How to get current available GPUs in TensorFlow

If you are using TensorFlow for deep learning tasks, it’s important to know how to check the current available GPUs on your system. This can help you utilize the full potential of your hardware and speed up your computations.

Here is a simple way to get the list of available GPUs in TensorFlow:

import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    for gpu in gpus:
        print("Device name:", gpu.name)
else:
    print("No GPU available")
  

The code snippet above uses the TensorFlow library to list the physical devices (i.e., GPUs) available on the system. If there are any GPUs present, it will print out the device name. Otherwise, it will print “No GPU available”.

By knowing the available GPUs, you can make use of TensorFlow’s built-in support for distributed computing and leverage the parallel processing power of your GPUs to accelerate your neural network training.

Remember to configure TensorFlow to use the available GPUs by setting the environment variable CUDA_VISIBLE_DEVICES or using the tf.config.experimental.set_visible_devices function before running your TensorFlow code.

By following these steps, you can ensure that your deep learning models are utilizing all the available GPUs on your system efficiently.