TensorFlow 2.3 has been released! The focus of this release is on new tools to make it easier for you to load and preprocess data, and to solve input-pipeline bottlenecks, whether you’re working on one machine, or many.
tf.data adds two mechanisms to solve input pipeline bottlenecks and improve resource utilization. For advanced users, the new service API provides a way to improve training speed when the host attached to a training device can’t keep up with the data consumption needs of your model. It allows you to offload input preprocessing to a CPU cluster of data-processing workers that run alongside your training job, increasing accelerator utilization. A second new feature is the tf.data snapshot API, which allows you to persist the output of your input preprocessing pipeline to disk, so you can reuse it on a different training run. This enables you to trade storage space to free up additional CPU time…
