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

Lesen Sie zunächst den Leitfaden zum verteilten Training von TensorFlow und vergleichen Sie dessen tf.distribute.Strategy API mit den Java-Bindings in diesem Repository. Ermitteln Sie, welche Funktionen für verteiltes Training und welcher Umfang der Java API im Rahmen liegen, und definieren Sie anschließend Tests oder Beispiele, die Training mit mehreren GPUs oder auf mehreren Maschinen vor der Implementierung demonstrieren.