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Could not load dynamic library 'libcudnn.so.8' in ubuntu

i am using Ubuntu 20.04.3 and trying to install tensorflow. Nvidia driver version is Driver Version: 470.63.01.
First, i have installed cuda 11.0 and checked it installation via following command
cat /usr/local/cuda/version.txt
It output CUDA Version 11.0.207.
Next i installed cudnn
tar -xzvf cudnn-11.0-linux-x64-v8.0.5.39
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
I also checked cuda files with this command
ls /usr/local |grep cuda
which result in
cuda
cuda-11.0
Cudnn files are tested by
ls /usr/local/cuda-11.0/lib64/libcudnn.so.8*
and output is
/usr/local/cuda-11.0/lib64/libcudnn.so.8
/usr/local/cuda-11.0/lib64/libcudnn.so.8.0.5
Then i installed tensorflow
pip install tensorflow==2.4.0
but when i run
import tensorflow as tf
tf.config.list_physical_devices('GPU')
It did not get GPU and error is Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory

I just faced the same issue. From your code snippet I see that you are referencing two different CUDA locations:
/usr/local/cuda/
/usr/local/cuda-11.0/
So you might double-check if that causes an issue.
From here I learned that TF reads the system information about available CUDA from LD_LIBRARY_PATH. Multiple CUDA versions are installed on the system I am using. Thus, exporting these paths explicitly fixed the issue for me:
export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda-11.0
Of course, you would need to adapt the path to your specific situation.

Tensorflow GPU / CUDA installation on Ubuntu

I have set up a Ubuntu 18.04 and tried to make Tensorflow 2.2 GPU work (I have an Nvidia/CUDA graphic card) with Python.
Even after reading the documentation https://www.tensorflow.org/install/gpu#linux_setup, it failed (see below for details about how it failed).
Question: would you have a canonical "todo" list (starting point: freshly installed Ubuntu server) on how to install tensorflow-gpu and make it work, with a few steps?
Notes:
I have read many similar forum posts, and I think that having a canonical "todo" (from a fresh Ubuntu install to having tensorflow-gpu working) would be interesting, with a few steps/bash commands
the documentation I used involved
export LD_LIBRARY_PATH...
# Add NVIDIA package repository
sudo apt-key adv --fetch-keys http://developer.download...
...
# Install CUDA and tools. Include optional NCCL 2.x
sudo apt install cuda9.0 cuda...
Even after a lot of trial and errors (I don't copy/paste all the different errors here, would be too long), then at the end:
import tensorflow
always failed. Some reasons included `ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory. I have already read the relevant question here, or this very long (!) Github issue.
After some trial and error, import tensorflow works, but it doesn't use the GPU (see also Tensorflow not running on GPU).

Well, I was facing the same problem. The first thing to do is to look up, which Tensorflow version is required. In your case Tensorflow 2.2. requires CUDA 10.1. The correct cuDNN version is also important. In your case it would be cuDNN 7.4. An additional point is the installed python version. I would recommend Python 3.5-3.8. If one those mismatch, a fully compatibility is almost impossible.
So if you want a check list, here you go:
Install CUDA 10.1 by installing nvidia-cuda-toolkit.
Install the cuDNN version compatible with CUDA 10.1.
Export CUDA environment variables.
If Bazel is not installed, you will be asked on that.
Install TensorFlow 2.2 using pip. I would highly recommend the usage of a virtual environment.
You can find the compatibility check list of Tensorflow and CUDA here
You can find the CUDA Toolkit here
Finally get cuDNN in the correct version here
That's all.

I faced the problem as well when using the Google Cloud Platform for two projects involving deep learning. They provide servers with nothing but a freshly installed Ubuntu OS. Regarding my experience, I recommend doing the following steps:
Look up the cuda and cuDNN version supported by the current Tensorflow release on the Tensorflow page.
Install the targeted cuda version from the deb package retrieved from Nvidias cuda page and be careful that more recent cuda versions might not work! This will automatically install the corresponding Nvidia drivers.
Install the targeted cuDNN version from this page and again be careful that a more recent cuDNN version might not work.
Install tensorflow-gpu using pip.
This should work. Your problem is probably that you are using a more recent cuda version than targeted by the current Tensorflow release.

