openboost
GPU-accelerated gradient boosting, written in Python.
Building Blocks
numba-cuda compiles Python to CUDA kernels.
The Problem
GBDT research is active:
- DART — dropout for trees
- NGBoost — probabilistic predictions
- GAMLSS — distributional regression
- GOSS — gradient-based sampling
- GBDT-PL — linear leaves
- Oblivious Trees — symmetric splits
OpenBoost lets you implement these in Python.
Goal
GPU GBDT core in Python. Extend it to build variants.
Install
pip install openboost
Status
WIP.
License
Apache-2.0
1 maintainer
Metadata
1 maintainer
Release files for openboost 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| openboost-0.1.0.tar.gz | 12.7 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openboost-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.0 kB
Release files / openboost-0.1.0.tar.gz
| Download URL | openboost-0.1.0.tar.gz |
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| Size | 12.7 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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| Download URL | openboost-0.1.0-py3-none-any.whl |
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| Size | 13.3 kB |
| Tags | Python 3 |
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