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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006458 / 0.011353 (-0.004894) | 0.003895 / 0.011008 (-0.007114) | 0.084280 / 0.038508 (0.045772) | 0.071304 / 0.023109 (0.048195) | 0.313910 / 0.275898 (0.038012) | 0.344070 / 0.323480 (0.020590) | 0.005413 / 0.007986 (-0.002573) | 0.003308 / 0.004328 (-0.001021) | 0.064570 / 0.004250 (0.060320) | 0.056824 / 0.037052 (0.019771) | 0.321102 / 0.258489 (0.062613) | 0.355834 / 0.293841 (0.061993) | 0.031252 / 0.128546 (-0.097294) | 0.008427 / 0.075646 (-0.067219) | 0.287348 / 0.419271 (-0.131924) | 0.053261 / 0.043533 (0.009728) | 0.324892 / 0.255139 (0.069753) | 0.335847 / 0.283200 (0.052647) | 0.023453 / 0.141683 (-0.118230) | 1.485456 / 1.452155 (0.033301) | 1.531329 / 1.492716 (0.038612) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201924 / 0.018006 (0.183918) | 0.447188 / 0.000490 (0.446698) | 0.005543 / 0.000200 (0.005343) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027586 / 0.037411 (-0.009825) | 0.082412 / 0.014526 (0.067886) | 0.094851 / 0.176557 (-0.081706) | 0.151331 / 0.737135 (-0.585804) | 0.094475 / 0.296338 (-0.201863) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399004 / 0.215209 (0.183795) | 3.974652 / 2.077655 (1.896997) | 1.991909 / 1.504120 (0.487789) | 1.811684 / 1.541195 (0.270489) | 1.869774 / 1.468490 (0.401283) | 0.487745 / 4.584777 (-4.097032) | 3.558945 / 3.745712 (-0.186768) | 5.530468 / 5.269862 (0.260606) | 3.293147 / 4.565676 (-1.272529) | 0.057531 / 0.424275 (-0.366744) | 0.007212 / 0.007607 (-0.000395) | 0.470325 / 0.226044 (0.244281) | 4.701652 / 2.268929 (2.432723) | 2.453020 / 55.444624 (-52.991605) | 2.110152 / 6.876477 (-4.766325) | 2.314669 / 2.142072 (0.172597) | 0.615039 / 4.805227 (-4.190189) | 0.133229 / 6.500664 (-6.367435) | 0.060821 / 0.075469 (-0.014648) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296708 / 1.841788 (-0.545079) | 18.717251 / 8.074308 (10.642943) | 14.325305 / 10.191392 (4.133913) | 0.147680 / 0.680424 (-0.532744) | 0.018312 / 0.534201 (-0.515889) | 0.392766 / 0.579283 (-0.186517) | 0.403319 / 0.434364 (-0.031045) | 0.453696 / 0.540337 (-0.086641) | 0.622564 / 1.386936 (-0.764372) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006483 / 0.011353 (-0.004870) | 0.004018 / 0.011008 (-0.006991) | 0.064436 / 0.038508 (0.025928) | 0.072365 / 0.023109 (0.049256) | 0.387532 / 0.275898 (0.111634) | 0.418175 / 0.323480 (0.094695) | 0.005453 / 0.007986 (-0.002533) | 0.003368 / 0.004328 (-0.000961) | 0.064896 / 0.004250 (0.060645) | 0.057018 / 0.037052 (0.019966) | 0.406596 / 0.258489 (0.148107) | 0.431194 / 0.293841 (0.137353) | 0.031788 / 0.128546 (-0.096759) | 0.008532 / 0.075646 (-0.067114) | 0.070605 / 0.419271 (-0.348666) | 0.053317 / 0.043533 (0.009785) | 0.391930 / 0.255139 (0.136791) | 0.406071 / 0.283200 (0.122872) | 0.028652 / 0.141683 (-0.113030) | 1.487677 / 1.452155 (0.035522) | 1.546071 / 1.492716 (0.053355) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220063 / 0.018006 (0.202056) | 0.441111 / 0.000490 (0.440621) | 0.006066 / 0.000200 (0.005867) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035179 / 0.037411 (-0.002232) | 0.096745 / 0.014526 (0.082219) | 0.108171 / 0.176557 (-0.068386) | 0.164590 / 0.737135 (-0.572545) | 0.109425 / 0.296338 (-0.186913) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408101 / 0.215209 (0.192892) | 4.062961 / 2.077655 (1.985306) | 2.101849 / 1.504120 (0.597730) | 1.935919 / 1.541195 (0.394724) | 1.993749 / 1.468490 (0.525259) | 0.487788 / 4.584777 (-4.096989) | 3.533972 / 3.745712 (-0.211740) | 3.218448 / 5.269862 (-2.051414) | 2.002322 / 4.565676 (-2.563355) | 0.057371 / 0.424275 (-0.366904) | 0.007704 / 0.007607 (0.000097) | 0.491695 / 0.226044 (0.265650) | 4.905009 / 2.268929 (2.636080) | 2.597879 / 55.444624 (-52.846745) | 2.252086 / 6.876477 (-4.624391) | 2.434439 / 2.142072 (0.292367) | 0.583071 / 4.805227 (-4.222156) | 0.133765 / 6.500664 (-6.366899) | 0.061276 / 0.075469 (-0.014193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.403111 / 1.841788 (-0.438676) | 19.218886 / 8.074308 (11.144578) | 13.981775 / 10.191392 (3.790383) | 0.167784 / 0.680424 (-0.512640) | 0.018401 / 0.534201 (-0.515800) | 0.392038 / 0.579283 (-0.187245) | 0.414776 / 0.434364 (-0.019587) | 0.476221 / 0.540337 (-0.064117) | 0.632724 / 1.386936 (-0.754212) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#420dbd92c42840d6c91ecf5d3560c6799ee0cca1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007595 / 0.011353 (-0.003758) | 0.004540 / 0.011008 (-0.006468) | 0.099350 / 0.038508 (0.060842) | 0.087062 / 0.023109 (0.063953) | 0.415980 / 0.275898 (0.140082) | 0.466390 / 0.323480 (0.142910) | 0.005958 / 0.007986 (-0.002027) | 0.003671 / 0.004328 (-0.000657) | 0.075714 / 0.004250 (0.071463) | 0.066062 / 0.037052 (0.029010) | 0.426527 / 0.258489 (0.168038) | 0.473282 / 0.293841 (0.179441) | 0.035669 / 0.128546 (-0.092878) | 0.009729 / 0.075646 (-0.065918) | 0.344035 / 0.419271 (-0.075237) | 0.061153 / 0.043533 (0.017620) | 0.428607 / 0.255139 (0.173468) | 0.445951 / 0.283200 (0.162752) | 0.026373 / 0.141683 (-0.115310) | 1.788725 / 1.452155 (0.336570) | 1.871055 / 1.492716 (0.378339) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230606 / 0.018006 (0.212600) | 0.489835 / 0.000490 (0.489345) | 0.005669 / 0.000200 (0.005469) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032197 / 0.037411 (-0.005214) | 0.099571 / 0.014526 (0.085045) | 0.112686 / 0.176557 (-0.063871) | 0.179478 / 0.737135 (-0.557658) | 0.112670 / 0.296338 (-0.183668) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449606 / 0.215209 (0.234397) | 4.503356 / 2.077655 (2.425701) | 2.190480 / 1.504120 (0.686361) | 1.986054 / 1.541195 (0.444860) | 2.071594 / 1.468490 (0.603104) | 0.566301 / 4.584777 (-4.018475) | 4.088460 / 3.745712 (0.342748) | 4.840100 / 5.269862 (-0.429761) | 2.857697 / 4.565676 (-1.707980) | 0.066718 / 0.424275 (-0.357557) | 0.008642 / 0.007607 (0.001034) | 0.539785 / 0.226044 (0.313740) | 5.383252 / 2.268929 (3.114323) | 2.878177 / 55.444624 (-52.566447) | 2.374577 / 6.876477 (-4.501899) | 2.590500 / 2.142072 (0.448428) | 0.675196 / 4.805227 (-4.130031) | 0.153544 / 6.500664 (-6.347120) | 0.070958 / 0.075469 (-0.004511) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.490403 / 1.841788 (-0.351385) | 22.085740 / 8.074308 (14.011432) | 16.588093 / 10.191392 (6.396701) | 0.188598 / 0.680424 (-0.491826) | 0.021567 / 0.534201 (-0.512634) | 0.472594 / 0.579283 (-0.106689) | 0.472903 / 0.434364 (0.038539) | 0.545305 / 0.540337 (0.004968) | 0.736399 / 1.386936 (-0.650537) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007635 / 0.011353 (-0.003718) | 0.004731 / 0.011008 (-0.006277) | 0.076482 / 0.038508 (0.037974) | 0.083666 / 0.023109 (0.060557) | 0.469596 / 0.275898 (0.193698) | 0.493068 / 0.323480 (0.169588) | 0.006014 / 0.007986 (-0.001971) | 0.003902 / 0.004328 (-0.000426) | 0.077142 / 0.004250 (0.072891) | 0.064355 / 0.037052 (0.027303) | 0.468859 / 0.258489 (0.210370) | 0.504002 / 0.293841 (0.210161) | 0.037606 / 0.128546 (-0.090940) | 0.010141 / 0.075646 (-0.065505) | 0.083790 / 0.419271 (-0.335482) | 0.060923 / 0.043533 (0.017390) | 0.464752 / 0.255139 (0.209613) | 0.500464 / 0.283200 (0.217264) | 0.031183 / 0.141683 (-0.110499) | 1.779294 / 1.452155 (0.327139) | 1.870848 / 1.492716 (0.378131) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246567 / 0.018006 (0.228560) | 0.477182 / 0.000490 (0.476693) | 0.000426 / 0.000200 (0.000226) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035377 / 0.037411 (-0.002034) | 0.106042 / 0.014526 (0.091516) | 0.119237 / 0.176557 (-0.057320) | 0.182145 / 0.737135 (-0.554991) | 0.119537 / 0.296338 (-0.176801) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.491352 / 0.215209 (0.276143) | 4.824220 / 2.077655 (2.746565) | 2.652039 / 1.504120 (1.147919) | 2.535310 / 1.541195 (0.994116) | 2.620009 / 1.468490 (1.151519) | 0.567865 / 4.584777 (-4.016912) | 4.158795 / 3.745712 (0.413082) | 6.042582 / 5.269862 (0.772721) | 3.957193 / 4.565676 (-0.608484) | 0.066647 / 0.424275 (-0.357628) | 0.008893 / 0.007607 (0.001285) | 0.570137 / 0.226044 (0.344093) | 5.687126 / 2.268929 (3.418198) | 3.137605 / 55.444624 (-52.307019) | 2.655979 / 6.876477 (-4.220498) | 2.893338 / 2.142072 (0.751265) | 0.698388 / 4.805227 (-4.106840) | 0.154897 / 6.500664 (-6.345767) | 0.071208 / 0.075469 (-0.004261) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.619346 / 1.841788 (-0.222441) | 22.782510 / 8.074308 (14.708202) | 16.317395 / 10.191392 (6.126003) | 0.197630 / 0.680424 (-0.482794) | 0.021795 / 0.534201 (-0.512406) | 0.466982 / 0.579283 (-0.112302) | 0.468609 / 0.434364 (0.034245) | 0.574380 / 0.540337 (0.034043) | 0.759827 / 1.386936 (-0.627109) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c1c5d8268ae59a0dcaea47da825e87c3f9528b4 \"CML watermark\")\n"
] | 2023-07-26T15:27:49 | 2023-07-26T16:24:43 | 2023-07-26T16:14:34 | CONTRIBUTOR | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/6079 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6079/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6079/comments | https://api.github.com/repos/huggingface/datasets/issues/6079/events | https://github.com/huggingface/datasets/issues/6079 | 1,822,597,471 | I_kwDODunzps5soqFf | 6,079 | Iterating over DataLoader based on HF datasets is stuck forever | {
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"When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here? ",
