autogluon / autogluon/autogluon

Benchmark Hummingbird for model compilation

Open
#3,037 2 comments 0 reactions 0 assignees View on GitHub
enhancement module: tabular priority: 2 resource: GPU
Dominant language
Python
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Description

Benchmark Hummingbird: https://github.com/microsoft/hummingbird

We should test on XGBoost & LightGBM, and try out the GPU support, to see if we can get a meaningful inference speedup.

Contributor guide

Open the contributing guide

Research direction

Start by reading the Hummingbird project at https://github.com/microsoft/hummingbird and identify how to benchmark XGBoost and LightGBM inference with GPU support. Run comparable benchmarks and document whether they produce a meaningful inference speedup; the issue does not name repository files, tests, or a specific success threshold.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning, performance
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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