Over the past year, Google’s TensorFlow has asserted itself as a popular open source toolkit for deep learning.

But training a TensorFlow model can be cumbersome and slow—especially when the mission is to take a dataset used by someone else and try to refine the training process it uses.

The sheer number of moving parts and variations in any model-training process is enough to make even deep-learning experts take a deep breath.This week, Google open-sourced a project intended to cut down on the amount of work in configuring a deep learning model for training.

Tensor2Tensor, or T2T for short, is a Python-powered workflow organization library for TensorFlow training jobs.
It lets developers specify the key elements used in a TensorFlow model and define the relationships among them.To read this article in full or to leave a comment, please click here

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