Yes, in TensorFlow on Apache Ignite we support distributed learning as you described it (please see details in this documentation <https://apacheignite.readme.io/docs/ignite-dataset> ).
Speaking about performance, TensorFlow supports distributed learning itself (please see details here <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/distribute> ). But to start distributed learning in pure TensorFlow you need to setup cluster manually, manually distribute training data between cluster nodes and handle node failures. In TensorFlow on Apache Ignite we do it for you automatically. Apache Ignite plays cluster manager role, it starts and maintains TensorFlow cluster with optimal configuration and handles node failures. At the same time, the training is fully performed by TensorFlow anyway. So, the training performance is absolutely equal to the case when you use pure TensorFlow with proper manually configured and started TensorFlow cluster because we don't participate in the training process when the cluster is running properly. -- Sent from: http://apache-ignite-users.70518.x6.nabble.com/
