tensorflow: training network on one GPU but occupy the space of the other

Question

I have two GPUs, and I just would like to use one GPU to train a network by tensorflow. When I train it, the code use all the memories of two GPUs, but only one GPU is working:

I do not know why and how to solve this problem.

do `export CUDA_VISIBLE_DEVICES=0` before running your script — Yaroslav Bulatov, Dec 14 '16 at 21:22

score 2 · Answer 1 · edited May 23 '17 at 12:13

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Try setting:

config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)

as in question.

edited May 23 '17 at 12:13

Community

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answered Dec 14 '16 at 19:50

sygi

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score 0 · Accepted Answer · edited May 23 '17 at 12:08

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By default tensorflow will occupy consume the memory of all available GPUs. You can either set allow growth as per @sygi's answer or make only one of the GPUs visible to tensorflow as per Yaroslav's comment. And as per this question.

edited May 23 '17 at 12:08

Community

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answered Dec 15 '16 at 04:22

Russell

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Finally, I use both ways. – karl_TUM Dec 15 '16 at 08:42

tensorflow: training network on one GPU but occupy the space of the other

2 Answers2