Could Not Load Dynamic Library Libcudnn.So.8 When Running Tensorflow On Ubuntu 20.04

“Encountering the ‘Could Not Load Dynamic Library Libcudnn.So.8’ error when running Tensorflow on Ubuntu 20.04 can be rectified by ensuring correct installation and configurations of CUDA and cuDNN libraries, vital for GPU-optimized computations.”The issue of “Could Not Load Dynamic Library Libcudnn.So.8” when running TensorFlow on Ubuntu 20.04 can often result from incorrect installation of either Tensorflow or CUDA toolkit, lack of support for the CUDA version in the installed TensorFlow version, or a mismatched version of the ‘libcudnn.so.8’ library.

Issue	Description	Possible Solutions
TensorFlow or CUDA Toolkit Incorrectly Installed	If the installation directions were not followed precisely for either TensorFlow or the CUDA toolkit, it could lead to this error.	Uninstall and reinstall both TensorFlow and the CUDA toolkit, ensuring all steps are correctly followed Check your system’s PATH variables
Lack of CUDA Version Support	TensorFlow supports specific versions of the CUDA toolkit. Using an unsupported version could cause this issue.	Revert to a supported CUDA version compatible with the installed TensorFlow Upgrade TensorFlow to a version that supports the installed CUDA toolkit
Mismatched ‘libcudnn.so.8’	If the version of ‘libcudnn.so.8’ is not aligned with the CUDA toolkit and TensorFlow, it may throw this error	Ensure you’re using the correct ‘libcudnn.so.8’ version as expected by your CUDA and TensorFlow versions

To check whether the ‘libcudnn.so.8’ library file exists in the system or if the system is able to access the library file, one might need to run the command:

locate libcudnn.so.8

. It should return the path(/path/to/libcudnn.so.8) where it is located. If there’s no output, it means the file does not exist or cannot be accessed.

Sometimes, the problem might not be missing files, but rather incorrect symbolic links. We need to ensure that libcudnn libraries are correctly linked by running these commands:

sudo ln -s /path/to/libcudnn.so.8 /usr/local/cuda-11.0/lib64/
sudo ldconfig

Always remember to replace ‘/path/to/’ with the actual directory location of your ‘libcudnn.so.8’. By doing this, we correctly link the library files which can help in resolving the said issue. The importance of these steps lies in their ability to accurately trace and resolve potential roots of problems related to TensorFlow and CUDA version compatibility, thereby making them essential when addressing such challenges.

Reference:
https://www.tensorflow.org/install/gpuThe error message

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

, encountered during the execution of TensorFlow on an Ubuntu 20.04 system, generally implies that the CUDA Deep Neural Network library (cuDNN) has not been correctly linked or installed in your environment.

This sort of issue might typically occur due to reasons like these:

– The required version of cuDNN library is absent.
– The linking path to the library may be incorrect.
– You might have installed a different version of cuDNN than what is compatible with your CUDA and TensorFlow version.

Here’s how you can solve this problem:

Solution 1: Verifying Your cuDNN Installation

Firstly, ensure that your cuDNN installation process was completed without errors. You may want to revisit the official NVIDIA website for their guide on correct cuDNN installation.

Solution 2: Checking the Compatibility of Your cuDNN Version

It’s important to use a version of cuDNN that pairs well with your installed versions of CUDA and TensorFlow. This handy compatibility guide by TensorFlow lets you check that.

Solution 3: Setting the LD_LIBRARY_PATH Correctly

If your cuDNN library has been correctly installed but is still throwing errors, it might be because the cuDNN library location isn’t included in your LD_LIBRARY_PATH environment variable. On Ubuntu systems, you can resolve this by appending this line at the end of your ~/.bashrc file:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

After saving the changes, use this command in your terminal to source the .bashrc file:

source ~/.bashrc

Remember to replace /usr/local/cuda/lib64 with the appropriate directory where your CUDA libraries are, if they’re in a different location.

Solution 4: Symlinking the Correct cuDNN Library Version

In some cases, creating symbolic links to your cuDNN library versions can also resolve the issue:

sudo ln -s /usr/local/cuda/lib64/libcudnn.so.8 /usr/local/cuda/lib64/libcudnn.so

Solution 5: Reinstalling cuDNN

If all else fails, try reinstalling your cuDNN library completely, ensuring that you follow the steps listed on the official NVIDIA instructions page carefully.

