url
stringlengths 61
61
| repository_url
stringclasses 1
value | labels_url
stringlengths 75
75
| comments_url
stringlengths 70
70
| events_url
stringlengths 68
68
| html_url
stringlengths 49
51
| id
int64 1.21B
1.85B
| node_id
stringlengths 18
19
| number
int64 4.19k
6.15k
| title
stringlengths 1
290
| user
dict | labels
list | state
stringclasses 2
values | locked
bool 1
class | assignee
dict | assignees
list | milestone
dict | comments
sequence | created_at
timestamp[s] | updated_at
timestamp[s] | closed_at
timestamp[s] | author_association
stringclasses 3
values | active_lock_reason
null | draft
bool 2
classes | pull_request
dict | body
stringlengths 2
33.9k
⌀ | reactions
dict | timeline_url
stringlengths 70
70
| performed_via_github_app
null | state_reason
stringclasses 3
values | is_pull_request
bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://api.github.com/repos/huggingface/datasets/issues/6145 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6145/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6145/comments | https://api.github.com/repos/huggingface/datasets/issues/6145/events | https://github.com/huggingface/datasets/pull/6145 | 1,847,811,310 | PR_kwDODunzps5Xx5If | 6,145 | Export to_iterable_dataset to document | {
"login": "npuichigo",
"id": 11533479,
"node_id": "MDQ6VXNlcjExNTMzNDc5",
"avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/npuichigo",
"html_url": "https://github.com/npuichigo",
"followers_url": "https://api.github.com/users/npuichigo/followers",
"following_url": "https://api.github.com/users/npuichigo/following{/other_user}",
"gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}",
"starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions",
"organizations_url": "https://api.github.com/users/npuichigo/orgs",
"repos_url": "https://api.github.com/users/npuichigo/repos",
"events_url": "https://api.github.com/users/npuichigo/events{/privacy}",
"received_events_url": "https://api.github.com/users/npuichigo/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [] | 2023-08-12T07:00:14 | 2023-08-12T07:00:14 | null | NONE | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6145",
"html_url": "https://github.com/huggingface/datasets/pull/6145",
"diff_url": "https://github.com/huggingface/datasets/pull/6145.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6145.patch",
"merged_at": null
} | Fix the export of a missing method of `Dataset` | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6145/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6145/timeline | null | null | true |
https://api.github.com/repos/huggingface/datasets/issues/6144 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6144/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6144/comments | https://api.github.com/repos/huggingface/datasets/issues/6144/events | https://github.com/huggingface/datasets/issues/6144 | 1,847,296,711 | I_kwDODunzps5uG4LH | 6,144 | NIH exporter file not found | {
"login": "brando90",
"id": 1855278,
"node_id": "MDQ6VXNlcjE4NTUyNzg=",
"avatar_url": "https://avatars.githubusercontent.com/u/1855278?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/brando90",
"html_url": "https://github.com/brando90",
"followers_url": "https://api.github.com/users/brando90/followers",
"following_url": "https://api.github.com/users/brando90/following{/other_user}",
"gists_url": "https://api.github.com/users/brando90/gists{/gist_id}",
"starred_url": "https://api.github.com/users/brando90/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/brando90/subscriptions",
"organizations_url": "https://api.github.com/users/brando90/orgs",
"repos_url": "https://api.github.com/users/brando90/repos",
"events_url": "https://api.github.com/users/brando90/events{/privacy}",
"received_events_url": "https://api.github.com/users/brando90/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [
"related: https://github.com/huggingface/datasets/issues/3504",
"another file not found:\r\n```\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 417, in _info\r\n await _file_info(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 837, in _file_info\r\n r.raise_for_status()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/aiohttp/client_reqrep.py\", line 1005, in raise_for_status\r\n raise ClientResponseError(\r\naiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 526, in <module>\r\n experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 475, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights\r\n column_names = next(iter(dataset)).keys()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 1353, in __iter__\r\n for key, example in ex_iterable:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 207, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py\", line 257, in _generate_examples\r\n for path, file in files[subset]:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 840, in __iter__\r\n yield from self.generator(*self.args, **self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 891, in _iter_from_urlpath\r\n with xopen(urlpath, \"rb\", download_config=download_config) as f:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 496, in xopen\r\n file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 134, in open\r\n return self.__enter__()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 102, in __enter__\r\n f = self.fs.open(self.path, mode=mode)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py\", line 1241, in open\r\n f = self._open(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 356, in _open\r\n size = size or self.info(path, **kwargs)[\"size\"]\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 121, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 106, in sync\r\n raise return_result\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 61, in _runner\r\n result[0] = await coro\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 430, in _info\r\n raise FileNotFoundError(url) from exc\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```",
"```\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```\r\nmost relevant line I think.",
"link to tweet: https://twitter.com/BrandoHablando/status/1690081313519489024?s=20 about issue"
] | 2023-08-11T19:05:25 | 2023-08-11T19:25:32 | null | NONE | null | null | null | ### Describe the bug
can't use or download the nih exporter pile data.
