How to add report_tensor_allocations_upon_oom to RunOptions in Keras

python tensorflow keras gpu
dspeyer picture dspeyer · Apr 5, 2018 · Viewed 15.9k times · Source

I'm trying to train a neural net on a GPU using Keras and am getting a "Resource exhausted: OOM when allocating tensor" error. The specific tensor it's trying to allocate isn't very big, so I assume some previous tensor consumed almost all the VRAM. The error message comes with a hint that suggests this:

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

That sounds good, but how do I do it? RunOptions appears to be a Tensorflow thing, and what little documentation I can find for it associates it with a "session". I'm using Keras, so Tensorflow is hidden under a layer of abstraction and its sessions under another layer below that.

How do I dig underneath everything to set this option in such a way that it will take effect?

Answer

Dr. Snoopy picture Dr. Snoopy · Apr 5, 2018

Its not as hard as it seems, what you need to know is that according to the documentation, the **kwargs parameter passed to model.compile will be passed to session.run

So you can do something like:

import tensorflow as tf
run_opts = tf.RunOptions(report_tensor_allocations_upon_oom = True)

model.compile(loss = "...", optimizer = "...", metrics = "..", options = run_opts)

And it should be passed directly each time session.run is called.

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