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Quantco / slim-trees

Pickle your ML models more efficiently for deployment 🚀
MIT License
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Make dtype casts safe #38

Closed pavelzw closed 1 year ago

pavelzw commented 1 year ago

Fixes #23

pavelzw commented 1 year ago

This adds safe casts to the old version of the LGBM compression algorithm (as well as the sklearn algorithm). @YYYasin19 needs to do something like this in #15 too.

github-actions[bot] commented 1 year ago
(benchmark 4470028469 / attempt 1) Base results / Our results / Change Model Size Dump Time Load Time
sklearn rf 77.2 MiB / 11.1 MiB / 6.94 x 0.04 s / 0.11 s / 2.43 x 0.06 s / 0.11 s / 1.76 x
sklearn rf LZMA 17.3 MiB / 5.3 MiB / 3.25 x 38.61 s / 6.69 s / 0.17 x 1.69 s / 0.58 s / 0.34 x
sklearn gb 2.2 MiB / 1.1 MiB / 2.08 x 0.03 s / 0.25 s / 7.47 x 0.04 s / 0.13 s / 3.66 x
sklearn gb LZMA 0.6 MiB / 0.2 MiB / 3.82 x 1.02 s / 0.45 s / 0.44 x 0.09 s / 0.15 s / 1.61 x
LGBM gbdt 5.3 MiB / 1.9 MiB / 2.81 x 0.17 s / 0.49 s / 2.91 x 0.03 s / 0.32 s / 12.11 x
LGBM gbdt LZMA 1.7 MiB / 0.8 MiB / 1.96 x 4.11 s / 1.15 s / 0.28 x 0.16 s / 0.38 s / 2.33 x
LGBM gbdt large 57.6 MiB / 18.9 MiB / 3.05 x 1.72 s / 5.11 s / 2.96 x 0.28 s / 3.55 s / 12.59 x
LGBM gbdt large LZMA 15.3 MiB / 6.7 MiB / 2.29 x 56.13 s / 14.17 s / 0.25 x 1.62 s / 4.00 s / 2.47 x
LGBM rf 1.6 MiB / 0.5 MiB / 3.42 x 0.05 s / 0.09 s / 1.91 x 0.01 s / 0.08 s / 12.93 x
LGBM rf LZMA 0.1 MiB / 0.1 MiB / 1.70 x 0.24 s / 0.13 s / 0.55 x 0.02 s / 0.08 s / 4.22 x