How to prevent tensorflow from allocating the totality of a GPU memory?

Question

I work in an environment in which computational resources are shared, i.e., we have a few server machines equipped with a few Nvidia Titan X GPUs each.

For small to m

Answer 1

Well I am new to tensorflow, I have Geforce 740m or something GPU with 2GB ram, I was running mnist handwritten kind of example for a native language with training data containing of 38700 images and 4300 testing images and was trying to get precision , recall , F1 using following code as sklearn was not giving me precise reults. once i added this to my existing code i started getting GPU errors.

TP = tf.count_nonzero(predicted * actual)
TN = tf.count_nonzero((predicted - 1) * (actual - 1))
FP = tf.count_nonzero(predicted * (actual - 1))
FN = tf.count_nonzero((predicted - 1) * actual)

prec = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * prec * recall / (prec + recall)

plus my model was heavy i guess, i was getting memory error after 147, 148 epochs, and then I thought why not create functions for the tasks so I dont know if it works this way in tensrorflow, but I thought if a local variable is used and when out of scope it may release memory and i defined the above elements for training and testing in modules, I was able to achieve 10000 epochs without any issues, I hope this will help..