[FIXED] Difference between installation libraries of Tensorflow GPU vs CPU

Issue

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 Also, the answer might be outdated as tensorflow keeps releasing new updates.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?

Solution

Updated answer 2023 (Tensorflow 2.x and above:)

Verify the CPU setup:

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([100, 100])))"

If a tensor is returned, you've installed TensorFlow successfully.

Verify the GPU setup:

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

If a list of GPU devices is returned, you've installed TensorFlow successfully.

Source: Tensorflow installation guide

Old answer(Tensorflow 1.x):

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.

Answered By - burglarhobbit