You are right, native Spark MLlib CrossValidation can't run *different *algorithms in parallel.
Thanks Yanbo On Tue, Sep 5, 2017 at 10:56 PM, Timsina, Prem <prem.tims...@mssm.edu> wrote: > Hi Yanboo, > > Thank You, I very much appreciate your help. > > For the current use case, the data can fit into a single node. So, > spark-sklearn seems to be good choice. > > > > *I have on question regarding this * > > *“If no, Spark MLlib provide CrossValidation which can run multiple > machine learning algorithms parallel on distributed dataset and do > parameter search. > FYI: https://spark.apache.org/docs/latest/ml-tuning.html#cross-validation > <https://urldefense.proofpoint.com/v2/url?u=https-3A__spark.apache.org_docs_latest_ml-2Dtuning.html-23cross-2Dvalidation&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=lVvXRRGoh5uXJw-K246dNzogKEfb2yFYtxpTB9xxizo&e=>”* > > If I understand correctly, it can run parameter search for > cross-validation in parallel. > > However, currently Spark does not support running multiple algorithms > (like Naïve Bayes, Random Forest, etc.) in parallel. Am I correct? > > If not, could you please point me to some resources where they have run > multiple algorithms in parallel. > > > > Thank You very much. It is great help, I will try spark-sklearn. > > Prem > > > > > > > > > > *From: *Yanbo Liang <yblia...@gmail.com> > *Date: *Tuesday, September 5, 2017 at 10:40 AM > *To: *Patrick McCarthy <pmccar...@dstillery.com> > *Cc: *"Timsina, Prem" <prem.tims...@mssm.edu>, "user@spark.apache.org" < > user@spark.apache.org> > *Subject: *Re: Apache Spark: Parallelization of Multiple Machine Learning > ALgorithm > > > > Hi Prem, > > > > How large is your dataset? Can it be fitted in a single node? > > If no, Spark MLlib provide CrossValidation which can run multiple machine > learning algorithms parallel on distributed dataset and do parameter > search. FYI: https://spark.apache.org/docs/latest/ml-tuning.html# > cross-validation > <https://urldefense.proofpoint.com/v2/url?u=https-3A__spark.apache.org_docs_latest_ml-2Dtuning.html-23cross-2Dvalidation&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=lVvXRRGoh5uXJw-K246dNzogKEfb2yFYtxpTB9xxizo&e=> > > If yes, you can also try spark-sklearn, which can distribute multiple > model training(single node training with sklearn) across a distributed > cluster and do parameter search. FYI: https://github.com/ > databricks/spark-sklearn > <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_databricks_spark-2Dsklearn&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=JfciAow01oTIYYCjhy83Q_nF85fKW9ZI-qYxfUa0BUU&e=> > > > > Thanks > > Yanbo > > > > On Tue, Sep 5, 2017 at 9:56 PM, Patrick McCarthy <pmccar...@dstillery.com> > wrote: > > You might benefit from watching this JIRA issue - > https://issues.apache.org/jira/browse/SPARK-19071 > <https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_SPARK-2D19071&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=hQZ6ldug0XZvo4q87r0BQatn55B6UtyVVs0Ge9UneW4&e=> > > > > On Sun, Sep 3, 2017 at 5:50 PM, Timsina, Prem <prem.tims...@mssm.edu> > wrote: > > Is there a way to parallelize multiple ML algorithms in Spark. My use case > is something like this: > > A) Run multiple machine learning algorithm (Naive Bayes, ANN, Random > Forest, etc.) in parallel. > > 1) Validate each algorithm using 10-fold cross-validation > > B) Feed the output of step A) in second layer machine learning algorithm. > > My question is: > > Can we run multiple machine learning algorithm in step A in parallel? > > Can we do cross-validation in parallel? Like, run 10 iterations of Naive > Bayes training in parallel? > > > > I was not able to find any way to run the different algorithm in parallel. > And it seems cross-validation also can not be done in parallel. > > I appreciate any suggestion to parallelize this use case. > > > > Prem > > > > >
