Yup, numpy calls into BLAS for matrix multiply. Sent from my iPad
> On 18 Jun 2015, at 8:54 PM, Ayman Farahat <ayman.fara...@yahoo.com> wrote: > > Thanks all for the help. > It turned out that using the bumpy matrix multiplication made a huge > difference in performance. I suspect that Numpy already uses BLAS optimized > code. > > Here is Python code > > #This is where i load and directly test the predictions > myModel = MatrixFactorizationModel.load(sc, "FlurryModelPath") > m1 = myModel.productFeatures().sample(False, 1.00) > m2 = m1.map(lambda (user,feature) : feature).collect() > m3 = matrix(m2).transpose() > > pf = sc.broadcast(m3) > uf = myModel.userFeatures() > > f1 = uf.map(lambda (userID, features): (userID, > squeeze(asarray(matrix(array(features)) * pf.value)))) > dog = f1.count() > >> On Jun 18, 2015, at 8:42 AM, Debasish Das <debasish.da...@gmail.com> wrote: >> >> Also in my experiments, it's much faster to blocked BLAS through cartesian >> rather than doing sc.union. Here are the details on the experiments: >> >> https://issues.apache.org/jira/browse/SPARK-4823 >> >>> On Thu, Jun 18, 2015 at 8:40 AM, Debasish Das <debasish.da...@gmail.com> >>> wrote: >>> 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 >>>>>>> >>>>>>> >>>>>>>> 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) >>>>>> +++ >>>>> >>>> >>> >> >