```
15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()
16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights
17 column_names = next(iter(dataset)).keys()
18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__
19 for key, example in ex_iterable:
20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__
21 yield from self.generate_examples_fn(**self.kwargs)
22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples
23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f:
24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper
25 return function(*args, download_config=download_config, **kwargs)
26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open
29 return self.__enter__()
30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__
31 f = self.fs.open(self.path, mode=mode)
32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open
33 f = self._open(
34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open
35 size = size or self.info(path, **kwargs)["size"]
36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper
37 return sync(self.loop, func, *args, **kwargs)
38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync
39 raise return_result
40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner
41 result[0] = await coro
42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info
43 raise FileNotFoundError(url) from exc
44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst
```
### Steps to reproduce the bug
run this:
```
from datasets import load_dataset
path, name = 'EleutherAI/pile', 'nih_exporter'
# -- Get data set
dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch")
batch = dataset.take(512)
print(f'{batch=}')
```
### Expected behavior
print the batch
### Environment info
```
(beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.14.4
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
``` | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6144/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6144/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6142 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6142/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6142/comments | https://api.github.com/repos/huggingface/datasets/issues/6142/events | https://github.com/huggingface/datasets/issues/6142 | 1,846,205,216 | I_kwDODunzps5uCtsg | 6,142 | the-stack-dedup fails to generate | {
"login": "michaelroyzen",
"id": 45830328,
"node_id": "MDQ6VXNlcjQ1ODMwMzI4",
"avatar_url": "https://avatars.githubusercontent.com/u/45830328?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/michaelroyzen",
"html_url": "https://github.com/michaelroyzen",
"followers_url": "https://api.github.com/users/michaelroyzen/followers",
"following_url": "https://api.github.com/users/michaelroyzen/following{/other_user}",
"gists_url": "https://api.github.com/users/michaelroyzen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/michaelroyzen/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/michaelroyzen/subscriptions",
"organizations_url": "https://api.github.com/users/michaelroyzen/orgs",
"repos_url": "https://api.github.com/users/michaelroyzen/repos",
"events_url": "https://api.github.com/users/michaelroyzen/events{/privacy}",
"received_events_url": "https://api.github.com/users/michaelroyzen/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [
"@severo "
] | 2023-08-11T05:10:49 | 2023-08-11T05:11:33 | null | NONE | null | null | null | ### Describe the bug
I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens.
### Steps to reproduce the bug
My code:
```
import os
import datasets as ds
MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local"
MY_TOKEN="my-token"
the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64)
```
The exception:
```
Generating train split: 233248251 examples [54:31, 57280.00 examples/s]
multiprocess.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1879, in _prepare_split_single
for _, table in generator:
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa
ged_modules/parquet/parquet.py", line 82, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa
ged_modules/parquet/parquet.py", line 61, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table
.py", line 2324, in table_cast
return cast_table_to_schema(table, schema)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table
.py", line 2282, in cast_table_to_schema
raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb
ecause column names don't match")
ValueError: Couldn't cast
hexsha: string
size: int64
ext: string
lang: string
max_stars_repo_path: string
max_stars_repo_name: string
max_stars_repo_head_hexsha: string
max_stars_repo_licenses: list<item: string>
child 0, item: string
max_stars_count: int64
max_stars_repo_stars_event_min_datetime: string
max_stars_repo_stars_event_max_datetime: string
max_issues_repo_path: string
max_issues_repo_name: string
max_issues_repo_head_hexsha: string
max_issues_repo_licenses: list<item: string>
child 0, item: string
max_issues_count: int64
max_issues_repo_issues_event_min_datetime: string
max_issues_repo_issues_event_max_datetime: string
max_forks_repo_path: string
max_forks_repo_name: string
max_forks_repo_head_hexsha: string
max_forks_repo_licenses: list<item: string>
child 0, item: string
max_forks_count: int64
max_forks_repo_forks_event_min_datetime: string
max_forks_repo_forks_event_max_datetime: string
content: string
avg_line_length: double
max_line_length: int64
alphanum_fraction: double
__id__: int64
-- schema metadata --
huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979
to
{'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p
ool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils
/py_utils.py", line 1328, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1912, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating th
e dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while genera
ting the dataset
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/download_the_stack.py", line 7, in <module>
the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr
ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us
e_auth_token=MY_TOKEN, num_proc=64)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load.
