rtlee9
commented
4 months ago My local asv benchmarking shows the regression was introduced in d9f70b3 rather than the previous commits
(venv) ➜ asv_bench git:(d9f70b397a) asv continuous -f 1.1 -E virtualenv HEAD~ HEAD -b inference.ToDatetimeFromIntsFloats.time_ Couldn't load asv.plugins._mamba_helpers because No module named 'libmambapy' · Creating environments · Discovering benchmarks ·· Uninstalling from virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter. ·· Installing d9f70b39 <v2.3.0.dev0~271> into virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter. · Running 12 total benchmarks (2 commits * 1 environments * 6 benchmarks) [ 0.00%] · For pandas commit e37ff77b <v2.3.0.dev0~272> (round 1/2): [ 0.00%] ·· Building for virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter..
[ 0.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter [ 4.17%] ··· Running (inference.ToDatetimeFromIntsFloats.time_nanosec_float64--)...... [25.00%] · For pandas commit d9f70b39 <v2.3.0.dev0~271> (round 1/2): [25.00%] ·· Building for virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter..
[25.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
[29.17%] ··· Running (inference.ToDatetimeFromIntsFloats.time_nanosec_float64--)...... [50.00%] · For pandas commit d9f70b39 <v2.3.0.dev0~271> (round 2/2): [50.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
[54.17%] ··· inference.ToDatetimeFromIntsFloats.time_nanosec_float64 260±3ms
[58.33%] ··· inference.ToDatetimeFromIntsFloats.time_nanosec_int64 3.16±0.09ms
[62.50%] ··· inference.ToDatetimeFromIntsFloats.time_nanosec_uint64 3.03±0.2ms [66.67%] ··· inference.ToDatetimeFromIntsFloats.time_sec_float64 262±2ms [70.83%] ··· inference.ToDatetimeFromIntsFloats.time_sec_int64 31.1±0.3ms
[75.00%] ··· inference.ToDatetimeFromIntsFloats.time_sec_uint64 30.9±0.2ms
[75.00%] · For pandas commit e37ff77b <v2.3.0.dev0~272> (round 2/2):
[75.00%] ·· Building for virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter..
[75.00%] ·· Benchmarking virtualenv-py3.10-Cython3.0.5-jinja2-matplotlib-meson-meson-python-numba-numexpr-odfpy-openpyxl-pyarrow-python-build-scipy-sqlalchemy-tables-xlrd-xlsxwriter
[79.17%] ··· inference.ToDatetimeFromIntsFloats.time_nanosec_float64 6.17±0.3ms
[83.33%] ··· inference.ToDatetimeFromIntsFloats.time_nanosec_int64 2.93±0.06ms
[87.50%] ··· inference.ToDatetimeFromIntsFloats.time_nanosec_uint64 2.91±0.06ms
[91.67%] ··· inference.ToDatetimeFromIntsFloats.time_sec_float64 5.51±0.3ms
[95.83%] ··· inference.ToDatetimeFromIntsFloats.time_sec_int64 31.0±0.3ms
[100.00%] ··· inference.ToDatetimeFromIntsFloats.time_sec_uint64 30.9±0.08ms
| Change | Before [e37ff77b] <v2.3.0.dev0~272> | After [d9f70b39] <v2.3.0.dev0~271> | Ratio | Benchmark (Parameter) |
|----------|---------------------------------------|--------------------------------------|---------|---------------------------------------------------------|
| + | 5.51±0.3ms | 262±2ms | 47.56 | inference.ToDatetimeFromIntsFloats.time_sec_float64 |
| + | 6.17±0.3ms | 260±3ms | 42.2 | inference.ToDatetimeFromIntsFloats.time_nanosec_float64 |
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
rhshadrach
PR #56037 may have induced a performance regression. If it was a necessary behavior change, this may have been expected and everything is okay.
Please check the links below. If any ASVs are parameterized, the combinations of parameters that a regression has been detected for appear as subbullets.
Subsequent benchmarks may have skipped some commits. The link below lists the commits that are between the two benchmark runs where the regression was identified.
https://github.com/pandas-dev/pandas/compare/05c32ba18f88921b78dc5984c70956247497ab4c...d9f70b397a010754ae41e7d201bba05834294559
cc @jbrockmendel