Sorry for chiming in late. GPUs on Flink. Till raised a good point- you need to be able to fall back to non-GPU resources if they aren't available.
Fun fact: this has already been developed for Flink vis-a-vis the Apache Mahout project. In short- Mahout exposes a number of tensor functions (vector %*% matrix, matrix %*% matrix, etc). If compiled for GPU support, those operations are completed via GPU- and if no GPUs are in fact available, Mahout math falls back to CPUs (and finally back to the JVM). How this should work is Flink takes care of shipping data around the cluster, and when data arrives at the local node- is dumped out to GPU for calculation, loaded back up and shipped back around cluster. In practice, the lack of a persist method for intermediate results makes this troublesome (not because of GPUs but for calculating any sort of complex algorithm we expect to be able to cache intermediate results). +1 to FLINK-1730 Everything in Mahout is modular- distributed engine (Flink/Spark/Write-your-own), Native Solvers (OpenMP / ViennaCL / CUDA / Write-your-own), algorithms, etc. So to sum up, you're noting the redundancy between ML packages in terms of algorithms- I would recommend checking out Mahout before rolling your own GPU integration (else risk redundantly integrating GPUs). If nothing else- it should give you some valuable insight regarding design considerations. Also FYI the goal of the Apache Mahout project is to address that problem precisely- implement an algorithm once in a mathematically expressive DSL, which is abstracted above the engine so the same code easily ports between engines / native solvers (i.e. CPU/GPU). https://github.com/apache/mahout/tree/master/viennacl-omp https://github.com/apache/mahout/tree/master/viennacl Best, tg Trevor Grant Data Scientist https://github.com/rawkintrevo http://stackexchange.com/users/3002022/rawkintrevo http://trevorgrant.org *"Fortunate is he, who is able to know the causes of things." -Virgil* On Fri, Feb 10, 2017 at 7:01 AM, Katherin Eri <katherinm...@gmail.com> wrote: > Thank you Felix, for provided information. > > Currently I analyze the provided integration of Flink with SystemML. > > And also gather the information for the ticket FLINK-1730 > <https://issues.apache.org/jira/browse/FLINK-1730>, maybe we will take it > to work, to unlock SystemML/Flink integration. > > > > чт, 9 февр. 2017 г. в 0:17, Felix Neutatz <neut...@googlemail.com. > invalid>: > > > Hi Kate, > > > > 1) - Broadcast: > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP- > 5%3A+Only+send+data+to+each+taskmanager+once+for+broadcasts > > - Caching: https://issues.apache.org/jira/browse/FLINK-1730 > > > > 2) I have no idea about the GPU implementation. The SystemML mailing list > > will probably help you out their. > > > > Best regards, > > Felix > > > > 2017-02-08 14:33 GMT+01:00 Katherin Eri <katherinm...@gmail.com>: > > > > > Thank you Felix, for your point, it is quite interesting. > > > > > > I will take a look at the code, of the provided Flink integration. > > > > > > 1) You have these problems with Flink: >>we realized that the lack > of > > a > > > caching operator and a broadcast issue highly effects the performance, > > have > > > you already asked about this the community? In case yes: please provide > > the > > > reference to the ticket or the topic of letter. > > > > > > 2) You have said, that SystemML provides GPU support. I have seen > > > SystemML’s source code and would like to ask: why you have decided to > > > implement your own integration with cuda? Did you try to consider ND4J, > > or > > > because it is younger, you support your own implementation? > > > > > > вт, 7 февр. 2017 г. в 18:35, Felix Neutatz <neut...@googlemail.com>: > > > > > > > Hi Katherin, > > > > > > > > we are also working in a similar direction. We implemented a > prototype > > to > > > > integrate with SystemML: > > > > https://github.com/apache/incubator-systemml/pull/119 > > > > SystemML provides many different matrix formats, operations, GPU > > support > > > > and a couple of DL algorithms. Unfortunately, we realized that the > lack > > > of > > > > a caching operator and a broadcast issue highly effects the > performance > > > > (e.g. compared to Spark). At the moment I am trying to tackle the > > > broadcast > > > > issue. But caching is still a problem for us. > > > > > > > > Best regards, > > > > Felix > > > > > > > > 2017-02-07 16:22 GMT+01:00 Katherin Eri <katherinm...@gmail.com>: > > > > > > > > > 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. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
