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Installing CUDA Toolkit on Linux using pip
Introduction:
The CUDA Toolkit is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general-purpose processing. In this tutorial, we will guide you through the process of installing the CUDA Toolkit on Linux using the pip package manager.
Prerequisites:
Steps to Install CUDA Toolkit using pip:
1. Verify GPU Compatibility:
Before installing the CUDA Toolkit, ensure that your GPU is compatible. You can check the Nvidia CUDA GPU support list on the official Nvidia website.
2. Install Nvidia GPU Drivers:
Make sure you have the latest Nvidia GPU drivers installed on your system. You can usually install them from the official Nvidia website or using your Linux distribution’s package manager.
3. Install Dependencies:
4. Download CUDA Toolkit:
Visit the Nvidia CUDA Toolkit download page and select the appropriate version for your system. For example, for Ubuntu, you can download the Debian package (deb) and install it using the following commands:
Replace version with the actual version number in the last two commands.
5. Set Environment Variables:
Add the CUDA Toolkit binaries to your PATH and update the LD_LIBRARY_PATH to include the CUDA libraries. Add the following lines to your shell profile file (e.g., ~/.bashrc or ~/.zshrc):
Replace version with the installed CUDA Toolkit version.
6. Install cuDNN:
cuDNN is a GPU-accelerated library for deep neural networks. Download the cuDNN library from the Nvidia website and follow the installation instructions.
7. Verify Installation:
Check if the CUDA Toolkit is correctly installed by running the following commands:
This command should display the installed CUDA Toolkit version.
8. Install GPU-accelerated Python packages:
Finally, install GPU-accelerated Python packages using pip:
Congratulations! You have successfully installed the CUDA Toolkit on Linux using pip, and your system is now ready for GPU-accelerated machine learning and deep learning tasks.
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