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add TensorWaves benchmark results (pytest) benchmark result for aa36e66
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Nov 8, 2023
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@@ -1,5 +1,5 @@ | ||
window.BENCHMARK_DATA = { | ||
"lastUpdate": 1696868022341, | ||
"lastUpdate": 1699450956098, | ||
"repoUrl": "https://github.com/ComPWA/tensorwaves", | ||
"entries": { | ||
"TensorWaves benchmark results": [ | ||
|
@@ -15014,6 +15014,142 @@ window.BENCHMARK_DATA = { | |
"extra": "mean: 798.6888098000122 msec\nrounds: 5" | ||
} | ||
] | ||
}, | ||
{ | ||
"commit": { | ||
"author": { | ||
"email": "[email protected]", | ||
"name": "Remco de Boer", | ||
"username": "redeboer" | ||
}, | ||
"committer": { | ||
"email": "[email protected]", | ||
"name": "GitHub", | ||
"username": "web-flow" | ||
}, | ||
"distinct": true, | ||
"id": "aa36e6636b5179d305a6186a56cc272f3678b52e", | ||
"message": "ENH: set data keys as first positional arguments (#488)", | ||
"timestamp": "2023-11-08T14:39:44+01:00", | ||
"tree_id": "6089e24e11f2b69ed0df9a4b6872c60e8bf1cb39", | ||
"url": "https://github.com/ComPWA/tensorwaves/commit/aa36e6636b5179d305a6186a56cc272f3678b52e" | ||
}, | ||
"date": 1699450955473, | ||
"tool": "pytest", | ||
"benches": [ | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_data[10000-jax]", | ||
"value": 0.43212293449461875, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 2.31415627399997 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_data[10000-numpy]", | ||
"value": 0.35976070162926915, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 2.7796254439999757 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_data[10000-tf]", | ||
"value": 0.3904975984805703, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 2.5608352110000396 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/ampform.py::TestJPsiToGammaPiPi::test_fit[10000-jax]", | ||
"value": 0.7198242756916121, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0", | ||
"extra": "mean: 1.3892279460000054 sec\nrounds: 1" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-jax]", | ||
"value": 24.933745065649873, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0025536172202358055", | ||
"extra": "mean: 40.10628958333484 msec\nrounds: 12" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-numpy]", | ||
"value": 172.19244861724295, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00011179184561905289", | ||
"extra": "mean: 5.807455600000466 msec\nrounds: 160" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-numba]", | ||
"value": 5.384394705612399, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0015094303684840972", | ||
"extra": "mean: 185.72189720000551 msec\nrounds: 5" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_data[3000-tf]", | ||
"value": 98.72208410799944, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00042555976493016223", | ||
"extra": "mean: 10.12944579761936 msec\nrounds: 84" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-jax]", | ||
"value": 9.352295111672042, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0005659957504415581", | ||
"extra": "mean: 106.92562500000236 msec\nrounds: 6" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-numpy]", | ||
"value": 9.772740356648908, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0013011033495843871", | ||
"extra": "mean: 102.32544440000879 msec\nrounds: 10" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-numba]", | ||
"value": 9.838227327021672, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0020212336935661233", | ||
"extra": "mean: 101.64432745454053 msec\nrounds: 11" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-Minuit2-tf]", | ||
"value": 1.2942062651987425, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0019623407254366334", | ||
"extra": "mean: 772.6743618 msec\nrounds: 5" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-jax]", | ||
"value": 8.800143036821254, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0004603690333550194", | ||
"extra": "mean: 113.63451659999555 msec\nrounds: 5" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-numpy]", | ||
"value": 9.531845132517335, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0023333682864347567", | ||
"extra": "mean: 104.91148209999324 msec\nrounds: 10" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-numba]", | ||
"value": 9.642699573915468, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0009152341490643514", | ||
"extra": "mean: 103.70539829998506 msec\nrounds: 10" | ||
}, | ||
{ | ||
"name": "benchmarks/expression.py::test_fit[1000-ScipyMinimizer-tf]", | ||
"value": 1.4733997971546289, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.004730094546128211", | ||
"extra": "mean: 678.7024146000022 msec\nrounds: 5" | ||
} | ||
] | ||
} | ||
] | ||
} | ||
|