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bump hugginggace_hub (#3199)
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@lhoestq
lhoestq committed Nov 2, 2021
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# for data streaming via http
"aiohttp",
# To get datasets from the Datasets Hub on huggingface.co
"huggingface_hub>=0.0.19,<0.1.0",
"huggingface_hub>=0.1.0,<1.0.0",
# Utilities from PyPA to e.g., compare versions
"packaging",
]
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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) 0.084662 / 0.011353 (0.073309) 0.004571 / 0.011008 (-0.006437) 0.037326 / 0.038508 (-0.001182) 0.044202 / 0.023109 (0.021092) 0.353365 / 0.275898 (0.077467) 0.409928 / 0.323480 (0.086449) 0.097899 / 0.007986 (0.089913) 0.005610 / 0.004328 (0.001282) 0.010720 / 0.004250 (0.006470) 0.047753 / 0.037052 (0.010701) 0.350439 / 0.258489 (0.091950) 0.407300 / 0.293841 (0.113459) 0.108410 / 0.128546 (-0.020136) 0.010363 / 0.075646 (-0.065283) 0.320038 / 0.419271 (-0.099233) 0.059175 / 0.043533 (0.015642) 0.365243 / 0.255139 (0.110104) 0.404121 / 0.283200 (0.120921) 0.100191 / 0.141683 (-0.041492) 2.132503 / 1.452155 (0.680348) 2.184092 / 1.492716 (0.691376)

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) 0.221074 / 0.018006 (0.203068) 0.479035 / 0.000490 (0.478545) 0.005797 / 0.000200 (0.005597) 0.000422 / 0.000054 (0.000368)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.046163 / 0.037411 (0.008752) 0.027644 / 0.014526 (0.013118) 0.032542 / 0.176557 (-0.144015) 0.244483 / 0.737135 (-0.492653) 0.035223 / 0.296338 (-0.261115)

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) 0.518124 / 0.215209 (0.302915) 5.215490 / 2.077655 (3.137835) 2.342011 / 1.504120 (0.837891) 2.024568 / 1.541195 (0.483373) 2.099787 / 1.468490 (0.631297) 0.538194 / 4.584777 (-4.046583) 5.953050 / 3.745712 (2.207338) 2.775973 / 5.269862 (-2.493889) 1.108936 / 4.565676 (-3.456740) 0.060659 / 0.424275 (-0.363616) 0.013330 / 0.007607 (0.005723) 0.647653 / 0.226044 (0.421608) 6.584351 / 2.268929 (4.315423) 2.870142 / 55.444624 (-52.574483) 2.373363 / 6.876477 (-4.503114) 2.525441 / 2.142072 (0.383369) 0.638728 / 4.805227 (-4.166500) 0.144737 / 6.500664 (-6.355927) 0.073431 / 0.075469 (-0.002038)

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) 1.928544 / 1.841788 (0.086756) 15.881883 / 8.074308 (7.807575) 33.367447 / 10.191392 (23.176055) 0.982080 / 0.680424 (0.301656) 0.680989 / 0.534201 (0.146788) 0.476698 / 0.579283 (-0.102585) 0.646573 / 0.434364 (0.212209) 0.327780 / 0.540337 (-0.212558) 0.353932 / 1.386936 (-1.033004)
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) 0.083338 / 0.011353 (0.071985) 0.004498 / 0.011008 (-0.006510) 0.035676 / 0.038508 (-0.002832) 0.039465 / 0.023109 (0.016356) 0.363734 / 0.275898 (0.087836) 0.419344 / 0.323480 (0.095864) 0.100363 / 0.007986 (0.092377) 0.005837 / 0.004328 (0.001509) 0.008484 / 0.004250 (0.004233) 0.044007 / 0.037052 (0.006954) 0.349983 / 0.258489 (0.091493) 0.408836 / 0.293841 (0.114996) 0.105293 / 0.128546 (-0.023254) 0.010774 / 0.075646 (-0.064872) 0.310446 / 0.419271 (-0.108826) 0.054645 / 0.043533 (0.011112) 0.367896 / 0.255139 (0.112757) 0.412620 / 0.283200 (0.129421) 0.095954 / 0.141683 (-0.045729) 2.045188 / 1.452155 (0.593033) 2.119511 / 1.492716 (0.626795)

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) 0.353313 / 0.018006 (0.335307) 0.468443 / 0.000490 (0.467954) 0.074240 / 0.000200 (0.074040) 0.001451 / 0.000054 (0.001397)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.040476 / 0.037411 (0.003064) 0.024162 / 0.014526 (0.009636) 0.032898 / 0.176557 (-0.143659) 0.246634 / 0.737135 (-0.490501) 0.037375 / 0.296338 (-0.258964)

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) 0.524639 / 0.215209 (0.309430) 5.387527 / 2.077655 (3.309872) 2.441611 / 1.504120 (0.937491) 2.162505 / 1.541195 (0.621310) 2.216681 / 1.468490 (0.748191) 0.511507 / 4.584777 (-4.073270) 6.124300 / 3.745712 (2.378588) 2.595995 / 5.269862 (-2.673866) 1.104242 / 4.565676 (-3.461434) 0.063814 / 0.424275 (-0.360461) 0.012979 / 0.007607 (0.005371) 0.679156 / 0.226044 (0.453112) 6.647291 / 2.268929 (4.378363) 2.988240 / 55.444624 (-52.456384) 2.541383 / 6.876477 (-4.335094) 2.666801 / 2.142072 (0.524728) 0.660144 / 4.805227 (-4.145083) 0.142400 / 6.500664 (-6.358264) 0.072163 / 0.075469 (-0.003307)

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) 1.905575 / 1.841788 (0.063787) 15.735696 / 8.074308 (7.661388) 32.678274 / 10.191392 (22.486882) 0.945227 / 0.680424 (0.264803) 0.695749 / 0.534201 (0.161548) 0.478601 / 0.579283 (-0.100682) 0.667267 / 0.434364 (0.232904) 0.349446 / 0.540337 (-0.190892) 0.334325 / 1.386936 (-1.052611)

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