"Thanks @mariosasko for your prompt response, here's the stack trace:\r\n\r\n```\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[12], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self)\r\n 954 # this is the shuffle buffer that we keep in memory\r\n 955 mem_buffer = []\r\n--> 956 for x in self.ex_iterable:\r\n 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n 958 i = next(indices_iterator)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self)\r\n 294 for key, pa_table in self.generate_tables_fn(**kwargs_with_shuffled_shards):\r\n 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n--> 296 formatted_batch = formatter.format_batch(pa_subtable)\r\n 297 for example in _batch_to_examples(formatted_batch):\r\n 298 yield key, example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table)\r\n 446 if self.lazy:\r\n 447 return LazyBatch(pa_table, self)\r\n--> 448 batch = self.python_arrow_extractor().extract_batch(pa_table)\r\n 449 batch = self.python_features_decoder.decode_batch(batch)\r\n 450 return batch\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table)\r\n 149 def extract_batch(self, pa_table: pa.Table) -> dict:\r\n--> 150 return pa_table.to_pydict()\r\n\r\nKeyboardInterrupt: \r\n```\r\n",
"Update: If i let it run, it eventually fails with:\r\n\r\n```\r\nRuntimeError Traceback (most recent call last)\r\nCell In[16], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self)\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n-> 1360 yield format_dict(example) if format_dict else example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct)\r\n 84 def recursive_tensorize(self, data_struct: dict):\r\n---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 463 mapped = [\r\n 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 463 mapped = [\r\n--> 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args)\r\n 364 # Singleton first to spare some computation\r\n 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 366 return function(data_struct)\r\n 368 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 369 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct)\r\n 80 elif isinstance(data_struct, (list, tuple)):\r\n 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])\r\n---> 82 return self._tensorize(data_struct)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value)\r\n 66 if isinstance(value, PIL.Image.Image):\r\n 67 value = np.asarray(value)\r\n---> 68 return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})\r\n\r\nRuntimeError: Could not infer dtype of decimal.Decimal\r\n```",
"PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.",
"I already have cast in collate_fn, in which I perform .astype(float) for each numerical field.\r\nOn the same instance, I installed a conda env with python 3.6, and this works well.\r\n\r\nSample:\r\n\r\n```\r\ndef streaming_data_collate_fn(batch):\r\n df = pd.DataFrame.from_dict(batch)\r\n feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float))))\r\n\r\n```",
"`collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format(\"numpy\")`, which should make this conversion faster.",
"Thanks! \r\nPython 3.10 conda-env: After replacing with_format(\"torch\") with with_format(\"numpy\"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nPython 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format(\"torch\") is the only thing that works, and executes in 1.6 seconds.\r\n\r\nI feel something else is also amiss here.",
"Can you share the `datasets` and `torch` versions installed in these conda envs?\r\n\r\n> Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nHmm, that's surprising. What are your dataset's `.features`?",
"Python 3.6: \r\ndatasets.__version__ 2.4.0\r\ntorch.__version__ 1.10.1+cu102\r\n\r\nPython 3.10:\r\ndatasets.__version__ 2.14.0\r\ntorch.__version__ 2.0.0\r\n\r\nAnonymized features are of the form (subset shown here):\r\n{\r\n'string_feature_i': Value(dtype='string', id=None),\r\n'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None),\r\n'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None),\r\n}\r\n\r\n\r\nThere is no output from .features in python 3.6 kernel BTW.",
"One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6.\r\n\r\n512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!",
"**Update**: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads:\r\n1. https://github.com/pytorch/pytorch/issues/102494\r\n2. https://github.com/pytorch/pytorch/issues/102269\r\n3. https://github.com/pytorch/pytorch/issues/99625\r\n\r\nOut of the suggested solutions, the one that worked in my case was:\r\n```\r\nos.environ['KMP_AFFINITY'] = \"disabled\"\r\n```\r\nIt is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.\r\n\r\n\r\n"
] | 2023-07-26T14:52:37 | 2023-07-26T19:14:16 | null | NONE | null | null | null | ### Describe the bug
I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here?
### Steps to reproduce the bug
```
train_dataset = load_dataset(
"parquet", data_files = {'train': tr_data_path + '*.parquet'},
split = 'train',
collate_fn = streaming_data_collate_fn,
streaming = True
).with_format('torch')
train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0)
t = time.time()
iter_ = 0
for batch in train_dataloader:
iter_ += 1
if iter_ == 1000:
break
print (time.time() - t)
```
### Expected behavior
The snippet should work normally and load the next batch of data.
### Environment info
datasets: '2.14.0'
pyarrow: '12.0.0'
torch: '2.0.0'
Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0]
!uname -r
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"Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). ",
"Ok thank you for your answer"
] | 2023-07-26T14:08:22 | 2023-07-26T21:10:40 | null | NONE | null | null | null | ### Describe the bug
I used:
```
dataset = load_dataset(
"oscar-corpus/OSCAR-2201",
token=True,
language="fr",
streaming=True,
split="train"
)
```
Unfortunately, the server had a problem during the training process. I saved the step my training stopped at.
But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset?
`download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True.
### Steps to reproduce the bug
```
from datasets import load_dataset, DownloadConfig
dataset = load_dataset(
"oscar-corpus/OSCAR-2201",
token=True,
language="fr",
streaming=True, # optional
split="train",
download_config=DownloadConfig(resume_download=True)
)
# interupt the run and try to relaunch it => this restart from scratch
```
### Expected behavior
I would expect a parameter to start streaming from a given index in the dataset.
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6077 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6077/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6077/comments | https://api.github.com/repos/huggingface/datasets/issues/6077/events | https://github.com/huggingface/datasets/issues/6077 | 1,822,486,810 | I_kwDODunzps5soPEa | 6,077 | Mapping gets stuck at 99% | {
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"The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass).\r\n\r\nAlso, these arrays are big, so it makes sense to reduce `batch_size`/`writer_batch_size` to avoid RAM issues and slow IO."
] | 2023-07-26T14:00:40 | 2023-07-26T18:29:10 | null | CONTRIBUTOR | null | null | null | ### Describe the bug
Hi !
I'm currently working with a large (~150GB) unnormalized dataset at work.
The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it.
I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset.
The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why.
Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me.
### Steps to reproduce the bug
I'm able to reproduce the problem using the following scripts:
```python
# random_data.py
import datasets
import torch
_VERSION = "1.0.0"
class RandomDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
version=_VERSION,
supervised_keys=None,
features=datasets.Features(
{
"positions": datasets.Array2D(
shape=(30000, 3),
dtype="float32",
),
"normals": datasets.Array2D(
shape=(30000, 3),
dtype="float32",
),
"features": datasets.Array2D(
shape=(30000, 6),
dtype="float32",
),
"scalars": datasets.Sequence(
feature=datasets.Value("float32"),
length=20,
),
},
),
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, # type: ignore
gen_kwargs={"nb_samples": 1000},
),
datasets.SplitGenerator(
name=datasets.Split.TEST, # type: ignore
gen_kwargs={"nb_samples": 100},
),
]
def _generate_examples(self, nb_samples: int):
for idx in range(nb_samples):
yield idx, {
"positions": torch.rand(30000, 3),
"normals": torch.rand(30000, 3),
"features": torch.rand(30000, 6),
"scalars": torch.rand(20),
}
```
```python
# main.py
import datasets
import torch
def compute_mean_std(
dataset: datasets.Dataset,
) -> dict[str, torch.Tensor]:
"""Compute the mean and standard deviation of each feature of the dataset.
Args:
dataset (`Dataset`): A huggingface dataset.
Returns:
dict: A dictionary containing the mean and standard deviation of each feature.
"""
result = {}
for key in dataset:
# extract data from dataset
data: torch.Tensor = dataset[key] # type: ignore
# reshape data, from (a, ..., b, c) -> (*, c)
data = data.reshape(-1, data.shape[-1])
# compute mean and std
mean = data.mean(dim=0) # (c)
std = data.std(dim=0) # (c)
# store in result
result[key] = torch.stack((mean, std))
return result
def apply_mean_std(
dataset: datasets.Dataset,
mean_std: datasets.Dataset,
) -> dict[str, torch.Tensor]:
"""Normalize the dataset using the mean and standard deviation of each feature.
Args:
dataset (`Dataset`): A huggingface dataset.
mean_std (`Dataset`): A huggingface dataset containing the mean and standard deviation of each feature.
Returns:
dict: A dictionary containing the normalized dataset.
