python cuda ubuntu
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CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface model created by Nvidia. It allows developers to use Nvidia GPUs for general-purpose processing (GPGPU). This tutorial will guide you through the process of setting up and using CUDA with Python on Ubuntu.
Nvidia GPU: Ensure that your system has an Nvidia GPU. CUDA is specifically designed to work with Nvidia GPUs.
Nvidia GPU Driver: Install the appropriate Nvidia GPU driver for your GPU. You can download and install the driver from the Nvidia website.
CUDA Toolkit: Download and install the CUDA Toolkit from the Nvidia CUDA Toolkit website.
cuDNN Library (Optional): If you plan to use deep learning libraries like TensorFlow or PyTorch with CUDA acceleration, consider installing the cuDNN library. You can download it from the Nvidia cuDNN website.
Python and Pip: Ensure that Python and Pip are installed on your system. You can install them using the following commands:
We will use the numba library, which provides a simple interface to CUDA programming with Python.
Let’s create a simple example to demonstrate how to use CUDA with Python using the numba library. Create a file named cuda_example.py with the following content:
Save the file and run it using the following command:
This example adds two arrays using a CUDA kernel. Make sure that your GPU is properly configured, and the CUDA toolkit is installed. If everything is set up correctly, you should see the input arrays and the result printed.
This tutorial provided a basic introduction to using CUDA with Python on Ubuntu. You can explore more complex CUDA programming with Python using libraries like numba or integrating CUDA with popular deep learning frameworks like TensorFlow and PyTorch.
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