autogluon/autogluon

Model Serving - AutoGluonOnnx

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#1,079 opened on Apr 23, 2021

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Description

For tabular model, or any model class, is there any easy way or plan in the coming release to integrate with ONNX for downstream serving ?

A side question is for any individual trained model, e.g.model = predictor._trainer.load_model('xgboost'), is this directly compatible to things like mlflow.xgboost or bentoml.frameworks.xgboost.XgboostModelArtifact? It is not hard to write a customised wrapper, however would like to hear from your plan. I think it would be super good to consider the overall ecosystem when we have a such user friendly autogluon already.

Contributor guide

Research direction
Investigate current ONNX support in AutoGluon and explore integration with existing serving frameworks like MLflow and BentoML. Look for any existing issues or PRs related to ONNX export.
Tech stack
python
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60