"""
result = {}
for key in mean_std.column_names:
# extract data from dataset
data: torch.Tensor = dataset[key] # type: ignore
# extract mean and std from dict
mean = mean_std[key][0] # type: ignore
std = mean_std[key][1] # type: ignore
# normalize data
normalized_data = (data - mean) / std
result[key] = normalized_data
return result
# hack to force the map function to use the entire dataset
MAX_MAP_BATCH_SIZE = 1_000_000_000
# get dataset
ds = datasets.load_dataset(
path="random_data.py",
split="train",
).with_format("torch")
# compute mean/std of each feature
mean_std = ds.map(
desc="Computing mean/std", # type: ignore
remove_columns=ds.column_names, # type: ignore
function=compute_mean_std,
batch_size=MAX_MAP_BATCH_SIZE,
batched=True,
)
# normalize each feature of the dataset
ds_normalized = ds.map(
desc="Applying mean/std", # type: ignore
function=apply_mean_std,
batched=False,
fn_kwargs={
"mean_std": mean_std,
},
)
```
### Expected behavior
Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/6076 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6076/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6076/comments | https://api.github.com/repos/huggingface/datasets/issues/6076/events | https://github.com/huggingface/datasets/pull/6076 | 1,822,345,597 | PR_kwDODunzps5WcGVR | 6,076 | No gzip encoding from github | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6076). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008191 / 0.011353 (-0.003162) | 0.004669 / 0.011008 (-0.006339) | 0.101315 / 0.038508 (0.062807) | 0.090235 / 0.023109 (0.067126) | 0.381265 / 0.275898 (0.105367) | 0.418266 / 0.323480 (0.094786) | 0.006292 / 0.007986 (-0.001693) | 0.003979 / 0.004328 (-0.000349) | 0.075946 / 0.004250 (0.071696) | 0.070678 / 0.037052 (0.033625) | 0.378006 / 0.258489 (0.119517) | 0.425825 / 0.293841 (0.131984) | 0.036325 / 0.128546 (-0.092221) | 0.009814 / 0.075646 (-0.065832) | 0.345687 / 0.419271 (-0.073584) | 0.063846 / 0.043533 (0.020313) | 0.386003 / 0.255139 (0.130864) | 0.400875 / 0.283200 (0.117675) | 0.027806 / 0.141683 (-0.113877) | 1.814810 / 1.452155 (0.362655) | 1.879897 / 1.492716 (0.387180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218684 / 0.018006 (0.200677) | 0.501715 / 0.000490 (0.501225) | 0.004808 / 0.000200 (0.004608) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035494 / 0.037411 (-0.001917) | 0.100949 / 0.014526 (0.086423) | 0.114639 / 0.176557 (-0.061917) | 0.188908 / 0.737135 (-0.548227) | 0.115794 / 0.296338 (-0.180545) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462537 / 0.215209 (0.247328) | 4.612469 / 2.077655 (2.534814) | 2.298065 / 1.504120 (0.793945) | 2.088738 / 1.541195 (0.547543) | 2.188072 / 1.468490 (0.719582) | 0.565412 / 4.584777 (-4.019364) | 4.180394 / 3.745712 (0.434681) | 3.848696 / 5.269862 (-1.421165) | 2.391381 / 4.565676 (-2.174296) | 0.067647 / 0.424275 (-0.356628) | 0.008847 / 0.007607 (0.001240) | 0.553288 / 0.226044 (0.327243) | 5.517962 / 2.268929 (3.249033) | 2.866622 / 55.444624 (-52.578002) | 2.439025 / 6.876477 (-4.437452) | 2.740156 / 2.142072 (0.598084) | 0.694796 / 4.805227 (-4.110431) | 0.159022 / 6.500664 (-6.341642) | 0.074471 / 0.075469 (-0.000998) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.534979 / 1.841788 (-0.306808) | 23.297273 / 8.074308 (15.222965) | 16.859178 / 10.191392 (6.667786) | 0.207594 / 0.680424 (-0.472830) | 0.021990 / 0.534201 (-0.512211) | 0.472059 / 0.579283 (-0.107224) | 0.497632 / 0.434364 (0.063268) | 0.565672 / 0.540337 (0.025335) | 0.772485 / 1.386936 (-0.614451) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007777 / 0.011353 (-0.003576) | 0.004679 / 0.011008 (-0.006329) | 0.077317 / 0.038508 (0.038809) | 0.087433 / 0.023109 (0.064324) | 0.437389 / 0.275898 (0.161491) | 0.479562 / 0.323480 (0.156082) | 0.006137 / 0.007986 (-0.001849) | 0.003938 / 0.004328 (-0.000390) | 0.074769 / 0.004250 (0.070518) | 0.066605 / 0.037052 (0.029553) | 0.454865 / 0.258489 (0.196376) | 0.485103 / 0.293841 (0.191262) | 0.036540 / 0.128546 (-0.092006) | 0.009983 / 0.075646 (-0.065664) | 0.083566 / 0.419271 (-0.335706) | 0.059527 / 0.043533 (0.015994) | 0.449154 / 0.255139 (0.194015) | 0.462542 / 0.283200 (0.179342) | 0.027581 / 0.141683 (-0.114102) | 1.776720 / 1.452155 (0.324565) | 1.847920 / 1.492716 (0.355204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246792 / 0.018006 (0.228786) | 0.494513 / 0.000490 (0.494024) | 0.004376 / 0.000200 (0.004176) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037837 / 0.037411 (0.000426) | 0.112752 / 0.014526 (0.098226) | 0.121742 / 0.176557 (-0.054815) | 0.189365 / 0.737135 (-0.547770) | 0.124366 / 0.296338 (-0.171973) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492890 / 0.215209 (0.277681) | 4.920270 / 2.077655 (2.842615) | 2.565350 / 1.504120 (1.061230) | 2.378679 / 1.541195 (0.837484) | 2.483794 / 1.468490 (1.015304) | 0.579623 / 4.584777 (-4.005154) | 4.195924 / 3.745712 (0.450212) | 3.903382 / 5.269862 (-1.366479) | 2.466884 / 4.565676 (-2.098793) | 0.064145 / 0.424275 (-0.360130) | 0.008695 / 0.007607 (0.001088) | 0.579300 / 0.226044 (0.353256) | 5.809064 / 2.268929 (3.540136) | 3.145393 / 55.444624 (-52.299232) | 2.832760 / 6.876477 (-4.043717) | 3.020460 / 2.142072 (0.878388) | 0.700235 / 4.805227 (-4.104992) | 0.161262 / 6.500664 (-6.339402) | 0.076484 / 0.075469 (0.001015) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606504 / 1.841788 (-0.235284) | 23.747863 / 8.074308 (15.673555) | 17.281712 / 10.191392 (7.090320) | 0.203874 / 0.680424 (-0.476550) | 0.021839 / 0.534201 (-0.512362) | 0.472365 / 0.579283 (-0.106918) | 0.475150 / 0.434364 (0.040786) | 0.571713 / 0.540337 (0.031376) | 0.759210 / 1.386936 (-0.627726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a7fc003b1d181d8e8ece24d5ebd442ec5d6519 \"CML watermark\")\n"
] | 2023-07-26T12:46:07 | 2023-07-26T14:01:21 | null | MEMBER | null | false | {
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} | Don't accept gzip encoding from github, otherwise some files are not streamable + seekable.
fix https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/discussions/2#64c0e0c1a04a514ba6303e84
and making sure https://github.com/huggingface/datasets/issues/2918 works as well | {
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https://api.github.com/repos/huggingface/datasets/issues/6075 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6075/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6075/comments | https://api.github.com/repos/huggingface/datasets/issues/6075/events | https://github.com/huggingface/datasets/issues/6075 | 1,822,341,398 | I_kwDODunzps5snrkW | 6,075 | Error loading music files using `load_dataset` | {
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"This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.",
"I upgraded the `soundfile` and it's working now! \r\nThanks @mariosasko for the help!"
] | 2023-07-26T12:44:05 | 2023-07-26T13:08:08 | 2023-07-26T13:08:08 | NONE | null | null | null | ### Describe the bug
I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test
I got the following error -
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__
return self._getitem(key)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem
formatted_output = format_table(
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table
return formatter(pa_table, query_type=query_type)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__
return self.format_column(pa_table)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column
return self.features.decode_column(column, column_name) if self.features else column
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp>
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example
array, sampling_rate = sf.read(f)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read
with SoundFile(file, 'r', samplerate, channels,
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__
self._file = self._open(file, mode_int, closefd)
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open
_error_check(_snd.sf_error(file_ptr),
File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check
raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised.
```
### Steps to reproduce the bug
Code to reproduce the error -
```python
from datasets import load_dataset
ds = load_dataset("susnato/pop2piano_real_music_test", split="test")
print(ds[0])
```
### Expected behavior
I should be able to read the music file without any error.
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35
- Python version: 3.9.16
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006616 / 0.011353 (-0.004737) | 0.003915 / 0.011008 (-0.007093) | 0.083271 / 0.038508 (0.044763) | 0.072595 / 0.023109 (0.049485) | 0.307224 / 0.275898 (0.031326) | 0.337244 / 0.323480 (0.013764) | 0.005296 / 0.007986 (-0.002690) | 0.003325 / 0.004328 (-0.001003) | 0.064589 / 0.004250 (0.060339) | 0.056369 / 0.037052 (0.019316) | 0.310829 / 0.258489 (0.052340) | 0.345563 / 0.293841 (0.051722) | 0.030551 / 0.128546 (-0.097995) | 0.008519 / 0.075646 (-0.067127) | 0.286368 / 0.419271 (-0.132903) | 0.052498 / 0.043533 (0.008966) | 0.308735 / 0.255139 (0.053596) | 0.329234 / 0.283200 (0.046034) | 0.022588 / 0.141683 (-0.119095) | 1.453135 / 1.452155 (0.000981) | 1.525956 / 1.492716 (0.033239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199417 / 0.018006 (0.181410) | 0.454621 / 0.000490 (0.454131) | 0.004928 / 0.000200 (0.004728) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028436 / 0.037411 (-0.008975) | 0.083722 / 0.014526 (0.069196) | 0.095162 / 0.176557 (-0.081395) | 0.153434 / 0.737135 (-0.583702) | 0.099480 / 0.296338 (-0.196859) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384647 / 0.215209 (0.169438) | 3.838406 / 2.077655 (1.760751) | 1.891267 / 1.504120 (0.387148) | 1.751432 / 1.541195 (0.210238) | 1.737443 / 1.468490 (0.268953) | 0.487758 / 4.584777 (-4.097019) | 3.635925 / 3.745712 (-0.109787) | 5.208718 / 5.269862 (-0.061144) | 3.029374 / 4.565676 (-1.536302) | 0.057613 / 0.424275 (-0.366662) | 0.007177 / 0.007607 (-0.000430) | 0.455596 / 0.226044 (0.229552) | 4.559969 / 2.268929 (2.291040) | 2.325321 / 55.444624 (-53.119303) | 2.034924 / 6.876477 (-4.841552) | 2.163869 / 2.142072 (0.021796) | 0.583477 / 4.805227 (-4.221750) | 0.132870 / 6.500664 (-6.367795) | 0.059618 / 0.075469 (-0.015851) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263751 / 1.841788 (-0.578037) | 19.740004 / 8.074308 (11.665696) | 14.410980 / 10.191392 (4.219588) | 0.170367 / 0.680424 (-0.510057) | 0.018225 / 0.534201 (-0.515976) | 0.390101 / 0.579283 (-0.189182) | 0.404298 / 0.434364 (-0.030066) | 0.455295 / 0.540337 (-0.085043) | 0.621179 / 1.386936 (-0.765757) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006580 / 0.011353 (-0.004773) | 0.004078 / 0.011008 (-0.006930) | 0.065842 / 0.038508 (0.027334) | 0.074494 / 0.023109 (0.051385) | 0.403644 / 0.275898 (0.127746) | 0.430204 / 0.323480 (0.106724) | 0.005343 / 0.007986 (-0.002643) | 0.003366 / 0.004328 (-0.000963) | 0.064858 / 0.004250 (0.060607) | 0.056252 / 0.037052 (0.019200) | 0.412556 / 0.258489 (0.154067) | 0.434099 / 0.293841 (0.140258) | 0.031518 / 0.128546 (-0.097028) | 0.008543 / 0.075646 (-0.067104) | 0.071658 / 0.419271 (-0.347613) | 0.049962 / 0.043533 (0.006430) | 0.398511 / 0.255139 (0.143372) | 0.415908 / 0.283200 (0.132708) | 0.025011 / 0.141683 (-0.116672) | 1.492350 / 1.452155 (0.040195) | 1.552996 / 1.492716 (0.060280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204971 / 0.018006 (0.186964) | 0.439965 / 0.000490 (0.439475) | 0.002071 / 0.000200 (0.001872) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031673 / 0.037411 (-0.005738) | 0.087529 / 0.014526 (0.073004) | 0.099882 / 0.176557 (-0.076675) | 0.156994 / 0.737135 (-0.580141) | 0.101421 / 0.296338 (-0.194918) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407480 / 0.215209 (0.192271) | 4.069123 / 2.077655 (1.991468) | 2.081288 / 1.504120 (0.577169) | 1.920367 / 1.541195 (0.379172) | 1.981053 / 1.468490 (0.512563) | 0.481995 / 4.584777 (-4.102782) | 3.546486 / 3.745712 (-0.199226) | 5.133150 / 5.269862 (-0.136712) | 3.056444 / 4.565676 (-1.509232) | 0.056650 / 0.424275 (-0.367625) | 0.007746 / 0.007607 (0.000139) | 0.490891 / 0.226044 (0.264847) | 4.902160 / 2.268929 (2.633232) | 2.564726 / 55.444624 (-52.879899) | 2.234988 / 6.876477 (-4.641489) | 2.387656 / 2.142072 (0.245583) | 0.576315 / 4.805227 (-4.228912) | 0.132065 / 6.500664 (-6.368599) | 0.060728 / 0.075469 (-0.014741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.370568 / 1.841788 (-0.471220) | 19.883159 / 8.074308 (11.808851) | 14.442066 / 10.191392 (4.250674) | 0.150119 / 0.680424 (-0.530305) | 0.018359 / 0.534201 (-0.515842) | 0.394128 / 0.579283 (-0.185155) | 0.411697 / 0.434364 (-0.022667) | 0.460580 / 0.540337 (-0.079757) | 0.608490 / 1.386936 (-0.778446) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#035d0cf842b82b14059999baa78e8d158dfbed12 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6074). All of your documentation changes will be reflected on that endpoint."
