Also not sure how threading helps here because Spark puts a partition to each core. On each core may be there are multiple threads if you are using intel hyperthreading but I will let Spark handle the threading.
On Thu, Jun 18, 2015 at 8:38 AM, Debasish Das <debasish.da...@gmail.com> wrote: > 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)* >> +++ >> >> >> >
