Yes you are write. I have many debate about this. I have an idea that if we have dl4j ( running over spark) what is the matter of doing run dl4j over ignite. previously i have this idea but after googling and share with you i think this is a waste time. Spark itself is in memory computing platform also ignite is. In distributed deep learning with are going to speed up learning via distribute model learning. Dl4j is a distributed deep learning data model and i think with integrating it with ignite we have no more speed up. It was in my opinion we can use igniterdd for speed up but i underestand that in deep learning we rarely shared data for using igniterdd. Do you agree with my interpretation?do you have any comment?
On Wednesday, January 9, 2019, dmitrievanthony <dmitrievanth...@gmail.com> wrote: > 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/ >
