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.