Fix CI by temporarily marking test_convert_to_parquet as expected to fail

[albertvillanova]

As a hotfix for CI, temporarily mark test_convert_to_parquet as expected to fail.

Fix #7073.

Revert once root cause is fixed.

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[github-actions]

Show benchmarks

PyArrow==8.0.0

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)	0.005168 / 0.011353 (-0.006185)	0.003572 / 0.011008 (-0.007436)	0.062755 / 0.038508 (0.024247)	0.030371 / 0.023109 (0.007262)	0.250240 / 0.275898 (-0.025658)	0.268091 / 0.323480 (-0.055389)	0.003260 / 0.007986 (-0.004726)	0.002706 / 0.004328 (-0.001622)	0.048957 / 0.004250 (0.044706)	0.044441 / 0.037052 (0.007389)	0.251801 / 0.258489 (-0.006688)	0.289401 / 0.293841 (-0.004440)	0.028991 / 0.128546 (-0.099555)	0.011871 / 0.075646 (-0.063775)	0.203722 / 0.419271 (-0.215549)	0.035911 / 0.043533 (-0.007622)	0.248070 / 0.255139 (-0.007069)	0.266480 / 0.283200 (-0.016720)	0.019831 / 0.141683 (-0.121852)	1.143429 / 1.452155 (-0.308726)	1.160102 / 1.492716 (-0.332614)

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)	0.096740 / 0.018006 (0.078734)	0.302473 / 0.000490 (0.301983)	0.000219 / 0.000200 (0.000019)	0.000043 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018367 / 0.037411 (-0.019045)	0.062346 / 0.014526 (0.047820)	0.074416 / 0.176557 (-0.102140)	0.120507 / 0.737135 (-0.616628)	0.076536 / 0.296338 (-0.219802)

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)	0.284093 / 0.215209 (0.068884)	2.738805 / 2.077655 (0.661150)	1.469263 / 1.504120 (-0.034856)	1.349122 / 1.541195 (-0.192073)	1.355578 / 1.468490 (-0.112912)	0.720364 / 4.584777 (-3.864413)	2.360339 / 3.745712 (-1.385373)	2.941134 / 5.269862 (-2.328728)	1.888692 / 4.565676 (-2.676984)	0.077111 / 0.424275 (-0.347164)	0.005070 / 0.007607 (-0.002537)	0.334122 / 0.226044 (0.108078)	3.298378 / 2.268929 (1.029450)	1.868514 / 55.444624 (-53.576111)	1.528561 / 6.876477 (-5.347916)	1.535319 / 2.142072 (-0.606754)	0.778591 / 4.805227 (-4.026636)	0.131364 / 6.500664 (-6.369300)	0.041697 / 0.075469 (-0.033773)

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)	0.970243 / 1.841788 (-0.871544)	11.324752 / 8.074308 (3.250443)	9.612381 / 10.191392 (-0.579011)	0.138842 / 0.680424 (-0.541582)	0.014479 / 0.534201 (-0.519722)	0.309415 / 0.579283 (-0.269868)	0.264654 / 0.434364 (-0.169710)	0.343695 / 0.540337 (-0.196642)	0.435323 / 1.386936 (-0.951613)

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)	0.005680 / 0.011353 (-0.005673)	0.003614 / 0.011008 (-0.007394)	0.060575 / 0.038508 (0.022067)	0.031103 / 0.023109 (0.007994)	0.269083 / 0.275898 (-0.006815)	0.291556 / 0.323480 (-0.031923)	0.004354 / 0.007986 (-0.003632)	0.002739 / 0.004328 (-0.001589)	0.049056 / 0.004250 (0.044806)	0.039759 / 0.037052 (0.002707)	0.280608 / 0.258489 (0.022119)	0.324798 / 0.293841 (0.030957)	0.032030 / 0.128546 (-0.096516)	0.011862 / 0.075646 (-0.063784)	0.060011 / 0.419271 (-0.359261)	0.033960 / 0.043533 (-0.009573)	0.271114 / 0.255139 (0.015975)	0.293922 / 0.283200 (0.010722)	0.019497 / 0.141683 (-0.122185)	1.137871 / 1.452155 (-0.314284)	1.180656 / 1.492716 (-0.312061)

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)	0.094201 / 0.018006 (0.076194)	0.306657 / 0.000490 (0.306167)	0.000215 / 0.000200 (0.000015)	0.000068 / 0.000054 (0.000014)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.022562 / 0.037411 (-0.014850)	0.077170 / 0.014526 (0.062644)	0.088915 / 0.176557 (-0.087642)	0.129455 / 0.737135 (-0.607680)	0.091571 / 0.296338 (-0.204767)

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)	0.300753 / 0.215209 (0.085544)	2.941929 / 2.077655 (0.864274)	1.613451 / 1.504120 (0.109331)	1.498365 / 1.541195 (-0.042830)	1.517124 / 1.468490 (0.048634)	0.709209 / 4.584777 (-3.875568)	0.950478 / 3.745712 (-2.795235)	2.799328 / 5.269862 (-2.470533)	1.872895 / 4.565676 (-2.692782)	0.078233 / 0.424275 (-0.346042)	0.005613 / 0.007607 (-0.001994)	0.349590 / 0.226044 (0.123545)	3.500213 / 2.268929 (1.231284)	2.001155 / 55.444624 (-53.443469)	1.704845 / 6.876477 (-5.171632)	1.810722 / 2.142072 (-0.331350)	0.795326 / 4.805227 (-4.009901)	0.132913 / 6.500664 (-6.367751)	0.041209 / 0.075469 (-0.034260)

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)	1.029513 / 1.841788 (-0.812274)	12.005617 / 8.074308 (3.931309)	10.119379 / 10.191392 (-0.072013)	0.139767 / 0.680424 (-0.540657)	0.015241 / 0.534201 (-0.518960)	0.301164 / 0.579283 (-0.278119)	0.121563 / 0.434364 (-0.312801)	0.336672 / 0.540337 (-0.203666)	0.431526 / 1.386936 (-0.955410)