On Sat, Jun 2, 2018 at 8:39 PM, Maximiliano Felice <maximilianofelice@gmail.com> wrote:

We're already in San Francisco waiting for the summit. We even think that we spotted @holdenk this afternoon.
Unless you happened to be walking by my garage probably not super likely, spent the day working on scooters/motorcycles (my style is a little less unique in SF :)). Also if you see me feel free to say hi unless I look like I haven't had my first coffee of the day, love chatting with folks IRL :)

@chris, we're really interested in the Meetup you're hosting. My team will probably join it since the beginning of you have room for us, and I'll join it later after discussing the topics on this thread. I'll send you an email regarding this request.


El vie., 1 de jun. de 2018 7:26 AM, Saikat Kanjilal <sxk1969@hotmail.com> escribió:
@Chris This sounds fantastic, please send summary notes for Seattle folks

@Felix I work in downtown Seattle, am wondering if we should a tech meetup around model serving in spark at my work or elsewhere close, thoughts?  I’m actually in the midst of building microservices to manage models and when I say models I mean much more than machine learning models (think OR, process models as well)


Sent from my iPhone

On May 31, 2018, at 10:32 PM, Chris Fregly <chris@fregly.com> wrote:

Hey everyone!

@Felix:  thanks for putting this together.  i sent some of you a quick calendar event - mostly for me, so i don’t forget!  :)

Coincidentally, this is the focus of June 6th's Advanced Spark and TensorFlow Meetup @5:30pm on June 6th (same night) here in SF!

Everybody is welcome to come.  Here’s the link to the meetup that includes the signup link:  https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/250924195/

We have an awesome lineup of speakers covered a lot of deep, technical ground.

For those who can’t attend in person, we’ll be broadcasting live - and posting the recording afterward.  

All details are in the meetup link above…

@holden/felix/nick/joseph/maximiliano/saikat/leif:  you’re more than welcome to give a talk. I can move things around to make room.

@joseph:  I’d personally like an update on the direction of the Databricks proprietary ML Serving export format which is similar to PMML but not a standard in any way.

Also, the Databricks ML Serving Runtime is only available to Databricks customers.  This seems in conflict with the community efforts described here.  Can you comment on behalf of Databricks?

Look forward to your response, joseph.

See you all soon!

Chris Fregly
Founder @ PipelineAI (100,000 Users)
Organizer @ Advanced Spark and TensorFlow Meetup (85,000 Global Members)

San Francisco - Chicago - Austin - 
Washington DC - London - Dusseldorf

Try our PipelineAI Community Edition with GPUs and TPUs!!

On May 30, 2018, at 9:32 AM, Felix Cheung <felixcheung_m@hotmail.com> wrote:


Thank you! Let’s meet then

June 6 4pm

Moscone West Convention Center

Ground floor (outside of conference area - should be available for all) - we will meet and decide where to go

(Would not send invite because that would be too much noise for dev@)

To paraphrase Joseph, we will use this to kick off the discusssion and post notes after and follow up online. As for Seattle, I would be very interested to meet in person lateen and discuss ;) 

From: Saikat Kanjilal <sxk1969@hotmail.com>
Sent: Tuesday, May 29, 2018 11:46 AM
Subject: Re: Revisiting Online serving of Spark models?
To: Maximiliano Felice <maximilianofelice@gmail.com>
Cc: Felix Cheung <felixcheung_m@hotmail.com>, Holden Karau <holden@pigscanfly.ca>, Joseph Bradley <joseph@databricks.com>, Leif Walsh <leif.walsh@gmail.com>, dev <dev@spark.apache.org>

Would love to join but am in Seattle, thoughts on how to make this work?


Sent from my iPhone

On May 29, 2018, at 10:35 AM, Maximiliano Felice <maximilianofelice@gmail.com> wrote:

Big +1 to a meeting with fresh air.

Could anyone send the invites? I don't really know which is the place Holden is talking about.

2018-05-29 14:27 GMT-03:00 Felix Cheung <felixcheung_m@hotmail.com>:
You had me at blue bottle!

From: Holden Karau <holden@pigscanfly.ca>
Sent: Tuesday, May 29, 2018 9:47 AM
Subject: Re: Revisiting Online serving of Spark models?
To: Felix Cheung <felixcheung_m@hotmail.com>
Cc: Saikat Kanjilal <sxk1969@hotmail.com>, Maximiliano Felice <maximilianofelice@gmail.com>, Joseph Bradley <joseph@databricks.com>, Leif Walsh <leif.walsh@gmail.com>, dev <dev@spark.apache.org>

I'm down for that, we could all go for a walk maybe to the mint plazaa blue bottle and grab coffee (if the weather holds have our design meeting outside :p)?

On Tue, May 29, 2018 at 9:37 AM, Felix Cheung <felixcheung_m@hotmail.com> wrote:

From: Felix Cheung <felixcheung_m@hotmail.com>
Sent: Saturday, May 26, 2018 1:05:29 PM
To: Saikat Kanjilal; Maximiliano Felice; Joseph Bradley
Cc: Leif Walsh; Holden Karau; dev

Subject: Re: Revisiting Online serving of Spark models?
Hi! How about we meet the community and discuss on June 6 4pm at (near) the Summit?

(I propose we meet at the venue entrance so we could accommodate people might not be in the conference)

From: Saikat Kanjilal <sxk1969@hotmail.com>
Sent: Tuesday, May 22, 2018 7:47:07 AM
To: Maximiliano Felice
Cc: Leif Walsh; Felix Cheung; Holden Karau; Joseph Bradley; dev
Subject: Re: Revisiting Online serving of Spark models?
I’m in the same exact boat as Maximiliano and have use cases as well for model serving and would love to join this discussion.

