There's some work going on to support PMML - https://issues.apache.org/jira/browse/SPARK-1406 - but it's not yet been merged into master.
What are you used to doing in other environments? In R I'm used to running save(), same with matlab. In python either pickling things or dumping to json seems pretty common. (even the scikit-learn docs recommend pickling - http://scikit-learn.org/stable/modules/model_persistence.html). These all seem basically equivalent java serialization to me.. Would some helper functions (in, say, mllib.util.modelpersistence or something) make sense to add? On Thu, Nov 6, 2014 at 11:36 PM, Duy Huynh <duy.huynh....@gmail.com> wrote: > that works. is there a better way in spark? this seems like the most > common feature for any machine learning work - to be able to save your > model after training it and load it later. > > On Fri, Nov 7, 2014 at 2:30 AM, Evan R. Sparks <evan.spa...@gmail.com> > wrote: > >> Plain old java serialization is one straightforward approach if you're in >> java/scala. >> >> On Thu, Nov 6, 2014 at 11:26 PM, ll <duy.huynh....@gmail.com> wrote: >> >>> what is the best way to save an mllib model that you just trained and >>> reload >>> it in the future? specifically, i'm using the mllib word2vec model... >>> thanks. >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/word2vec-how-to-save-an-mllib-model-and-reload-it-tp18329.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 >>> >>> >> >