] | 2023-07-26T12:20:54 | 2023-07-26T14:42:56 | null | CONTRIBUTOR | null | false | {
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} | Removes the warning about requiring to write a dataset loading script to define multiple configurations, as the README YAML can be used instead (for simple cases). Also, deletes the section about using the `BatchSampler` in `torch<=1.12.1` to speed up loading, as `torch 1.12.1` is over a year old (and `torch 2.0` has been out for a while). | {
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"Version 2.3.2 is over one year old, so please use the latest release (2.14.0) to get the expected behavior. Version 2.3.2 does not contain some fixes we made to fix resolving hidden files/directories (starting with a dot)."
] | 2023-07-26T11:09:31 | 2023-07-26T12:34:45 | null | NONE | null | null | null | ### Describe the bug
First, I cd workdir.
Then, I just use load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"})
that couldn't work and
<FileNotFoundError: Unable to find
'/a/b/c/.d/train/train.jsonl' at
/a/b/c/.d/>
And I debug, it is fine in version2.1.2
So there maybe a bug in path join.
Here is the whole bug report:
/x/datasets/loa │
│ d.py:1656 in load_dataset │
│ │
│ 1653 │ ignore_verifications = ignore_verifications or save_infos │
│ 1654 │ │
│ 1655 │ # Create a dataset builder │
│ ❱ 1656 │ builder_instance = load_dataset_builder( │
│ 1657 │ │ path=path, │
│ 1658 │ │ name=name, │
│ 1659 │ │ data_dir=data_dir, │
│ │
│ x/datasets/loa │
│ d.py:1439 in load_dataset_builder │
│ │
│ 1436 │ if use_auth_token is not None: │
│ 1437 │ │ download_config = download_config.copy() if download_config e │
│ 1438 │ │ download_config.use_auth_token = use_auth_token │
│ ❱ 1439 │ dataset_module = dataset_module_factory( │
│ 1440 │ │ path, │
│ 1441 │ │ revision=revision, │
│ 1442 │ │ download_config=download_config, │
│ │
│ x/datasets/loa │
│ d.py:1097 in dataset_module_factory │
│ │
│ 1094 │ │
│ 1095 │ # Try packaged │
│ 1096 │ if path in _PACKAGED_DATASETS_MODULES: │
│ ❱ 1097 │ │ return PackagedDatasetModuleFactory( │
│ 1098 │ │ │ path, │
│ 1099 │ │ │ data_dir=data_dir, │
│ 1100 │ │ │ data_files=data_files, │
│ │
│x/datasets/loa │
│ d.py:743 in get_module │
│ │
│ 740 │ │ │ if self.data_dir is not None │
│ 741 │ │ │ else get_patterns_locally(str(Path().resolve())) │
│ 742 │ │ ) │
│ ❱ 743 │ │ data_files = DataFilesDict.from_local_or_remote( │
│ 744 │ │ │ patterns, │
│ 745 │ │ │ use_auth_token=self.download_config.use_auth_token, │
│ 746 │ │ │ base_path=str(Path(self.data_dir).resolve()) if self.data │
│ │
│ x/datasets/dat │
│ a_files.py:590 in from_local_or_remote │
│ │
│ 587 │ │ out = cls() │
│ 588 │ │ for key, patterns_for_key in patterns.items(): │
│ 589 │ │ │ out[key] = ( │
│ ❱ 590 │ │ │ │ DataFilesList.from_local_or_remote( │
│ 591 │ │ │ │ │ patterns_for_key, │
│ 592 │ │ │ │ │ base_path=base_path, │
│ 593 │ │ │ │ │ allowed_extensions=allowed_extensions, │
│ │
│ /x/datasets/dat │
│ a_files.py:558 in from_local_or_remote │
│ │
│ 555 │ │ use_auth_token: Optional[Union[bool, str]] = None, │
│ 556 │ ) -> "DataFilesList": │
│ 557 │ │ base_path = base_path if base_path is not None else str(Path() │
│ ❱ 558 │ │ data_files = resolve_patterns_locally_or_by_urls(base_path, pa │
│ 559 │ │ origin_metadata = _get_origin_metadata_locally_or_by_urls(data │
│ 560 │ │ return cls(data_files, origin_metadata) │
│ 561 │
│ │
│ /x/datasets/dat │
│ a_files.py:195 in resolve_patterns_locally_or_by_urls │
│ │
│ 192 │ │ if is_remote_url(pattern): │
│ 193 │ │ │ data_files.append(Url(pattern)) │
│ 194 │ │ else: │
│ ❱ 195 │ │ │ for path in _resolve_single_pattern_locally(base_path, pat │
│ 196 │ │ │ │ data_files.append(path) │
│ 197 │ │
│ 198 │ if not data_files: │
│ │
│ /x/datasets/dat │
│ a_files.py:145 in _resolve_single_pattern_locally │
│ │
│ 142 │ │ error_msg = f"Unable to find '{pattern}' at {Path(base_path).r │
│ 143 │ │ if allowed_extensions is not None: │
│ 144 │ │ │ error_msg += f" with any supported extension {list(allowed │
│ ❱ 145 │ │ raise FileNotFoundError(error_msg) │
│ 146 │ return sorted(out) │
│ 147
### Steps to reproduce the bug
1. Version=2.3.2
2. In shell, cd workdir.(cd /a/b/c/.d/)
3. load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"})
### Expected behavior
fix it please~
### Environment info
2.3.2 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6072). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007617 / 0.011353 (-0.003736) | 0.004580 / 0.011008 (-0.006428) | 0.100913 / 0.038508 (0.062405) | 0.087703 / 0.023109 (0.064594) | 0.424159 / 0.275898 (0.148261) | 0.467195 / 0.323480 (0.143715) | 0.006890 / 0.007986 (-0.001096) | 0.003765 / 0.004328 (-0.000564) | 0.077513 / 0.004250 (0.073262) | 0.064889 / 0.037052 (0.027837) | 0.422349 / 0.258489 (0.163860) | 0.477391 / 0.293841 (0.183550) | 0.036025 / 0.128546 (-0.092522) | 0.009939 / 0.075646 (-0.065707) | 0.342409 / 0.419271 (-0.076862) | 0.061568 / 0.043533 (0.018035) | 0.431070 / 0.255139 (0.175931) | 0.462008 / 0.283200 (0.178809) | 0.027480 / 0.141683 (-0.114203) | 1.802271 / 1.452155 (0.350116) | 1.861336 / 1.492716 (0.368620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255806 / 0.018006 (0.237800) | 0.507969 / 0.000490 (0.507479) | 0.010060 / 0.000200 (0.009860) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032286 / 0.037411 (-0.005125) | 0.104468 / 0.014526 (0.089942) | 0.112707 / 0.176557 (-0.063850) | 0.181285 / 0.737135 (-0.555850) | 0.113180 / 0.296338 (-0.183158) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449265 / 0.215209 (0.234056) | 4.465941 / 2.077655 (2.388287) | 2.177889 / 1.504120 (0.673769) | 1.969864 / 1.541195 (0.428669) | 2.077502 / 1.468490 (0.609011) | 0.561607 / 4.584777 (-4.023170) | 4.281873 / 3.745712 (0.536161) | 4.975352 / 5.269862 (-0.294510) | 2.907121 / 4.565676 (-1.658555) | 0.070205 / 0.424275 (-0.354070) | 0.009164 / 0.007607 (0.001557) | 0.581921 / 0.226044 (0.355876) | 5.538667 / 2.268929 (3.269739) | 2.798853 / 55.444624 (-52.645771) | 2.314015 / 6.876477 (-4.562462) | 2.584836 / 2.142072 (0.442763) | 0.672333 / 4.805227 (-4.132894) | 0.153828 / 6.500664 (-6.346836) | 0.069757 / 0.075469 (-0.005712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559670 / 1.841788 (-0.282118) | 23.994639 / 8.074308 (15.920331) | 16.856160 / 10.191392 (6.664768) | 0.195555 / 0.680424 (-0.484869) | 0.021586 / 0.534201 (-0.512615) | 0.469295 / 0.579283 (-0.109989) | 0.481582 / 0.434364 (0.047218) | 0.588667 / 0.540337 (0.048329) | 0.734347 / 1.386936 (-0.652589) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009614 / 0.011353 (-0.001739) | 0.004616 / 0.011008 (-0.006392) | 0.077223 / 0.038508 (0.038715) | 0.103074 / 0.023109 (0.079965) | 0.447834 / 0.275898 (0.171936) | 0.524696 / 0.323480 (0.201216) | 0.007120 / 0.007986 (-0.000866) | 0.003890 / 0.004328 (-0.000438) | 0.076406 / 0.004250 (0.072156) | 0.073488 / 0.037052 (0.036436) | 0.466221 / 0.258489 (0.207732) | 0.532206 / 0.293841 (0.238365) | 0.037596 / 0.128546 (-0.090950) | 0.010029 / 0.075646 (-0.065617) | 0.084313 / 0.419271 (-0.334959) | 0.060088 / 0.043533 (0.016555) | 0.437792 / 0.255139 (0.182653) | 0.512850 / 0.283200 (0.229650) | 0.032424 / 0.141683 (-0.109259) | 1.762130 / 1.452155 (0.309975) | 1.946097 / 1.492716 (0.453381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250774 / 0.018006 (0.232768) | 0.506869 / 0.000490 (0.506379) | 0.008232 / 0.000200 (0.008032) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037779 / 0.037411 (0.000368) | 0.111933 / 0.014526 (0.097407) | 0.122385 / 0.176557 (-0.054172) | 0.190372 / 0.737135 (-0.546763) | 0.122472 / 0.296338 (-0.173866) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488502 / 0.215209 (0.273293) | 4.878114 / 2.077655 (2.800459) | 2.504144 / 1.504120 (1.000024) | 2.321077 / 1.541195 (0.779883) | 2.416797 / 1.468490 (0.948307) | 0.583582 / 4.584777 (-4.001195) | 4.277896 / 3.745712 (0.532184) | 3.874780 / 5.269862 (-1.395082) | 2.540099 / 4.565676 (-2.025577) | 0.068734 / 0.424275 (-0.355541) | 0.009158 / 0.007607 (0.001550) | 0.578401 / 0.226044 (0.352357) | 5.763354 / 2.268929 (3.494426) | 3.167771 / 55.444624 (-52.276853) | 2.675220 / 6.876477 (-4.201257) | 2.920927 / 2.142072 (0.778855) | 0.673948 / 4.805227 (-4.131280) | 0.157908 / 6.500664 (-6.342756) | 0.071672 / 0.075469 (-0.003797) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.635120 / 1.841788 (-0.206668) | 24.853480 / 8.074308 (16.779172) | 17.162978 / 10.191392 (6.971586) | 0.209577 / 0.680424 (-0.470847) | 0.030110 / 0.534201 (-0.504091) | 0.546970 / 0.579283 (-0.032313) | 0.581912 / 0.434364 (0.147548) | 0.571460 / 0.540337 (0.031123) | 0.823411 / 1.386936 (-0.563525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83b792dddd074ccd007c407f942f6870aac7ee84 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006674 / 0.011353 (-0.004679) | 0.004198 / 0.011008 (-0.006810) | 0.084859 / 0.038508 (0.046351) | 0.076065 / 0.023109 (0.052955) | 0.316065 / 0.275898 (0.040167) | 0.352097 / 0.323480 (0.028617) | 0.005610 / 0.007986 (-0.002376) | 0.003600 / 0.004328 (-0.000729) | 0.064921 / 0.004250 (0.060671) | 0.054493 / 0.037052 (0.017441) | 0.318125 / 0.258489 (0.059636) | 0.370183 / 0.293841 (0.076342) | 0.031141 / 0.128546 (-0.097405) | 0.008755 / 0.075646 (-0.066891) | 0.288241 / 0.419271 (-0.131030) | 0.052379 / 0.043533 (0.008846) | 0.328147 / 0.255139 (0.073008) | 0.347548 / 0.283200 (0.064348) | 0.024393 / 0.141683 (-0.117290) | 1.480646 / 1.452155 (0.028492) | 1.575867 / 1.492716 (0.083151) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268978 / 0.018006 (0.250971) | 0.586470 / 0.000490 (0.585980) | 0.003190 / 0.000200 (0.002990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030595 / 0.037411 (-0.006816) | 0.083037 / 0.014526 (0.068511) | 0.103706 / 0.176557 (-0.072850) | 0.164104 / 0.737135 (-0.573031) | 0.104536 / 0.296338 (-0.191802) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382274 / 0.215209 (0.167065) | 3.811878 / 2.077655 (1.734223) | 1.840098 / 1.504120 (0.335978) | 1.670949 / 1.541195 (0.129754) | 1.763755 / 1.468490 (0.295264) | 0.479526 / 4.584777 (-4.105251) | 3.544443 / 3.745712 (-0.201269) | 3.263004 / 5.269862 (-2.006858) | 2.092801 / 4.565676 (-2.472875) | 0.057167 / 0.424275 (-0.367108) | 0.007450 / 0.007607 (-0.000157) | 0.463731 / 0.226044 (0.237686) | 4.624630 / 2.268929 (2.355701) | 2.327078 / 55.444624 (-53.117546) | 1.977734 / 6.876477 (-4.898743) | 2.237152 / 2.142072 (0.095079) | 0.573210 / 4.805227 (-4.232018) | 0.132095 / 6.500664 (-6.368569) | 0.060283 / 0.075469 (-0.015186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243404 / 1.841788 (-0.598384) | 20.306778 / 8.074308 (12.232470) | 14.561660 / 10.191392 (4.370268) | 0.170826 / 0.680424 (-0.509598) | 0.018574 / 0.534201 (-0.515627) | 0.392367 / 0.579283 (-0.186916) | 0.402918 / 0.434364 (-0.031446) | 0.476629 / 0.540337 (-0.063708) | 0.653709 / 1.386936 (-0.733227) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006562 / 0.011353 (-0.004791) | 0.004092 / 0.011008 (-0.006916) | 0.065951 / 0.038508 (0.027443) | 0.078090 / 0.023109 (0.054981) | 0.369679 / 0.275898 (0.093781) | 0.411442 / 0.323480 (0.087962) | 0.005646 / 0.007986 (-0.002339) | 0.003537 / 0.004328 (-0.000791) | 0.066024 / 0.004250 (0.061773) | 0.058947 / 0.037052 (0.021895) | 0.389219 / 0.258489 (0.130730) | 0.414200 / 0.293841 (0.120359) | 0.030372 / 0.128546 (-0.098174) | 0.008631 / 0.075646 (-0.067015) | 0.071692 / 0.419271 (-0.347580) | 0.048035 / 0.043533 (0.004502) | 0.376960 / 0.255139 (0.121821) | 0.389847 / 0.283200 (0.106648) | 0.023940 / 0.141683 (-0.117743) | 1.487633 / 1.452155 (0.035479) | 1.561680 / 1.492716 (0.068964) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.301467 / 0.018006 (0.283461) | 0.544159 / 0.000490 (0.543669) | 0.000408 / 0.000200 (0.000208) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030939 / 0.037411 (-0.006472) | 0.087432 / 0.014526 (0.072906) | 0.103263 / 0.176557 (-0.073293) | 0.154551 / 0.737135 (-0.582585) | 0.104631 / 0.296338 (-0.191707) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422348 / 0.215209 (0.207139) | 4.206003 / 2.077655 (2.128348) | 2.212619 / 1.504120 (0.708499) | 2.049616 / 1.541195 (0.508421) | 2.139093 / 1.468490 (0.670603) | 0.489647 / 4.584777 (-4.095130) | 3.523291 / 3.745712 (-0.222422) | 3.277657 / 5.269862 (-1.992205) | 2.111353 / 4.565676 (-2.454324) | 0.057597 / 0.424275 (-0.366679) | 0.007675 / 0.007607 (0.000068) | 0.493068 / 0.226044 (0.267023) | 4.939493 / 2.268929 (2.670565) | 2.695995 / 55.444624 (-52.748630) | 2.374904 / 6.876477 (-4.501573) | 2.600110 / 2.142072 (0.458038) | 0.586306 / 4.805227 (-4.218921) | 0.134137 / 6.500664 (-6.366527) | 0.061897 / 0.075469 (-0.013572) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330628 / 1.841788 (-0.511160) | 20.557964 / 8.074308 (12.483656) | 14.251632 / 10.191392 (4.060240) | 0.148772 / 0.680424 (-0.531652) | 0.018383 / 0.534201 (-0.515817) | 0.392552 / 0.579283 (-0.186731) | 0.403959 / 0.434364 (-0.030405) | 0.462154 / 0.540337 (-0.078184) | 0.608832 / 1.386936 (-0.778104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a291b2b659a356199dff0ab004ad3845459034b \"CML watermark\")\n"
] | 2023-07-26T10:44:23 | 2023-07-26T19:26:48 | null | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/6071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6071/comments | https://api.github.com/repos/huggingface/datasets/issues/6071/events | https://github.com/huggingface/datasets/issues/6071 | 1,821,990,749 | I_kwDODunzps5smV9d | 6,071 | storage_options provided to load_dataset not fully piping through since datasets 2.14.0 | {
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"Hi ! Thanks for reporting, I opened a PR to fix this\r\n\r\nWhat filesystem are you using ?",
"Hi @lhoestq ! Thank you so much 🙌 \r\n\r\nIt's a bit of a custom setup, but in practice I am using a [pyarrow.fs.S3FileSystem](https://arrow.apache.org/docs/python/generated/pyarrow.fs.S3FileSystem.html) (wrapped in a `fsspec.implementations.arrow.ArrowFSWrapper` [to make it](https://arrow.apache.org/docs/python/filesystems.html#using-arrow-filesystems-with-fsspec) `fsspec` compatible). I also register it as an entrypoint with `fsspec` so that it's the one that gets automatically resolved when looking for filesystems for the `s3` protocol\r\n\r\nIn my case the `storage_option` that seemed not getting piped through was the filesystem's `endpoint_override` that I use in some tests to point at a mock S3 bucket"
] | 2023-07-26T09:37:20 | 2023-07-26T11:04:35 | null | NONE | null | null | null | ### Describe the bug
Since the latest release of `datasets` (`2.14.0`), custom filesystem `storage_options` passed to `load_dataset()` do not seem to propagate through all the way - leading to problems if loading data files that need those options to be set.
I think this is because of the new `_prepare_path_and_storage_options()` (https://github.com/huggingface/datasets/pull/6028), which returns the right `storage_options` to use given a path and a `DownloadConfig` - but which might not be taking into account the extra `storage_options` explicitly provided e.g. through `load_dataset()`
### Steps to reproduce the bug
```python
import fsspec
import pandas as pd
import datasets
# Generate mock parquet file
data_files = "demo.parquet"
pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}).to_parquet(data_files)
_storage_options = {"x": 1, "y": 2}
fs = fsspec.filesystem("file", **_storage_options)
dataset = datasets.load_dataset(
"parquet",
data_files=data_files,
storage_options=fs.storage_options
)
```
Looking at the `storage_options` resolved here:
https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L331
they end up being `{}`, instead of propagating through the `storage_options` that were provided to `load_dataset` (`fs.storage_options`). As these then get used for the filesystem operation a few lines below
https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L339
the call will fail if the user-provided `storage_options` were needed.
---
A temporary workaround that seemed to work locally to bypass the problem was to bundle a duplicate of the `storage_options` into the `download_config`, so that they make their way all the way to `_prepare_path_and_storage_options()` and get extracted correctly:
```python
dataset = datasets.load_dataset(
"parquet",
data_files=data_files,
storage_options=fs.storage_options,
download_config=datasets.DownloadConfig(storage_options={fs.protocol: fs.storage_options}),
)
```
### Expected behavior
`storage_options` provided to `load_dataset` take effect in all backend filesystem operations.
