Re: Some interesting use case

[Boris Lublinsky]

Ron,
If you are talking about Tensorflow Saved model format, I personally think that 
it is overkill for model serving. My preferred option is to used traditional TF 
export, which can be optimized for serving.
As for processing I am using TF Java APIs, which basically is a population of 
the tensor column.

But if you are really interested, we can talk about it in San Jose or set up a 
config call if you want to discuss it sooner.

Boris Lublinsky
FDP Architect
boris.lublin...@lightbend.com
https://www.lightbend.com/

> On Jan 16, 2018, at 10:53 PM, Ron Gonzalez  wrote:
> 
> Yes you're right. I believe this is the use case that I'm after. So if I 
> understand correctly, transforms that do aggregations just assume that the 
> batch of data being aggregated is passed as part of a tensor column. Is it 
> possible to hook up a lookup call to another Tensorflow Serving servable for 
> a join in batch mode?
> 
> Will a saved model when loaded into a tensorflow serving model actually have 
> the definitions of the metadata when retrieved using the tensorflow serving 
> metadata api?
> 
> Thanks,
> Ron
> 
> On Tuesday, January 16, 2018, 6:16:01 PM PST, Charles Chen  
> wrote:
> 
> 
> This sounds similar to the use case for tf.Transform, a library that depends 
> on Beam: https://github.com/tensorflow/transform 
> 
> On Tue, Jan 16, 2018 at 5:51 PM Ron Gonzalez  > wrote:
> Hi,
>   I was wondering if anyone has encountered or used Beam in the following 
> manner:
>  
>   1. During machine learning training, use Beam to create the event table. 
> The flow may consist of some joins, aggregations, row-based transformations, 
> etc...
>   2. Once the model is created, deploy the model to some scoring service via 
> PMML (or some other scoring service).
>   3. Enable the SAME transformations used in #1 by using a separate engine 
> but thereby guaranteeing that it will transform the data identically as the 
> engine used in #1.
> 
>   I think this is a pretty interesting use case where Beam is used to 
> guarantee portability across engines and deployment (batch to true streaming, 
> not micro-batch). What's not clear to me is with respect to how batch joins 
> would translate during one-by-one scoring (probably lookups) or how 
> aggregations given that some kind of history would need to be stored (and how 
> much is kept is configurable too).
> 
>   Thoughts?
> 
> Thanks,
> Ron

Re: Some interesting use case

[zlgonzalez]

Thanks Boris.Yeah we can talk about it at the BoF...
Thanks,Ron


Sent via the Samsung Galaxy S7 active, an AT 4G LTE smartphone
 Original message From: Boris Lublinsky 
<boris.lublin...@lightbend.com> Date: 1/17/18  6:10 AM  (GMT-08:00) To: 
u...@beam.apache.org Cc: dev@beam.apache.org, Charles Chen <c...@google.com> 
Subject: Re: Some interesting use case 
Ron,If you are talking about Tensorflow Saved model format, I personally think 
that it is overkill for model serving. My preferred option is to used 
traditional TF export, which can be optimized for serving.As for processing I 
am using TF Java APIs, which basically is a population of the tensor column.
But if you are really interested, we can talk about it in San Jose or set up a 
config call if you want to discuss it sooner.

Boris Lublinsky
FDP Architect
boris.lublin...@lightbend.com
https://www.lightbend.com/



On Jan 16, 2018, at 10:53 PM, Ron Gonzalez <zlgonza...@yahoo.com> wrote:

Yes you're right. I believe this is the use case that I'm after. So 
if I understand correctly, transforms that do aggregations just assume that the 
batch of data being aggregated is passed as part of a tensor column. Is it 
possible to hook up a lookup call to another Tensorflow Serving servable for a 
join in batch mode?
Will a saved model when loaded into a tensorflow serving model actually have 
the definitions of the metadata when retrieved using the tensorflow serving 
metadata api?
Thanks,Ron






On Tuesday, January 16, 2018, 6:16:01 PM PST, Charles 
Chen <c...@google.com> wrote:





This sounds similar to the use case for tf.Transform, a 
library that depends on Beam: https://github.com/tensorflow/transform
On Tue, Jan 16, 2018 at 5:51 PM Ron Gonzalez <zlgonza...@yahoo.com> wrote:
Hi,  I was wondering if anyone has encountered or used Beam in the following 
manner:   1. During machine learning training, use Beam to create the event 
table. The flow may consist of some joins, aggregations, row-based 
transformations, etc...  2. Once the model is created, deploy the model to some 
scoring service via PMML (or some other scoring service).  3. Enable the SAME 
transformations used in #1 by using a separate engine but thereby guaranteeing 
that it will transform the data identically as the engine used in #1.
  I think this is a pretty interesting use case where Beam is used to guarantee 
portability across engines and deployment (batch to true streaming, not 
micro-batch). What's not clear to me is with respect to how batch joins would 
translate during one-by-one scoring (probably lookups) or how aggregations 
given that some kind of history would need to be stored (and how much is kept 
is configurable too).
  Thoughts?
Thanks,Ron

Re: Some interesting use case

[Charles Chen]

This sounds similar to the use case for tf.Transform, a library that
depends on Beam: https://github.com/tensorflow/transform

On Tue, Jan 16, 2018 at 5:51 PM Ron Gonzalez  wrote:

> Hi,
>   I was wondering if anyone has encountered or used Beam in the following
> manner:
>
>   1. During machine learning training, use Beam to create the event table.
> The flow may consist of some joins, aggregations, row-based
> transformations, etc...
>   2. Once the model is created, deploy the model to some scoring service
> via PMML (or some other scoring service).
>   3. Enable the SAME transformations used in #1 by using a separate engine
> but thereby guaranteeing that it will transform the data identically as the
> engine used in #1.
>
>   I think this is a pretty interesting use case where Beam is used to
> guarantee portability across engines and deployment (batch to true
> streaming, not micro-batch). What's not clear to me is with respect to how
> batch joins would translate during one-by-one scoring (probably lookups) or
> how aggregations given that some kind of history would need to be stored
> (and how much is kept is configurable too).
>
>   Thoughts?
>
> Thanks,
> Ron
>

Some interesting use case

[Ron Gonzalez]

Hi,  I was wondering if anyone has encountered or used Beam in the following 
manner:   1. During machine learning training, use Beam to create the event 
table. The flow may consist of some joins, aggregations, row-based 
transformations, etc...  2. Once the model is created, deploy the model to some 
scoring service via PMML (or some other scoring service).  3. Enable the SAME 
transformations used in #1 by using a separate engine but thereby guaranteeing 
that it will transform the data identically as the engine used in #1.
  I think this is a pretty interesting use case where Beam is used to guarantee 
portability across engines and deployment (batch to true streaming, not 
micro-batch). What's not clear to me is with respect to how batch joins would 
translate during one-by-one scoring (probably lookups) or how aggregations 
given that some kind of history would need to be stored (and how much is kept 
is configurable too).
  Thoughts?
Thanks,Ron