Tensorflow Could Not Load Dynamic Library Libcudart.So.10.0 On Ubuntu 18.04

Indeed, Tensorflow leverages the power of GPUs via CUDA, a parallel computing platform developed by NVIDIA. To serve this purpose, Tensorflow relies on certain dynamic libraries from the CUDA toolkit, and one of these is `libcudart.so.10.0`. If your system doesn’t contain this library or if it’s stored in a directory which isn’t recognized, you’ll encounter the mentioned error.

To fix the problem, follow these steps:

1. **Confirmation**: Confirm whether the CUDA toolkit is already installed by running the command

nvidia-smi

. If the toolkit is installed, this code will provide information about the driver and CUDA toolkit version.

2. **Reinstallation/Installation**: You can either reinstall the toolkit, if it exists, or install it afresh. Download the correct version from the official NVIDIA website.

For instance, here’s how you would go about re-installing the toolkit:

sudo apt-get --purge remove cuda

Then, after removing, use the following commands to update the system’s database and install Cuda 10.0 toolkit:

sudo apt-get update

sudo apt-get install cuda-10-0

3. **Access Permissions**: Ensure the new libraries are accessible to your operating system by adding them to the PATH and LD_LIBRARY_PATH environment variables in the .bashrc file, then source your bash profile or log out and log back in for changes to take effect:

• export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}

• export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64\

4. **Validation**: Validate that everything is working properly by importing TensorFlow in a Python environment –

python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

By following these steps, you should be able to successfully fix the “Tensorflow Could Not Load Dynamic Library Libcudart.So.10.0” error on Ubuntu 18.04. Nevertheless, troubleshooting such errors may prove different and unique in some instances depending on the setup of each machine. Willingly interact with online communities that have traversed the same path remains a pertinent course of action in case any problems appear.When an error message pops up saying “Tensorflow Could Not Load Dynamic Library Libcudart.So.10.0 On Ubuntu 18.04” while trying to run TensorFlow on your machine, it indicates that there’s a missing dynamic library associated with CUDA Toolkit.

Let’s understand the components involved here:

• TensorFlow: A powerful open-source software library for dataflow programming across a range of tasks, widely used for machine learning applications such as neural networks.
• CUDA Toolkit: NVIDIA’s comprehensive development environment for creating high performance GPU-accelerated applications.
• Libcudart.so.10.0: The specific dynamic library file related to CUDA runtime. This is essentially a piece of the larger CUDA codebase that helps Tensorflow leverage GPU architecture.

The missing libcudart library can lead to limitations in TensorFlow’s functionality, particularly if you’re looking to use GPU acceleration capabilities. It’s important to note that this issue resides within the operating system’s inability to locate this library, which can happen due to several reasons including incompatible versions of TensorFlow and CUDA, incorrect environment variables, or simply because the library isn’t installed.

To solve this issue, you can try these steps:

1. Install the right CUDA version: Match the CUDA version with what TensorFlow expects. As per TensorFlow’s official compatibility guide (source), TensorFlow v2.3.0 uses CUDA 10.1. If you have a different CUDA version installed, you need to either upgrade/downgrade it or install the required one concurrently. You can fetch the CUDA Toolkit v10.1 from NVIDIA’s archive (link).
2. Exporting CUDA paths: Sometimes, just installing isn’t enough – Your system needs to know where to find them. To do this, add the following lines to the end of your ~/.bashrc file (

   sudo nano ~/.bashrc

   ).

   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}}

3. Reinstall TensorFlow: Lastly, reinstall TensorFlow after correctly installing CUDA. Use pip or conda commands to uninstall and reinstall tensorflow-gpu (

   pip uninstall tensorflow-gpu && pip install tensorflow-gpu

   ).

Upon successful completion of these steps, you should hopefully no longer see the error about “Tensorflow Could Not Load Dynamic Library Libcudart.So.10.0 On Ubuntu 18.04”. Always make sure your system’s hardware, software, and drivers are compatible with each other, especially while working with complex libraries like TensorFlow and CUDA.