Knowing how to handle and troubleshoot ‘Could Not Load Dynamic Library’ errors will enhance your experience running ML programs like TensorFlow on your GPU-enabled Ubuntu platform. A little diligence about matching software & library versions and following recommended installation & setup processes can save one from such issues.Assuming that you’ve installed TensorFlow on your Ubuntu 20.04, but you encounter the error ‘Could Not Load Dynamic Library libcudnn.so.8’, this points towards a few possible core issues:

– Incomplete installation of TensorFlow
– Incompatibility between CUDA toolkit and cuDNN versions
– Issues with environment variables

Let’s dive deep to understand each of these issues in detail:

Table of Contents

Toggle

Incomplete Tensorflow Installation

The first potential issue might be an incomplete or corrupted installation of Tensorflow. When installing complex libraries like TensorFlow, there is always the risk of certain dependencies failing to install correctly or entirely.

Solutions:

Instead of manually downloading and installing TensorFlow, it might be better to use

pip

, which manages dependencies for you.

Use this code snippet:

pip install tensorflow

This command should ideally solve most of the missing dependencies issues.

However, if despite trying this, the problem persists, read on for more potential issues and solutions.

CUDA Toolkit and cuDNN Incompatibility

TensorFlow requires specific versions of CUDA and cuDNN. However, sometimes we might have different versions installed which can lead to conflicts.

In your case, the missing dynamic library

libcudnn.so.8

indicates a version mismatch between your installed CUDA toolkit and its required cuDNN versions.

Effective management of CUDA and cuDNN versions becomes crucial because TensorFlow’s functionality depends heavily on these libraries.

Solutions:

Fixing the CUDA and cuDNN compatibility issue involves two steps:
– Uninstall the existing CUDA toolkit and cuDNN
– Install the appropriate versions as per the requirement of your TensorFlow version. For instance, TensorFlow 2.X supports CUDA 11.0 and cuDNN 8.

To uninstall the existing CUDA toolkit & cuDNN, use these commands:

sudo apt-get --purge remove cuda-*

sudo apt-get autoremove

reboot

Then, download and install the correct version of CUDA toolkit from NVIDIA’s official website (https://developer.nvidia.com/cuda-toolkit-archive) and follow the same process for cuDNN (https://developer.nvidia.com/rdp/cudnn-download).

Environment Variables

Correct setting up of environment variables is crucial for TensorFlow to function properly. The absence of adequate PATH environment variable settings could also trigger the error: ‘Could Not Load Dynamic Library libcudnn.so.8’.

Solution:

Setting up your PATH environment variable correctly can resolve this issue. Use the below code in your

.bashrc

or

.zshrc

file:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.0/lib64

These are the common potential issues and fixes related to the ‘Could Not Load Dynamic Library libcudnn.so.8’ error when running TensorFlow on Ubuntu 20.04. Diagnosing and addressing these issues should help you rectify the errors and run TensorFlow smoothly on your system.

The problem you’re experiencing is not uncommon amongst those who are delving into the realm of CUDA (Compute Unified Device Architecture) and cuDNN (CUDA Deep Neural Network library). These are parallel processing platforms and programming models allows you to utilize NVIDIA hardware to accelerate computing applications in a much more efficient way by utilizing the GPU’s (Graphics Processing Unit) power.

Error Explanation:

Your application could not locate the

libcudnn.so.8

file, which is an aspect of the cuDNN library. This issue typically arises when either the cuDNN library isn’t installed, or if it is, the environment variable pointing to the location of the library on your system isn’t set properly.

Solving the Problem:

Let’s break down how to tackle this challenge:

• Ensure that cuDNN is Installed: Firstly, confirm the installation of cuDNN. If you haven’t got it installed, visit the official NVIDIA cuDNN website or look for installable packages from trusted resources like Ubuntu Software Center.
• Extract and move the cuDNN files: Once it’s downloaded, decompress the tarball using the command:

  tar -xzvf cudnn-10.0-linux-x64-v7.5.0.56.tgz

  And move the resulting files to appropriate folders using commands similar to:

  sudo cp cuda/include/cudnn.h /usr/local/cuda/include
  sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
  

  Replace cuda/lib64/ with cuda/lib/ if on a 32-bit machine.

• Set the Environment Path Variable: Your machine needs to know where to find these libraries. You can do this by setting the LD_LIBRARY_PATH environment variable. Here’s one way of doing it in bash terminal:

  export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

• Include Paths in Configuration Files: Lastly, ensure paths are included in the respective configuration files. For example, add these lines to your .bashrc or equivalent file:

  export CUDA_HOME=/usr/local/cuda
  export PATH=$PATH:${CUDA_HOME}/bin
  

Now, every new shell or terminal will have the correct path variables needed for Tensorflow to find cuDNN and CUDA!

This is a nuanced but integral part of configuring your development environment with CUDA and cuDNN on an Ubuntu 20.04 system, especially given their potential some tools like Machine Learning & Artificial Intelligence-based workloads built on platforms like TensorFlow.

If followed diligently, the approach mentioned here should relieve you of that pesky “could not load dynamic library ‘libcudnn.so.8′” error.

