Difference between installation libraries of Tensorflow GPU vs CPU
Question:
Recently, I wanted to move my Python libraries to a pendrive to keep all the libraries constant while switching between my workstation and laptop. (Also so that if I update one, it’s updated on other also.)
For this, I have installed a tensorflow-gpu version on my pendrive (my laptop doesn’t have a GPU). Everything works fine without a problem on both PC (it detects and uses my GPU without a problem) and laptop (it automatically uses my CPU).
That’s where my question lies. What is the difference between a
tensorflow-gpu
AND just
tensorflow
? (Because when no GPU is found, tensorflow-gpu automatically uses the CPU version.)
Does the difference lie only in the GPU support? Then why at all have a non GPU version of tensorflow?
Also, is it alright to proceed like this? Or should I create virtual environments to keep separate installations for CPU and GPU?
The closest answer I can find is
How to develop for tensor flow with gpu without a gpu.
But it only specifies that it’s completely okay to use tensorflow-gpu on a CPU platform, but it still does not answer my first question. Also, the answer might be outdated as tensorflow keeps releasing new updates.
I had installed the tensorflow-gpu version on my workstation with GTX 1070 (Thus a successful install).
Also I understand the difference is that pip install tensorflow-gpu will require CUDA enabled device to install, but my question is more towards the usage of the libraries because I am not getting any problems when using the tensorflow-gpu version on my laptop (with no GPU) and all my scripts run without any error.
(Also removed pip install from above to avoid confusion)
Also, isn’t running tensorflow-gpu on a system with no GPU the same as setting CUDA_VISIBLE_DEVICES=-1?
Answers:
tensorflow-gpu requires cuda/cudnn. tensorflow does not. pip doesn’t install cuda for you (conda does), so pip install tensorflow-gpu won’t work out of the box on most systems without a nvidia gpu.
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).
Recently, I wanted to move my Python libraries to a pendrive to keep all the libraries constant while switching between my workstation and laptop. (Also so that if I update one, it’s updated on other also.)
For this, I have installed a tensorflow-gpu version on my pendrive (my laptop doesn’t have a GPU). Everything works fine without a problem on both PC (it detects and uses my GPU without a problem) and laptop (it automatically uses my CPU).
That’s where my question lies. What is the difference between a
tensorflow-gpu
AND just
tensorflow
? (Because when no GPU is found, tensorflow-gpu automatically uses the CPU version.)
Does the difference lie only in the GPU support? Then why at all have a non GPU version of tensorflow?
Also, is it alright to proceed like this? Or should I create virtual environments to keep separate installations for CPU and GPU?
The closest answer I can find is
How to develop for tensor flow with gpu without a gpu.
But it only specifies that it’s completely okay to use tensorflow-gpu on a CPU platform, but it still does not answer my first question. Also, the answer might be outdated as tensorflow keeps releasing new updates.
I had installed the tensorflow-gpu version on my workstation with GTX 1070 (Thus a successful install).
Also I understand the difference is that pip install tensorflow-gpu will require CUDA enabled device to install, but my question is more towards the usage of the libraries because I am not getting any problems when using the tensorflow-gpu version on my laptop (with no GPU) and all my scripts run without any error.
(Also removed pip install from above to avoid confusion)
Also, isn’t running tensorflow-gpu on a system with no GPU the same as setting CUDA_VISIBLE_DEVICES=-1?
tensorflow-gpu requires cuda/cudnn. tensorflow does not. pip doesn’t install cuda for you (conda does), so pip install tensorflow-gpu won’t work out of the box on most systems without a nvidia gpu.
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).