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Implement Custom Transformer to Model Deployment #15

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merged 19 commits into from
Nov 3, 2020

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ariefrahmansyah
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@ariefrahmansyah ariefrahmansyah commented Oct 29, 2020

This PR allows users to deploy their Transformer Docker image that contains pre/post-processing code.

Changes:

  • API
    • Append Transformer Spec to InferenceService Spec
    • Update Model Version Endpoint API (Deploy, Redeploy, Get, List)
    • Add unit test
    • Update Swagger spec
  • UI - Update Model Version Deployment page to include Transformer configuration
  • SDK - Update deploy() function to accept new optional arg: transformer
    • Add E2E test

Model version deployment page preview:

transformer-deploy

@pradithya
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You'll need to include end to end test to the to do list

@ariefrahmansyah
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You'll need to include end to end test to the to do list

Yes. The e2e tests will be included in Python SDK test

@pradithya
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After a model+transformer is deployed, is there a way to see the configuration in UI?

@ariefrahmansyah
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After a model+transformer is deployed, is there a way to see the configuration in UI?

Great question. There's no special page for displaying model version configuration, but we can use the redeploy page to display it. Something like this:

image

We can have separate PR for dedicated model version page.

@pradithya
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After a model+transformer is deployed, is there a way to see the configuration in UI?

Great question. There's no special page for displaying model version configuration, but we can use the redeploy page to display it. Something like this:

image

We can have separate PR for dedicated model version page.

Can you create an issue for this?

@pradithya
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LGTM

@ariefrahmansyah ariefrahmansyah merged commit 6ffeda3 into main Nov 3, 2020
@ariefrahmansyah ariefrahmansyah deleted the add-transformer branch November 3, 2020 02:31
pradithya pushed a commit that referenced this pull request Oct 3, 2022
* Implement Transformer to Version Endpoint

* Add transformer config on deployment page UI

* Update API port to 8080 as 3000 used by UI

* Update swagger api spec -- add transformer

* Add env vars to transformer object

* Display existing transformer spec in UI

* Update Transformer models gorm relation

* Integrate transformer to sdk

* Preload transformers without joining

* Update merlin-sdk version for pyfunc-server

* Address review - move transformer to models.Service

* Propagate transformer related env vars

* Increase timeout for local pyfunc server test

* Revert timeout for local pyfunc server test

* Add e2e test for transformer via python sdk test

* Fix sdk syntax

* Update API unit test

* Propagate docker registries list

* Cast env vars to string
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2 participants