Understanding the integral role of cuDNN in TensorFlow is key when troubleshooting problems related to loading dynamic libraries, specifically

libcudnn.so.8

. cuDNN (CUDA Deep Neural Network) library, a GPU-accelerated library for Deep Neural Networks, is an essential component for running machine learning operations optimized on NVIDIA’s graphic hardware [source].

In the context of TensorFlow, a high-performance machine learning library, the cuDNN facilitates efficient computation, thereby speeding up training and inference of deep neural networks [source]. Effectively, it allows TensorFlow to leverage NVIDIA GPUs for enhanced performance.

The error message “Could Not Load Dynamic Library ‘libcudnn.so.8′” typically surfaces when TensorFlow attempts to access the cuDNN library but fails to find it. This problem primarily occurs due to one of the following issues:

• An incorrect or incomplete installation of the cuDNN library.
• Mismatch between TensorFlow, CUDA, and cuDNN versions.
• Incorrect configuration of environment variables.

To remedy the error, one could:

1. Verify that cuDNN is Installed: A quick way to check if libcudnn is indeed installed would be by running the command

   ldconfig -p | grep libcudnn

   .

2. Match Correct Versions: It’s crucial to have compatible versions of TensorFlow, CUDA and cuDNN installed. TensorFlow maintains a guide mapping respective compatible versions [source].
3. Set Environment Variables: Setting environment variables can help your system locate necessary files. Run these commands to set correctly:

   • export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

   • export CUDA_HOME=/usr/local/cuda

4. Re-install cuDNN: If all else fails, re-installing cuDNN could help resolve underlying installation problems.

Software	Version
TensorFlow	2.4.0
CUDA	11.0
cuDNN	8.0.4

This table illustrates an example of correct version alignment.

Effectively, understanding how cuDNN factors into TensorFlow’s operation empowers you, as a coder, to effectively troubleshoot and rectify any dynamic library loading issues, ensuring seamless neural network training and real-time incentive processing.This is a common issue that tends to crop up when developers attempt to run TensorFlow on Ubuntu 20.04. Essentially, TensorFlow utilizes CUDA for its processing requirements. CUDA has specific libraries that it uses, one of which is libcudnn.so.8. It’s critical to have the correct version of libcudnn at hand because if there’s an issue with the loading of this library, TensorFlow won’t operate correctly.

One reason why you might see such kind of errors could be due to inconsistent versions of the CUDA toolkit or cuDNN and your TensorFlow installation. TensorFlow provides clear guidance on which versions are compatible with each other. Here’s the link to the official TensorFlow GPU support Page.

It could also be caused by invalid paths of these libraries. Please ensure that PATH and LD_LIBRARY_PATH environment variables include directories containing binaries and shared libraries.

Here is how you set these variables:

export PATH=/usr/local/cuda-11.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

To persistently set them every time you open a new terminal session, you can add the command lines into the end of file ~/.bashrc

Important note: Change cuda-11.0 to the folder name of your CUDA Toolkit on your system.

For solving this issue, you might want to consider following these steps:

1. Make sure your NVIDIA driver is correctly installed.
2. Install the CUDA Toolkit and cuDNN that matches your TensorFlow installation.
3. Check your environment variables as mentioned above.

Another noteworthy thing here is that while working with such technologies, always keep checking the dependencies and specifications provided by official documentation, like installing python, TensorFlow, CUDA, and cuDNN, all should have compatible versions to remove any conflicts.

The following table will help you understand which TensorFlow version requires what level of CUDA and cuDNN:

TensorFlow	CUDA	cuDNN
TF 2.5	CUDA 11.2	cuDNN 8.1.0
TF 2.4	CUDA 11.0	cuDNN 8.0.4
TF 2.3	CUDA 11.0	cuDNN 7.6.5
TF 2.2	CUDA 10.1	cuDNN 7.6.5
TF 2.1	CUDA 10.1	cuDNN 7.6.5
TF 2.0	CUDA 10.0	cuDNN 7.6.5

Hopefully, this comprehensive walk-through has provided you with a better grasp over why the error “Could Not Load Dynamic Library Libcudnn.So.8 When Running Tensorflow On Ubuntu 20.04” arises and how you can address it in logical sequence. Happy coding!Let’s dive into the exciting realm of TensorFlow installation and setup on Ubuntu 20.04, particularly focusing on the compatibility with NVIDIA drivers and troubleshooting the often encountered error “Could not load Dynamic Library libcudnn.so.8”.

Firstly, let’s understand that TensorFlow GPU support requires a plethora of software dependencies including the NVIDIA driver and CUDA Toolkit to begin. Here is a step-by-step guide to cater the requisite needs:

sudo apt-get update
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
ubuntu-drivers devices
sudo ubuntu-drivers autoinstall

This set of commands will automatically identify and install the correct NVIDIA driver needed for your system.

