I know that TensorFlow comes in two different versions - one for GPU acceleration and one for CPU only. I’m working on a Linux system and need to figure out which version I currently have installed.
I’m also curious about what happens when you have the GPU version but no GPU hardware is available. Does TensorFlow automatically fall back to CPU processing or does it crash with an error?
Another thing I want to understand is how to make sure TensorFlow actually uses the GPU when it’s available. Do I need to set any special configuration parameters or does it happen automatically? I want to make sure my neural network training is taking advantage of GPU acceleration when possible.
you can also try tf.test.is_gpu_available() while training to see real-time what’s being used. Generally, TensorFlow detects the GPU on its own, so if you spot /device:GPU:0, all’s well!
Run tf.config.list_physical_devices('GPU') to check your TensorFlow setup. If it returns nothing, you’ve got either a CPU-only install or your GPU isn’t configured right. Don’t worry about crashes though - TensorFlow will just fall back to CPU if there’s no compatible GPU (you might see some CUDA warnings). TensorFlow handles GPU stuff automatically, so operations that can use GPU acceleration will get sent there without you doing anything. Just watch the console output when training to see which device it’s actually using.
Hold up - did you check if your GPU drivers are actually installed correctly? sometimes TensorFlow sees the GPU but can’t use it because the drivers are messed up. What GPU are you running? and are you seeing any speed difference between CPU and GPU with your network size?