Multi-GPU not working when using model(inputs) nor when computing the gradients

[miguelCalado]

Hi,

I've been using tf-keras-vis for a while and wanted to speed things up by using more than one GPU to compute the saliency maps of multiple images. I noticed that when using the interpretability methods (the ones that use tape.gradients and model(inputs)) only one GPU is being used.

I don't know if this is normal behavior, a bug on TensorFlow's part, or a problem on my end, but I decided to open an issue, maybe someone has tried it.

Here are a couple of useful links:

• Stackoverflow: I opened this issue on StackOverflow and added a bounty to it. You can find a list of things that I've tried and also more information on this issue.
• Working Example: A trained CNN on the dogs_vs_cats dataset. This replicates the problem of not being able to distribute the performance between the GPUs. You can also download the models in HDF5 and Saved formats.

[keisen]

@miguelCalado , Thank you for letting us know that and sharing links! They will be great helps for us.

Unfortunately, tf-keras-vis has never been tested on a machine that has multiple GPUs.
I don't know the cause of the problem you're facing.
If I get an environment where can be used multiple GPUs, I can tackle this problem.
However, it will take some time to do so.

Thanks!