The next step in the preparatory phase includes installing the CUDA Toolkit:

sudo apt install nvidia-cuda-toolkit

Now we’re ready for the actual TensorFlow installation. Opt for a virtual environment for easy package management. Python’s built-in `venv` module is great for this:

python3 -m venv tf-gpu
source tf-gpu/bin/activate
pip install tensorflow

Alright, so that wraps up the key steps to reach our desired configuration. But what about the issue, “Could Not Load Dynamic Library ‘libcudnn.so.8’?” Well, it is due to missing or incorrectly configured cuDNN library which is indeed important as it is NVIDIA’s library for deep neural networks.

To resolve this issue, perform the following:

1. Download the appropriate cuDNN library from the NVIDIA website.
2. Extract and copy files to the CUDA directory:

shell
tar -xzvf cudnn-x.x-linux-x64-v8.x.x.x.tgz
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn*.h

3. Finally, add the following lines to the .bashrc file for path exportation:

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

Please adapt the version numbers given in the examples to fit your specific setup.

Interestingly, if you still face an issue, uninstalling and reinstalling TensorFlow could help detect and adhere to the newly installed NVIDIA drivers and libraries better:

pip uninstall tensorflow
pip install tensorflow

Mercifully, we have carefully tread through all relevant corners of our TensorFlow-NVIDIA journey; right from initiating the driver installation to independently resolving prevalent errors and glitches. This handy guide ought to leapfrog common pitfalls and catalyze your seamless transition into exploiting TensorFlow’s limitless capabilities on GPU.

When running TensorFlow on Ubuntu 20.04, a common error that you may come across is “Could not load dynamic library ‘libcudnn.so.8’.” This error usually occurs due to the Linux environment not being set up correctly to use the CUDA Deep Neural Network Library (cuDNN), which is a GPU-accelerated library of primitives for deep neural networks.

Why does this error occur?

The libcudnn.so.8 error message means that TensorFlow is unable to find cuDNN’s shared library file. The common reasons why this error occurs are:

• Your NVIDIA driver might be out-dated or in-compatible with your CUDA toolkit version. NVIDIA drivers are regularly updated, and hence there might be compatibility issues with different CUDA Toolkit versions.
• You may have installed the required NVIDIA CUDA Toolkit and cuDNN, but these systems may not have been configured correctly so that TensorFlow can use them. Your system likely failed to locate the necessary cuDNN library because it was not added to the system’s PATH.

How Can we Resolve This Error?

This issue can usually be fixed by following these steps:

1. Install NVIDIA driver, CUDA Toolkit, and cuDNN:

The first step involves installing or updating the NVIDIA driver, CUDA Toolkit, and cuDNN. Detailed instructions specific to an Ubuntu system can be found at the official NVIDIA cuDNN installation guide.

$ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt update
$ sudo apt install libcudnn8

2. Modify the System PATH:

You may need to modify your environment path to include directories that contain needed libraries:

$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu/

Note: Always make sure to replace /usr/local/cuda/lib64/ with the directory where you installed the CUDA libraries.

If you’re still facing the same problem, don’t hesitate to refer to the TensorFlow GPU installation documentation. They have some great detailed guides that can help. It’s also helpful to check on forums or community websites such as StackOverflow. If all else fails, you can always reach out to the support team for whichever software you’re using.

I hope this helps you get past this annoying little hiccup. With the right libraries in place, and TensorFlow correctly configured, you’ll be ready to dive into the wonderful world of machine learning programming!

So you’re faced with the error “Could not load dynamic library ‘libcudnn.so.8′”. This is a common issue experienced when trying to run TensorFlow on Ubuntu 20.04, which arises due to the absence of the CUDA Deep Neural Network library (libCUDNN) that TensorFlow requires for operation. Emphatically put, the problem is primarily tied to the inaccessibility of libCUDNN.

Detecting exactly what’s causing this issue can be a challenging task, but we’ll direct our focus towards proprietary NVIDIA drivers installation, the version compatibility between TensorFlow, CUDA, and libcudnn, and the setting of appropriate environmental paths for these libraries. Using these as our troubleshooting guideposts, let’s delve into potential solutions.

Ensuring Correct Installation of NVIDIA Drivers
The NVidia Graphics Card serves as the foundation that the CUDA Toolkit builds upon. You can check the proper working of your NVidia graphics card by running the command:

nvidia-smi

If your NVIDIA drivers are correctly installed, adequate information about your GPU should display. If not, consider reinstalling them. Visit NVidia’s official website for guidance on choosing the right driver for your GPU.

Verifying Version Synchronization Between CUDA, cuDNN and TensorFlow
Equally important is ensuring a match amid the versions of CUDA, cuDNN and TensorFlow. For instance, if you’re running TensorFlow 2.4.0, you’ll need CUDA 11.0 and cuDNN 8.0.2.

