What do you think about using a general purpose registry, that can also be used 
to discover cluster machines, or microservices?
Something like consul.io or docker swarm with and ASF compatible license? This 
would be a real step into the future and since some work is needed anyway…

I think Donald is right that much of this can be made optional—with a mind 
towards making a single machine install easy and a cluster install almost as 
easy


On Sep 21, 2016, at 1:18 PM, Donald Szeto <don...@apache.org> wrote:

I second with removing engine manifests and add a separate registry for
other meta data (such as where to push engine code, models, and misc.
discovery).

The current design is a result of realizing the need that producing
predictions from the model requires custom code (scoring function) as well.
We have bundled training code, predicting (scoring) code together as an
engine, different input parameters as different engine variants, and engine
instances as an immutable list of metadata that points to an engine, engine
variant, and trained models. We can definitely draw clearer boundaries and
names. We should start a design doc somewhere. Any suggestions?

I propose to start by making registration optional, then start to refactor
manifest and build a proper engine registry.

Regards,
Donald

On Wed, Sep 21, 2016 at 12:29 PM, Marcin Ziemiński <ziem...@gmail.com>
wrote:

> I think that getting rid of the manifest.json and introducing a new kind
> of resourse-id for an engine to be registered is a good idea.
> 
> Currently in the repository there are three important keys:
> * engine id
> * engine version - depends only on the path the engine was built at to
> distinguish copies
> * engine instance id - because of the name may be associated with the
> engine itself, but in fact is the identificator of trained models for an
> engine.
> When running deploy you either get the latest trained model for the
> engine-id and engine-version, what strictly ties it to the location it was
> compiled at or you specify engine instance id. I am not sure, but I think
> that in the latter case you could get a model for a completely different
> engine, what could potentially fail because of initialization with improper
> parameters.
> What is more, the engine object creation relies only on the full name of
> the EngineFactory, so the actual engine, which gets loaded is determined by
> the current CLASSPATH. I guess that it is probably the place, which should
> be modified if we want a multi-tenant architecture.
> I have to admit that these things hadn't been completely clear to me,
> until I went through the code.
> 
> We could introduce a new type of service for engine and model management.
> I like the idea of the repository to push built engines under chosen ids.
> We could also add some versioning of them if necessary.
> I treat this approach purely as some kind of package management system.
> 
> As Pat said, a similar approach would let us rely only on the repository
> and thanks to that run pio commands regardless of the machine and location.
> 
> Separating the engine part from the rest of PIO could potentially enable
> us to come up with different architectures in the future and push us
> towards micro-services ecosystem.
> 
> What do you think of separating models from engines in more visible way? I
> mean that engine variants in terms of algorithm parameters are more like
> model variants. I just see an engine only as code being a dependency for
> application related models/algorithms. So you would register an engine - as
> a code once and run training for some domain specific data (app) and
> algorithm parameters, what would result in a different identifier, that
> would be later used for deployment.
> 
> Regards,
> Marcin
> 
> 
> 
> 
> 
> niedz., 18.09.2016 o 20:02 użytkownik Pat Ferrel <p...@occamsmachete.com>
> napisał:
> 
>> This sounds like a good case for Donald’s suggestion.
>> 
>> What I was trying to add to the discussion is a way to make all commands
>> rely on state in the megastore, rather than any file on any machine in a
>> cluster or on ordering of execution or execution from a location in a
>> directory structure. All commands would then be stateless.
>> 
>> This enables real use cases like provisioning PIO machines and running
>> `pio deploy <resource-id>` to get a new PredictionServer. Provisioning can
>> be container and discovery based rather cleanly.
>> 
>> 
>> On Sep 17, 2016, at 5:26 PM, Mars Hall <m...@heroku.com> wrote:
>> 
>> Hello folks,
>> 
>> Great to hear about this possibility. I've been working on running
>> PredictionIO on Heroku https://www.heroku.com
>> 
>> Heroku's 12-factor architecture https://12factor.net prefers "stateless
>> builds" to ensure that compiled artifacts result in processes which may be
>> cheaply restarted, replaced, and scaled via process count & size. I imagine
>> this stateless property would be valuable for others as well.
>> 
>> The fact that `pio build` inserts stateful metadata into a database
>> causes ripples throughout the lifecycle of PIO engines on Heroku:
>> 
>> * An engine cannot be built for production without the production
>> database available. When a production database contains PII (personally
>> identifiable information) which has security compliance requirements, the
>> build system may not be privileged to access that PII data. This also
>> affects CI (continuous integration/testing), where engines would need to be
>> rebuilt in production, defeating assurances CI is supposed to provide.
>> 
>> * The build artifacts cannot be reliably reused. "Slugs" at Heroku are
>> intended to be stateless, so that you can rollback to a previous version
>> during the lifetime of an app. With `pio build` causing database
>> side-effects, there's a greater-than-zero probability of slug-to-metadata
>> inconsistencies eventually surfacing in a long-running system.
>> 
>> 
>> From my user-perspective, a few changes to the CLI would fix it:
>> 
>> 1. add a "skip registration" option, `pio build
>> --without-engine-registration`
>> 2. a new command `pio app register` that could be run separately in the
>> built engine (before training)
>> 
>> Alas, I do not know PredictionIO internals, so I can only offer a
>> suggestion for how this might be solved.
>> 
>> 
>> Donald, one specific note,
>> 
>> Regarding "No automatic version matching of PIO binary distribution and
>> artifacts version used in the engine template":
>> 
>> The Heroku slug contains the PredictionIO binary distribution used to
>> build the engine, so there's never a version matching issue. I guess some
>> systems might deploy only the engine artifacts to production where a
>> pre-existing PIO binary is available, but that seems like a risky practice
>> for long-running systems.
>> 
>> 
>> Thanks for listening,
>> 
>> *Mars Hall
>> Customer Facing Architect
>> Salesforce App Cloud / Heroku
>> San Francisco, California
>> 
>>> On Sep 16, 2016, at 10:42, Donald Szeto <don...@apache.org> wrote:
>>> 
>>> Hi all,
>>> 
>>> I want to start the discussion of removing engine registration. How
>> many people actually take advantage of being able to run pio commands
>> everywhere outside of an engine template directory? This will be a
>> nontrivial change on the operational side so I want to gauge the potential
>> impact to existing users.
>>> 
>>> Pros:
>>> - Stateless build. This would work well with many PaaS.
>>> - Eliminate the "pio build" command once and for all.
>>> - Ability to use your own build system, i.e. Maven, Ant, Gradle, etc.
>>> - Potentially better experience with IDE since engine templates no
>> longer depends on an SBT plugin.
>>> 
>>> Cons:
>>> - Inability to run pio engine training and deployment commands outside
>> of engine template directory.
>>> - No automatic version matching of PIO binary distribution and
>> artifacts version used in the engine template.
>>> - A less unified user experience: from pio-build-train-deploy to build,
>> then pio-train-deploy.
>>> 
>>> Regards,
>>> Donald
>> 
>> 
>> 

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