Re: Distributed Training in tensorflow
Hi, Ignite ML natively has a multilayer perceptron (see https://apacheignite.readme.io/docs/multilayer-perceptron), so you can use it to build a simple neural network. But, if we are talking about deep learning the answer will "no", Ignite ML provides it only via integration with TensorFlow. -- Sent from: http://apache-ignite-users.70518.x6.nabble.com/
Re: Distributed Training in tensorflow
i read your documentation. i have another question in my mind. you said that tensorflow itself support distributed learning and we can pure TensorFlow you'll have to start TensorFlow workers manually and distribute data manually as well. in you project you have considered tensorflow as distributed or pure tensorflow? On Wed, Jan 9, 2019 at 12:37 PM dmitrievanthony 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/ >
Re: Distributed Training in tensorflow
i have another question. is it possible to implement neural network algorithm on apache ignite directly? i think for example we implement RNN on ignite node and execute them? do you have seen this subject in ignite ML? On Fri, Jan 11, 2019 at 2:29 PM zaleslaw wrote: > Yes, I agree with your conclusion. I have no benchmarks, of course, but it > seems that no speedup in DL4j on Spark on Ignite RDD > > > > -- > Sent from: http://apache-ignite-users.70518.x6.nabble.com/ >
Re: Distributed Training in tensorflow
Yes, I agree with your conclusion. I have no benchmarks, of course, but it seems that no speedup in DL4j on Spark on Ignite RDD -- Sent from: http://apache-ignite-users.70518.x6.nabble.com/
Re: Distributed Training in tensorflow
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 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/ >
Re: Distributed Training in tensorflow
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/
Re: Distributed Training in tensorflow
Dear Mehdi Sey
First of all, we should have running Ignite cluster with a dataset loaded
into caches.
NOTE: This dataset could be reached via "from tensorflow.contrib.ignite
import IgniteDataset" in your Jupiter Notebook.
In the second, we shouldn't forget about tf.device("...") call
The whole documentation could be found here
Short answer: Yes, we must
--
Sent from: http://apache-ignite-users.70518.x6.nabble.com/
