Remove beam

[albertvillanova]

Remove beam, as part of the 3.0 release.

[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.

[lhoestq]

Awesome !

[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)	0.005931 / 0.011353 (-0.005422)	0.004127 / 0.011008 (-0.006881)	0.063854 / 0.038508 (0.025346)	0.034687 / 0.023109 (0.011577)	0.251397 / 0.275898 (-0.024501)	0.280348 / 0.323480 (-0.043132)	0.005008 / 0.007986 (-0.002977)	0.002930 / 0.004328 (-0.001398)	0.050703 / 0.004250 (0.046452)	0.047109 / 0.037052 (0.010057)	0.258525 / 0.258489 (0.000035)	0.288759 / 0.293841 (-0.005081)	0.030547 / 0.128546 (-0.097999)	0.102184 / 0.075646 (0.026537)	0.207934 / 0.419271 (-0.211338)	0.036477 / 0.043533 (-0.007056)	0.338160 / 0.255139 (0.083021)	0.310735 / 0.283200 (0.027535)	0.018637 / 0.141683 (-0.123045)	1.228539 / 1.452155 (-0.223616)	1.168004 / 1.492716 (-0.324713)

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)	0.098355 / 0.018006 (0.080348)	0.302310 / 0.000490 (0.301820)	0.000215 / 0.000200 (0.000015)	0.000044 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.019607 / 0.037411 (-0.017804)	0.063795 / 0.014526 (0.049269)	0.075029 / 0.176557 (-0.101528)	0.121293 / 0.737135 (-0.615842)	0.076480 / 0.296338 (-0.219858)

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)	0.285285 / 0.215209 (0.070076)	2.747455 / 2.077655 (0.669801)	1.454190 / 1.504120 (-0.049929)	1.330777 / 1.541195 (-0.210418)	1.358292 / 1.468490 (-0.110198)	0.724991 / 4.584777 (-3.859786)	2.374889 / 3.745712 (-1.370823)	2.985868 / 5.269862 (-2.283994)	1.921521 / 4.565676 (-2.644156)	0.078589 / 0.424275 (-0.345686)	0.005104 / 0.007607 (-0.002503)	0.333898 / 0.226044 (0.107853)	3.317702 / 2.268929 (1.048773)	1.887161 / 55.444624 (-53.557463)	1.510700 / 6.876477 (-5.365777)	1.544175 / 2.142072 (-0.597898)	0.804262 / 4.805227 (-4.000965)	0.134015 / 6.500664 (-6.366649)	0.042819 / 0.075469 (-0.032650)

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)	1.012142 / 1.841788 (-0.829645)	11.861780 / 8.074308 (3.787472)	9.797285 / 10.191392 (-0.394107)	0.142114 / 0.680424 (-0.538310)	0.013984 / 0.534201 (-0.520217)	0.302412 / 0.579283 (-0.276871)	0.265060 / 0.434364 (-0.169304)	0.337510 / 0.540337 (-0.202828)	0.432197 / 1.386936 (-0.954739)

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)	0.005920 / 0.011353 (-0.005433)	0.003991 / 0.011008 (-0.007017)	0.049874 / 0.038508 (0.011366)	0.033771 / 0.023109 (0.010662)	0.264789 / 0.275898 (-0.011109)	0.287554 / 0.323480 (-0.035926)	0.004341 / 0.007986 (-0.003644)	0.002888 / 0.004328 (-0.001441)	0.049383 / 0.004250 (0.045133)	0.040757 / 0.037052 (0.003704)	0.286067 / 0.258489 (0.027578)	0.311105 / 0.293841 (0.017264)	0.031482 / 0.128546 (-0.097064)	0.012358 / 0.075646 (-0.063288)	0.060298 / 0.419271 (-0.358973)	0.033237 / 0.043533 (-0.010296)	0.265804 / 0.255139 (0.010665)	0.281273 / 0.283200 (-0.001927)	0.017879 / 0.141683 (-0.123804)	1.154059 / 1.452155 (-0.298096)	1.156758 / 1.492716 (-0.335958)

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)	0.004677 / 0.018006 (-0.013329)	0.300768 / 0.000490 (0.300278)	0.000212 / 0.000200 (0.000013)	0.000043 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.023032 / 0.037411 (-0.014379)	0.077498 / 0.014526 (0.062973)	0.089134 / 0.176557 (-0.087422)	0.129691 / 0.737135 (-0.607444)	0.091372 / 0.296338 (-0.204967)

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)	0.290823 / 0.215209 (0.075613)	2.873159 / 2.077655 (0.795504)	1.563361 / 1.504120 (0.059241)	1.447048 / 1.541195 (-0.094147)	1.490473 / 1.468490 (0.021983)	0.715642 / 4.584777 (-3.869135)	0.996223 / 3.745712 (-2.749489)	2.861466 / 5.269862 (-2.408396)	1.915581 / 4.565676 (-2.650096)	0.077892 / 0.424275 (-0.346383)	0.005463 / 0.007607 (-0.002144)	0.339670 / 0.226044 (0.113626)	3.412830 / 2.268929 (1.143902)	1.908676 / 55.444624 (-53.535949)	1.625358 / 6.876477 (-5.251119)	1.769437 / 2.142072 (-0.372635)	0.792505 / 4.805227 (-4.012722)	0.133007 / 6.500664 (-6.367657)	0.041305 / 0.075469 (-0.034164)

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)	0.986882 / 1.841788 (-0.854905)	12.368101 / 8.074308 (4.293793)	10.367439 / 10.191392 (0.176047)	0.141248 / 0.680424 (-0.539176)	0.016144 / 0.534201 (-0.518057)	0.300962 / 0.579283 (-0.278321)	0.126863 / 0.434364 (-0.307501)	0.341107 / 0.540337 (-0.199230)	0.439819 / 1.386936 (-0.947117)