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fix token invalid error in CI (#3200)
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@lhoestq
lhoestq authored Nov 3, 2021
1 parent 4b9bfcf commit 27aa053
Showing 1 changed file with 2 additions and 2 deletions.
4 changes: 2 additions & 2 deletions tests/hub_fixtures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ def hf_private_dataset_repo_txt_data_(hf_api: HfApi, hf_token, text_file):
yield repo_id
try:
hf_api.delete_repo(token=hf_token, name=repo_name, repo_type="dataset")
except requests.exceptions.HTTPError:
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass


Expand All @@ -70,7 +70,7 @@ def hf_private_dataset_repo_zipped_txt_data_(hf_api: HfApi, hf_token, zip_csv_pa
yield repo_id
try:
hf_api.delete_repo(token=hf_token, name=repo_name, repo_type="dataset")
except requests.exceptions.HTTPError:
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass


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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) 0.070219 / 0.011353 (0.058867) 0.003799 / 0.011008 (-0.007209) 0.031412 / 0.038508 (-0.007096) 0.034998 / 0.023109 (0.011889) 0.287485 / 0.275898 (0.011587) 0.317261 / 0.323480 (-0.006219) 0.079176 / 0.007986 (0.071190) 0.004098 / 0.004328 (-0.000230) 0.008964 / 0.004250 (0.004714) 0.040141 / 0.037052 (0.003088) 0.283937 / 0.258489 (0.025448) 0.325885 / 0.293841 (0.032044) 0.084794 / 0.128546 (-0.043752) 0.008720 / 0.075646 (-0.066926) 0.253668 / 0.419271 (-0.165603) 0.046682 / 0.043533 (0.003149) 0.292241 / 0.255139 (0.037102) 0.314001 / 0.283200 (0.030802) 0.091262 / 0.141683 (-0.050421) 1.724436 / 1.452155 (0.272282) 1.775118 / 1.492716 (0.282402)

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) 0.234047 / 0.018006 (0.216041) 0.431288 / 0.000490 (0.430798) 0.002779 / 0.000200 (0.002579) 0.000250 / 0.000054 (0.000195)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.036720 / 0.037411 (-0.000692) 0.021633 / 0.014526 (0.007107) 0.026136 / 0.176557 (-0.150420) 0.194375 / 0.737135 (-0.542760) 0.027039 / 0.296338 (-0.269299)

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) 0.427402 / 0.215209 (0.212193) 4.277312 / 2.077655 (2.199657) 1.949029 / 1.504120 (0.444909) 1.698047 / 1.541195 (0.156852) 1.717447 / 1.468490 (0.248957) 0.426268 / 4.584777 (-4.158509) 4.603711 / 3.745712 (0.857999) 3.673909 / 5.269862 (-1.595953) 0.893564 / 4.565676 (-3.672112) 0.050420 / 0.424275 (-0.373855) 0.010768 / 0.007607 (0.003161) 0.529211 / 0.226044 (0.303166) 5.283117 / 2.268929 (3.014189) 2.322604 / 55.444624 (-53.122020) 1.942410 / 6.876477 (-4.934066) 2.020139 / 2.142072 (-0.121934) 0.538697 / 4.805227 (-4.266531) 0.114503 / 6.500664 (-6.386161) 0.056952 / 0.075469 (-0.018518)

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) 1.521954 / 1.841788 (-0.319834) 11.801271 / 8.074308 (3.726963) 26.816357 / 10.191392 (16.624965) 0.767462 / 0.680424 (0.087038) 0.506856 / 0.534201 (-0.027345) 0.366025 / 0.579283 (-0.213258) 0.500154 / 0.434364 (0.065790) 0.253524 / 0.540337 (-0.286814) 0.262828 / 1.386936 (-1.124108)
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) 0.068091 / 0.011353 (0.056738) 0.003809 / 0.011008 (-0.007199) 0.029443 / 0.038508 (-0.009065) 0.032671 / 0.023109 (0.009562) 0.297153 / 0.275898 (0.021255) 0.322693 / 0.323480 (-0.000787) 0.080868 / 0.007986 (0.072882) 0.004081 / 0.004328 (-0.000248) 0.007171 / 0.004250 (0.002920) 0.035664 / 0.037052 (-0.001389) 0.286768 / 0.258489 (0.028279) 0.332186 / 0.293841 (0.038345) 0.084256 / 0.128546 (-0.044291) 0.008703 / 0.075646 (-0.066943) 0.251162 / 0.419271 (-0.168109) 0.045409 / 0.043533 (0.001876) 0.295514 / 0.255139 (0.040375) 0.315770 / 0.283200 (0.032570) 0.077851 / 0.141683 (-0.063832) 1.635726 / 1.452155 (0.183571) 1.694209 / 1.492716 (0.201493)

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) 0.235552 / 0.018006 (0.217546) 0.433350 / 0.000490 (0.432861) 0.003067 / 0.000200 (0.002867) 0.000214 / 0.000054 (0.000160)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.033537 / 0.037411 (-0.003874) 0.020769 / 0.014526 (0.006243) 0.026186 / 0.176557 (-0.150371) 0.195065 / 0.737135 (-0.542070) 0.025803 / 0.296338 (-0.270536)

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) 0.423793 / 0.215209 (0.208584) 4.246638 / 2.077655 (2.168984) 1.895172 / 1.504120 (0.391052) 1.705882 / 1.541195 (0.164688) 1.761361 / 1.468490 (0.292871) 0.417049 / 4.584777 (-4.167727) 4.662190 / 3.745712 (0.916478) 2.064798 / 5.269862 (-3.205063) 0.875712 / 4.565676 (-3.689965) 0.049808 / 0.424275 (-0.374467) 0.011180 / 0.007607 (0.003573) 0.534631 / 0.226044 (0.308586) 5.324474 / 2.268929 (3.055545) 2.295478 / 55.444624 (-53.149147) 1.940887 / 6.876477 (-4.935590) 2.041347 / 2.142072 (-0.100725) 0.532982 / 4.805227 (-4.272246) 0.113792 / 6.500664 (-6.386872) 0.056808 / 0.075469 (-0.018661)

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) 1.530232 / 1.841788 (-0.311556) 16.568211 / 8.074308 (8.493903) 27.020202 / 10.191392 (16.828810) 0.737006 / 0.680424 (0.056583) 0.535194 / 0.534201 (0.000993) 0.377989 / 0.579283 (-0.201294) 0.513235 / 0.434364 (0.078871) 0.266532 / 0.540337 (-0.273805) 0.292938 / 1.386936 (-1.093998)

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