autogluon / autogluon/autogluon
Benchmark Hummingbird for model compilation
Open
enhancement
module: tabular
priority: 2
resource: GPU
- Dominant language
- Python
- Stars
- 10.7k
- Forks
- 1.2k
- Avg merge
- 21h 29m
- Merged PRs (30d)
- 57
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
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.
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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