Skip to content

Commit

Permalink
set dev version
Browse files Browse the repository at this point in the history
  • Loading branch information
lhoestq committed Jul 25, 2022
1 parent 401d4c4 commit 10b1355
Show file tree
Hide file tree
Showing 2 changed files with 2 additions and 2 deletions.
2 changes: 1 addition & 1 deletion setup.py
Original file line number Diff line number Diff line change
Expand Up @@ -201,7 +201,7 @@

setup(
name="datasets",
version="2.4.0", # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
version="2.4.1.dev0", # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
description="HuggingFace community-driven open-source library of datasets",
long_description=open("README.md", encoding="utf-8").read(),
long_description_content_type="text/markdown",
Expand Down
2 changes: 1 addition & 1 deletion src/datasets/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__version__ = "2.4.0"
__version__ = "2.4.1.dev0"

import pyarrow
from packaging import version
Expand Down

1 comment on commit 10b1355

@github-actions
Copy link

Choose a reason for hiding this comment

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

Show benchmarks

PyArrow==6.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.007822 / 0.011353 (-0.003531) 0.003679 / 0.011008 (-0.007330) 0.027432 / 0.038508 (-0.011076) 0.029614 / 0.023109 (0.006505) 0.300321 / 0.275898 (0.024423) 0.333945 / 0.323480 (0.010465) 0.005529 / 0.007986 (-0.002457) 0.004193 / 0.004328 (-0.000136) 0.006591 / 0.004250 (0.002340) 0.032393 / 0.037052 (-0.004660) 0.299747 / 0.258489 (0.041258) 0.334687 / 0.293841 (0.040846) 0.028475 / 0.128546 (-0.100071) 0.009375 / 0.075646 (-0.066271) 0.236520 / 0.419271 (-0.182751) 0.043001 / 0.043533 (-0.000532) 0.305732 / 0.255139 (0.050593) 0.328682 / 0.283200 (0.045482) 0.084903 / 0.141683 (-0.056780) 1.846445 / 1.452155 (0.394290) 1.881841 / 1.492716 (0.389125)

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) 0.194155 / 0.018006 (0.176148) 0.417377 / 0.000490 (0.416887) 0.012822 / 0.000200 (0.012622) 0.000584 / 0.000054 (0.000529)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.023851 / 0.037411 (-0.013560) 0.096063 / 0.014526 (0.081537) 0.107809 / 0.176557 (-0.068747) 0.152948 / 0.737135 (-0.584187) 0.105164 / 0.296338 (-0.191174)

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) 0.465527 / 0.215209 (0.250318) 4.637431 / 2.077655 (2.559776) 2.133044 / 1.504120 (0.628924) 1.908879 / 1.541195 (0.367684) 1.967754 / 1.468490 (0.499263) 0.463747 / 4.584777 (-4.121029) 4.192389 / 3.745712 (0.446677) 2.117655 / 5.269862 (-3.152207) 0.839207 / 4.565676 (-3.726469) 0.054666 / 0.424275 (-0.369609) 0.011503 / 0.007607 (0.003896) 0.578181 / 0.226044 (0.352136) 5.778651 / 2.268929 (3.509722) 2.592022 / 55.444624 (-52.852602) 2.221479 / 6.876477 (-4.654998) 2.282202 / 2.142072 (0.140129) 0.591198 / 4.805227 (-4.214029) 0.121952 / 6.500664 (-6.378712) 0.064041 / 0.075469 (-0.011428)

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) 1.677125 / 1.841788 (-0.164662) 14.007095 / 8.074308 (5.932787) 28.189789 / 10.191392 (17.998397) 0.844399 / 0.680424 (0.163975) 0.591117 / 0.534201 (0.056916) 0.431067 / 0.579283 (-0.148216) 0.489012 / 0.434364 (0.054648) 0.274222 / 0.540337 (-0.266116) 0.282481 / 1.386936 (-1.104455)
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) 0.008395 / 0.011353 (-0.002958) 0.003679 / 0.011008 (-0.007329) 0.028641 / 0.038508 (-0.009867) 0.032260 / 0.023109 (0.009151) 0.364121 / 0.275898 (0.088223) 0.389269 / 0.323480 (0.065789) 0.005946 / 0.007986 (-0.002040) 0.002961 / 0.004328 (-0.001368) 0.006809 / 0.004250 (0.002559) 0.034486 / 0.037052 (-0.002566) 0.366433 / 0.258489 (0.107944) 0.389141 / 0.293841 (0.095300) 0.031682 / 0.128546 (-0.096864) 0.009738 / 0.075646 (-0.065909) 0.248020 / 0.419271 (-0.171251) 0.047744 / 0.043533 (0.004211) 0.355307 / 0.255139 (0.100168) 0.400608 / 0.283200 (0.117408) 0.087268 / 0.141683 (-0.054415) 1.885793 / 1.452155 (0.433639) 2.049056 / 1.492716 (0.556340)

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) 0.194977 / 0.018006 (0.176971) 0.415036 / 0.000490 (0.414546) 0.013501 / 0.000200 (0.013301) 0.000230 / 0.000054 (0.000176)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.022539 / 0.037411 (-0.014873) 0.093625 / 0.014526 (0.079099) 0.104771 / 0.176557 (-0.071786) 0.147840 / 0.737135 (-0.589295) 0.104226 / 0.296338 (-0.192113)

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) 0.460235 / 0.215209 (0.245026) 4.590741 / 2.077655 (2.513086) 2.170689 / 1.504120 (0.666569) 1.941178 / 1.541195 (0.399983) 2.033543 / 1.468490 (0.565052) 0.458412 / 4.584777 (-4.126365) 4.133142 / 3.745712 (0.387430) 3.102985 / 5.269862 (-2.166877) 0.837300 / 4.565676 (-3.728377) 0.054896 / 0.424275 (-0.369379) 0.011658 / 0.007607 (0.004050) 0.569776 / 0.226044 (0.343732) 5.753247 / 2.268929 (3.484319) 2.509080 / 55.444624 (-52.935544) 2.139344 / 6.876477 (-4.737133) 2.338817 / 2.142072 (0.196744) 0.583492 / 4.805227 (-4.221735) 0.120774 / 6.500664 (-6.379890) 0.063903 / 0.075469 (-0.011566)

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) 1.696273 / 1.841788 (-0.145515) 13.511827 / 8.074308 (5.437519) 28.226114 / 10.191392 (18.034722) 0.859854 / 0.680424 (0.179430) 0.586718 / 0.534201 (0.052517) 0.422685 / 0.579283 (-0.156598) 0.467942 / 0.434364 (0.033578) 0.267850 / 0.540337 (-0.272488) 0.275926 / 1.386936 (-1.111010)

CML watermark

Please sign in to comment.