py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1004, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build
er.py", line 1796, in _prepare_split
for job_id, done, content in iflatmap_unordered(
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils
/py_utils.py", line 1354, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils
/py_utils.py", line 1354, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p
ool.py", line 774, in get
raise self._value
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
The dataset downloads properly. @lhoestq @loub
### Environment info
Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04 | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6142/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6142/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6141 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6141/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6141/comments | https://api.github.com/repos/huggingface/datasets/issues/6141/events | https://github.com/huggingface/datasets/issues/6141 | 1,846,117,729 | I_kwDODunzps5uCYVh | 6,141 | TypeError: ClientSession._request() got an unexpected keyword argument 'https' | {
"login": "q935970314",
"id": 35994018,
"node_id": "MDQ6VXNlcjM1OTk0MDE4",
"avatar_url": "https://avatars.githubusercontent.com/u/35994018?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/q935970314",
"html_url": "https://github.com/q935970314",
"followers_url": "https://api.github.com/users/q935970314/followers",
"following_url": "https://api.github.com/users/q935970314/following{/other_user}",
"gists_url": "https://api.github.com/users/q935970314/gists{/gist_id}",
"starred_url": "https://api.github.com/users/q935970314/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/q935970314/subscriptions",
"organizations_url": "https://api.github.com/users/q935970314/orgs",
"repos_url": "https://api.github.com/users/q935970314/repos",
"events_url": "https://api.github.com/users/q935970314/events{/privacy}",
"received_events_url": "https://api.github.com/users/q935970314/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [] | 2023-08-11T02:40:32 | 2023-08-11T02:45:05 | null | NONE | null | null | null | ### Describe the bug
Hello, when I ran the [code snippet](https://huggingface.co/docs/datasets/v2.14.4/en/loading#json) on the document, I encountered the following problem:
```
Python 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
>>> dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset
builder_instance = load_dataset_builder(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1413, in dataset_module_factory
).get_module()
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 949, in get_module
data_files = DataFilesDict.from_patterns(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 672, in from_patterns
DataFilesList.from_patterns(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 578, in from_patterns
resolve_pattern(
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern
for filepath, info in fs.glob(pattern, detail=True).items()
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 113, in wrapper
return sync(self.loop, func, *args, **kwargs)
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 98, in sync
raise return_result
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 53, in _runner
result[0] = await coro
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 449, in _glob
elif await self._exists(path):
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 306, in _exists
r = await session.get(self.encode_url(path), **kw)
File "/home/liushuai/anaconda3/lib/python3.10/site-packages/aiohttp/client.py", line 922, in get
self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs)
TypeError: ClientSession._request() got an unexpected keyword argument 'https'
```
### Steps to reproduce the bug
```
from datasets import load_dataset
base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data")
```
### Expected behavior
able to load normally
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.4.54-2-x86_64-with-glibc2.27
- Python version: 3.10.9
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6141/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6141/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6140 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6140/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6140/comments | https://api.github.com/repos/huggingface/datasets/issues/6140/events | https://github.com/huggingface/datasets/issues/6140 | 1,845,384,712 | I_kwDODunzps5t_lYI | 6,140 | Misalignment between file format specified in configs metadata YAML and the inferred builder | {
"login": "albertvillanova",
"id": 8515462,
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/albertvillanova",
"html_url": "https://github.com/albertvillanova",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"type": "User",
"site_admin": false
} | [
{
"id": 1935892857,
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/bug",
"name": "bug",
"color": "d73a4a",
"default": true,
"description": "Something isn't working"
}
] | open | false | null | [] | null | [] | 2023-08-10T15:07:34 | 2023-08-10T15:07:34 | null | MEMBER | null | null | null | There is a misalignment between the format of the `data_files` specified in the configs metadata YAML (CSV):
```yaml
configs:
- config_name: default
data_files:
- split: train
path: data.csv
```
and the inferred builder (JSON). Note there are multiple JSON files in the repo, but they do not appear in the configs metadata YAML.