To verify that you have these essential components installed at compatible versions:

– Run the command

nvidia-smi

to check your CUDA version.
– Use the command

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

to see your installed cuDNN version.

If there’s any mismatch among your CUDA and cuDNN versions in relation to TensorFlow’s requirements, an upgrade will do the trick. Find pertinent guides from the official CUDA toolkit page and the official cuDNN SDK webpage, respectively.

Setting Environmental Paths Correctly
Lastly, correctly setting the environmental paths for CUDA and cuDNN is crucial since it enables TensorFlow to find these libraries during runtime. Two paths need to be set: the dynamic library path LD_LIBRARY_PATH and the bin path PATH.

Here’s how to set them up:

For LD_LIBRARY_PATH:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

For PATH:

export PATH=$PATH:/usr/local/cuda/bin

After running those commands, make sure to add them to your .bashrc file so they persist across sessions by using these commands:

echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64' >> ~/.bashrc

echo 'export PATH=$PATH:/usr/local/cuda/bin' >> ~/.bashrc

Ultimately, the essence of displaying code snippets and providing hyperlinks to relevant resources is intended to keep you independently informed and solution-oriented throughout your troubleshooting endeavors.The error message “Could not load dynamic library ‘libcudnn.so.8′” is quite a common issue when running Python TensorFlow applications on Ubuntu 20.04 environment. This problem might be due to several reasons.

1. CUDA Toolkit and cuDNN Library’s Incompatible Versions

TensorFlow requires certain versions of CUDA Toolkit and cuDNN library, and problems occur when these dependencies aren’t matched. For instance, TensorFlow 2.X needs CUDA v10.1 to operate efficiently along with cuDNN v7.6. If you install different versions of CUDA or cuDNN, TensorFlow may not function correctly or even fail to see the libraries.

How to counter this:
It’s crucial that you ensure your installed CUDA Toolkit and cuDNN version are compatible with your TensorFlow version. This info is always provided in the TensorFlow’s installation guide Official TensorFlow Documentation.

    tensorflow_version = 2.x 
    cuda_toolkit_version = 10.1
    cudnn_version = 7.6

    export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
    export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

How to counter this:
Uninstall CUDA and cuDNN and reinstall them. While installing, make sure you follow the steps listed on the official NVIDIA guides: CUDA Installation Guide and cuDNN Installation Guide

    sudo apt-get --purge remove cuda
    sudo apt-get autoremove

In summary, facing an error with libcudnn.so.8 when running TensorFlow on Ubuntu can be frustrating, but proper debugging can resolve it. Verify your CUDA and cuDNN installations, their versions, and the PATH variables. Besides that, don’t overlook the installation guidance from both TensorFlow and NVIDIA, as they provide invaluable information on tackling such issues.Fixing the incompatible versions of CUDA Toolkit & TensorFlow libraries issue, and specifically addressing the case when you encounter “Could not load dynamic library ‘libcudnn.so.8’ when running TensorFlow on Ubuntu 20.04,” requires several tactical steps. Let’s dive into it.

1. Identify Version Compatibility: Understanding the version compatibility is fundamental when dealing with software development tools and libraries. TensorFlow typically releases a guide about compatible versions in their documentation^source. This problem may occur if the installed version of CUDA or cuDNN isn’t compatible with the TensorFlow version you are using.

To check the version of TensorFlow installed in your system, open terminal and type:

python -c 'import tensorflow as tf; print(tf.__version__)'

2. Determine Installed CUDA and cuDNN Versions: The next step is to understand which versions of CUDA and cuDNN exist on your system. To find out the CUDA version use:

cat /usr/local/cuda/version.txt

And for knowing the cuDNN version, you can utilize:

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

3. Address Incompatibility by Installing Correct Library Version: If diagnosed that the issue resides in incompatible versions, you need to un-install existing libraries and install the correct versions identified from TensorFlow’s tested source builds page.

For removing CUDA, use:

sudo apt-get --purge remove cuda
sudo apt-get autoremove

Afterward, download the appropriate version of CUDA from NVIDIA’s archive ^source:

wget https://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run
sudo sh cuda-10.1.....run

This guides you through an interactive CUDA installation.

As for cuDNN, delete incorrect lib files before installing correct ones with:

sudo rm /usr/lib/x86_64-linux-gnu/libcudnn*

You’ll then download the correct cuDNN version from NVIDIA’s site^source and extract the downloaded file:

tar xvzf cudnn-x.x-linux-x64-v*.tgz

Transfer bin, include, and lib64 folders contents to the CUDA directory (usually ‘/usr/local/cuda’):

sudo cp -P cuda/lib64/* /usr/local/cuda/lib64/
sudo cp  cuda/include/* /usr/local/cuda/include/

Ultimately, reinstall TensorFlow and verify its access to GPU:

pip uninstall tensorflow
pip install tensorflow==x.x   # the version corresponding to CUDA and cuDNN you've installed

Then run a simple TensorFlow program to ensure setup correctness.

