how to use gpu in python

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How to Use GPU in Python

Introduction

Using a Graphics Processing Unit (GPU) in Python can greatly accelerate certain computations, especially those that involve heavy number crunching or require parallel processing. In this tutorial, we will discuss how to enable and utilize GPU in Python using the popular library, TensorFlow.

Prerequisites

Before getting started, make sure you have the following prerequisites installed:
– Python (version 3.6 or above)
– TensorFlow (version 2.0 or above)
– CUDA Toolkit (for Nvidia GPUs)
– cuDNN (for Nvidia GPUs)

Step 1: Check GPU Availability

To determine whether your system has a compatible GPU, run the following code in Python:

python
import tensorflow as tf
print("GPU is available: ", tf.test.is_gpu_available())

If the output is True, your system has a compatible GPU.

Step 2: Install CUDA Toolkit and cuDNN (for Nvidia GPUs)

If you are using an Nvidia GPU, you need to install the CUDA Toolkit and cuDNN to enable GPU support. Follow these steps to install the necessary libraries:

  1. Download and install the latest version of CUDA Toolkit from the Nvidia website.
  2. Download and install cuDNN from the Nvidia Developer website. Make sure to choose the version compatible with your CUDA Toolkit installation.
  3. Add the CUDA binaries to your system’s PATH variable.

Step 3: Configure TensorFlow to Use the GPU

By default, TensorFlow uses the CPU for computations. To enable GPU support, you need to configure TensorFlow appropriately. Here’s how you can do it:

“`python
import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices(‘GPU’)
if gpus:
try:
tf.config.experimental.set_visible_devices(gpus[0], ‘GPU’)
tf.config.experimental.set_memory_growth(gpus[0], True)
print(“GPU configuration is successful.”)
except RuntimeError as e:
print(e)
“`

Step 4: Write GPU-Enabled Code

Once the GPU is successfully configured, you can write code that utilizes the GPU for computations. For example, let’s consider a simple matrix multiplication using TensorFlow:

“`python
import tensorflow as tf

Enable GPU support (if not done already)

…

Perform matrix multiplication

a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 6], [7, 8]])
c = tf.matmul(a, b)

Print the result

print(c)
“`

When executed on a GPU-enabled system, the matrix multiplication will be performed using the GPU, resulting in faster computation.

Conclusion

Utilizing the GPU in Python can significantly improve the performance of computationally intensive tasks. By following the steps outlined in this tutorial, you can enable and utilize GPU support in Python using TensorFlow.