To install tensorflow-gpu, the guidelines which are provided on official website are very tedious for beginers, instead we can do these simple steps:
Note : NVIDIA driver must be installed before this(you can verify this using command nvidia-smi).
Install Anaconda https://www.anaconda.com/distribution/?
Create an virtual environment using command "conda create -n envname"
Then activate env using command "conda activate envname"
Finally install tensorflow using command "conda install tensorflow-gpu"
With the given code
import tensorflow as tf
if tf.test.gpu_device_name():
print('Default GPU Device{}'.format(tf.test.gpu_device_name()))
else:
print("not using gpu")
You can find the tutorial on link given below
https://www.pugetsystems.com/labs/hpc/Install-TensorFlow-with-GPU-Support-the-Easy-Way-on-Ubuntu-18-04-without-installing-CUDA-1170/?

I would suggest to first check the availability of GPU using nvidia-smi command.
I had faced the same issue, i was able to resolve it by using docker container, you can install docker using Install Docker Engine on Ubuntu or use the Digital Ocean guide (i used this one) How To Install and Use Docker on Ubuntu 18.04
After that it is simple just run the following command based on the requirements
NV_GPU='0' nvidia-docker run --runtime=nvidia -it -v /path/to/folder:/path/to/folder/for/docker/container nvcr.io/nvidia/tensorflow:17.11
NV_GPU='0' nvidia-docker run --runtime=nvidia -it -v /storage/research/:/storage/research/ nvcr.io/nvidia/tensorflow:20.12-tf2-py3
Here '0' represents the GPU number, if you want to use more than one GPU just use '0,1,2' and so on ....
Hope this solves the issue.

Which versions of TensorFlow work with which versions of CUDA [duplicate]

I have noticed that some newer TensorFlow versions are incompatible with older CUDA and cuDNN versions. Does an overview of the compatible versions or even a list of officially tested combinations exist? I can't find it in the TensorFlow documentation.

TL;DR) See this table: https://www.tensorflow.org/install/source#gpu
Generally:
Check the CUDA version:
cat /usr/local/cuda/version.txt
and cuDNN version:
grep CUDNN_MAJOR -A 2 /usr/local/cuda/include/cudnn.h
and install a combination as given below in the images or here.
The following images and the link provide an overview of the officially supported/tested combinations of CUDA and TensorFlow on Linux, macOS and Windows:
Minor configurations:
Since the given specifications below in some cases might be too broad, here is one specific configuration that works:
tensorflow-gpu==1.12.0
cuda==9.0
cuDNN==7.1.4
The corresponding cudnn can be downloaded here.
Tested build configurations
Please refer to https://www.tensorflow.org/install/source#gpu for a up-to-date compatibility chart (for official TF wheels).
(figures updated May 20, 2020)
Linux GPU
Linux CPU
macOS GPU
macOS CPU
Windows GPU
Windows CPU
Updated as of Dec 5 2020: For the updated information please refer Link for Linux and Link for Windows.

The compatibility table given in the tensorflow site does not contain specific minor versions for cuda and cuDNN. However, if the specific versions are not met, there will be an error when you try to use tensorflow.
For tensorflow-gpu==1.12.0 and cuda==9.0, the compatible cuDNN version is 7.1.4, which can be downloaded from here after registration.
You can check your cuda version using
nvcc --version
cuDNN version using
cat /usr/include/cudnn.h | grep CUDNN_MAJOR -A 2
tensorflow-gpu version using
pip freeze | grep tensorflow-gpu
UPDATE:
Since tensorflow 2.0, has been released, I will share the compatible cuda and cuDNN versions for it as well (for Ubuntu 18.04).
tensorflow-gpu = 2.0.0
cuda = 10.0
cuDNN = 7.6.0

if you are coding in jupyter notebook, and want to check which cuda version tf is using, run the follow command directly into jupyter cell:
!conda list cudatoolkit
!conda list cudnn
and to check if the gpu is visible to tf:
tf.test.is_gpu_available(
cuda_only=False, min_cuda_compute_capability=None
)

You can use this configuration for cuda 10.0 (10.1 does not work as of 3/18), this runs for me:
tensorflow>=1.12.0
tensorflow_gpu>=1.4
Install version tensorflow gpu:
pip install tensorflow-gpu==1.4.0