Sent from my iPhone

On May 22, 2018, at 6:39 AM, Maximiliano Felice <maximilianofelice@gmail.com> wrote:


I'm don't usually write a lot on this list but I keep up to date with the discussions and I'm a heavy user of Spark. This topic caught my attention, as we're currently facing this issue at work. I'm attending to the summit and was wondering if it would it be possible for me to join that meeting. I might be able to share some helpful usecases and ideas.

Maximiliano Felice

El mar., 22 de may. de 2018 9:14 AM, Leif Walsh <leif.walsh@gmail.com> escribió:
I’m with you on json being more readable than parquet, but we’ve had success using pyarrow’s parquet reader and have been quite happy with it so far. If your target is python (and probably if not now, then soon, R), you should look in to it. 

On Mon, May 21, 2018 at 16:52 Joseph Bradley <joseph@databricks.com> wrote:
Regarding model reading and writing, I'll give quick thoughts here:
* Our approach was to use the same format but write JSON instead of Parquet.  It's easier to parse JSON without Spark, and using the same format simplifies architecture.  Plus, some people want to check files into version control, and JSON is nice for that.
* The reader/writer APIs could be extended to take format parameters (just like DataFrame reader/writers) to handle JSON (and maybe, eventually, handle Parquet in the online serving setting).

This would be a big project, so proposing a SPIP might be best.  If people are around at the Spark Summit, that could be a good time to meet up & then post notes back to the dev list.

On Sun, May 20, 2018 at 8:11 PM, Felix Cheung <felixcheung_m@hotmail.com> wrote:
Specifically I’d like bring part of the discussion to Model and PipelineModel, and various ModelReader and SharedReadWrite implementations that rely on SparkContext. This is a big blocker on reusing  trained models outside of Spark for online serving.

What’s the next step? Would folks be interested in getting together to discuss/get some feedback?

From: Felix Cheung <felixcheung_m@hotmail.com>
Sent: Thursday, May 10, 2018 10:10 AM
Subject: Re: Revisiting Online serving of Spark models?
To: Holden Karau <holden@pigscanfly.ca>, Joseph Bradley <joseph@databricks.com>
Cc: dev <dev@spark.apache.org>

Huge +1 on this!

From:holden.karau@gmail.com <holden.karau@gmail.com> on behalf of Holden Karau <holden@pigscanfly.ca>
Sent: Thursday, May 10, 2018 9:39:26 AM
To: Joseph Bradley
Cc: dev
Subject: Re: Revisiting Online serving of Spark models?

On Thu, May 10, 2018 at 9:25 AM, Joseph Bradley <joseph@databricks.com> wrote:
Thanks for bringing this up Holden!  I'm a strong supporter of this.

Awesome! I'm glad other folks think something like this belongs in Spark.
This was one of the original goals for mllib-local: to have local versions of MLlib models which could be deployed without the big Spark JARs and without a SparkContext or SparkSession.  There are related commercial offerings like this : ) but the overhead of maintaining those offerings is pretty high.  Building good APIs within MLlib to avoid copying logic across libraries will be well worth it.

We've talked about this need at Databricks and have also been syncing with the creators of MLeap.  It'd be great to get this functionality into Spark itself.  Some thoughts:
* It'd be valuable to have this go beyond adding transform() methods taking a Row to the current Models.  Instead, it would be ideal to have local, lightweight versions of models in mllib-local, outside of the main mllib package (for easier deployment with smaller & fewer dependencies).
* Supporting Pipelines is important.  For this, it would be ideal to utilize elements of Spark SQL, particularly Rows and Types, which could be moved into a local sql package.
* This architecture may require some awkward APIs currently to have model prediction logic in mllib-local, local model classes in mllib-local, and regular (DataFrame-friendly) model classes in mllib.  We might find it helpful to break some DeveloperApis in Spark 3.0 to facilitate this architecture while making it feasible for 3rd party developers to extend MLlib APIs (especially in Java).
I agree this could be interesting, and feed into the other discussion around when (or if) we should be considering Spark 3.0
I _think_ we could probably do it with optional traits people could mix in to avoid breaking the current APIs but I could be wrong on that point.
* It could also be worth discussing local DataFrames.  They might not be as important as per-Row transformations, but they would be helpful for batching for higher throughput.
That could be interesting as well. 

I'll be interested to hear others' thoughts too!


On Wed, May 9, 2018 at 7:18 AM, Holden Karau <holden@pigscanfly.ca> wrote:
Hi y'all,

With the renewed interest in ML in Apache Spark now seems like a good a time as any to revisit the online serving situation in Spark ML. DB & other's have done some excellent working moving a lot of the necessary tools into a local linear algebra package that doesn't depend on having a SparkContext.

There are a few different commercial and non-commercial solutions round this, but currently our individual transform/predict methods are private so they either need to copy or re-implement (or put them selves in org.apache.spark) to access them. How would folks feel about adding a new trait for ML pipeline stages to expose to do transformation of single element inputs (or local collections) that could be optionally implemented by stages which support this? That way we can have less copy and paste code possibly getting out of sync with our model training.

I think continuing to have on-line serving grow in different projects is probably the right path, forward (folks have different needs), but I'd love to see us make it simpler for other projects to build reliable serving tools.

I realize this maybe puts some of the folks in an awkward position with their own commercial offerings, but hopefully if we make it easier for everyone the commercial vendors can benefit as well.


Holden :)


Joseph Bradley
Software Engineer - Machine Learning
Databricks, Inc.


Joseph Bradley
Software Engineer - Machine Learning
Databricks, Inc.