“Watching paint dry is faster than training my deep learning model.”

“If only I had ten more GPUs, I could train my model in time.” “I want to run my model on a cheap smartphone, but it’s probably too heavy and slow.”

If this sounds like you, then you might like this talk.

Exploring the landscape of training and inference, we cover a myriad of tricks that step-by-step improve the efficiency of most deep learning pipelines, reduce wasted hardware cycles, and make them cost-effective. We identify and fix inefficiencies across different parts of the pipeline, including data preparation, reading and augmentation, training, and inference.

With a data-driven approach and easy-to-replicate TensorFlow examples, finely tune the knobs of your deep learning pipeline to get the best out of your hardware.