4. Set Environment Variables: To guarantee these settings withstand system restarts, append the following lines to the end of your ‘.bashrc’ file:

export PATH=/usr/local/cuda/bin:${PATH}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}

Once completed, reboot the computer, after which the library loading error should be resolved.

Go through each step carefully as this process involves multiple dependencies and precise version matches.
It isn’t uncommon for developers to encounter issues due to library conflicts while harnessing Machine Learning frameworks like TensorFlow. Hence, this troubleshooting guidance is crucial in maintaining productive work progress.

Debugging Runtime Issues: Could Not Load Dynamic Library ‘libcudnn.so.8’ When Running Tensorflow On Ubuntu 20.04

The error message “Could Not Load Dynamic Library ‘libcudnn.so.8′” points to a mismatch or absence of some critical files related to NVIDIA’s CUDA Deep Neural Network library (cuDNN) that Tensorflow relies on for GPU computing.

import tensorflow as tf 

print(tf.__version__) 

If executing the above code results in a similar error, not being able to load ‘libcudnn.so.8’, then we have a cuDNN issue we need to resolve. Let’s delve into how we can debug and rectify this problem step-by-step.

See also

Linux

Docker Alpine Executable Binary Not Found Even If In Path

– Check Your System’s CUDA and cuDNN Versions:

Ensure that your system has the exact versions of CUDA and cuDNN required by the TensorFlow version you’ve installed. You can verify this by using:

nvidia-smi 

nvcc --version 

Cross-reference your CUDA version against the Tensorflow compatibility documentation HERE.

– Verify the Installation Directory:

The error could be precipitated by the incorrect installation of cuDNN in a directory not accessible by TensorFlow. Ensure cuDNN is installed in a directory present in your `LD_LIBRARY_PATH`. You can check your current path with `echo $LD_LIBRARY_PATH`.

If it isn’t there, add it like so:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

– Reinstall cuDNN:

If previous steps did not stop the error, try reinstalling cuDNN, ensuring to follow the instructions provided by NVIDIA thoroughly.
After downloading the cuDNN package corresponding to your CUDA version from NVIDIA’s site HERE, install it via the command line:

tar -zxvf cudnn-10.0-linux-x64-v7.6.5.32.tgz 

sudo cp cuda/include/cudnn*.h /usr/local/cuda/include 

sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 

sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

And finally, reboot the machine and attempt to import TensorFlow once more. Debugging runtime issues such as loading dynamic libraries when implementing Machine Learning models can seem daunting initially. However, a step-wise methodology as outlined above can assist us greatly in zeroing in on the causes to iron out the bugs effectively.

A primary cause of the reported problem is generally the mismatch between the installed versions of CUDA and cuDNN vis-a-vis the required versions according to Tensorflow’s documentation. Furthermore, examining installation directories and performing reinstallation can aid in problem-solving.

Remember, maintaining an up-to-date understanding of Tensorflow’s requirements is crucial as new advancements roll out frequently and are often accompanied by updated dependencies. Therefore, keep an eye on the Tensorflow website and forums, which provide a rich repository of information and troubleshooting experiences shared by the global community. We all know the power and potential of CUDA libraries when it comes to GPU programmable interfaces for high performance computing and deep learning applications, especially with Tensorflow. However, it’s no secret that there can be bumps on the road in setting them up properly. Let’s focus our sight on one common issue which pops up during Tensorflow setup on Ubuntu 20.04 – “Could not load dynamic library libcudnn.so.8”.

The error directly points towards the unavailability or incorrect linking of cuDNN (CUDA Deep Neural Network) library version 8. The key point here is – each tensorflow version works optimally with a specific CUDA and cuDNN version. As of now, Tensorflow 2.4 works like a charmer with CUDA 11.0 and cuDNN 8.0.

Firstly, ensure you’ve installed the correct CUDA version for your TensorFlow. Check your cuda version using:

nvcc --version

If you don’t have CUDA 11.0 for matching with TensorFlow 2.4, follow these instructions;
• Go to NVIDIA Toolkit Archive.
• Select and download the package for Ubuntu 20.04.
• Run the downloaded file (

sudo sh cuda_11.0.*

) to install CUDA 11.0.

