The tf distribute 101 guide explains how to scale Keras model training using TensorFlow's tf distribute API for environments with multiple GPUs on a single host or multi-worker configurations
In a single-server environment with multiple GPUs, MirroredStrategy can be used to replicate the model on each GPU, synchronizing gradients in real time, which improves performance and reduces training time
For distributed scenarios across multiple machines, MultiWorkerMirroredStrategy is recommended, which enables fault tolerance and dynamic task reordering, as well as efficient use of resources in public clouds
To optimize performance, it is advisable to adjust the batch size, choose a good optimizer, and measure the impact of asynchronous synchronization versus synchronous synchronization depending on the use case
In case of failures, tf distribute offers fast recovery mechanisms by saving checkpoints and automatically resuming training without data loss
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