Expand no-code dataset info with datasets-server info

[mariosasko]

E.g., to have info about a dataset's number of examples for more informative TQDM bars.

[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]

Nice ! Pretty useful for big datasets

[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)	0.005237 / 0.011353 (-0.006116)	0.003614 / 0.011008 (-0.007394)	0.063349 / 0.038508 (0.024841)	0.027297 / 0.023109 (0.004187)	0.236203 / 0.275898 (-0.039695)	0.260029 / 0.323480 (-0.063451)	0.003096 / 0.007986 (-0.004889)	0.003342 / 0.004328 (-0.000987)	0.048703 / 0.004250 (0.044453)	0.043121 / 0.037052 (0.006069)	0.257491 / 0.258489 (-0.000998)	0.282861 / 0.293841 (-0.010980)	0.027701 / 0.128546 (-0.100845)	0.010634 / 0.075646 (-0.065012)	0.207369 / 0.419271 (-0.211903)	0.035799 / 0.043533 (-0.007734)	0.240445 / 0.255139 (-0.014694)	0.261977 / 0.283200 (-0.021223)	0.018175 / 0.141683 (-0.123508)	1.143964 / 1.452155 (-0.308191)	1.230057 / 1.492716 (-0.262659)

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)	0.096656 / 0.018006 (0.078650)	0.303434 / 0.000490 (0.302944)	0.000225 / 0.000200 (0.000025)	0.000051 / 0.000054 (-0.000004)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018454 / 0.037411 (-0.018957)	0.061792 / 0.014526 (0.047266)	0.073384 / 0.176557 (-0.103172)	0.120148 / 0.737135 (-0.616988)	0.074221 / 0.296338 (-0.222118)

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)	0.290291 / 0.215209 (0.075082)	2.822908 / 2.077655 (0.745254)	1.483139 / 1.504120 (-0.020981)	1.349619 / 1.541195 (-0.191576)	1.356588 / 1.468490 (-0.111902)	0.571723 / 4.584777 (-4.013054)	2.402696 / 3.745712 (-1.343016)	2.832215 / 5.269862 (-2.437647)	1.794962 / 4.565676 (-2.770714)	0.062707 / 0.424275 (-0.361568)	0.004997 / 0.007607 (-0.002610)	0.343093 / 0.226044 (0.117049)	3.383028 / 2.268929 (1.114100)	1.818624 / 55.444624 (-53.626000)	1.549859 / 6.876477 (-5.326618)	1.667838 / 2.142072 (-0.474235)	0.648574 / 4.805227 (-4.156653)	0.119181 / 6.500664 (-6.381484)	0.042074 / 0.075469 (-0.033395)

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)	0.982039 / 1.841788 (-0.859748)	11.411759 / 8.074308 (3.337451)	9.783405 / 10.191392 (-0.407987)	0.129577 / 0.680424 (-0.550847)	0.014091 / 0.534201 (-0.520110)	0.297925 / 0.579283 (-0.281358)	0.263884 / 0.434364 (-0.170480)	0.346032 / 0.540337 (-0.194305)	0.444806 / 1.386936 (-0.942130)

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)	0.005527 / 0.011353 (-0.005826)	0.003677 / 0.011008 (-0.007332)	0.050245 / 0.038508 (0.011737)	0.030070 / 0.023109 (0.006961)	0.272640 / 0.275898 (-0.003258)	0.296555 / 0.323480 (-0.026925)	0.004247 / 0.007986 (-0.003738)	0.003833 / 0.004328 (-0.000495)	0.049341 / 0.004250 (0.045091)	0.046604 / 0.037052 (0.009552)	0.282765 / 0.258489 (0.024276)	0.314924 / 0.293841 (0.021084)	0.029749 / 0.128546 (-0.098797)	0.010524 / 0.075646 (-0.065122)	0.057859 / 0.419271 (-0.361412)	0.053172 / 0.043533 (0.009640)	0.274906 / 0.255139 (0.019767)	0.290566 / 0.283200 (0.007366)	0.019299 / 0.141683 (-0.122384)	1.164092 / 1.452155 (-0.288062)	1.205074 / 1.492716 (-0.287642)

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)	0.093943 / 0.018006 (0.075936)	0.298746 / 0.000490 (0.298256)	0.000232 / 0.000200 (0.000032)	0.000054 / 0.000054 (-0.000000)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.022098 / 0.037411 (-0.015313)	0.075523 / 0.014526 (0.060997)	0.086784 / 0.176557 (-0.089773)	0.124610 / 0.737135 (-0.612525)	0.087743 / 0.296338 (-0.208595)

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)	0.298555 / 0.215209 (0.083346)	2.951493 / 2.077655 (0.873838)	1.611448 / 1.504120 (0.107328)	1.481503 / 1.541195 (-0.059692)	1.497937 / 1.468490 (0.029447)	0.580402 / 4.584777 (-4.004375)	2.433308 / 3.745712 (-1.312404)	2.712717 / 5.269862 (-2.557145)	1.766286 / 4.565676 (-2.799391)	0.063973 / 0.424275 (-0.360303)	0.005006 / 0.007607 (-0.002601)	0.354541 / 0.226044 (0.128497)	3.486448 / 2.268929 (1.217519)	1.972779 / 55.444624 (-53.471846)	1.709018 / 6.876477 (-5.167458)	1.864242 / 2.142072 (-0.277831)	0.678213 / 4.805227 (-4.127014)	0.119525 / 6.500664 (-6.381140)	0.041387 / 0.075469 (-0.034082)

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)	1.021337 / 1.841788 (-0.820451)	12.049563 / 8.074308 (3.975255)	10.424701 / 10.191392 (0.233309)	0.131444 / 0.680424 (-0.548980)	0.015644 / 0.534201 (-0.518557)	0.293712 / 0.579283 (-0.285571)	0.279160 / 0.434364 (-0.155204)	0.327991 / 0.540337 (-0.212346)	0.435455 / 1.386936 (-0.951481)