Hello all,
@Gabor, we have discussed the idea of using the streaming API to write all of our ML algorithms with a couple of people offline, and I think it might be possible and is generally worth a shot. The approach we would take would be close to Vowpal Wabbit, not exactly "online", but rather "fast-batch". There will be problems popping up again, even for very simple algos like on line linear regression with SGD [1], but hopefully fixing those will be more aligned with the priorities of the community. @Katherin, my understanding is that given the limited resources, there is no development effort focused on batch processing right now. So to summarize, it seems like there are people willing to work on ML on Flink, but nobody is sure how to do it. There are many directions we could take (batch, online, batch on streaming), each with its own merits and downsides. If you want we can start a design doc and move the conversation there, come up with a roadmap and start implementing. Regards, Theodore [1] http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Understanding-connected-streams-use-without-timestamps-td10241.html On Tue, Feb 21, 2017 at 11:17 PM, Gábor Hermann <m...@gaborhermann.com> wrote: > It's great to see so much activity in this discussion :) > I'll try to add my thoughts. > > I think building a developer community (Till's 2. point) can be slightly > separated from what features we should aim for (1. point) and showcasing > (3. point). Thanks Till for bringing up the ideas for restructuring, I'm > sure we'll find a way to make the development process more dynamic. I'll > try to address the rest here. > > It's hard to choose directions between streaming and batch ML. As Theo has > indicated, not much online ML is used in production, but Flink concentrates > on streaming, so online ML would be a better fit for Flink. However, as > most of you argued, there's definite need for batch ML. But batch ML seems > hard to achieve because there are blocking issues with persisting, > iteration paths etc. So it's no good either way. > > I propose a seemingly crazy solution: what if we developed batch > algorithms also with the streaming API? The batch API would clearly seem > more suitable for ML algorithms, but there a lot of benefits of this > approach too, so it's clearly worth considering. Flink also has the high > level vision of "streaming for everything" that would clearly fit this > case. What do you all think about this? Do you think this solution would be > feasible? I would be happy to make a more elaborate proposal, but I push my > main ideas here: > > 1) Simplifying by using one system > It could simplify the work of both the users and the developers. One could > execute training once, or could execute it periodically e.g. by using > windows. Low-latency serving and training could be done in the same system. > We could implement incremental algorithms, without any side inputs for > combining online learning (or predictions) with batch learning. Of course, > all the logic describing these must be somehow implemented (e.g. > synchronizing predictions with training), but it should be easier to do so > in one system, than by combining e.g. the batch and streaming API. > > 2) Batch ML with the streaming API is not harder > Despite these benefits, it could seem harder to implement batch ML with > the streaming API, but in my opinion it's not. There are more flexible, > lower-level optimization potentials with the streaming API. Most > distributed ML algorithms use a lower-level model than the batch API > anyway, so sometimes it feels like forcing the algorithm logic into the > training API and tweaking it. Although we could not use the batch > primitives like join, we would have the E.g. in my experience with > implementing a distributed matrix factorization algorithm [1], I couldn't > do a simple optimization because of the limitations of the iteration API > [2]. Even if we pushed all the development effort to make the batch API > more suitable for ML there would be things we couldn't do. E.g. there are > approaches for updating a model iteratively without locks [3,4] (i.e. > somewhat asynchronously), and I don't see a clear way to implement such > algorithms with the batch API. > > 3) Streaming community (users and devs) benefit > The Flink streaming community in general would also benefit from this > direction. There are many features needed in the streaming API for ML to > work, but this is also true for the batch API. One really important is the > loops API (a.k.a. iterative DataStreams) [5]. There has been a lot of > effort (mostly from Paris) for making it mature enough [6]. Kate mentioned > using GPUs, and I'm sure they have uses in streaming generally [7]. Thus, > by improving the streaming API to allow ML algorithms, the streaming API > benefit too (which is important as they have a lot more production users > than the batch API). > > 4) Performance can be at least as good > I believe the same performance could be achieved with the streaming API as > with the batch API. Streaming API is much closer to the runtime than the > batch API. For corner-cases, with runtime-layer optimizations of batch API, > we could find a way to do the same (or similar) optimization for the > streaming API (see my previous point). Such case could be using managed > memory (and spilling to disk). There are also benefits by default, e.g. we > would have a finer grained fault tolerance with the streaming API. > > 5) We could keep batch ML API > For the shorter term, we should not throw away all the algorithms > implemented with the batch API. By pushing forward the development with > side inputs we could make them usable with streaming API. Then, if the > library gains some popularity, we could replace the algorithms in the batch > API with streaming ones, to avoid the performance costs of e.g. not being > able to persist. > > 6) General tools for implementing ML algorithms > Besides implementing algorithms one by one, we could give more general > tools for making it easier to implement algorithms. E.g. parameter server > [8,9]. Theo also mentioned in another thread that TensorFlow has a similar > model to Flink streaming, we could look into that too. I think often when > deploying a production ML system, much more configuration and tweaking > should be done than e.g. Spark MLlib allows. Why not allow that? > > 7) Showcasing > Showcasing this could be easier. We could say that we're doing batch ML > with a streaming API. That's interesting in its own. IMHO this integration > is also a more approachable way towards end-to-end ML. > > > Thanks for reading so far :) > > [1] https://github.com/apache/flink/pull/2819 > [2] https://issues.apache.org/jira/browse/FLINK-2396 > [3] https://people.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf > [4] https://www.usenix.org/system/files/conference/hotos13/hotos > 13-final77.pdf > [5] https://cwiki.apache.org/confluence/display/FLINK/FLIP-15+ > Scoped+Loops+and+Job+Termination > [6] https://github.com/apache/flink/pull/1668 > [7] http://lsds.doc.ic.ac.uk/sites/default/files/saber-sigmod16.pdf > [8] https://www.cs.cmu.edu/~muli/file/parameter_server_osdi14.pdf > [9] http://apache-flink-mailing-list-archive.1008284.n3.nabble. > com/Using-QueryableState-inside-Flink-jobs-and- > Parameter-Server-implementation-td15880.html > > Cheers, > Gabor >
