Let me also add that it depends on what you want to achieve. TensorFlow supports distributed training and it does it on it's own. But if you use pure TensorFlow you'll have to start TensorFlow workers manually and distribute data manually as well. And you can do it, I mean start workers manually on the nodes Ignite cluster occupies or even some other nodes. It will work and perhaps work well in some cases and work very well in case of accurate manual setup.
At the same time, Apache Ignite provides a cluster management functionality for TensorFlow that allows to start workers automatically on the same nodes Apache Ignite keeps the data. From our perspective it's the most efficient way to setup TensorFlow cluster on top of Apache Ignite cluster because it allows to reduce data transfers. You can find more details about this in readme: https://apacheignite.readme.io/docs/ignite-dataset and https://apacheignite.readme.io/docs/tf-command-line-tool. -- Sent from: http://apache-ignite-users.70518.x6.nabble.com/
