Honestly I don't believe this kind of functionality belongs within
spark-jobserver.
For serving of factor-type models, you are typically in the realm of
recommendations or ad-serving scenarios - i.e. needing to score a user /
context against many possible items and return a top-k list of those.
Model sizes are 10m x rank, 100k x rank range.
For recommendation/topic modeling I can run batch recommendAll and then
keep serving the model using a distributed cache but then I can't
incorporate per user model re-predict if user feedback is making the
current topk stale. I have to wait for next
Ok
My view is with only 100k items, you are better off serving in-memory for
items vectors. i.e. store all item vectors in memory, and compute user *
item score on-demand. In most applications only a small proportion of users
are active, so really you don't need all 10m user vectors in memory.
On Wed, Jun 24, 2015 at 12:02 PM, Nick Pentreath
nick.pentre...@gmail.com wrote:
Oryx does almost the same but Oryx1 kept all user and item vectors in memory
(though I am not sure about whether Oryx2 still stores all user and item
vectors in memory or partitions in some way).
(Yes, this is a
Thanks Nick, Sean for the great suggestions...
Since you guys have already hit these issues before I think it will be
great if we can add the learning to Spark Job Server and enhance it for
community.
Nick, do you see any major issues in using Spray over Scalatra ?
Looks like Model Server API
Yes, and typically needs are 100ms. Now imagine even 10 concurrent
requests. My experience has been that this approach won't nearly
scale. The best you could probably do is async mini-batch
near-real-time scoring, pushing results to some store for retrieval,
which could be entirely suitable for
Models that I am looking for are mostly factorization based models (which
includes both recommendation and topic modeling use-cases).
For recommendation models, I need a combination of Spark SQL and ml model
prediction api...I think spark job server is what I am looking for and it
has fast http
How large are your models?
Spark job server does allow synchronous job execution and with a warm
long-lived context it will be quite fast - but still in the order of a second
or a few seconds usually (depending on model size - for very large models
possibly quite a lot more than that).
Out of curiosity why netty?
What model are you serving?
Velox doesn't look like it is optimized for cases like ALS recs, if that's
what you mean. I think scoring ALS at scale in real time takes a fairly
different approach.
The servlet engine probably doesn't matter at all in comparison.
On Sat,
Oryx 2 has a scala client
https://github.com/OryxProject/oryx/blob/master/framework/oryx-api/src/main/scala/com/cloudera/oryx/api/
2015-06-20 11:39 GMT-07:00 Debasish Das debasish.da...@gmail.com:
After getting used to Scala, writing Java is too much work :-)
I am looking for scala based
Is there a presentation up about this end-to-end example?
I'm looking into velox now - our internal model pipeline just saves factors to
S3 and model server loads them periodically from S3
—
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On Sat, Jun 20, 2015 at 9:46 PM, Debasish Das debasish.da...@gmail.com
wrote:
Is velox NOT open source?
On Saturday, June 20, 2015, Debasish Das debasish.da...@gmail.com wrote:
Hi,
The demo of end-to-end ML pipeline including the model server component at
Spark Summit was really cool.
I was wondering if the Model Server component is based upon Velox or it
uses a
Hi Debasish,
The Oryx project (https://github.com/cloudera/oryx), which is Apache 2
licensed, contains a model server that can serve models built with MLlib.
-Sandy
On Sat, Jun 20, 2015 at 8:00 AM, Charles Earl charles.ce...@gmail.com
wrote:
Is velox NOT open source?
On Saturday, June 20,
Hi,
The demo of end-to-end ML pipeline including the model server component at
Spark Summit was really cool.
I was wondering if the Model Server component is based upon Velox or it
uses a completely different architecture.
https://github.com/amplab/velox-modelserver
We are looking for an open
Mind if I ask what 1.3/1.4 ML features that you are looking for?
On Saturday, June 20, 2015, Debasish Das debasish.da...@gmail.com wrote:
After getting used to Scala, writing Java is too much work :-)
I am looking for scala based project that's using netty at its core (spray
is one example).
Integration of model server with ML pipeline API.
On Sat, Jun 20, 2015 at 12:25 PM, Donald Szeto don...@prediction.io wrote:
Mind if I ask what 1.3/1.4 ML features that you are looking for?
On Saturday, June 20, 2015, Debasish Das debasish.da...@gmail.com wrote:
After getting used to
Oops, that link was for Oryx 1. Here's the repo for Oryx 2:
https://github.com/OryxProject/oryx
On Sat, Jun 20, 2015 at 10:20 AM, Sandy Ryza sandy.r...@cloudera.com
wrote:
Hi Debasish,
The Oryx project (https://github.com/cloudera/oryx), which is Apache 2
licensed, contains a model server
After getting used to Scala, writing Java is too much work :-)
I am looking for scala based project that's using netty at its core (spray
is one example).
prediction.io is an option but that also looks quite complicated and not
using all the ML features that got added in 1.3/1.4
Velox built on
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