### Environment info
datasets==2.14.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6070 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6070/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6070/comments | https://api.github.com/repos/huggingface/datasets/issues/6070/events | https://github.com/huggingface/datasets/pull/6070 | 1,820,836,330 | PR_kwDODunzps5WXDLc | 6,070 | Fix Quickstart notebook link | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008473 / 0.011353 (-0.002880) | 0.004734 / 0.011008 (-0.006274) | 0.103895 / 0.038508 (0.065387) | 0.071838 / 0.023109 (0.048729) | 0.379949 / 0.275898 (0.104051) | 0.397375 / 0.323480 (0.073895) | 0.006695 / 0.007986 (-0.001290) | 0.004536 / 0.004328 (0.000207) | 0.076151 / 0.004250 (0.071901) | 0.058690 / 0.037052 (0.021638) | 0.379937 / 0.258489 (0.121448) | 0.411833 / 0.293841 (0.117992) | 0.046805 / 0.128546 (-0.081741) | 0.013689 / 0.075646 (-0.061958) | 0.327896 / 0.419271 (-0.091375) | 0.063873 / 0.043533 (0.020340) | 0.378451 / 0.255139 (0.123312) | 0.398725 / 0.283200 (0.115525) | 0.034961 / 0.141683 (-0.106722) | 1.604999 / 1.452155 (0.152845) | 1.748370 / 1.492716 (0.255654) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224634 / 0.018006 (0.206628) | 0.548468 / 0.000490 (0.547979) | 0.005049 / 0.000200 (0.004849) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028144 / 0.037411 (-0.009267) | 0.092184 / 0.014526 (0.077659) | 0.102987 / 0.176557 (-0.073570) | 0.176987 / 0.737135 (-0.560149) | 0.103093 / 0.296338 (-0.193246) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578410 / 0.215209 (0.363201) | 5.664781 / 2.077655 (3.587126) | 2.487763 / 1.504120 (0.983643) | 2.254213 / 1.541195 (0.713018) | 2.239693 / 1.468490 (0.771202) | 0.810380 / 4.584777 (-3.774397) | 5.036540 / 3.745712 (1.290828) | 7.064695 / 5.269862 (1.794834) | 4.215101 / 4.565676 (-0.350575) | 0.089792 / 0.424275 (-0.334483) | 0.008487 / 0.007607 (0.000879) | 0.692292 / 0.226044 (0.466248) | 6.780226 / 2.268929 (4.511297) | 3.245510 / 55.444624 (-52.199114) | 2.575984 / 6.876477 (-4.300493) | 2.747546 / 2.142072 (0.605473) | 0.956604 / 4.805227 (-3.848623) | 0.198937 / 6.500664 (-6.301727) | 0.070849 / 0.075469 (-0.004620) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.536469 / 1.841788 (-0.305319) | 21.750583 / 8.074308 (13.676275) | 20.559532 / 10.191392 (10.368140) | 0.241244 / 0.680424 (-0.439180) | 0.030078 / 0.534201 (-0.504123) | 0.462204 / 0.579283 (-0.117079) | 0.600103 / 0.434364 (0.165739) | 0.535074 / 0.540337 (-0.005264) | 0.764427 / 1.386936 (-0.622509) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009712 / 0.011353 (-0.001641) | 0.005036 / 0.011008 (-0.005972) | 0.073683 / 0.038508 (0.035175) | 0.078684 / 0.023109 (0.055574) | 0.445096 / 0.275898 (0.169198) | 0.496233 / 0.323480 (0.172754) | 0.006231 / 0.007986 (-0.001755) | 0.004720 / 0.004328 (0.000392) | 0.076444 / 0.004250 (0.072194) | 0.060932 / 0.037052 (0.023880) | 0.505727 / 0.258489 (0.247238) | 0.498702 / 0.293841 (0.204861) | 0.047115 / 0.128546 (-0.081431) | 0.014028 / 0.075646 (-0.061618) | 0.099292 / 0.419271 (-0.319980) | 0.061571 / 0.043533 (0.018038) | 0.468435 / 0.255139 (0.213296) | 0.481747 / 0.283200 (0.198547) | 0.033962 / 0.141683 (-0.107721) | 1.665397 / 1.452155 (0.213242) | 1.830488 / 1.492716 (0.337772) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268217 / 0.018006 (0.250211) | 0.555123 / 0.000490 (0.554633) | 0.000451 / 0.000200 (0.000251) | 0.000156 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034262 / 0.037411 (-0.003150) | 0.107807 / 0.014526 (0.093281) | 0.115631 / 0.176557 (-0.060926) | 0.175914 / 0.737135 (-0.561221) | 0.118775 / 0.296338 (-0.177564) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583260 / 0.215209 (0.368051) | 5.934976 / 2.077655 (3.857321) | 2.752304 / 1.504120 (1.248184) | 2.382746 / 1.541195 (0.841551) | 2.389402 / 1.468490 (0.920912) | 0.794213 / 4.584777 (-3.790564) | 5.215269 / 3.745712 (1.469557) | 7.083595 / 5.269862 (1.813733) | 3.776136 / 4.565676 (-0.789540) | 0.091141 / 0.424275 (-0.333135) | 0.008803 / 0.007607 (0.001196) | 0.726510 / 0.226044 (0.500465) | 6.926860 / 2.268929 (4.657931) | 3.475612 / 55.444624 (-51.969012) | 2.730237 / 6.876477 (-4.146240) | 2.879145 / 2.142072 (0.737073) | 0.959956 / 4.805227 (-3.845271) | 0.189812 / 6.500664 (-6.310852) | 0.071624 / 0.075469 (-0.003845) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748184 / 1.841788 (-0.093603) | 23.764520 / 8.074308 (15.690212) | 19.502461 / 10.191392 (9.311069) | 0.233987 / 0.680424 (-0.446437) | 0.028116 / 0.534201 (-0.506085) | 0.478838 / 0.579283 (-0.100445) | 0.560952 / 0.434364 (0.126588) | 0.529902 / 0.540337 (-0.010435) | 0.735095 / 1.386936 (-0.651841) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dda3e389212f44117a40b44bb0cdf358cfd9f71e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006735 / 0.011353 (-0.004618) | 0.004131 / 0.011008 (-0.006878) | 0.085619 / 0.038508 (0.047111) | 0.076973 / 0.023109 (0.053864) | 0.315175 / 0.275898 (0.039277) | 0.354703 / 0.323480 (0.031223) | 0.005409 / 0.007986 (-0.002577) | 0.003438 / 0.004328 (-0.000891) | 0.064773 / 0.004250 (0.060523) | 0.056117 / 0.037052 (0.019064) | 0.313825 / 0.258489 (0.055336) | 0.354654 / 0.293841 (0.060813) | 0.031384 / 0.128546 (-0.097163) | 0.008537 / 0.075646 (-0.067109) | 0.288528 / 0.419271 (-0.130744) | 0.053036 / 0.043533 (0.009504) | 0.312213 / 0.255139 (0.057074) | 0.335952 / 0.283200 (0.052752) | 0.023165 / 0.141683 (-0.118518) | 1.497559 / 1.452155 (0.045404) | 1.561949 / 1.492716 (0.069233) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212558 / 0.018006 (0.194552) | 0.456555 / 0.000490 (0.456065) | 0.000334 / 0.000200 (0.000134) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028571 / 0.037411 (-0.008840) | 0.085154 / 0.014526 (0.070628) | 0.095961 / 0.176557 (-0.080596) | 0.153041 / 0.737135 (-0.584094) | 0.099234 / 0.296338 (-0.197105) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.381796 / 0.215209 (0.166587) | 3.806948 / 2.077655 (1.729294) | 1.829597 / 1.504120 (0.325477) | 1.659065 / 1.541195 (0.117870) | 1.738524 / 1.468490 (0.270034) | 0.483379 / 4.584777 (-4.101398) | 3.540648 / 3.745712 (-0.205064) | 3.269188 / 5.269862 (-2.000673) | 2.042113 / 4.565676 (-2.523564) | 0.056905 / 0.424275 (-0.367370) | 0.007235 / 0.007607 (-0.000373) | 0.460581 / 0.226044 (0.234537) | 4.597451 / 2.268929 (2.328522) | 2.334284 / 55.444624 (-53.110340) | 1.960026 / 6.876477 (-4.916450) | 2.172118 / 2.142072 (0.030045) | 0.576758 / 4.805227 (-4.228470) | 0.131196 / 6.500664 (-6.369468) | 0.060053 / 0.075469 (-0.015417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289466 / 1.841788 (-0.552322) | 19.713059 / 8.074308 (11.638750) | 14.292390 / 10.191392 (4.100998) | 0.146199 / 0.680424 (-0.534225) | 0.018123 / 0.534201 (-0.516078) | 0.392492 / 0.579283 (-0.186791) | 0.416544 / 0.434364 (-0.017820) | 0.457166 / 0.540337 (-0.083171) | 0.645490 / 1.386936 (-0.741446) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006508 / 0.011353 (-0.004845) | 0.004010 / 0.011008 (-0.006998) | 0.065201 / 0.038508 (0.026693) | 0.076322 / 0.023109 (0.053213) | 0.364198 / 0.275898 (0.088300) | 0.398251 / 0.323480 (0.074771) | 0.005328 / 0.007986 (-0.002658) | 0.003298 / 0.004328 (-0.001031) | 0.064378 / 0.004250 (0.060128) | 0.056053 / 0.037052 (0.019000) | 0.365431 / 0.258489 (0.106942) | 0.402777 / 0.293841 (0.108936) | 0.031014 / 0.128546 (-0.097532) | 0.008507 / 0.075646 (-0.067140) | 0.071471 / 0.419271 (-0.347801) | 0.048300 / 0.043533 (0.004768) | 0.359700 / 0.255139 (0.104561) | 0.382244 / 0.283200 (0.099044) | 0.023783 / 0.141683 (-0.117900) | 1.517518 / 1.452155 (0.065363) | 1.569732 / 1.492716 (0.077015) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257447 / 0.018006 (0.239440) | 0.452598 / 0.000490 (0.452109) | 0.015187 / 0.000200 (0.014987) | 0.000164 / 0.000054 (0.000109) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030958 / 0.037411 (-0.006454) | 0.090066 / 0.014526 (0.075540) | 0.101120 / 0.176557 (-0.075437) | 0.154295 / 0.737135 (-0.582840) | 0.103582 / 0.296338 (-0.192756) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415945 / 0.215209 (0.200736) | 4.146464 / 2.077655 (2.068809) | 2.121414 / 1.504120 (0.617294) | 1.956885 / 1.541195 (0.415690) | 2.047955 / 1.468490 (0.579465) | 0.486334 / 4.584777 (-4.098443) | 3.506263 / 3.745712 (-0.239449) | 4.942274 / 5.269862 (-0.327587) | 2.907836 / 4.565676 (-1.657841) | 0.057344 / 0.424275 (-0.366931) | 0.007813 / 0.007607 (0.000206) | 0.497888 / 0.226044 (0.271844) | 4.978017 / 2.268929 (2.709089) | 2.600447 / 55.444624 (-52.844177) | 2.335050 / 6.876477 (-4.541427) | 2.480373 / 2.142072 (0.338301) | 0.597954 / 4.805227 (-4.207274) | 0.134794 / 6.500664 (-6.365870) | 0.062605 / 0.075469 (-0.012864) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.344390 / 1.841788 (-0.497398) | 20.020067 / 8.074308 (11.945759) | 14.344626 / 10.191392 (4.153234) | 0.172101 / 0.680424 (-0.508322) | 0.018549 / 0.534201 (-0.515652) | 0.393589 / 0.579283 (-0.185694) | 0.438401 / 0.434364 (0.004037) | 0.463800 / 0.540337 (-0.076537) | 0.618269 / 1.386936 (-0.768667) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0177910b32712f28d147879395e511207e39958 \"CML watermark\")\n"
] | 2023-07-25T17:48:37 | 2023-07-25T18:19:01 | 2023-07-25T18:10:16 | CONTRIBUTOR | null | false | {
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} | Reported in https://github.com/huggingface/datasets/pull/5902#issuecomment-1649885621 (cc @alvarobartt) | {
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"You can list the dataset's columns with `ds.column_names` before `.map` to check whether the dataset has an `image` column. If it doesn't, then this is a bug. Otherwise, please paste the line with the `.map` call.\r\n\r\n\r\n",
"This is the piece of code I am running:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef resize(examples):\r\n examples[\"pixel_values\"] = [image.convert(\"RGB\").resize((300, 300)) for image in examples[\"image\"]]\r\n return examples\r\n\r\ndef preprocess_train(example_batch):\r\n print(f\"Example batch: \\n{example_batch}\")\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\nimage_dataset = image_dataset.map(resize, remove_columns=[\"image\"], batched=True)\r\n\r\nimage_dataset[\"train\"].set_transform(preprocess_train)\r\nimage_dataset[\"validation\"].set_transform(preprocess_val)\r\n```\r\n\r\nWhen I print ds.column_names I get the following\r\n`{'train': ['image', 'label'], 'validation': ['image', 'label'], 'test': ['image', 'label']}`\r\n\r\nThe `print(f\"Example batch: \\n{example_batch}\")` in the `preprocess_train` function outputs only labels without images:\r\n```\r\nExample batch: \r\n{'label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}\r\n```\r\n\r\nThe weird part of it all is that a sample code runs in a jupyter lab notebook without any bugs, but when I run my scripts from the terminal I get the bug. The same code.",
"The `remove_columns=[\"image\"]` argument in the `.map` call removes the `image` column from the output, so drop this argument to preserve it.",
"The problem is not with the removal of the image key. The bug is why only the labels are sent to be process, instead of all the featues or dictionary keys.\r\n\r\nP.S. I just dropped the removal argument as you've suggested, but that didn't solve the problem, because only the labels are being sent to be processed",
"All the `image_dataset.column_names` after the `map` call should also be present in `preprocess_train `/`preprocess_val` unless (input) `columns` in `set_transform` are specified.\r\n\r\nIf that's not the case, we need a full reproducer (not snippets) with the environment info."
] | 2023-07-25T17:45:50 | 2023-07-26T17:33:49 | null | NONE | null | null | null | ### Describe the bug
I've loaded a local image dataset with:
`ds = laod_dataset("imagefolder", data_dir=path-to-data)`
And defined a transform to process the data, following the Datasets docs.
However, I get a keyError error, indicating there's no "image" key in my dataset. When I printed out the example_batch sent to the transformation function, it shows only the labels are being sent to the function.
For some reason, the images are not in the example batches.
