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Update warning message in __init__.py
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mariosasko committed Nov 24, 2021
1 parent 846e4d9 commit 3d21bd0
Showing 1 changed file with 2 additions and 2 deletions.
4 changes: 2 additions & 2 deletions src/datasets/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -25,9 +25,9 @@
from pyarrow import total_allocated_bytes


if _version.parse(pyarrow.__version__).major < 1:
if _version.parse(pyarrow.__version__).major < 3:
raise ImportWarning(
"To use `datasets`, the module `pyarrow>=1.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
"To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
"If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
)

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Show benchmarks

PyArrow==3.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.072051 / 0.011353 (0.060698) 0.004217 / 0.011008 (-0.006791) 0.032181 / 0.038508 (-0.006327) 0.036929 / 0.023109 (0.013819) 0.301445 / 0.275898 (0.025547) 0.339388 / 0.323480 (0.015908) 0.087354 / 0.007986 (0.079368) 0.004453 / 0.004328 (0.000124) 0.009330 / 0.004250 (0.005080) 0.044679 / 0.037052 (0.007627) 0.299173 / 0.258489 (0.040684) 0.338886 / 0.293841 (0.045045) 0.085655 / 0.128546 (-0.042891) 0.009090 / 0.075646 (-0.066556) 0.254868 / 0.419271 (-0.164403) 0.046736 / 0.043533 (0.003204) 0.296864 / 0.255139 (0.041725) 0.323055 / 0.283200 (0.039855) 0.088747 / 0.141683 (-0.052935) 1.714763 / 1.452155 (0.262608) 1.786236 / 1.492716 (0.293520)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.360642 / 0.018006 (0.342636) 0.551545 / 0.000490 (0.551055) 0.019236 / 0.000200 (0.019036) 0.000413 / 0.000054 (0.000359)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.036206 / 0.037411 (-0.001206) 0.023827 / 0.014526 (0.009302) 0.031734 / 0.176557 (-0.144823) 0.199146 / 0.737135 (-0.537989) 0.031958 / 0.296338 (-0.264380)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.418871 / 0.215209 (0.203662) 4.199916 / 2.077655 (2.122261) 1.805759 / 1.504120 (0.301640) 1.590973 / 1.541195 (0.049778) 1.712746 / 1.468490 (0.244256) 0.413169 / 4.584777 (-4.171608) 4.761378 / 3.745712 (1.015666) 2.157049 / 5.269862 (-3.112812) 0.882629 / 4.565676 (-3.683047) 0.049680 / 0.424275 (-0.374595) 0.011211 / 0.007607 (0.003604) 0.520876 / 0.226044 (0.294832) 5.216294 / 2.268929 (2.947365) 2.274083 / 55.444624 (-53.170541) 1.905460 / 6.876477 (-4.971016) 2.059639 / 2.142072 (-0.082434) 0.526222 / 4.805227 (-4.279005) 0.119398 / 6.500664 (-6.381266) 0.057017 / 0.075469 (-0.018452)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 1.536839 / 1.841788 (-0.304948) 12.772264 / 8.074308 (4.697956) 26.890802 / 10.191392 (16.699410) 0.805086 / 0.680424 (0.124662) 0.528092 / 0.534201 (-0.006109) 0.373740 / 0.579283 (-0.205543) 0.513558 / 0.434364 (0.079194) 0.257327 / 0.540337 (-0.283011) 0.270644 / 1.386936 (-1.116292)
PyArrow==latest
Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.069434 / 0.011353 (0.058081) 0.004480 / 0.011008 (-0.006528) 0.030219 / 0.038508 (-0.008289) 0.034076 / 0.023109 (0.010967) 0.321944 / 0.275898 (0.046046) 0.354871 / 0.323480 (0.031391) 0.090723 / 0.007986 (0.082738) 0.004666 / 0.004328 (0.000337) 0.007538 / 0.004250 (0.003288) 0.039083 / 0.037052 (0.002031) 0.322374 / 0.258489 (0.063885) 0.363881 / 0.293841 (0.070040) 0.085079 / 0.128546 (-0.043467) 0.009180 / 0.075646 (-0.066466) 0.252177 / 0.419271 (-0.167094) 0.047568 / 0.043533 (0.004035) 0.321011 / 0.255139 (0.065872) 0.341964 / 0.283200 (0.058764) 0.090966 / 0.141683 (-0.050717) 1.669882 / 1.452155 (0.217728) 1.718122 / 1.492716 (0.225405)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.347093 / 0.018006 (0.329086) 0.528565 / 0.000490 (0.528075) 0.001980 / 0.000200 (0.001780) 0.000082 / 0.000054 (0.000027)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.034079 / 0.037411 (-0.003332) 0.021974 / 0.014526 (0.007448) 0.029700 / 0.176557 (-0.146857) 0.200571 / 0.737135 (-0.536564) 0.030588 / 0.296338 (-0.265750)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.419295 / 0.215209 (0.204086) 4.208799 / 2.077655 (2.131144) 1.798755 / 1.504120 (0.294635) 1.592361 / 1.541195 (0.051167) 1.700465 / 1.468490 (0.231975) 0.412631 / 4.584777 (-4.172146) 4.732810 / 3.745712 (0.987098) 3.434846 / 5.269862 (-1.835016) 0.893085 / 4.565676 (-3.672592) 0.050040 / 0.424275 (-0.374235) 0.011043 / 0.007607 (0.003436) 0.534167 / 0.226044 (0.308122) 5.297885 / 2.268929 (3.028957) 2.284325 / 55.444624 (-53.160300) 1.913981 / 6.876477 (-4.962496) 2.074645 / 2.142072 (-0.067428) 0.528160 / 4.805227 (-4.277067) 0.115246 / 6.500664 (-6.385419) 0.056341 / 0.075469 (-0.019128)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 1.530273 / 1.841788 (-0.311514) 12.309415 / 8.074308 (4.235107) 27.236960 / 10.191392 (17.045568) 0.751434 / 0.680424 (0.071010) 0.521091 / 0.534201 (-0.013110) 0.368626 / 0.579283 (-0.210657) 0.507584 / 0.434364 (0.073220) 0.253072 / 0.540337 (-0.287265) 0.261702 / 1.386936 (-1.125234)

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