Comments (4)
The RAPIDS Forest Inference Library has a treelite front end for GPU inference. See this blog post: https://medium.com/rapids-ai/rapids-forest-inference-library-prediction-at-100-million-rows-per-second-19558890bc35
The released version (0.10) supports XGBoost and LightGBM models, but the newest nightlies can work with treelite protobufs and sklearn RandomForest models as well. For large-batch inference, it's very very fast, though PCI transfers etc. start to add up if you do single-row inference or very small batches.
(Using treelite as a front-end has been awesome to support all these libraries - many thanks to Philip for his work here.)
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@Denisevi4 Interesting suggestion. Only problem is the added latency due to PCIe transfer and kernel launch overhead. For now, we are focusing on getting the best throughput using desktop/server-grade CPUs, and getting a good throughput for small-ish batches (~100) requires low latency.
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@Denisevi4 #67 may make GPU inference more practical. See the RapidScorer paper: https://www.kdd.org/kdd2018/accepted-papers/view/rapidscorer-fast-tree-ensemble-evaluation-by-maximizing-compactness-in-data
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Please check out RAPIDS cuML, whose GPU-accelerated Forest Inference Library (FIL) integrates with Treelite. Thanks!
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Related Issues (20)
- Document Treelite serialization format.
- Adopt Four-Document System to organize docs
- Refactor sklearn loader using mix-in classes
- Implement v4 serialization format
- Revamp JSON importer to make it easy to use HOT 3
- Drop "max_index" postprocessor
- Add directory exist check in _load_lib for add_dll_directory HOT 1
- scikit-learn 1.4.0 breaks unit test HOT 1
- Multi-class, multi-output RandomForestClassifier in scikit-learn produces error
- Test multi-target models where each tree predicts multiple regression targets HOT 1
- Export model back to scikit-learn HOT 1
- NumPy 2.0 support HOT 4
- xgboost json model fails to load HOT 3
- Release 4.2.0
- LightGBM to Treelite conversion fails when some leaves are very deep HOT 1
- Add support UBJSON XGBoost models
- Typo in the editing models tutorial HOT 2
- Ability to combine tree models HOT 2
- Release 4.3.0
- [Qst] Numerical Instability when using double as input
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