h2oai / h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
http://h2o.ai
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Implement a convertor from GBM MOJO to ONNX #8583

Closed exalate-issue-sync[bot] closed 1 year ago

exalate-issue-sync[bot] commented 1 year ago

ONNX is a standardized format used to represent ML models: https://onnx.ai/

Popular ML frameworks provide support for ONNX via convertors: https://github.com/onnx/onnxmltools/tree/master/onnxmltools/convert (XGBoost, Spark).

The goal of this task is to create a convertor from H2O GBM MOJO to ONNX representation. Inspiration could be taken from XGBoost implementation of the convertor: https://github.com/onnx/onnxmltools/tree/master/onnxmltools/convert/xgboost

Not all distributions / objective functions needs to be supported on Day 1 (XGBoost doesn't support Tweedie for example). Main challenge will be most likely to handle categorical encoding. For initial POC it is also okay to do just 1-hot encoded models.

exalate-issue-sync[bot] commented 1 year ago

Jan Sterba commented: was merged into onnx https://github.com/onnx/onnxmltools/pull/358 will be nex onnxmltools release

maybe we might want to add a chapter to Productionizing H2O doc chapter? [~accountid:557058:04659f86-fbfe-4d01-90c9-146c34df6ee6] WDYT?

exalate-issue-sync[bot] commented 1 year ago

Michal Kurka commented: [~accountid:5bd82be4216ccf49622e5137] sounds like good idea to me, cc: [~accountid:557058:6e44bc1a-dd50-499b-a331-2e049f28773b]

h2o-ops commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-7057 Assignee: Jan Sterba Reporter: Michal Kurka State: Closed Fix Version: 3.28.0.2 Attachments: N/A Development PRs: Available

Linked PRs from JIRA

https://github.com/h2oai/h2o-3/pull/4104