### Steps to reproduce the bug
I'm using the latest stable version of datasets
### Expected behavior
I expect the example_batches to contain both images and labels
### Environment info
I'm using the latest stable version of datasets | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006573 / 0.011353 (-0.004780) | 0.004014 / 0.011008 (-0.006994) | 0.084999 / 0.038508 (0.046491) | 0.074965 / 0.023109 (0.051855) | 0.313294 / 0.275898 (0.037396) | 0.349678 / 0.323480 (0.026198) | 0.005401 / 0.007986 (-0.002585) | 0.003401 / 0.004328 (-0.000927) | 0.065363 / 0.004250 (0.061112) | 0.057159 / 0.037052 (0.020107) | 0.313260 / 0.258489 (0.054771) | 0.354654 / 0.293841 (0.060813) | 0.030895 / 0.128546 (-0.097651) | 0.008605 / 0.075646 (-0.067042) | 0.289190 / 0.419271 (-0.130081) | 0.052474 / 0.043533 (0.008942) | 0.316193 / 0.255139 (0.061054) | 0.339966 / 0.283200 (0.056767) | 0.024112 / 0.141683 (-0.117571) | 1.515606 / 1.452155 (0.063452) | 1.571428 / 1.492716 (0.078711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203284 / 0.018006 (0.185278) | 0.452720 / 0.000490 (0.452230) | 0.003891 / 0.000200 (0.003691) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028992 / 0.037411 (-0.008419) | 0.083170 / 0.014526 (0.068644) | 0.097739 / 0.176557 (-0.078817) | 0.153401 / 0.737135 (-0.583734) | 0.098628 / 0.296338 (-0.197711) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390190 / 0.215209 (0.174981) | 3.901272 / 2.077655 (1.823617) | 1.887194 / 1.504120 (0.383074) | 1.723696 / 1.541195 (0.182501) | 1.800537 / 1.468490 (0.332047) | 0.481758 / 4.584777 (-4.103019) | 3.605098 / 3.745712 (-0.140614) | 3.304482 / 5.269862 (-1.965380) | 2.053515 / 4.565676 (-2.512161) | 0.056997 / 0.424275 (-0.367278) | 0.007347 / 0.007607 (-0.000260) | 0.461367 / 0.226044 (0.235323) | 4.606152 / 2.268929 (2.337223) | 2.470048 / 55.444624 (-52.974576) | 2.060019 / 6.876477 (-4.816458) | 2.320507 / 2.142072 (0.178435) | 0.575050 / 4.805227 (-4.230178) | 0.133030 / 6.500664 (-6.367634) | 0.061508 / 0.075469 (-0.013962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275430 / 1.841788 (-0.566357) | 19.725453 / 8.074308 (11.651145) | 14.396360 / 10.191392 (4.204968) | 0.157980 / 0.680424 (-0.522443) | 0.018516 / 0.534201 (-0.515685) | 0.394717 / 0.579283 (-0.184566) | 0.404948 / 0.434364 (-0.029415) | 0.474001 / 0.540337 (-0.066336) | 0.668463 / 1.386936 (-0.718474) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006697 / 0.011353 (-0.004656) | 0.004206 / 0.011008 (-0.006802) | 0.065458 / 0.038508 (0.026950) | 0.075845 / 0.023109 (0.052735) | 0.365051 / 0.275898 (0.089153) | 0.400919 / 0.323480 (0.077439) | 0.005347 / 0.007986 (-0.002638) | 0.003386 / 0.004328 (-0.000943) | 0.065398 / 0.004250 (0.061148) | 0.057320 / 0.037052 (0.020268) | 0.379161 / 0.258489 (0.120672) | 0.406892 / 0.293841 (0.113051) | 0.031986 / 0.128546 (-0.096560) | 0.008674 / 0.075646 (-0.066972) | 0.071723 / 0.419271 (-0.347549) | 0.049897 / 0.043533 (0.006364) | 0.372034 / 0.255139 (0.116895) | 0.394293 / 0.283200 (0.111094) | 0.023681 / 0.141683 (-0.118002) | 1.479793 / 1.452155 (0.027639) | 1.553105 / 1.492716 (0.060389) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233660 / 0.018006 (0.215654) | 0.454412 / 0.000490 (0.453923) | 0.004473 / 0.000200 (0.004273) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031115 / 0.037411 (-0.006296) | 0.090541 / 0.014526 (0.076015) | 0.104363 / 0.176557 (-0.072193) | 0.161022 / 0.737135 (-0.576114) | 0.105114 / 0.296338 (-0.191225) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427668 / 0.215209 (0.212459) | 4.263145 / 2.077655 (2.185490) | 2.247043 / 1.504120 (0.742923) | 2.082554 / 1.541195 (0.541360) | 2.170505 / 1.468490 (0.702015) | 0.491802 / 4.584777 (-4.092975) | 3.587295 / 3.745712 (-0.158417) | 3.344697 / 5.269862 (-1.925165) | 2.060529 / 4.565676 (-2.505148) | 0.057829 / 0.424275 (-0.366446) | 0.007780 / 0.007607 (0.000173) | 0.503374 / 0.226044 (0.277330) | 5.034742 / 2.268929 (2.765814) | 2.701957 / 55.444624 (-52.742667) | 2.479002 / 6.876477 (-4.397474) | 2.622055 / 2.142072 (0.479982) | 0.591363 / 4.805227 (-4.213864) | 0.133834 / 6.500664 (-6.366830) | 0.062276 / 0.075469 (-0.013193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.338788 / 1.841788 (-0.503000) | 20.333599 / 8.074308 (12.259291) | 14.783196 / 10.191392 (4.591804) | 0.168695 / 0.680424 (-0.511729) | 0.018478 / 0.534201 (-0.515723) | 0.397398 / 0.579283 (-0.181885) | 0.409900 / 0.434364 (-0.024464) | 0.475315 / 0.540337 (-0.065023) | 0.644267 / 1.386936 (-0.742669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb0b324e0bae4c93bb5509b2f0731bc346adb21b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007315 / 0.011353 (-0.004038) | 0.004294 / 0.011008 (-0.006714) | 0.100300 / 0.038508 (0.061792) | 0.077780 / 0.023109 (0.054670) | 0.353728 / 0.275898 (0.077830) | 0.400538 / 0.323480 (0.077058) | 0.005807 / 0.007986 (-0.002178) | 0.003649 / 0.004328 (-0.000680) | 0.077548 / 0.004250 (0.073297) | 0.058834 / 0.037052 (0.021781) | 0.352064 / 0.258489 (0.093574) | 0.399951 / 0.293841 (0.106110) | 0.036472 / 0.128546 (-0.092074) | 0.008653 / 0.075646 (-0.066994) | 0.323089 / 0.419271 (-0.096182) | 0.075127 / 0.043533 (0.031594) | 0.334412 / 0.255139 (0.079273) | 0.375718 / 0.283200 (0.092519) | 0.027915 / 0.141683 (-0.113768) | 1.698795 / 1.452155 (0.246640) | 1.781447 / 1.492716 (0.288730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216111 / 0.018006 (0.198104) | 0.507706 / 0.000490 (0.507216) | 0.000851 / 0.000200 (0.000651) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030451 / 0.037411 (-0.006960) | 0.087488 / 0.014526 (0.072962) | 0.105094 / 0.176557 (-0.071462) | 0.168130 / 0.737135 (-0.569006) | 0.106791 / 0.296338 (-0.189547) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426291 / 0.215209 (0.211082) | 4.281046 / 2.077655 (2.203391) | 2.162268 / 1.504120 (0.658148) | 1.909503 / 1.541195 (0.368309) | 1.943165 / 1.468490 (0.474675) | 0.516667 / 4.584777 (-4.068110) | 4.113218 / 3.745712 (0.367506) | 5.931372 / 5.269862 (0.661510) | 3.563521 / 4.565676 (-1.002155) | 0.062415 / 0.424275 (-0.361860) | 0.007577 / 0.007607 (-0.000030) | 0.534588 / 0.226044 (0.308543) | 5.183490 / 2.268929 (2.914561) | 2.790662 / 55.444624 (-52.653962) | 2.258630 / 6.876477 (-4.617846) | 2.499930 / 2.142072 (0.357857) | 0.606154 / 4.805227 (-4.199073) | 0.136093 / 6.500664 (-6.364571) | 0.061151 / 0.075469 (-0.014318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.398392 / 1.841788 (-0.443396) | 21.482150 / 8.074308 (13.407842) | 15.477336 / 10.191392 (5.285944) | 0.192878 / 0.680424 (-0.487546) | 0.021764 / 0.534201 (-0.512437) | 0.437149 / 0.579283 (-0.142134) | 0.439976 / 0.434364 (0.005612) | 0.514498 / 0.540337 (-0.025840) | 0.762642 / 1.386936 (-0.624294) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007504 / 0.011353 (-0.003849) | 0.004526 / 0.011008 (-0.006482) | 0.071008 / 0.038508 (0.032500) | 0.078305 / 0.023109 (0.055195) | 0.436160 / 0.275898 (0.160262) | 0.439048 / 0.323480 (0.115568) | 0.006061 / 0.007986 (-0.001925) | 0.003681 / 0.004328 (-0.000648) | 0.069445 / 0.004250 (0.065195) | 0.059258 / 0.037052 (0.022206) | 0.437745 / 0.258489 (0.179256) | 0.464247 / 0.293841 (0.170406) | 0.033286 / 0.128546 (-0.095260) | 0.009846 / 0.075646 (-0.065800) | 0.076330 / 0.419271 (-0.342941) | 0.051919 / 0.043533 (0.008386) | 0.432817 / 0.255139 (0.177678) | 0.426295 / 0.283200 (0.143095) | 0.029818 / 0.141683 (-0.111865) | 1.747640 / 1.452155 (0.295485) | 1.726653 / 1.492716 (0.233937) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251253 / 0.018006 (0.233247) | 0.483394 / 0.000490 (0.482904) | 0.003992 / 0.000200 (0.003793) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032180 / 0.037411 (-0.005231) | 0.095425 / 0.014526 (0.080900) | 0.105908 / 0.176557 (-0.070648) | 0.164732 / 0.737135 (-0.572403) | 0.115903 / 0.296338 (-0.180435) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469467 / 0.215209 (0.254258) | 4.633239 / 2.077655 (2.555584) | 2.517557 / 1.504120 (1.013437) | 2.352726 / 1.541195 (0.811531) | 2.314618 / 1.468490 (0.846128) | 0.548446 / 4.584777 (-4.036331) | 3.908797 / 3.745712 (0.163085) | 3.525941 / 5.269862 (-1.743921) | 2.178858 / 4.565676 (-2.386819) | 0.057614 / 0.424275 (-0.366661) | 0.008604 / 0.007607 (0.000997) | 0.554756 / 0.226044 (0.328711) | 5.325635 / 2.268929 (3.056706) | 3.014266 / 55.444624 (-52.430359) | 2.844165 / 6.876477 (-4.032312) | 2.903019 / 2.142072 (0.760947) | 0.617750 / 4.805227 (-4.187478) | 0.144259 / 6.500664 (-6.356405) | 0.065944 / 0.075469 (-0.009525) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.504625 / 1.841788 (-0.337163) | 22.400787 / 8.074308 (14.326479) | 15.223702 / 10.191392 (5.032310) | 0.213357 / 0.680424 (-0.467067) | 0.019310 / 0.534201 (-0.514891) | 0.456596 / 0.579283 (-0.122687) | 0.473811 / 0.434364 (0.039447) | 0.517800 / 0.540337 (-0.022537) | 0.792468 / 1.386936 (-0.594468) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#03750f4a4c664125c7de910be004710b181dd354 