrix (cuda 11.7 vs 11.2 so minor superior version). But python 3 version was downgraded according to the tensorflow comp matrix (3.10 to 3.7).
Note that you can have multiple cuda version installed and manage it by symlink on linux. (win should be different a bit)
setup with conda and python 3.7
sudo pacman -S base-devel cudnn
conda activate tf-2.9
conda uninstall cudatoolkit && conda install cudnn
I've also had to update gcc for another lib (out of topic)
conda install -c conda-forge gcc=12.1.0
added the snippet for debug according to tf-gpu docs
import tensorflow as tf
tf.config.list_physical_devices('GPU')
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
I now see 2 gpu detected instead of 0, training time is divided by 10.
nvidia-smi reports ram usage maxed and power level raised from 9W to 150W validating the usage of the gpu (the other was left idle).
Rootcause: cudnn was not installed system-wide.