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I am training a neural net with a DataFeeder which is a bit slow (because it reads non-contiguous data from a h5 file); so the GPU satays idle (GPU-Util is at 0 %) half of the time.

Is there a way, in either TensorFlow or skflow, to have multiple DataFeeders running in parallel, to avoid this bottleneck?

P. Camilleri
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1 Answers1

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Tensorflow has reader library that can in parallel (and in C++) read and queue data. This should remove bottleneck you are talking about.

We are currently (this/next week) adding it's support to tf.learn (new name for skflow) to make it easy to use. You will still need to convert your data into one of the formats readers support (fixed len vectors, Example proto).

If you want to try make it work yourself - you can create a separate DataFeeder, that would use the ops from reader library in input_builder function and return no-op in the get_feed_dict_fn.

ilblackdragon
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