Thanks for the first answer.
Something about backward compatibility.
I can successfully install tensorflow-2.4.0 with cuda-11.1 and cudnn 8.0.5.
Source: https://www.tensorflow.org/install/source#gpu

I had installed CUDA 10.1 and CUDNN 7.6 by mistake. You can use following configurations (This worked for me - as of 9/10). :
Tensorflow-gpu == 1.14.0
CUDA 10.1
CUDNN 7.6
Ubuntu 18.04
But I had to create symlinks for it to work as tensorflow originally works with CUDA 10.
sudo ln -s /opt/cuda/targets/x86_64-linux/lib/libcublas.so /opt/cuda/targets/x86_64-linux/lib/libcublas.so.10.0
sudo cp /usr/lib/x86_64-linux-gnu/libcublas.so.10 /usr/local/cuda-10.1/lib64/
sudo ln -s /usr/local/cuda-10.1/lib64/libcublas.so.10 /usr/local/cuda-10.1/lib64/libcublas.so.10.0
sudo ln -s /usr/local/cuda/targets/x86_64-linux/lib/libcusolver.so.10 /usr/local/cuda/lib64/libcusolver.so.10.0
sudo ln -s /usr/local/cuda/targets/x86_64-linux/lib/libcurand.so.10 /usr/local/cuda/lib64/libcurand.so.10.0
sudo ln -s /usr/local/cuda/targets/x86_64-linux/lib/libcufft.so.10 /usr/local/cuda/lib64/libcufft.so.10.0
sudo ln -s /usr/local/cuda/targets/x86_64-linux/lib/libcudart.so /usr/local/cuda/lib64/libcudart.so.10.0
sudo ln -s /usr/local/cuda/targets/x86_64-linux/lib/libcusparse.so.10 /usr/local/cuda/lib64/libcusparse.so.10.0
And add the following to my ~/.bashrc -
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-10.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda/targets/x86_64-linux/lib/

I had a similar problem after upgrading to TF 2.0. The CUDA version that TF was reporting did not match what Ubuntu 18.04 thought I had installed. It said I was using CUDA 7.5.0, but apt thought I had the right version installed.
What I eventually had to do was grep recursively in /usr/local for CUDNN_MAJOR, and I found that /usr/local/cuda-10.0/targets/x86_64-linux/include/cudnn.h did indeed specify the version as 7.5.0.
/usr/local/cuda-10.1 got it right, and /usr/local/cuda pointed to /usr/local/cuda-10.1, so it was (and remains) a mystery to me why TF was looking at /usr/local/cuda-10.0.
Anyway, I just moved /usr/local/cuda-10.0 to /usr/local/old-cuda-10.0 so TF couldn't find it any more and everything then worked like a charm.
It was all very frustrating, and I still feel like I just did a random hack. But it worked :) and perhaps this will help someone with a similar issue.

How to install CUDA in Google Colab GPU's

It seems that Google Colab GPU's doesn't come with CUDA Toolkit, how can I install CUDA in Google Colab GPU's. I am getting this error in installing mxnet in Google Colab.
Installing collected packages: mxnet
Successfully installed mxnet-1.2.0
ERROR: Incomplete installation for leveraging GPUs for computations.
Please make sure you have CUDA installed and run the following line in
your terminal and try again:
pip uninstall -y mxnet && pip install mxnet-cu90==1.1.0
Adjust 'cu90' depending on your CUDA version ('cu75' and 'cu80' are
also available).
You can also disable GPU usage altogether by invoking turicreate.config.set_num_gpus(0).
An exception has occurred, use %tb to see the full traceback.
SystemExit: 1

Cuda is not showing on your notebook because you have not enabled GPU in Colab.
The Google Colab comes with both options GPU or without GPU.
You can enable or disable GPU in runtime settings
Go to Menu > Runtime > Change runtime.
Change hardware acceleration to GPU.
To check if GPU is running or not, run the following command
!nvidia-smi
If the output is like the following image it means your GPU and cuda are working. You can see the CUDA version also.
After that to check if PyTorch is capable of using GPU, run the following code.
import torch
torch.cuda.is_available()
# Output would be True if Pytorch is using GPU otherwise it would be False.
To check if TensorFlow is capable of using GPU, run the following code.
import tensorflow as tf
tf.test.gpu_device_name()
# Standard output is '/device:GPU:0'