Troubleshooting Approaches to Tensorflow’s Dynamic Loading Error: Libcudart.so.10.0 on Ubuntu 18.04

This error usually occurs when you’re trying to run TensorFlow on a system with an incompatible, missing or incorrectly installed Nvidia CUDA toolkit (which the libcudart.so.10.0 library is a part of). Here are my analytical insights and suggested troubleshooting steps for this issue:

Install Compatible CUDA Toolkit Version

The most straightforward solution – ensure that the CUDA toolkit version matching your installed Tensorflow’s requirements is present. Each Tensorflow release corresponds to a specific CUDA and cuDNN version. For example, TensorFlow 1.x works better with CUDA 10.0.

sudo apt-get install nvidia-cuda-toolkit

You can verify the installation using the nvcc command:

nvcc --version

Download and installation instructions could be found in the official NVIDIA website.

Update Environment Variables

After installing the CUDA toolkit, ensure the operating system knows where to find it by setting the appropriate environmental variables.

export CUDA_HOME=/usr/local/cuda-10.0 
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64

Ensure to replace “/usr/local/cuda-10.0” with your CUDA installation directory path.

Full Upgrade

Performing a full upgrade of the system sometimes rectifies the issue as it might sync up some disparate components to their latest update circles.

sudo apt-get update && sudo apt-get dist-upgrade

TensorFlow GPU Support Check

Confirm if the TensorFlow-GPU version is installed on your system. The regular version doesn’t support GPU acceleration and hence not CUDA compatible.

import tensorflow as tf
print(tf.test.is_gpu_available())

The above code should return True for GPU versions.

Assuming all these steps are correctly carried out, the ‘lib cudart.so.10.0’ dynamic loading error should ideally be resolved.

It is essential to note that your actual problem may slightly vary based on your development environment and TensorFlow usage specifics. Always cross reference your issues against TensorFlow’s own documentation and discussions on platforms like StackOverflow and the TensorFlow GitHub repository.As a professional coder, I often encounter complex problems that require thoughtful solutions. One such issue you may have stumbled upon is the “Could not load dynamic library ‘libcudart.so.10.0’ error when working with Tensorflow on Ubuntu 18.04. The root cause of this error is a missing or improperly linked CUDA runtime library (libcudart). Here’s how you can fix this issue:

Firstly, let’s clarify that CUDA Toolkit version compatibility is vital in resolving these errors. Tensorflow needs specific versions of CUDA to work correctly. According to Tensorflow’s official GPU support page, Tensorflow requires CUDA 10.0.

Now, Ubuntu 18.04 repositories by default provide CUDA 10.1 or even newer, which might be causing your problem. So, we will need to manually install the correct version of CUDA.

While installing CUDA Toolkit via the runfile method might seem intuitive, it might conflict with system-level NVIDIA drivers that come pre-installed in Ubuntu distributions. Thus, my suggestion would be to go for the deb(local) method.

Here’s an outline of the steps involved:

– Uninstall any existing CUDA installations:

sudo apt-get purge cuda
sudo apt autoremove
sudo apt-get clean

– Download CUDA 10.0 toolkit via wget command. You would find the needed link from NVIDIA’s archived CUDA releases page.

wget [CUDA 10.0 download link]

– Install CUDA Toolkit 10.0 using dpkg and apt:

dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda=10.0.130-1

– Ensure that PATH variable includes cuda-10.0 folders:

export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}

After the necessary installation steps and environmental settings are done, perform a reboot or restart your terminal session to apply the changes.

Next, check the installed versions for CUDA and libcudart:

See also

Linux

Vault Error

nvcc --version
ldconfig -p | grep libcudart

If everything is as expected, now Tensorflow should be able to find the libCudart.so.10.0 file without any issues because ldconfig updates the links and cache to dynamic libraries, ensuring that our newly installed CUDA libraries become available system-wide.

Remember, if there are still issues, they might relate to Tensorflow installation itself or its compatibility with your system. It’s always good to use the tensorflow-gpu==1.* version corresponding to CUDA 10.0 for the best results.

