The TensorFlow Dataset API provides various facilities for creating scalable input pipelines for TensorFlow models, including:

• Reading data from a variety of formats including CSV files and TFRecords files (the standard binary format for TensorFlow training data).

• Transforming datasets in a variety of ways including mapping arbitrary functions against them.

• Shuffling, batching, and repeating datasets over a number of epochs.

• Streaming interface to data for reading arbitrarily large datasets.

• Reading and transforming data are TensorFlow graph operations, so are executed in C++ and in parallel with model training.

The R interface to TensorFlow datasets provides access to the Dataset API, including high-level convenience functions for easy integration with the keras package.

For documentation on using tfdatasets, see the package website at https://tensorflow.rstudio.com/tools/tfdatasets/.