Thank you Theodore, for your reply. 1) Regarding GPU, your point is clear and I agree with it, ND4J looks appropriate. But, my current understanding is that, we also need to cover some resource management questions -> when we need to provide GPU support we also need to manage it like resource. For example, Mesos has already supported GPU like resource item: Initial support for GPU resources. <https://issues.apache.org/jira/browse/MESOS-4424?jql=text%20~%20GPU> Flink uses Mesos as cluster manager, and this means that this feature of Mesos could be reused. Also memory managing questions in Flink regarding GPU should be clarified.
2) Regarding integration with DL4J: what stops us to initialize ticket and start the discussion around this topic? We need some user story or the community is not sure that DL is really helpful? Why the discussion with Adam Gibson just finished with no implementation of any idea? What concerns do we have? пн, 6 февр. 2017 г. в 15:01, Theodore Vasiloudis < theodoros.vasilou...@gmail.com>: > Hell all, > > This is point that has come up in the past: Given the multitude of ML > libraries out there, should we have native implementations in FlinkML or > try to integrate other libraries instead? > > We haven't managed to reach a consensus on this before. My opinion is that > there is definitely value in having ML algorithms written natively in > Flink, both for performance optimization, > but more importantly for engineering simplicity, we don't want to force > users to use yet another piece of software to run their ML algos (at least > for a basic set of algorithms). > > We have in the past discussed integrations with DL4J (particularly ND4J) > with Adam Gibson, the core developer of the library, but we never got > around to implementing anything. > > Whether it makes sense to have an integration with DL4J as part of the > Flink distribution would be up for discussion. I would suggest to make it > an independent repo to allow for > faster dev/release cycles, and because it wouldn't be directly related to > the core of Flink so it would add extra reviewing burden to an already > overloaded group of committers. > > Natively supporting GPU calculations in Flink would be much better achieved > through a library like ND4J, the engineering burden would be too much > otherwise. > > Regards, > Theodore > > On Mon, Feb 6, 2017 at 11:26 AM, Katherin Eri <katherinm...@gmail.com> > wrote: > > > Hello, guys. > > > > Theodore, last week I started the review of the PR: > > https://github.com/apache/flink/pull/2735 related to *word2Vec for > Flink*. > > > > > > > > During this review I have asked myself: why do we need to implement such > a > > very popular algorithm like *word2vec one more time*, when there is > already > > available implementation in java provided by deeplearning4j.org > > <https://deeplearning4j.org/word2vec> library (DL4J -> Apache 2 > licence). > > This library tries to promote itself, there is a hype around it in ML > > sphere, and it was integrated with Apache Spark, to provide scalable > > deeplearning calculations. > > > > > > *That's why I thought: could we integrate with this library or not also > and > > Flink? * > > > > 1) Personally I think, providing support and deployment of > > *Deeplearning(DL) > > algorithms/models in Flink* is promising and attractive feature, because: > > > > a) during last two years DL proved its efficiency and these > algorithms > > used in many applications. For example *Spotify *uses DL based algorithms > > for music content extraction: Recommending music on Spotify with deep > > learning AUGUST 05, 2014 > > <http://benanne.github.io/2014/08/05/spotify-cnns.html> for their music > > recommendations. Developers need to scale up DL manually, that causes a > lot > > of work, so that’s why such platforms like Flink should support these > > models deployment. > > > > b) Here is presented the scope of Deeplearning usage cases > > <https://deeplearning4j.org/use_cases>, so many of this scenarios > related > > to scenarios, that could be supported on Flink. > > > > > > 2) But DL uncover such questions like: > > > > a) scale up calculations over machines > > > > b) perform these calculations both over CPU and GPU. GPU is required > to > > train big DL models, otherwise learning process could have very slow > > convergence. > > > > > > 3) I have checked this DL4J library, which already have reach support of > > many attractive DL models like: Recurrent Networks and LSTMs, > Convolutional > > Networks (CNN), Restricted Boltzmann Machines (RBM) and others. So we > won’t > > need to implement them independently, but only provide the ability of > > execution of this models over Flink cluster, the quite similar way like > it > > was integrated with Apache Spark. > > > > > > Because of all of this I propose: > > > > 1) To create new ticket in Flink’s JIRA for integration of Flink with > > DL4J and decide on which side this integration should be implemented. > > > > 2) Support natively GPU resources in Flink and allow calculations over > > them, like that is described in this publication > > https://www.oreilly.com/learning/accelerating-spark-workloads-using-gpus > > > > > > > > *Regarding original issue Implement Word2Vec > > <https://issues.apache.org/jira/browse/FLINK-2094>in Flink, *I have > > investigated its implementation in DL4J and that implementation of > > integration DL4J with Apache Spark, and got several points: > > > > It seems that idea of building of our own implementation of word2vec in > > Flink not such a bad solution, because: This DL4J was forced to > reimplement > > its original word2Vec over Spark. I have checked the integration of DL4J > > with Spark, and found that it is too strongly coupled with Spark API, so > > that it is impossible just to take some DL4J API and reuse it, instead we > > need to implement independent integration for Flink. > > > > *That’s why we simply finish implementation of current PR > > **independently **from > > integration to DL4J.