Hello all, I am running the Spark recommendation algorithm in MLlib and I have been studying its output with various model configurations. Ideally I would like to be able to run one job that trains the recommendation model with many different configurations to try to optimize for performance. A sample code in python is copied below.
The issue I have is that each new model which is trained caches a set of RDDs and eventually the executors run out of memory. Is there any way in Pyspark to unpersist() these RDDs after each iteration? The names of the RDDs which I gather from the UI is: itemInBlocks itemOutBlocks Products ratingBlocks userInBlocks userOutBlocks users I am using Spark 1.3. Thank you for any help! Regards, Jonathan data_train, data_cv, data_test = data.randomSplit([99,1,1], 2) functions = [rating] #defined elsewhere ranks = [10,20] iterations = [10,20] lambdas = [0.01,0.1] alphas = [1.0,50.0] results = [] for ratingFunction, rank, numIterations, m_lambda, m_alpha in itertools.product( functions, ranks, iterations, lambdas, alphas ): #train model ratings_train = data_train.map(lambda l: Rating( l.user, l.product, ratingFunction(l) ) ) model = ALS.trainImplicit( ratings_train, rank, numIterations, lambda_=float(m_lambda), alpha=float(m_alpha) ) #test performance on CV data ratings_cv = data_cv.map(lambda l: Rating( l.uesr, l.product, ratingFunction(l) ) ) auc = areaUnderCurve( ratings_cv, model.predictAll ) #save results result = ",".join(str(l) for l in [ratingFunction.__name__,rank,numIterations,m_lambda,m_alpha,auc]) results.append(result) ________________________________________________________ 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.