Thanks a lot Evan...
On Wed, Dec 4, 2013 at 8:31 PM, Evan R. Sparks wrote:
> Ah, actually - I just remembered that the user and product features of the
> model are RDDs, so - you might be better off saving those components to
> HDFS and then at load time reading them back in and creating a new
Ah, actually - I just remembered that the user and product features of the
model are RDDs, so - you might be better off saving those components to
HDFS and then at load time reading them back in and creating a new
MatrixFactorizationModel. Sorry for the confusion!
Note, the above solution only wo
I thought to convert model to RDD and save to HDFS, and then load it.
I will try your method. Thanks a lot.
On Wed, Dec 4, 2013 at 7:41 PM, Evan R. Sparks wrote:
> The model is serializable - so you should be able to write it out to disk
> and load it up in another program.
>
> See, e.g. - htt
The model is serializable - so you should be able to write it out to disk
and load it up in another program.
See, e.g. - https://gist.github.com/ramn/5566596 (Note, I haven't tested
this particular example, but it looks alright).
Spark makes use of this type of scala (and kryo, etc.) serializatio
Hi All,
I am creating a model by calling train method of ALS.
val model = ALS.train(ratings.)
I need to persist this model. Use it from different clients, enable
clients to make predictions using this model. In other words, persist and
reload this model.
Any suggestions, please?
BR,
Aslan