* > > > > > > > > Could you please provide your opinion regarding my questions and points, > > what do you think about them? > > > > > > > > пн, 6 февр. 2017 г. в 12:51, Katherin Eri <katherinm...@gmail.com>: > > > > > Sorry, guys I need to finish this letter first. > > > Full version of it will come shortly. > > > > > > пн, 6 февр. 2017 г. в 12:49, Katherin Eri <katherinm...@gmail.com>: > > > > > > Hello, guys. > > > Theodore, last week I started the review of the PR: > > > https://github.com/apache/flink/pull/2735 related to *word2Vec for > > Flink*. > > > > > > During this review I have asked myself: why do we need to implement > such > > a > > > very popular algorithm like *word2vec one more time*, when there is > > > already availabe implementation in java provided by deeplearning4j.org > > > <https://deeplearning4j.org/word2vec> library (DL4J -> Apache 2 > > licence). > > > This library tries to promote it self, there is a hype around it in ML > > > sphere, and it was integrated with Apache Spark, to provide scalable > > > deeplearning calculations. > > > That's why I thought: could we integrate with this library or not also > > and > > > Flink? > > > 1) Personally I think, providing support and deployment of Deeplearning > > > algorithms/models in Flink is promising and attractive feature, > because: > > > a) during last two years deeplearning proved its efficiency and > this > > > algorithms used in many applications. For example *Spotify *uses DL > based > > > algorithms for music content extraction: Recommending music on Spotify > > > with deep learning AUGUST 05, 2014 > > > <http://benanne.github.io/2014/08/05/spotify-cnns.html> for their > music > > > recommendations. Doing this natively scalable is very attractive. > > > > > > > > > I have investigated that implementation of integration DL4J with Apache > > > Spark, and got several points: > > > > > > 1) It seems that idea of building of our own implementation of word2vec > > > not such a bad solution, because the integration of DL4J with Spark is > > too > > > strongly coupled with Saprk API and it will take time from the side of > > DL4J > > > to adopt this integration to Flink. Also I have expected that we will > be > > > able to call just some API, it is not such thing. > > > 2) > > > > > > https://deeplearning4j.org/use_cases > > > https://www.analyticsvidhya.com/blog/2017/01/t-sne- > > implementation-r-python/ > > > > > > > > > чт, 19 янв. 2017 г. в 13:29, Till Rohrmann <trohrm...@apache.org>: > > > > > > Hi Katherin, > > > > > > welcome to the Flink community. Always great to see new people joining > > the > > > community :-) > > > > > > Cheers, > > > Till > > > > > > On Tue, Jan 17, 2017 at 1:02 PM, Katherin Sotenko < > > katherinm...@gmail.com> > > > wrote: > > > > > > > ok, I've got it. > > > > I will take a look at https://github.com/apache/flink/pull/2735. > > > > > > > > вт, 17 янв. 2017 г. в 14:36, Theodore Vasiloudis < > > > > theodoros.vasilou...@gmail.com>: > > > > > > > > > Hello Katherin, > > > > > > > > > > Welcome to the Flink community! > > > > > > > > > > The ML component definitely needs a lot of work you are correct, we > > are > > > > > facing similar problems to CEP, which we'll hopefully resolve with > > the > > > > > restructuring Stephan has mentioned in that thread. > > > > > > > > > > If you'd like to help out with PRs we have many open, one I have > > > started > > > > > reviewing but got side-tracked is the Word2Vec one [1]. > > > > > > > > > > Best, > > > > > Theodore > > > > > > > > > > [1] https://github.com/apache/flink/pull/2735 > > > > > > > > > > On Tue, Jan 17, 2017 at 12:17 PM, Fabian Hueske <fhue...@gmail.com > > > > > > wrote: > > > > > > > > > > > Hi Katherin, > > > > > > > > > > > > welcome to the Flink community! > > > > > > Help with reviewing PRs is always very welcome and a great way to > > > > > > contribute. > > > > > > > > > > > > Best, Fabian > > > > > > > > > > > > > > > > > > > > > > > > 2017-01-17 11:17 GMT+01:00 Katherin Sotenko < > > katherinm...@gmail.com > > > >: > > > > > > > > > > > > > Thank you, Timo. > > > > > > > I have started the analysis of the topic. > > > > > > > And if it necessary, I will try to perform the review of other > > > pulls) > > > > > > > > > > > > > > > > > > > > > вт, 17 янв. 2017 г. в 13:09, Timo Walther <twal...@apache.org > >: > > > > > > > > > > > > > > > Hi Katherin, > > > > > > > > > > > > > > > > great to hear that you would like to contribute! Welcome! > > > > > > > > > > > > > > > > I gave you contributor permissions. You can now assign issues > > to > > > > > > > > yourself. I assigned FLINK-1750 to you. > > > > > > > > Right now there are many open ML pull requests, you are very > > > > welcome > > > > > to > > > > > > > > review the code of others, too. > > > > > > > > > > > > > > > > Timo > > > > > > > > > > > > > > > > > > > > > > > > Am 17/01/17 um 10:39 schrieb Katherin Sotenko: > > > > > > > > > Hello, All! > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > I'm Kate Eri, I'm java developer with 6-year enterprise > > > > experience, > > > > > > > also > > > > > > > > I > > > > > > > > > have some expertise with scala (half of the year). > > > > > > > > > > > > > > > > > > Last 2 years I have participated in several BigData > projects > > > that > > > > > > were > > > > > > > > > related to Machine Learning (Time series analysis, > > Recommender > > > > > > systems, > > > > > > > > > Social networking) and ETL. I have experience with Hadoop, > > > Apache > > > > > > Spark > > > > > > > > and > > > > > > > > > Hive. > > > > > > > > > > > > > > > > > > > > > > > > > > > I’m fond of ML topic, and I see that Flink project requires > > > some > > > > > work > > > > > > > in > > > > > > > > > this area, so that’s why I would like to join Flink and ask > > me > > > to > > > > > > grant > > > > > > > > the > > > > > > > > > assignment of the ticket > > > > > > > > https://issues.apache.org/jira/browse/FLINK-1750 > > > > > > > > > to me. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