Next step is to get cuDNN v8. You can obtain cuDNN from NVIDIA’s site here. Opt for Download cuDNN v8.0.5 (November 9th, 2020), for CUDA 11.0. Unzip the downloaded package and move the contents to your cuDNN directory (Usually /usr/local/cuda-11.0). Here are the commands for it:

tar -xzvf cudnn-11.0-linux-x64-v8.0.5.39.tgz
sudo cp cuda/include/cudnn*.h /usr/local/cuda-11.0/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda-11.0/lib64
sudo chmod a+r /usr/local/cuda-11.0/include/cudnn*.h /usr/local/cuda-11.0/lib64/libcudnn*

Ensure that CUDA path is set in your environment variables.

export PATH={CUDA_HOME}/lib64:${PATH}

Where {CUDA_HOME} would usually be /usr/local/cuda-11.0 unless you’ve changed it during CUDA installation.

After completing these steps, your Tensorflow should run without any errors. If at this point your Tensorflow still throws the same error, then quite possibly the problem lies in wrongly linked libraries. To diagnose this, go through the linked libraries and look for the culprit. Input below command in the terminal.

ldd $(python -c 'import tensorflow as tf; print(tf.sysconfig.get_lib())')/libtensorflow_framework.so.2

In the output check the libcudnn.so.8 part, it should point to /usr/local/cuda-11.0/lib64/libcudnn.so.8 (or whatever your appropriate CUDA path is). If it’s pointing to some other cuDNN copy in your system, what you need is some house-cleaning. Delete the old versions making it point to the right place. When libraries conflict, the trouble starts brewing.

Be meticulous with software versions and careful linking of libraries to avoid such pitfalls. Mistakes happen, but knowing where to look will guide your correction process.

Sources:
Tensorflow Official Guide
NVIDIA Toolkit Archive
NVIDIA cuDNNGetting the “Could not load dynamic library ‘libcudnn.so .8′” error when running Tensorflow on Ubuntu 20.04 can seem daunting at first, but don’t worry, there are effective solution protocols to address this issue. The root cause of the problem often lies in the CUDNN Library either missing or not properly installed. Here’s a complete guide for you.

Solution Protocol 1: Validate and Install Correct CUDA Toolkit Version

The CUDA toolkit and CUDA version play a vital role in TensorFlow operations. To check the current CUDA version:

cat /usr/local/cuda/version.txt

After confirming your CUDA version, you must install the correct version of cuDNN that this specific version of CUDA supports.

Now, if you find that the CUDA Toolkit or CUDA version is not installed, visit NVIDIA’s official site here to install them.

Solution Protocol 2: Validate and Install Correct libcudnn Version

The next step is to ensure the right version of libcudnn is installed. Generally, for TensorFlow, you require cuDNN 7.6 for CUDA 10.0 (TensorFlow <= 2.3) or cuDNN 8.0 for CUDA 11.0 (TensorFlow >=2.4).

You should validate your current libcudnn version with:

cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2

If you identify that cuDNN is not installed correctly or missing, download it from their official website over here.

Solution Protocol 3: Setting Library Path

On some occasions, TensorFlow might fail to locate the cuDNN library even though it exists in our system. This happens when the path environmental variable does not point to the library directory.

To solve this, add these lines to your .bashrc file:

export LD_LIBRARY_PATH="/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64"

and then run:

source ~/.bashrc

Solution Protocol 4: Reinstalling Tensorflow

An incorrectly installed version of TensorFlow could be the root of this error. To sidestep this, uninstall it:

pip uninstall tensorflow

Then, reinstall it according to compatibility with CUDA:

For CUDA 11.0 (TensorFlow >=2.4),

pip install tensorflow

For CUDA 10.0 (TensorFlow <= 2.3)

pip install tensorflow==2.3.0

By following these steps, the “Could not load dynamic library ‘libcudnn.so .8′” issue when running TensorFlow on Ubuntu 20.04 should now be resolved. Remember the key factor here: having synchronized, corresponding versions of CUDA, cuDNN, and TensorFlow.
When working with deep learning libraries such as TensorFlow (TF) on Ubuntu 20.04, one common roadblock that you might encounter is the “Could not load dynamic library ‘libcudnn.so.8′” error. This issue is typically a sign of incorrect installation or improper setup in terms of our CUDA Toolkit and cuDNN library paths.

Let’s delve into setting the right pathways, initializing post-installation tweaks, and ensuring smooth operations to rectify this error.

Setting Right Pathways

Ensuring correct environment variable paths for both your CUDA toolkit and cuDNN library is crucial. These paths guide your operating system to correctly locate necessary programming resources. The following export commands can be used in your terminal to set these paths:

<pre>
export PATH=/usr/local/cuda-11.0/bin${PATH:+:}$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:
                   ${LD_LIBRARY_PATH:+:}$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:
                   ${LD_LIBRARY_PATH:+:}$LD_LIBRARY_PATH
</pre>

Ensure to replace ‘cuda-11.0’ with your version of CUDA toolkit throughout all three export statements.

You can add these lines to the end of your ~/.bashrc file to ensure they are automatically executed each time a new terminal is opened.

Post-Installation Checks

Once you have set up the appropriate environment variables, it’s important to verify the installation of CUDA and cuDNN was performed correctly.

