Google open-sources TensorFlow training tools

From InfoWorld: 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.

T2T comes with defaults for each element, which is what’s most immediately useful about it. Several common models and datasets come baked into T2T, so you can quickly get started by reusing or expanding on an existing mode and deploy one of the defaults and tinker with it as needed.

What T2T doesn’t do is provide a larger context beyond TensorFlow for how to organize a deep learning project. Theoretically, it could become part of an end-to-end, data-to-prediction system for building machine learning solutions, but right now it simply makes the job of using TensorFlow easier—and that is absolutely worth having.

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