TensorFlow 2.3 aims for program 'understanding', resource economy • DEVCLASS

A good two months after its last big release, the TensorFlow team has bestowed version 2.3 upon followers of the self-proclaimed machine learning framework for everyone. TensorFlow 2.3 seems to have put a special focus on understanding and reducing resource usage, with new mechanisms in the data library and fresh profiler tools being among the most highlighted additions. An experimental snapshot API in tf.data for example is meant to store the output of a preprocessing pipeline to disk, so that already processed data can be reused, saving CPU resources needed to compute them again in other steps. Moreover, the tf.data service aims to speed up the training process in cases where the attached host isn't able to "keep up with the data consumption of the model". If a model for example can process more images then the host can generate, the service can take over leveraging a cluster of workers to prepare the needed amount of training data.