By carefully following this guide, hopefully, I believe you should be successful in resolving the “‘Could not load dynamic library ‘libcudart.so.10.0′” error on your Ubuntu 18.04 system. Happy Coding!When dealing with TensorFlow, especially on Ubuntu 18.04, one of the most common issues that developers encounter is the “TensorFlow could not load dynamic library libcudart.so.10.0” error. This is essentially your TensorFlow installation informing you it’s having trouble locating or accessing the Canonical Ubuntu 18.04 CUDA Runtime (libcudart) software libraries.

Why this happens:

1. An improper or failed CUDA toolkit installation. The CUDA toolkit provides essential development tools for developers looking to capitalize on the power of NVIDIA GPUs. It includes the CUDA runtime library (libcudart).
2. You might have installed a wrong version of the CUDA toolkit. TensorFlow is notably specific with the versions of CUDA and cuDNN it requires.
3. Your system’s environment variables are not correctly set up. These play a role in helping TensorFlow locate where the necessary CUDA libraries are located.

Preventing Future Errors:

To avoid such errors in the future, Here are some preventive measures:

1. 1. Correct CUDA Installation: Always ensure that your CUDA toolkit installation is done correctly. Doing so will make certain that all the necessary libraries, including libcudart.so.10.0 expose their full functionality. It’s also important to download your CUDA Toolkit only from the official NVIDIA site to avoid getting compromised files.
   2. Correct CUDA Version: TensorFlow works best with specific CUDA and cuDNN versions. Therefore, when installing these prerequisites, it’s recommended to first check the TensorFlow GPU Software requirements to know precisely what versions to install.
   3. Properly Set Environment Variables: After successful CUDA and cuDNN installation, setting up your system environment variables correctly plays a critical role in preventing this issue. By doing so, you’ll be providing TensorFlow with paths detailing where exactly to find installed CUDA libraries. We can achieve this by adding the following lines to our ~/.bashrc file:

export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

1. TensorFlow Installation: While installing TensorFlow, use pip within a virtual environment. This will isolate your TensorFlow environment and prevent potential conflicts with other Python packages. Following these steps guarantees sutability between your TensorFlow and installed CUDA version thus preventing future ‘library not loaded’ errors.

In summary, while the ‘TensorFlow could not load dynamic library libcudart.so.10.0’ error may seem quite intimidating at first sight, its resolution mostly lies in ensuring correct CUDA Toolkit installations, settling on suitable versions, correctly structuring system environment variables, and maintaining a tidy TensorFlow installation. By taking note of the above-stated factors, you significantly minimize the risk of subsequently experiencing this error.In context of your asked query, the situation where

TensorFlow

cannot dynamically load the library

libcudart.so.10.0

on Ubuntu 18.04 usually arises due to incorrect CUDA installation or incompatibility issues between the TensorFlow version and CUDA toolkit version.

As a first step in resolving this issue, it’s important to ensure that the appropriate CUDA toolkit version is installed. Tensorflow requires different versions of CUDA for different releases. Hence it’s crucial to install the correct CUDA version which matches your tensorflow version. You can find the required CUDA version at tensorflow GPU dependencies.

To verify your CUDA toolkit installation,

nvcc --version

This should return your current CUDA version used in your environment.

If you have installed the correct CUDA version but still encounter the same error, there could be an issue with the paths configured in your .bashrc file. Specifically, the CUDA environment variables possibly might not have been set correctly. This can be checked by using:

echo $LD_LIBRARY_PATH

The output should contain paths leading to your CUDA libraries.

If in case, these paths aren’t correctly set, they can be updated in your bash configuration files:

export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Replace `/usr/local/cuda-10.0` with your own CUDA installation path.

A failure in resolution even post the above steps might indicate an incompatibility issue between GPU drivers and CUDA version. Make sure to match your GPU driver version with the compatible CUDA version as shown in the NVIDIA documentation.

The table shows the mapping correlations between TensorFlow Versions, CUDA Toolkit Versions and GPU Driver Versions. Following such systematic troubleshooting steps can resolve “libcudart.so.10.0” loading issue effectively related to TensorFlow on Ubuntu 18.04.