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007420 / 0.011353 (-0.003933) | 0.004502 / 0.011008 (-0.006506) | 0.097882 / 0.038508 (0.059374) | 0.079084 / 0.023109 (0.055975) | 0.361797 / 0.275898 (0.085899) | 0.416563 / 0.323480 (0.093083) | 0.006106 / 0.007986 (-0.001879) | 0.003803 / 0.004328 (-0.000526) | 0.074669 / 0.004250 (0.070418) | 0.062168 / 0.037052 (0.025116) | 0.378844 / 0.258489 (0.120355) | 0.426601 / 0.293841 (0.132760) | 0.035619 / 0.128546 (-0.092927) | 0.009686 / 0.075646 (-0.065960) | 0.336481 / 0.419271 (-0.082790) | 0.065553 / 0.043533 (0.022021) | 0.362501 / 0.255139 (0.107362) | 0.399752 / 0.283200 (0.116552) | 0.028685 / 0.141683 (-0.112998) | 1.683495 / 1.452155 (0.231340) | 1.786105 / 1.492716 (0.293388) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220792 / 0.018006 (0.202786) | 0.501936 / 0.000490 (0.501447) | 0.000389 / 0.000200 (0.000189) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032180 / 0.037411 (-0.005232) | 0.093079 / 0.014526 (0.078553) | 0.107967 / 0.176557 (-0.068589) | 0.171747 / 0.737135 (-0.565389) | 0.107920 / 0.296338 (-0.188418) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444431 / 0.215209 (0.229222) | 4.454934 / 2.077655 (2.377279) | 2.140265 / 1.504120 (0.636145) | 1.960126 / 1.541195 (0.418931) | 2.049649 / 1.468490 (0.581158) | 0.557861 / 4.584777 (-4.026916) | 4.046240 / 3.745712 (0.300528) | 4.513748 / 5.269862 (-0.756114) | 2.593643 / 4.565676 (-1.972034) | 0.066795 / 0.424275 (-0.357480) | 0.008302 / 0.007607 (0.000694) | 0.535643 / 0.226044 (0.309599) | 5.299429 / 2.268929 (3.030500) | 2.656019 / 55.444624 (-52.788606) | 2.281214 / 6.876477 (-4.595263) | 2.302910 / 2.142072 (0.160837) | 0.661696 / 4.805227 (-4.143532) | 0.149787 / 6.500664 (-6.350877) | 0.069609 / 0.075469 (-0.005860) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.509842 / 1.841788 (-0.331946) | 21.717504 / 8.074308 (13.643196) | 15.825102 / 10.191392 (5.633710) | 0.168115 / 0.680424 (-0.512309) | 0.021637 / 0.534201 (-0.512564) | 0.454270 / 0.579283 (-0.125013) | 0.458531 / 0.434364 (0.024167) | 0.523052 / 0.540337 (-0.017285) | 0.711219 / 1.386936 (-0.675717) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007189 / 0.011353 (-0.004164) | 0.004437 / 0.011008 (-0.006571) | 0.075111 / 0.038508 (0.036603) | 0.079245 / 0.023109 (0.056136) | 0.423169 / 0.275898 (0.147270) | 0.455007 / 0.323480 (0.131527) | 0.006076 / 0.007986 (-0.001909) | 0.003819 / 0.004328 (-0.000509) | 0.074976 / 0.004250 (0.070726) | 0.062127 / 0.037052 (0.025075) | 0.456809 / 0.258489 (0.198320) | 0.474707 / 0.293841 (0.180867) | 0.036221 / 0.128546 (-0.092325) | 0.009428 / 0.075646 (-0.066218) | 0.082842 / 0.419271 (-0.336429) | 0.057086 / 0.043533 (0.013553) | 0.436121 / 0.255139 (0.180982) | 0.453934 / 0.283200 (0.170734) | 0.026045 / 0.141683 (-0.115638) | 1.789782 / 1.452155 (0.337627) | 1.820934 / 1.492716 (0.328218) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230790 / 0.018006 (0.212784) | 0.497987 / 0.000490 (0.497497) | 0.002775 / 0.000200 (0.002575) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034418 / 0.037411 (-0.002994) | 0.105567 / 0.014526 (0.091041) | 0.113134 / 0.176557 (-0.063423) | 0.173742 / 0.737135 (-0.563394) | 0.115936 / 0.296338 (-0.180403) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502259 / 0.215209 (0.287050) | 4.969877 / 2.077655 (2.892222) | 2.684860 / 1.504120 (1.180740) | 2.484386 / 1.541195 (0.943192) | 2.543061 / 1.468490 (1.074571) | 0.545733 / 4.584777 (-4.039044) | 4.029660 / 3.745712 (0.283948) | 5.927883 / 5.269862 (0.658021) | 3.528372 / 4.565676 (-1.037305) | 0.065957 / 0.424275 (-0.358318) | 0.008933 / 0.007607 (0.001326) | 0.601630 / 0.226044 (0.375585) | 5.825872 / 2.268929 (3.556944) | 3.230721 / 55.444624 (-52.213904) | 2.891308 / 6.876477 (-3.985169) | 3.054994 / 2.142072 (0.912922) | 0.665480 / 4.805227 (-4.139747) | 0.154815 / 6.500664 (-6.345849) | 0.072997 / 0.075469 (-0.002472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549892 / 1.841788 (-0.291896) | 22.337484 / 8.074308 (14.263176) | 16.308286 / 10.191392 (6.116894) | 0.189594 / 0.680424 (-0.490830) | 0.021844 / 0.534201 (-0.512357) | 0.456958 / 0.579283 (-0.122325) | 0.459957 / 0.434364 (0.025593) | 0.529014 / 0.540337 (-0.011323) | 0.700359 / 1.386936 (-0.686577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e4df86b5fb0bc164433ce615af641ec3ba437e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009050 / 0.011353 (-0.002303) | 0.004968 / 0.011008 (-0.006040) | 0.114315 / 0.038508 (0.075807) | 0.084475 / 0.023109 (0.061366) | 0.426325 / 0.275898 (0.150427) | 0.457870 / 0.323480 (0.134390) | 0.007076 / 0.007986 (-0.000910) | 0.004635 / 0.004328 (0.000307) | 0.082950 / 0.004250 (0.078700) | 0.065414 / 0.037052 (0.028361) | 0.441936 / 0.258489 (0.183447) | 0.476983 / 0.293841 (0.183142) | 0.048575 / 0.128546 (-0.079972) | 0.013929 / 0.075646 (-0.061717) | 0.377498 / 0.419271 (-0.041774) | 0.081503 / 0.043533 (0.037970) | 0.426706 / 0.255139 (0.171567) | 0.460374 / 0.283200 (0.177175) | 0.046052 / 0.141683 (-0.095631) | 1.894896 / 1.452155 (0.442741) | 1.998639 / 1.492716 (0.505923) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313267 / 0.018006 (0.295261) | 0.607501 / 0.000490 (0.607012) | 0.003369 / 0.000200 (0.003169) | 0.000102 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032266 / 0.037411 (-0.005145) | 0.120138 / 0.014526 (0.105613) | 0.115044 / 0.176557 (-0.061513) | 0.181374 / 0.737135 (-0.555761) | 0.114681 / 0.296338 (-0.181657) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.648039 / 0.215209 (0.432830) | 6.005048 / 2.077655 (3.927394) | 2.674524 / 1.504120 (1.170404) | 2.284831 / 1.541195 (0.743637) | 2.360150 / 1.468490 (0.891660) | 0.888021 / 4.584777 (-3.696756) | 5.419840 / 3.745712 (1.674128) | 4.825816 / 5.269862 (-0.444046) | 3.140876 / 4.565676 (-1.424801) | 0.099511 / 0.424275 (-0.324764) | 0.009176 / 0.007607 (0.001569) | 0.735646 / 0.226044 (0.509602) | 7.224026 / 2.268929 (4.955097) | 3.551146 / 55.444624 (-51.893478) | 2.844374 / 6.876477 (-4.032103) | 3.145307 / 2.142072 (1.003235) | 1.077636 / 4.805227 (-3.727591) | 0.217754 / 6.500664 (-6.282910) | 0.081755 / 0.075469 (0.006286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670956 / 1.841788 (-0.170831) | 25.524961 / 8.074308 (17.450653) | 23.061596 / 10.191392 (12.870204) | 0.247524 / 0.680424 (-0.432899) | 0.031712 / 0.534201 (-0.502489) | 0.513049 / 0.579283 (-0.066234) | 0.614568 / 0.434364 (0.180204) | 0.574669 / 0.540337 (0.034331) | 0.816621 / 1.386936 (-0.570315) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009384 / 0.011353 (-0.001969) | 0.004959 / 0.011008 (-0.006049) | 0.084782 / 0.038508 (0.046274) | 0.098086 / 0.023109 (0.074977) | 0.544395 / 0.275898 (0.268497) | 0.585157 / 0.323480 (0.261677) | 0.006507 / 0.007986 (-0.001479) | 0.004151 / 0.004328 (-0.000178) | 0.088596 / 0.004250 (0.084345) | 0.069149 / 0.037052 (0.032097) | 0.533109 / 0.258489 (0.274620) | 0.604117 / 0.293841 (0.310276) | 0.047685 / 0.128546 (-0.080861) | 0.013651 / 0.075646 (-0.061996) | 0.096566 / 0.419271 (-0.322705) | 0.062022 / 0.043533 (0.018489) | 0.561897 / 0.255139 (0.306758) | 0.617636 / 0.283200 (0.334436) | 0.034636 / 0.141683 (-0.107047) | 1.854667 / 1.452155 (0.402512) | 1.908923 / 1.492716 (0.416207) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260633 / 0.018006 (0.242627) | 0.622268 / 0.000490 (0.621778) | 0.002116 / 0.000200 (0.001916) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035161 / 0.037411 (-0.002250) | 0.103707 / 0.014526 (0.089181) | 0.115467 / 0.176557 (-0.061090) | 0.180077 / 0.737135 (-0.557059) | 0.118871 / 0.296338 (-0.177467) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.628481 / 0.215209 (0.413271) | 6.304929 / 2.077655 (4.227275) | 3.027775 / 1.504120 (1.523655) | 2.753880 / 1.541195 (1.212686) | 2.820442 / 1.468490 (1.351952) | 0.851103 / 4.584777 (-3.733674) | 5.427383 / 3.745712 (1.681670) | 7.434310 / 5.269862 (2.164449) | 4.418790 / 4.565676 (-0.146887) | 0.101733 / 0.424275 (-0.322542) | 0.009701 / 0.007607 (0.002094) | 0.763033 / 0.226044 (0.536989) | 7.497927 / 2.268929 (5.228998) | 3.735335 / 55.444624 (-51.709290) | 3.149200 / 6.876477 (-3.727277) | 3.306214 / 2.142072 (1.164141) | 1.085440 / 4.805227 (-3.719787) | 0.207562 / 6.500664 (-6.293102) | 0.078091 / 0.075469 (0.002622) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.820097 / 1.841788 (-0.021691) | 25.525539 / 8.074308 (17.451231) | 21.874219 / 10.191392 (11.682827) | 0.228391 / 0.680424 (-0.452033) | 0.029584 / 0.534201 (-0.504617) | 0.511546 / 0.579283 (-0.067737) | 0.602719 / 0.434364 (0.168355) | 0.581874 / 0.540337 (0.041537) | 0.802861 / 1.386936 (-0.584075) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6063ea2069c8b5641b983ba2c1d39b60afe7c00a \"CML watermark\")\n"
] | 2023-07-25T11:17:25 | 2023-07-25T15:29:39 | 2023-07-25T15:17:50 | MEMBER | null | false | {
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