I’m really new to tensorflow and just found something unusual when instantiating a keras metric object as follows.
import tensorflow as tf m = tf.keras.metrics.Mean(name='test')
Once executing two lines above in python, GPU memory consumption soars from 0% to around 95% (about 10GiB) in a moment. And it never goes down until I terminate the program or delete the instance. I checked it on
nvtop gpu monitor.
My machine is Ubuntu server with eight RTX2080Ti GPUs equipped. Plus, I’m using docker image provided by the Nvidia NGC (specifically, nvcr.io/nvidia/tensorflow:20.03-tf2-py3)
I observed the same issue on TitanXp machine. And another docker image (nvcr.io/nvidia/tensorflow:20.01-tf2-py3) showed the same issue.
Do you guys get the same issue? Is it a bug of tensorflow or the docker image?