We added SPARK-3066 for this. In 1.4 you should get the code to do BLAS dgemm based calculation.
On Thu, Jun 18, 2015 at 8:20 AM, Ayman Farahat < ayman.fara...@yahoo.com.invalid> wrote: > Thanks Sabarish and Nick > Would you happen to have some code snippets that you can share. > Best > Ayman > > On Jun 17, 2015, at 10:35 PM, Sabarish Sasidharan < > sabarish.sasidha...@manthan.com> wrote: > > Nick is right. I too have implemented this way and it works just fine. In > my case, there can be even more products. You simply broadcast blocks of > products to userFeatures.mapPartitions() and BLAS multiply in there to get > recommendations. In my case 10K products form one block. Note that you > would then have to union your recommendations. And if there lots of product > blocks, you might also want to checkpoint once every few times. > > Regards > Sab > > On Thu, Jun 18, 2015 at 10:43 AM, Nick Pentreath <nick.pentre...@gmail.com > > wrote: > >> One issue is that you broadcast the product vectors and then do a dot >> product one-by-one with the user vector. >> >> You should try forming a matrix of the item vectors and doing the dot >> product as a matrix-vector multiply which will make things a lot faster. >> >> Another optimisation that is avalailable on 1.4 is a recommendProducts >> method that blockifies the factors to make use of level 3 BLAS (ie >> matrix-matrix multiply). I am not sure if this is available in The Python >> api yet. >> >> But you can do a version yourself by using mapPartitions over user >> factors, blocking the factors into sub-matrices and doing matrix multiply >> with item factor matrix to get scores on a block-by-block basis. >> >> Also as Ilya says more parallelism can help. I don't think it's so >> necessary to do LSH with 30,000 items. >> >> — >> Sent from Mailbox <https://www.dropbox.com/mailbox> >> >> >> On Thu, Jun 18, 2015 at 6:01 AM, Ganelin, Ilya < >> ilya.gane...@capitalone.com> wrote: >> >>> Actually talk about this exact thing in a blog post here >>> http://blog.cloudera.com/blog/2015/05/working-with-apache-spark-or-how-i-learned-to-stop-worrying-and-love-the-shuffle/. >>> Keep in mind, you're actually doing a ton of math. Even with proper caching >>> and use of broadcast variables this will take a while defending on the size >>> of your cluster. To get real results you may want to look into locality >>> sensitive hashing to limit your search space and definitely look into >>> spinning up multiple threads to process your product features in parallel >>> to increase resource utilization on the cluster. >>> >>> >>> >>> Thank you, >>> Ilya Ganelin >>> >>> >>> >>> -----Original Message----- >>> *From: *afarahat [ayman.fara...@yahoo.com] >>> *Sent: *Wednesday, June 17, 2015 11:16 PM Eastern Standard Time >>> *To: *user@spark.apache.org >>> *Subject: *Matrix Multiplication and mllib.recommendation >>> >>> Hello; >>> I am trying to get predictions after running the ALS model. >>> The model works fine. In the prediction/recommendation , I have about 30 >>> ,000 products and 90 Millions users. >>> When i try the predict all it fails. >>> I have been trying to formulate the problem as a Matrix multiplication >>> where >>> I first get the product features, broadcast them and then do a dot >>> product. >>> Its still very slow. Any reason why >>> here is a sample code >>> >>> def doMultiply(x): >>> a = [] >>> #multiply by >>> mylen = len(pf.value) >>> for i in range(mylen) : >>> myprod = numpy.dot(x,pf.value[i][1]) >>> a.append(myprod) >>> return a >>> >>> >>> myModel = MatrixFactorizationModel.load(sc, "FlurryModelPath") >>> #I need to select which products to broadcast but lets try all >>> m1 = myModel.productFeatures().sample(False, 0.001) >>> pf = sc.broadcast(m1.collect()) >>> uf = myModel.userFeatures() >>> f1 = uf.map(lambda x : (x[0], doMultiply(x[1]))) >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/Matrix-Multiplication-and-mllib-recommendation-tp23384.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> For additional commands, e-mail: user-h...@spark.apache.org >>> >>> >>> ------------------------------ >>> The information contained in this e-mail is confidential and/or >>> proprietary to Capital One and/or its affiliates and may only be used >>> solely in performance of work or services for Capital One. The information >>> transmitted herewith is intended only for use by the individual or entity >>> to which it is addressed. If the reader of this message is not the intended >>> recipient, you are hereby notified that any review, retransmission, >>> dissemination, distribution, copying or other use of, or taking of any >>> action in reliance upon this information is strictly prohibited. If you >>> have received this communication in error, please contact the sender and >>> delete the material from your computer. >>> >> >> > > > -- > > Architect - Big Data > Ph: +91 99805 99458 > > Manthan Systems | *Company of the year - Analytics (2014 Frost and > Sullivan India ICT)* > +++ > > >
