Distributed Training with TensorFlow Java

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System information

• TensorFlow version (you are using): 2.X
• Are you willing to contribute it (Yes/No): Yes, when able and available

Describe the feature and the current behavior/state.
Tensorflow on Python has tf.distribute.Strategy API to distribute training across multiple GPUs or multiple machines.

Will this change the current api? How?
Yes, it will add a new awesome feature

Who will benefit with this feature?

• Anyone that requires to speed up training a DL model
• Anyone that requires to train a DL model with big data
• Anyone who wants to create or add Java support for APIs that leverages tf.distribute.Strategy such as TensorflowOnSpark, Spark Tensorflow Distributor or Horovod

Any Other info.
https://www.tensorflow.org/guide/distributed_training

Inizia leggendo la guida di TensorFlow sull'addestramento distribuito e confrontando la sua API tf.distribute.Strategy con i binding Java presenti in questo repository. Determina quali funzionalità di addestramento distribuito e quale superficie dell'API Java rientrano nell'ambito, quindi definisci test o esempi che dimostrino l'addestramento con più GPU o su più macchine prima dell'implementazione.