I pretty much believe that Google Colab has Cuda pre-installed... You can make sure by opening a new notebook and type !nvcc --version which would return the installed Cuda version.
Here is mine:

Go here: https://developer.nvidia.com/cuda-downloads
Select Linux -> x86_64 -> Ubuntu -> 16.04 -> deb (local)
Copy link from the download button.
Now you have to compose the sequence of commands. First one will be the call to wget that will download CUDA installer from the link you saved on step 3
There will be installation instruction under "Base installer" section. Copy them as well, but remove sudo from all the lines.
Preface each line with commands with !, insert into a cell and run
For me the command sequence was the following:
!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
!apt-get update
!apt-get install cuda
Now finally install mxnet. As cuda version I installed above is 9.2 I had to slighly change your command: !pip install mxnet-cu92
Successfully installed graphviz-0.8.3 mxnet-cu92-1.2.0

If you switch to using GPU then CUDA will be available on your VM. Basically what you need to do is to match MXNet's version with installed CUDA version.
Here's what I used to install MXNet on Colab:
First check the CUDA version
!cat /usr/local/lib/python3.6/dist-packages/external/local_config_cuda/cuda/cuda/cuda_config.h |\
grep TF_CUDA_VERSION
For me it outputted #define TF_CUDA_VERSION "8.0"
Then I installed MXNet with
!pip install mxnet-cu80

I think the easiest way here is to install mxnet-cu80. Just use the following code:
!pip install mxnet-cu80
import mxnet as mx
And you could check whether it works by:
a = mx.nd.ones((2, 3), mx.gpu())
b = a * 2 + 1
b.asnumpy()
I think colab right now just support cu80 and higher versions won't work.
For more information, you could see the following two websites:
Google Colab Free GPU Tutorial
Installing mxnet

This solution worked for me in November, 2022. Query the version of Ubuntu that Colab is running on (run in notebook using ! or in terminal without):
!lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 18.04.6 LTS
Release: 18.04
Codename: bionic
Query the current cuda version in Colab (only for comparision):
!nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_14_21:12:58_PST_2021
Cuda compilation tools, release 11.2, V11.2.152
Build cuda_11.2.r11.2/compiler.29618528_0
Next, got to the cuda toolkit archive or latest builds and configure the desired cuda version and os version. The Distribution is Ubuntu.
Copy the installation instructions:
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda-repo-ubuntu1804-11-7-local_11.7.0-515.43.04-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1804-11-7-local_11.7.0-515.43.04-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu1804-11-7-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda
Change the last line to include your cuda-version e.g., apt-get -y install cuda-11-7. Otherwise a more recent version might be installed.
!wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
!mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
!wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda-!repo-ubuntu1804-11-7-local_11.7.0-515.43.04-1_amd64.deb
!dpkg -i cuda-repo-ubuntu1804-11-7-local_11.7.0-515.43.04-1_amd64.deb
!cp /var/cuda-repo-ubuntu1804-11-7-local/cuda-*-keyring.gpg /usr/share/keyrings/
!apt-get update
!apt-get -y install cuda-11-7
Your cuda version will now be updated:
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Jun__8_16:49:14_PDT_2022
Cuda compilation tools, release 11.7, V11.7.99
Build cuda_11.7.r11.7/compiler.31442593_0

To run in Colab, you need CUDA 8 (mxnet 1.1.0 for cuda 9+ is broken). But Google Colab runs now 9.2. There is, however the way to uninstall 9.2, install 8.0 and then install mxnet 1.1.0 cu80.
The complete jupyter code is here : Medium

problems with installation tensorflow from source on ubuntu 16 with gpu

I have ubuntu 16.04
I have installed CUDA 7.5 from Ubuntu repo and cuDNN 5.1.3 for CUDA 7.5
and ran CUDA examples that works, same for pycuda
and I want to install tensorflow from source with gpu support,
tensorflow configuration is stuck on nvvm, and I can't find it in system also find isn't any helpful
$ find / -name nvvm*
/usr/include/nvvm.h
/usr/share/doc/nvidia-cuda-doc/html/nvvm-ir-spec
where can I find nvvm?

Your find command is wrong. You need to quote the -name argument to prevent shell globbing:
$ find / -name 'nvvm*'