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

Commencez par lire le guide de TensorFlow sur l’entraînement distribué et comparer son API tf.distribute.Strategy avec les bindings Java de ce dépôt. Déterminez quelles fonctionnalités d’entraînement distribué et quelle surface de l’API Java sont dans le périmètre, puis définissez des tests ou des exemples démontrant l’entraînement avec plusieurs GPU ou sur plusieurs machines avant l’implémentation.