Quantco / slim-trees

Pickle your ML models more efficiently for deployment 🚀
MIT License
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Bump pypa/gh-action-pypi-publish from 1.8.8 to 1.8.10 #82

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps pypa/gh-action-pypi-publish from 1.8.8 to 1.8.10.

Release notes

Sourced from pypa/gh-action-pypi-publish's releases.

v1.8.10

:bug: What's Fixed

@​woodruffw fixed decoding OIDC claims in debug output on failure by applying correct padding to the encoded payload via pypa/gh-action-pypi-publish#177.

Full Diff: https://github.com/pypa/gh-action-pypi-publish/compare/v1.8.9...v1.8.10

v1.8.9

:nail_care: Cosmetic output improvements

:hammer_and_wrench: Internal dependencies

Full Diff: https://github.com/pypa/gh-action-pypi-publish/compare/v1.8.8...v1.8.9

Commits
  • b7f401d Merge PR #177 into unstable/v1
  • ba3ecc9 oidc-exchange: fix padding
  • ade57f5 Merge PRs #174 #175 and #172 into unstable/v1
  • 637917e README: re-add "pro tip" language
  • 4864f13 README: use semantic callouts
  • 326f9ad oidc-exchange: add-trailing-comma
  • e5f0690 oidc-exchange: ignore a nested function
  • 8bdd0cc oidc-exchange: lintage
  • 71a0032 oidc-exchange: render claims if exchange fails
  • adef75a Bump cryptography from 41.0.2 to 41.0.3 in /requirements
  • Additional commits viewable in compare view


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github-actions[bot] commented 1 year ago
(benchmark 5859459791 / attempt 1) Base results / Our results / Change Model Size Dump Time Load Time
sklearn rf 20M 23.4 MiB / 3.0 MiB / 7.67 x 0.02 s / 0.04 s / 2.02 x 0.02 s / 0.05 s / 2.94 x
sklearn rf 20M lzma 7.6 MiB / 2.0 MiB / 3.78 x 15.78 s / 1.30 s / 0.08 x 0.71 s / 0.24 s / 0.34 x
sklearn rf 200M 238.8 MiB / 30.8 MiB / 7.75 x 0.17 s / 0.36 s / 2.16 x 0.22 s / 0.50 s / 2.27 x
sklearn rf 200M lzma 48.4 MiB / 14.8 MiB / 3.26 x 124.62 s / 20.22 s / 0.16 x 5.25 s / 1.86 s / 0.35 x
sklearn rf 1G 1302.2 MiB / 168.0 MiB / 7.75 x 1.31 s / 2.01 s / 1.53 x 1.31 s / 2.50 s / 1.91 x
sklearn rf 1G lzma 295.9 MiB / 99.2 MiB / 2.98 x 791.00 s / 110.78 s / 0.14 x 29.43 s / 10.64 s / 0.36 x
sklearn gb 2M 2.5 MiB / 1.1 MiB / 2.16 x 0.04 s / 0.34 s / 8.54 x 0.04 s / 0.26 s / 5.86 x
sklearn gb 2M lzma 0.7 MiB / 0.2 MiB / 4.42 x 1.20 s / 0.46 s / 0.39 x 0.11 s / 0.30 s / 2.81 x
lgbm gbdt 2M 2.6 MiB / 1.0 MiB / 2.78 x 0.10 s / 0.32 s / 3.22 x 0.02 s / 0.14 s / 9.18 x
lgbm gbdt 2M lzma 0.9 MiB / 0.5 MiB / 1.90 x 1.66 s / 0.57 s / 0.35 x 0.09 s / 0.17 s / 1.98 x
lgbm gbdt 5M 5.3 MiB / 1.9 MiB / 2.81 x 0.17 s / 0.57 s / 3.35 x 0.03 s / 0.29 s / 10.62 x
lgbm gbdt 5M lzma 1.7 MiB / 0.8 MiB / 1.96 x 3.99 s / 1.19 s / 0.30 x 0.17 s / 0.36 s / 2.08 x
lgbm gbdt 20M 22.7 MiB / 7.6 MiB / 3.00 x 0.74 s / 2.26 s / 3.05 x 0.13 s / 1.25 s / 9.75 x
lgbm gbdt 20M lzma 6.3 MiB / 3.0 MiB / 2.09 x 21.65 s / 5.56 s / 0.26 x 0.67 s / 1.45 s / 2.16 x
lgbm gbdt 100M 101.1 MiB / 33.0 MiB / 3.06 x 3.13 s / 9.75 s / 3.12 x 0.59 s / 107.62 s / 181.20 x
lgbm gbdt 100M lzma 25.6 MiB / 10.6 MiB / 2.41 x 95.82 s / 26.86 s / 0.28 x 2.74 s / 6.09 s / 2.22 x
lgbm rf 10M 10.9 MiB / 3.2 MiB / 3.46 x 0.35 s / 0.75 s / 2.12 x 0.06 s / 0.49 s / 8.15 x
lgbm rf 10M lzma 0.7 MiB / 0.4 MiB / 1.86 x 2.10 s / 1.01 s / 0.48 x 0.13 s / 0.54 s / 4.10 x