Skip to content

Commit

Permalink
fix test
Browse files Browse the repository at this point in the history
  • Loading branch information
@lhoestq
lhoestq committed Sep 2, 2022
1 parent 247e3cf commit 0418808
Showing 1 changed file with 1 addition and 1 deletion.
2 changes: 1 addition & 1 deletion src/datasets/info.py
View file
Expand Up @@ -339,7 +339,7 @@ def write_to_directory(self, dataset_infos_dir, overwrite=False, pretty_print=Fa
if os.path.exists(dataset_readme_path):
dataset_metadata = DatasetMetadata.from_readme(Path(dataset_readme_path))
else:
dataset_metadata = {}
dataset_metadata = DatasetMetadata()
if total_dataset_infos:
dataset_metadata["dataset_infos"] = [dset_info._to_yaml_dict() for dset_info in total_dataset_infos.values()]
if len(dataset_metadata["dataset_infos"]) == 1:
Expand Down

1 comment on commit 0418808

@github-actions
Copy link

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Show benchmarks

PyArrow==6.0.0

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) 0.007946 / 0.011353 (-0.003407) 0.003947 / 0.011008 (-0.007061) 0.030721 / 0.038508 (-0.007787) 0.039715 / 0.023109 (0.016605) 0.298253 / 0.275898 (0.022355) 0.360518 / 0.323480 (0.037039) 0.005921 / 0.007986 (-0.002065) 0.004694 / 0.004328 (0.000365) 0.007095 / 0.004250 (0.002845) 0.045702 / 0.037052 (0.008649) 0.311052 / 0.258489 (0.052563) 0.344683 / 0.293841 (0.050842) 0.031538 / 0.128546 (-0.097009) 0.009660 / 0.075646 (-0.065986) 0.272330 / 0.419271 (-0.146942) 0.051703 / 0.043533 (0.008170) 0.298887 / 0.255139 (0.043748) 0.318437 / 0.283200 (0.035237) 0.106409 / 0.141683 (-0.035274) 1.563785 / 1.452155 (0.111630) 1.552564 / 1.492716 (0.059847)

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) 0.205414 / 0.018006 (0.187408) 0.445103 / 0.000490 (0.444613) 0.001205 / 0.000200 (0.001006) 0.000093 / 0.000054 (0.000039)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.025335 / 0.037411 (-0.012076) 0.103292 / 0.014526 (0.088766) 0.115013 / 0.176557 (-0.061544) 0.153917 / 0.737135 (-0.583218) 0.117812 / 0.296338 (-0.178526)

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) 0.398276 / 0.215209 (0.183067) 3.976629 / 2.077655 (1.898975) 1.795889 / 1.504120 (0.291769) 1.610551 / 1.541195 (0.069356) 1.666398 / 1.468490 (0.197908) 0.422567 / 4.584777 (-4.162210) 3.666307 / 3.745712 (-0.079405) 2.030560 / 5.269862 (-3.239302) 1.398145 / 4.565676 (-3.167531) 0.051108 / 0.424275 (-0.373168) 0.010891 / 0.007607 (0.003284) 0.510770 / 0.226044 (0.284725) 5.107732 / 2.268929 (2.838803) 2.259792 / 55.444624 (-53.184833) 1.908584 / 6.876477 (-4.967893) 2.006283 / 2.142072 (-0.135790) 0.531167 / 4.805227 (-4.274060) 0.117837 / 6.500664 (-6.382827) 0.060639 / 0.075469 (-0.014830)

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) 1.453799 / 1.841788 (-0.387989) 13.458091 / 8.074308 (5.383783) 24.960772 / 10.191392 (14.769380) 0.826823 / 0.680424 (0.146399) 0.545202 / 0.534201 (0.011001) 0.384675 / 0.579283 (-0.194608) 0.424087 / 0.434364 (-0.010277) 0.287539 / 0.540337 (-0.252799) 0.281608 / 1.386936 (-1.105328)
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) 0.006145 / 0.011353 (-0.005208) 0.004048 / 0.011008 (-0.006960) 0.027882 / 0.038508 (-0.010626) 0.033740 / 0.023109 (0.010631) 0.384108 / 0.275898 (0.108210) 0.445877 / 0.323480 (0.122397) 0.003989 / 0.007986 (-0.003997) 0.003540 / 0.004328 (-0.000789) 0.004881 / 0.004250 (0.000631) 0.041094 / 0.037052 (0.004042) 0.385739 / 0.258489 (0.127250) 0.425602 / 0.293841 (0.131761) 0.030421 / 0.128546 (-0.098126) 0.009777 / 0.075646 (-0.065869) 0.263941 / 0.419271 (-0.155330) 0.055050 / 0.043533 (0.011517) 0.382527 / 0.255139 (0.127388) 0.399722 / 0.283200 (0.116522) 0.103586 / 0.141683 (-0.038097) 1.474725 / 1.452155 (0.022570) 1.517475 / 1.492716 (0.024759)

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) 0.218550 / 0.018006 (0.200543) 0.438699 / 0.000490 (0.438210) 0.003963 / 0.000200 (0.003763) 0.000093 / 0.000054 (0.000038)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.025807 / 0.037411 (-0.011605) 0.106122 / 0.014526 (0.091596) 0.117532 / 0.176557 (-0.059025) 0.162535 / 0.737135 (-0.574601) 0.121325 / 0.296338 (-0.175014)

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) 0.423996 / 0.215209 (0.208787) 4.212741 / 2.077655 (2.135086) 1.997649 / 1.504120 (0.493529) 1.808605 / 1.541195 (0.267411) 1.869901 / 1.468490 (0.401410) 0.429381 / 4.584777 (-4.155396) 3.760445 / 3.745712 (0.014733) 3.355812 / 5.269862 (-1.914050) 1.710245 / 4.565676 (-2.855432) 0.053677 / 0.424275 (-0.370598) 0.011515 / 0.007607 (0.003908) 0.517061 / 0.226044 (0.291016) 5.167808 / 2.268929 (2.898879) 2.476599 / 55.444624 (-52.968025) 2.130048 / 6.876477 (-4.746429) 2.263109 / 2.142072 (0.121037) 0.535689 / 4.805227 (-4.269539) 0.118879 / 6.500664 (-6.381785) 0.062073 / 0.075469 (-0.013396)

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) 1.543103 / 1.841788 (-0.298684) 14.174495 / 8.074308 (6.100187) 24.668134 / 10.191392 (14.476742) 0.939152 / 0.680424 (0.258729) 0.604989 / 0.534201 (0.070788) 0.387276 / 0.579283 (-0.192007) 0.428876 / 0.434364 (-0.005488) 0.267685 / 0.540337 (-0.272653) 0.275330 / 1.386936 (-1.111606)

CML watermark

Please sign in to comment.