Determining if Keras TensorFlow backend is using GPU or CPU

Backend
1

Hey everyone! I’m trying to figure out how to tell if my Keras TensorFlow backend is running on GPU or CPU. I’m on Linux and I’m not sure which version I installed.

Does anyone know how to check this? I’m also wondering about a couple of things:

  1. If I have the GPU version, will it automatically switch to CPU if there’s no GPU available? Or will it just crash?

  2. When a GPU is present, do I need to change any settings to make sure it’s actually using the GPU?

I’d really appreciate any help or tips on this. Thanks in advance!

2

hey owen, i’ve been there! to check, run this in python:

import tensorflow as tf
print(tf.test.is_built_with_cuda())

it’ll say True if gpu-enabled. it should auto-switch to cpu if no gpu. no extra settings needed usually, but u can force gpu use with:

import os
os.environ[‘CUDA_VISIBLE_DEVICES’] = ‘0’

3

hey there! have u tried checking ur tensorflow version? it might give u a clue. run print(tf.__version__) to see. im curious, what kinda project r u working on? GPUs can be game-changers for deep learning. ever thought about cloud options if ur local setup isnt cutting it?

4

To determine if your Keras TensorFlow backend is using GPU or CPU, you can use the following code:

import tensorflow as tf
print(tf.config.list_physical_devices(‘GPU’))

This will display available GPUs. If the list is empty, it’s using CPU. TensorFlow typically switches to CPU automatically if no GPU is available, preventing crashes. For optimal GPU usage, ensure you have the correct CUDA and cuDNN versions installed. You can also check GPU utilization during model training using the nvidia-smi command in the terminal. Remember, even with a GPU present, some operations may still run on CPU depending on the task complexity and available memory.