Thanks Simone for pointing this out. On the Apache Mahout project we have distributed linear algebra with R-like semantics that can be executed on Spark/Flink/H2O.
@Kam: the document u point out is old and outdated, the most up-to-date reference to the Samsara api is the book - 'Apache Mahout: Beyond MapReduce". (shameless marketing here on behalf of fellow committers :) ) We added Flink DataSet API in the recent Mahout 0.12.0 release (April 11, 2016) and has been called out in my talk at ApacheBigData in Vancouver last week. The Mahout community would definitely be interested in being involved with this and sharing notes. IMHO, the focus should be first on building a good linalg foundations before embarking on building algos and pipelines. Adding @dlyubimov to this. ---------- Forwarded message ---------- From: Simone Robutti <simone.robu...@radicalbit.io> Date: Tue, May 17, 2016 at 9:48 AM Subject: Fwd: machine learning API, common models To: Suneel Marthi <smar...@apache.org> ---------- Forwarded message ---------- From: Kavulya, Soila P <soila.p.kavu...@intel.com> Date: 2016-05-17 1:53 GMT+02:00 Subject: RE: machine learning API, common models To: "dev@beam.incubator.apache.org" <dev@beam.incubator.apache.org> Thanks Simone, You have raised a valid concern about how different frameworks will have different implementations and parameter semantics for the same algorithm. I agree that it is important to keep this in mind. Hopefully, through this exercise, we will identify a good set of common ML abstractions across different frameworks. Feel free to edit the document. We had limited the first pass of the comparison matrix to the machine learning pipeline APIs, but we can extend it to include other ML building blocks like linear algebra operations, and APIs for optimizers like gradient descent. Soila -----Original Message----- From: Kam Kasravi [mailto:kamkasr...@gmail.com] Sent: Monday, May 16, 2016 8:22 AM To: dev@beam.incubator.apache.org Subject: Re: machine learning API, common models Thanks Simone - yes I had read your concerns on dev and I think they're well founded. Thanks for the samsura reference - I've been looking at the spark/scala bindings http://mahout.apache.org/users/sparkbindings/ScalaSparkBindings.pdf . I think we should expand the document to include linear algebraic ops or least pay due diligence to it. If you're doing anything on the flink side in this regard let us or feel free to suggest edits/updates to the document. Thanks Kam On Mon, May 16, 2016 at 6:05 AM, Simone Robutti < simone.robu...@radicalbit.io> wrote: > Hello, > > I'm Simone and I just began contributing to Flink ML (actually on the > distributed linalg part). I already expressed my concerns about the > idea of an high level API relying on specific frameworks' implementations: > different implementations produce different results and may vary in > quality. Also the semantics of parameters may change from one > implementation to the other. This could hinder portability and > transparency. I believe these problems could be handled paying the due > attention to the details of every single implementation but I invite > you not to underestimate these problems. > > On the other hand the API in itself looks good to me. From my side, I > hope to fill some of the gaps in Flink you underlined in the comparison matrix. > > Talking about matrices, proper matrices this time, I believe it would > be useful to include in this API support for linear algebra operations. > Something similar is already present in Mahout's Samsara and it looks > really good but clearly a similar implementation on Beam would be way > more interesting and powerful. > > My 2 cents, > > Simone > > > 2016-05-14 4:53 GMT+02:00 Kavulya, Soila P <soila.p.kavu...@intel.com>: > > > Hi Tyler, > > > > Thank you so much for your feedback. I agree that starting with the > > high-level API is a good direction. We are interested in Python > > because > it > > is the language that our data scientists are most familiar with. I > > think starting with Java would be the best approach, because the > > Python API can be a thin wrapper for Java API. > > > > In Spark, the Scala, Java and Python APIs are identical. Flink does > > not have a Python API for ML pipelines at present. > > > > Could you point me to the updated runner API? > > > > Soila > > > > -----Original Message----- > > From: Tyler Akidau [mailto:taki...@google.com.INVALID] > > Sent: Friday, May 13, 2016 6:34 PM > > To: dev@beam.incubator.apache.org > > Subject: Re: machine learning API, common models > > > > Hi Kam & Soila, > > > > Thanks a lot for writing this up. I ran the doc past some of the > > folks who've been doing ML work here at Google, and they were > > generally happy with the distillation of common methods in the doc. > > I'd be curious to > hear > > what folks on the Flink- and Spark- runner sides think. > > > > To me, this seems like a good direction for a high-level API. > > Presumably, once a high-level API is in place, we could begin > > looking at what it > would > > take to add lower-level ML algorithm support (e.g. iterative) to the > > Beam Model. Is this essentially what you're thinking? > > > > Some more specific questions/comments: > > > > - Presumably you'd want to tackle this in Java first, since that's the > > only language we currently support? Given that half of your > > examples are in > > Python, I'm also assuming Python will be interesting once it's > > available. > > > > - Along those lines, what languages are represented in the capability > > matrix? E.g. is Spark ML support as detailed there identical across > > Java/Scala and Python? > > > > - Have you thought about how this would tie in at the runner level, > > particularly given the updated Runner API changes that are coming? I'm > > assuming they'd be provided as composite transforms that (for > > now) > would > > have no default implementation, given the lack of low-level > > primitives for > > ML algorithms, but am curious what your thoughts are there. > > > > - I still don't fully understand how incremental updates due to model > > drift would tie in at the API level. There's a comment thread in > > the > doc > > still open tracking this, so no need to comment here additionally. > Just > > pointing it out as one of the things that stands out as > > potentially having > > API-level impacts to me that doesn't seem 100% fleshed out in the > > doc yet > > (thought that admittedly may just be my limited understanding at > > this point > > :-). > > > > -Tyler > > > > > > > > > > On Fri, May 13, 2016 at 10:48 AM Kam Kasravi <kamkasr...@gmail.com> > wrote: > > > > > Hi Tyler - my bad. Comments should be enabled now. > > > > > > On Fri, May 13, 2016 at 10:45 AM, Tyler Akidau > > > <taki...@google.com.invalid > > > > > > > wrote: > > > > > > > Thanks a lot, Kam. Can you please enable comment access on the doc? > > > > I > > > seem > > > > to have view access only. > > > > > > > > -Tyler > > > > > > > > On Fri, May 13, 2016 at 9:54 AM Kam Kasravi > > > > <kamkasr...@gmail.com> > > > wrote: > > > > > > > > > Hi > > > > > > > > > > A number of readers have made comments on this topic recently. > > > > > We have created a document that does some analysis of common > > > > > ML models and > > > > related > > > > > APIs. We hope this can drive an approach that will result in > > > > > an API, compatibility matrix and involvement from the same > > > > > groups that are implementing transformation runners (spark, flink, etc). > > > > > We welcome comments here or in the document itself. > > > > > > > > > > > > > > > > > > > > > > https://docs.google.com/document/d/17cRZk_yqHm3C0fljivjN66MbLkeKS1 > > > yjo4 > > > PBECHb-xA/edit?usp=sharing > > > > > > > > > > > > > > >
