Thank you, Till. 1) Regarding ND4J, I didn’t know about such a pity and critical restriction of it -> lack of sparsity optimizations, and you are right: this issue is still actual for them. I saw that Flink uses Breeze, but I thought its usage caused by some historical reasons.
2) Regarding integration with DL4J, I have read the source code of DL4J/Spark integration, that’s why I have declined my idea of reuse of their word2vec implementation for now, for example. I can perform deeper investigation of this topic, if it required. So I feel that we have the following picture: 1) DL integration investigation, could be part of Apache Bahir. I can perform futher investigation of this topic, but I thik we need some separated ticket for this to track this activity. 2) GPU support, required for DL is interesting, but requires ND4J for example. 3) ND4J couldn’t be incorporated because it doesn’t support sparsity <https://deeplearning4j.org/roadmap.html> [1]. Regarding ND4J is this the single blocker for incorporation of it or may be some others known? [1] https://deeplearning4j.org/roadmap.html вт, 7 февр. 2017 г. в 16:26, Till Rohrmann <trohrm...@apache.org>: Thanks for initiating this discussion Katherin. I think you're right that in general it does not make sense to reinvent the wheel over and over again. Especially if you only have limited resources at hand. So if we could integrate Flink with some existing library that would be great. In the past, however, we couldn't find a good library which provided enough freedom to integrate it with Flink. Especially if you want to have distributed and somewhat high-performance implementations of ML algorithms you would have to take Flink's execution model (capabilities as well as limitations) into account. That is mainly the reason why we started implementing some of the algorithms "natively" on Flink. If I remember correctly, then the problem with ND4J was and still is that it does not support sparse matrices which was a requirement from our side. As far as I know, it is quite common that you have sparse data structures when dealing with large scale problems. That's why we built our own abstraction which can have different implementations. Currently, the default implementation uses Breeze. I think the support for GPU based operations and the actual resource management are two orthogonal things. The implementation would have to work with no GPUs available anyway. If the system detects that GPUs are available, then ideally it would exploit them. Thus, we could add this feature later and maybe integrate it with FLINK-5131 [1]. Concerning the integration with DL4J I think that Theo's proposal to do it in a separate repository (maybe as part of Apache Bahir) is a good idea. We're currently thinking about outsourcing some of Flink's libraries into sub projects. This could also be an option for the DL4J integration then. In general I think it should be feasible to run DL4J on Flink given that it also runs on Spark. Have you already looked at it closer? [1] https://issues.apache.org/jira/browse/FLINK-5131 Cheers, Till On Tue, Feb 7, 2017 at 11:47 AM, Katherin Eri <katherinm...@gmail.com> wrote: > 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. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
