running python script on gpu
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In this tutorial, we'll explore how to run Python scripts on a GPU (Graphics Processing Unit) for accelerated performance using the popular deep learning library TensorFlow and its GPU-compatible version. This tutorial assumes you have a compatible NVIDIA GPU and have installed the necessary drivers and libraries.
First, make sure you have Python installed on your system. You can install TensorFlow using:
Ensure that you have a compatible GPU and have installed the necessary NVIDIA GPU drivers and CUDA Toolkit. You can find information on installing CUDA Toolkit on the NVIDIA website.
You can check if TensorFlow is detecting your GPU by running the following Python code:
If you see the name of your GPU, it means TensorFlow has successfully detected it.
Now, let's create a simple Python script that utilizes the GPU for computation. Create a file named gpu_script.py and add the following code:
This script generates two random matrices and performs matrix multiplication using TensorFlow. The @tf.function decorator optimizes the function for better performance.
Save the file and run it using the following command:
The script should print the result shape and the time taken for the matrix multiplication on your GPU.
Congratulations! You've successfully run a Python script on your GPU for accelerated computation using TensorFlow. You can now explore more complex tasks and machine learning models that can benefit from GPU acceleration.
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