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Certainly! CUDA, which stands for 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 (parallel computing). The CUDA_VISIBLE_DEVICES environment variable is used to control which GPUs are visible to a CUDA-enabled application.
In this tutorial, I'll show you how to use the CUDA_VISIBLE_DEVICES environment variable in Python from the command line, allowing you to control which GPU devices your CUDA-enabled Python script will utilize.
Before you start, make sure you have the necessary libraries installed. You can use the following command to install the required package:
Create a Python script (e.g., cuda_example.py) that utilizes CUDA. Here's a simple example using the PyCUDA library:
This script performs a simple addition operation on the GPU.
Now, let's modify the script to accept the GPU device indices as command-line arguments and set the CUDA_VISIBLE_DEVICES environment variable accordingly:
Now, you can run the script from the command line, specifying the GPU indices you want to use:
This script dynamically sets the CUDA_VISIBLE_DEVICES environment variable based on the provided GPU indices, allowing you to control which GPUs your CUDA-enabled Python script will use.
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