Re: ml and mllib persistence

I see. I could not find any explanation about this. Could you tell me what sort of portability issue is this? Isn't JVM supposed to give the abstraction of that? Thanks! On 12.07.2016 20:04, Reynold Xin wrote: Platform as a general word, eg language platforms, OS, different JVM versions,

Re: ml and mllib persistence

Platform as a general word, eg language platforms, OS, different JVM versions, different JVM vendors, different Spark versions... On Tuesday, July 12, 2016, Salih Gedik wrote: > Hi Reynold, > > I was wondering if you meant cross language or cross platform? Thanks > On 12.07.2016

Re: ml and mllib persistence

Hi Reynold, I was wondering if you meant cross language or cross platform? Thanks On 12.07.2016 19:57, Reynold Xin wrote: Also Java serialization isn't great for cross platform compatibility. On Tuesday, July 12, 2016, aka.fe2s > wrote:

Re: ml and mllib persistence

Also Java serialization isn't great for cross platform compatibility. On Tuesday, July 12, 2016, aka.fe2s wrote: > Okay, I think I found an answer on my question. Some models (for instance > org.apache.spark.mllib.recommendation.MatrixFactorizationModel) hold RDDs, > so just

Re: ml and mllib persistence

Okay, I think I found an answer on my question. Some models (for instance org.apache.spark.mllib.recommendation.MatrixFactorizationModel) hold RDDs, so just serializing these objects will not work. -- Oleksiy Dyagilev On Tue, Jul 12, 2016 at 5:40 PM, aka.fe2s wrote: > What