Install Nvidia Drivers with Latest CUDA Version

Table of Contents:

1. Introduction
2. Determining GPU Compatibility
3. Purging Existing Nvidia Drivers
4. Installing Nvidia Drivers
5. Checking Driver and CUDA Versions
6. Downloading and Installing CUDA Toolkit
7. Configuring the Bash RC
8. Verifying CUDA Toolkit Installation
9. Downloading and Installing cuDNN
10. Finalizing Installation
11. Conclusion

Introduction

In this Tutorial, we will guide you through the process of installing and configuring video drivers on Ubuntu 18.04, enabling you to use the latest version of CUDA. By successfully installing the necessary drivers, you will be able to leverage the power of your GPU for tasks such as training neural networks and using deep learning libraries like TensorFlow and PyTorch. Additionally, we will show you how to install the cuDNN library, which provides high-performance GPU acceleration for deep learning applications. Let's get started!

Determining GPU Compatibility

The first step is to check if your computer has an NVIDIA GPU. Open the terminal and enter the command lspci | grep Nvidia to determine your GPU's product type, series, and model.

Pros: This step ensures that your computer is compatible with the installation process.

Purging Existing Nvidia Drivers

Before installing new drivers, it is essential to remove any existing Nvidia drivers from your computer. Run the command sudo apt purge nvidia* to purge all Nvidia-related packages.

Pros: Removes conflicting drivers and prepares the system for fresh installations.

Cons: May take some time to complete.

Installing Nvidia Drivers

To install the latest Nvidia drivers, add a software source by running the command sudo add-apt-repository ppa:graphics-drivers/ppa. Next, execute sudo apt-get update to search for up-to-date software. Finally, install the proper Nvidia driver using the command sudo apt install nvidia-driver-410.

Pros: Installs the most recent Nvidia drivers, ensuring optimal performance and compatibility with CUDA.

Checking Driver and CUDA Versions

After rebooting your computer, confirm that the correct driver version and CUDA version have been installed. Enter the command nvidia-smi to display a table containing the driver version and CUDA version.

Pros: Verifies successful driver installation and CUDA compatibility.

Downloading and Installing CUDA Toolkit

Visit the developer.nvidia.com/cuda-downloads page and select the appropriate target platform. Download and extract the CUDA toolkit file, then navigate to the extracted directory in the terminal. Run the provided command to install CUDA, accepting the license agreement and selecting the installation option.

Pros: Installs the CUDA toolkit, enabling GPU-accelerated computations.

Configuring the Bash RC

To ensure that the CUDA toolkit is recognized, add the following lines to your .bashrc file:

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

Pros: Allows easy access to the CUDA toolkit and ensures compatibility with other dependencies.

Verifying CUDA Toolkit Installation

Reload the .bashrc file by running the command source ~/.bashrc. Verify the CUDA toolkit installation by using the command nvcc --version.

Pros: Confirms successful CUDA installation and displays the version number.

Downloading and Installing cuDNN

Visit developer.nvidia.com/cudnn and sign in to your NVIDIA Developer account. Download the appropriate version of cuDNN that matches your CUDA version. Extract the files and note the file path to the cudnn.h header file. Copy the header file to the user include directory using the command sudo cp -P include/cudnn.h /usr/local/cuda/include. Next, copy the contents of lib64/cudnn to /usr/local/cuda/lib64 with the command sudo cp -P lib64/libcudnn* /usr/local/cuda/lib64. Finally, change the permissions of the copied files using the command sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*.

Pros: Provides GPU acceleration and high-performance for deep learning applications.

Finalizing Installation

Congratulations! You have successfully installed the Nvidia drivers, CUDA toolkit, and cuDNN library. Your system is now ready to use GPU acceleration for deep learning tasks.

Conclusion

In this tutorial, we guided you through the process of installing and configuring Nvidia drivers, CUDA toolkit, and cuDNN library on Ubuntu 18.04. By following these steps, you can leverage the power of your GPU for deep learning applications, such as training neural networks and using TensorFlow and PyTorch. Enjoy exploring the capabilities of your GPU and optimizing your machine learning workflows!

Highlights:

• Learn how to install and configure Nvidia drivers, CUDA toolkit, and cuDNN library on Ubuntu 18.04
• Leverage the power of your GPU for deep learning tasks
• Optimize machine learning workflows with GPU acceleration
• Ensure compatibility with the latest versions of CUDA, TensorFlow, and PyTorch

FAQ:

Q: What is CUDA? A: CUDA is a parallel computing platform and programming model developed by NVIDIA for the efficient execution of tasks on GPUs.

Q: Why is it essential to install the latest Nvidia drivers? A: Installing the latest Nvidia drivers ensures optimal performance and compatibility with CUDA and deep learning libraries.

Q: What is cuDNN? A: cuDNN is a GPU-accelerated library for deep neural networks that provides highly optimized implementations of convolutional and recurrent neural networks.

Q: Can I use GPU acceleration for deep learning on Ubuntu 18.04? A: Yes, by following the steps outlined in this tutorial, you can leverage your GPU for deep learning tasks on Ubuntu 18.04.

Q: How can I verify the installation of CUDA and cuDNN? A: You can use the command nvcc --version to verify the CUDA installation and nvcc -V to check the cuDNN installation.