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I am training convnets with Tensorflow and skflow, on an EC2 instance I share with other people. For all of us to be able to work at the same time, I'd like to limit the fraction of available GPU memory which is allocated.

This question does it with Tensorflow, but since I'm using sklfow I'm never using a tf.Session().

Is it possible to do the same thing through skflow ?

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P. Camilleri
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1 Answers1

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At this moment, you can only control the number of cores (num_cores) to be used in estimators by passing this parameter to estimator.

One can add gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) to tf.ConfigProto as suggested by this question you linked to achieve what you need.

Feel free to submit a PR to make changes here as well as adding this additional parameters to all estimators. Otherwise, I'll make the changes some time this week.

Edit:

I have made the changes to allow those options. Please check "Building A Model Using Different GPU Configurations" example in examples folder. Let me know if there's any particular need or other options you want to add. Pull requests are always welcomed!

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Yuan Tang
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