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Pin version exclusion for Markdown (#3293)
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@albertvillanova
albertvillanova authored Nov 18, 2021
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"sphinx-panels",
"sphinx-inline-tabs",
"myst-parser",
"Markdown!=3.3.5",
],
}

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Show benchmarks

PyArrow==3.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.076242 / 0.076242 (0.064889) 0.005166 / 0.005166 (-0.005842) 0.038230 / 0.038230 (-0.000278) 0.039390 / 0.039390 (0.016281) 0.350184 / 0.350184 (0.074286) 0.427388 / 0.427388 (0.103908) 0.098406 / 0.098406 (0.090421) 0.006316 / 0.006316 (0.001987) 0.010635 / 0.010635 (0.006385) 0.045496 / 0.045496 (0.008443) 0.367143 / 0.367143 (0.108654) 0.395103 / 0.395103 (0.101262) 0.108427 / 0.108427 (-0.020120) 0.012961 / 0.012961 (-0.062685) 0.316546 / 0.316546 (-0.102726) 0.058556 / 0.058556 (0.015024) 0.366233 / 0.366233 (0.111094) 0.407953 / 0.407953 (0.124753) 0.092835 / 0.092835 (-0.048848) 2.041841 / 2.041841 (0.589687) 2.115541 / 2.115541 (0.622825)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.280707 / 0.280707 (0.262700) 0.510590 / 0.510590 (0.510100) 0.006041 / 0.006041 (0.005841) 0.000110 / 0.000110 (0.000056)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.039478 / 0.039478 (0.002067) 0.026802 / 0.026802 (0.012277) 0.031095 / 0.031095 (-0.145461) 0.232289 / 0.232289 (-0.504847) 0.032256 / 0.032256 (-0.264082)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.641800 / 0.641800 (0.426591) 6.491097 / 6.491097 (4.413442) 2.502391 / 2.502391 (0.998271) 2.123746 / 2.123746 (0.582551) 2.119712 / 2.119712 (0.651222) 0.719786 / 0.719786 (-3.864991) 6.984980 / 6.984980 (3.239268) 3.142570 / 3.142570 (-2.127291) 1.418939 / 1.418939 (-3.146738) 0.081256 / 0.081256 (-0.343019) 0.012469 / 0.012469 (0.004862) 0.782455 / 0.782455 (0.556411) 7.970374 / 7.970374 (5.701445) 3.063717 / 3.063717 (-52.380908) 2.334900 / 2.334900 (-4.541577) 2.548310 / 2.548310 (0.406238) 0.886360 / 0.886360 (-3.918867) 0.182001 / 0.182001 (-6.318663) 0.067645 / 0.067645 (-0.007824)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 1.981827 / 1.981827 (0.140039) 14.080762 / 14.080762 (6.006453) 42.500053 / 42.500053 (32.308661) 0.929624 / 0.929624 (0.249201) 0.649798 / 0.649798 (0.115597) 0.468254 / 0.468254 (-0.111029) 0.709330 / 0.709330 (0.274966) 0.338301 / 0.338301 (-0.202036) 0.350012 / 0.350012 (-1.036924)
PyArrow==latest
Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.074190 / 0.074190 (0.062837) 0.005439 / 0.005439 (-0.005569) 0.036500 / 0.036500 (-0.002009) 0.034865 / 0.034865 (0.011756) 0.381667 / 0.381667 (0.105769) 0.405888 / 0.405888 (0.082408) 0.088772 / 0.088772 (0.080786) 0.005118 / 0.005118 (0.000789) 0.008414 / 0.008414 (0.004164) 0.039095 / 0.039095 (0.002042) 0.393325 / 0.393325 (0.134836) 0.414910 / 0.414910 (0.121069) 0.105873 / 0.105873 (-0.022673) 0.013783 / 0.013783 (-0.061864) 0.316409 / 0.316409 (-0.102863) 0.057946 / 0.057946 (0.014413) 0.358076 / 0.358076 (0.102937) 0.408283 / 0.408283 (0.125083) 0.103616 / 0.103616 (-0.038067) 2.109486 / 2.109486 (0.657331) 2.173414 / 2.173414 (0.680698)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.294367 / 0.294367 (0.276361) 0.550176 / 0.550176 (0.549686) 0.004667 / 0.004667 (0.004467) 0.000143 / 0.000143 (0.000088)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.037645 / 0.037645 (0.000234) 0.027283 / 0.027283 (0.012757) 0.032508 / 0.032508 (-0.144049) 0.226337 / 0.226337 (-0.510798) 0.038787 / 0.038787 (-0.257551)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.662622 / 0.662622 (0.447412) 6.575183 / 6.575183 (4.497528) 2.815302 / 2.815302 (1.311182) 2.473566 / 2.473566 (0.932371) 2.551093 / 2.551093 (1.082602) 0.747930 / 0.747930 (-3.836847) 6.889078 / 6.889078 (3.143366) 4.884008 / 4.884008 (-0.385854) 1.483932 / 1.483932 (-3.081745) 0.079951 / 0.079951 (-0.344324) 0.014113 / 0.014113 (0.006506) 0.832572 / 0.832572 (0.606528) 8.240020 / 8.240020 (5.971092) 3.579663 / 3.579663 (-51.864962) 2.807537 / 2.807537 (-4.068939) 2.929375 / 2.929375 (0.787303) 0.871474 / 0.871474 (-3.933754) 0.167361 / 0.167361 (-6.333303) 0.068408 / 0.068408 (-0.007062)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 1.938393 / 1.938393 (0.096605) 14.487945 / 14.487945 (6.413637) 43.885884 / 43.885884 (33.694492) 1.059319 / 1.059319 (0.378895) 0.716647 / 0.716647 (0.182446) 0.494106 / 0.494106 (-0.085177) 0.736737 / 0.736737 (0.302373) 0.348998 / 0.348998 (-0.191339) 0.402944 / 0.402944 (-0.983992)

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