The following commands help confirm that everything is in order:

<pre>
nvcc -V //Check CUDA version
nvidia-smi //Check Graphics card information
sudo dpkg -l | grep libcudnn //Check cuDNN version
</pre>

Ensuring Smooth Operations

With the paths properly set and installations confirmed, running TensorFlow should ideally yield no error. However, if circumstances still result in the ‘Could not load dynamic library ‘libcudnn.so.8’ error, an additional step may be necessitated.

In many cases, creating a symbolic link to the missing library solves the problem:

<pre>
sudo ln -s /path/to/your/cudnn/lib/libcudnn.so.8 /usr/local/cuda/lib64/
</pre>

Once again, make sure to replace ‘/path/to/your/cudnn/lib/’ with the exact location in which libcudnn.so.8 resides in your file system.

Through the above approach, we can settle issues revolving around the ‘Could not load dynamic library ‘libcudnn.so.8’ error message. By effectively managing system environment variables post-installation, detecting any potential installation problems, and performing any essential corrective steps, we enable seamless functioning of the TensorFlow setup on Ubuntu 20.04.Not being able to load the dynamic library libcudnn.so.8 when running TensorFlow on Ubuntu 20.04 might be caused by incompatible versions of software libraries. Optimizing for performance often requires using compatible versions of various software libraries.

In this case, the error message signifies that libcudnn.so.8 is not found. This is crucial since cuDNN (CUDA Deep Neural Network library), indicated by libcudnn.so.8, plays a big role in TensorFlow’s computation process, particularly if you’re leveraging a GPU’s power.

Below are a few steps to resolve the situation and ensure optimal performance:

Step 1: Check the Installed CUDA Version

Before we proceed, it’s important to know the version of CUDA installed on your system. You could run:

nvcc --version

Step 2: Download an Appropriate cuDNN Version

Based on your CUDA version, you should select an appropriate cuDNN version from the [official NVIDIA website](https://developer.nvidia.com/rdp/cudnn-archive). If you have CUDA version 11.0 (for instance), you’d download cuDNN v8.0.4 for CUDA 11.0.

Step 3: Installing cuDNN Library Files

After downloading, extract the cuDNN package and move the resulting files to the CUDA path:

sudo tar -xzvf cudnn-11.0-linux-x64-v8.0.4.30.tgz
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

Step 4: Setting System-Level Environmental Variables

To enable your system to detect the cuDNN library, consider adding its directory to your `LD_LIBRARY_PATH`:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

Add this line permanently to your path by appending it to the end of the ~/.bashrc file:

echo 'export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"' >> ~/.bashrc

Step 5: Confirming cuDNN Installation

Upon successfully completing the above steps, you can test if cuDNN is installed correctly:

cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2

You should see output showing the cuDNN version you previously installed.

For ideal performance when using TensorFlow, align the versions of TensorFlow, CUDA, Python, and cuDNN based on the given compatibility matrix on TensorFlow’s official [website](https://www.tensorflow.org/install/source#gpu).

Moreover, keep your hardware specifications in mind. Different GPUs may require different CUDA toolkit versions. Always refer to NVIDIA’s official guides to verify supported versions.

Source code compatibility ensures minimal software conflicts, maximal usage of resources, and therefore optimal performance.The predicament of encountering the “Could not load dynamic library libcudnn.so.8” error when running TensorFlow in Ubuntu 20.04 can be dissected down to a few likely causes and solutions.

Firstly, this error might arise due to the absence or incorrect version of CUDA and its corresponding cuDNN. While TensorFlow requires CUDA for GPU-accelerated computation, cuDNN is NVIDIA’s GPU-accelerated library for deep neural networks. Essentially, it provides a high-level API that enables seamless integration into any deep learning framework, such as TensorFlow. Fundamentally, TensorFlow won’t function properly without these two components; CUDA because it handles the heavy computation, and cuDNN since it has specific commands and functions dedicated to neural networks that TensorFlow uses.

# Check CUDA/cuDNN version
nvcc --version
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

Secondly, let’s break down how to resolve this. If no version of CUDA or cuDNN is installed, we need to:
– Download the correct versions from NVIDIA’s website.
– Then install them according to the given instructions.

In case an incorrect version is installed, they should be uninstalled first before installing the right ones. So, either we need to:
– Uninstall the old versions
– Or update them if possible

Another point of consideration could be that Linux distribution doesn’t know where to find the libraries, even if they are correctly installed. A quick fix to this involves appending the path of your CUDA toolkit directory to LD_LIBRARY_PATH.

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

By understanding these points of contention, finding the cause, and correcting the issue becomes much simpler. Notably, this challenge emphasizes the criticality of ensuring that all interdependent programs and components installed on a device have the right versions and are correctly installed for harmonious functioning.