Difference between installation libraries of Tensorflow GPU vs CPU
One thing to Note: CUDA can be installed even if you don't have a GPU in your system.
For packages tensorflow
and tensorflow-gpu
I hope this clears the confusion. yes/no means "Will the package work out of the box when executing import tensorflow as tf
"? Here are the differences:
| Support for TensorFlow libraries | tensorflow | tensorflow-gpu |
| for hardware type: | tf | tf-gpu |
|----------------------------------|------------|-----------------|
| cpu-only | yes | no (~tf-like) |
| gpu with cuda+cudnn installed | yes | yes |
| gpu without cuda+cudnn installed | yes | no (~tf-like) |
Edit: Confirmed the no
answers on a cpu-only
system and the gpu without cuda+cudnn installed
(by removing CUDA+CuDNN env variables).
~tf-like
means even though the library is tensorflow-gpu
, it would behave like tensorflow
library.
Just a quick (unnecessary?) note... from TensorFlow2.0 onwards these are not separated, and you simply install tensorflow (as this includes GPU support if you have an appropriate card/CUDA installed).