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[D] Why is Tensorflow so slow (compared to FFM)?

I have ~500GB of extremely sparse, tabular data with millions of features and a single binary target. I have been training FFM models with https://github.com/cttsai1985/libffm and they take around 6 hours to train. This model creates millions of cross-features on top of the millions of original features and trains parameters for all of them.

As complex of a model as is it, that FFM model only takes 6 hours to train and converge after ~10 epochs on 500GB data on a single machine with ~ 8 cores. On the other hand, training an extremely simple tensorflow model on this dataset (embedding layer -> sigmoid output neuron) takes almost 2 days for a single epoch on this dataset. I’ve optimized everywhere I can think of, using tf.data api, etc, it seems like tensorflow is just really slow compared to FFM. What’s the deal?

submitted by /u/DstnB3
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.