See: https://huggingface.co/datasets/freddyaboulton/chatinterface_with_image_csv/discussions/1
CC: @freddyaboulton @polinaeterna | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6140/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6140/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6139 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6139/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6139/comments | https://api.github.com/repos/huggingface/datasets/issues/6139/events | https://github.com/huggingface/datasets/issues/6139 | 1,844,991,583 | I_kwDODunzps5t-FZf | 6,139 | Offline dataset viewer | {
"login": "yuvalkirstain",
"id": 57996478,
"node_id": "MDQ6VXNlcjU3OTk2NDc4",
"avatar_url": "https://avatars.githubusercontent.com/u/57996478?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/yuvalkirstain",
"html_url": "https://github.com/yuvalkirstain",
"followers_url": "https://api.github.com/users/yuvalkirstain/followers",
"following_url": "https://api.github.com/users/yuvalkirstain/following{/other_user}",
"gists_url": "https://api.github.com/users/yuvalkirstain/gists{/gist_id}",
"starred_url": "https://api.github.com/users/yuvalkirstain/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/yuvalkirstain/subscriptions",
"organizations_url": "https://api.github.com/users/yuvalkirstain/orgs",
"repos_url": "https://api.github.com/users/yuvalkirstain/repos",
"events_url": "https://api.github.com/users/yuvalkirstain/events{/privacy}",
"received_events_url": "https://api.github.com/users/yuvalkirstain/received_events",
"type": "User",
"site_admin": false
} | [
{
"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] | open | false | null | [] | null | [
"Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc.\r\n\r\nIf you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39."
] | 2023-08-10T11:30:00 | 2023-08-10T14:37:38 | null | NONE | null | null | null | ### Feature request
The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset.
### Motivation
I want to easily view my dataset even when it is hosted locally.
### Your contribution
N.A. | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6139/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6139/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6138 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6138/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6138/comments | https://api.github.com/repos/huggingface/datasets/issues/6138/events | https://github.com/huggingface/datasets/pull/6138 | 1,844,952,496 | PR_kwDODunzps5XoH2V | 6,138 | Ignore CI lint rule violation in Pickler.memoize | {
"login": "albertvillanova",
"id": 8515462,
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/albertvillanova",
"html_url": "https://github.com/albertvillanova",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"type": "User",
"site_admin": false
} | [] | closed | false | null | [] | null | [
"<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) | 0.006536 / 0.011353 (-0.004817) | 0.003890 / 0.011008 (-0.007118) | 0.084044 / 0.038508 (0.045536) | 0.071893 / 0.023109 (0.048784) | 0.346926 / 0.275898 (0.071028) | 0.397487 / 0.323480 (0.074007) | 0.004065 / 0.007986 (-0.003921) | 0.003218 / 0.004328 (-0.001111) | 0.064670 / 0.004250 (0.060420) | 0.052414 / 0.037052 (0.015362) | 0.355413 / 0.258489 (0.096924) | 0.398894 / 0.293841 (0.105053) | 0.030763 / 0.128546 (-0.097783) | 0.008590 / 0.075646 (-0.067056) | 0.286857 / 0.419271 (-0.132415) | 0.051126 / 0.043533 (0.007593) | 0.346125 / 0.255139 (0.090986) | 0.395673 / 0.283200 (0.112474) | 0.025766 / 0.141683 (-0.115917) | 1.466238 / 1.452155 (0.014084) | 1.543117 / 1.492716 (0.050400) |\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) | 0.213210 / 0.018006 (0.195204) | 0.451981 / 0.000490 (0.451491) | 0.003784 / 0.000200 (0.003585) | 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) | 0.027756 / 0.037411 (-0.009655) | 0.082446 / 0.014526 (0.067920) | 0.095414 / 0.176557 (-0.081142) | 0.151812 / 0.737135 (-0.585323) | 0.096296 / 0.296338 (-0.200042) |\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) | 0.383729 / 0.215209 (0.168520) | 3.835126 / 2.077655 (1.757471) | 1.891972 / 1.504120 (0.387852) | 1.719934 / 1.541195 (0.178739) | 1.899980 / 1.468490 (0.431490) | 0.488741 / 4.584777 (-4.096036) | 3.634120 / 3.745712 (-0.111592) | 3.243314 / 5.269862 (-2.026547) | 2.028382 / 4.565676 (-2.537294) | 0.057355 / 0.424275 (-0.366920) | 0.007717 / 0.007607 (0.000110) | 0.459835 / 0.226044 (0.233790) | 4.591793 / 2.268929 (2.322864) | 2.346861 / 55.444624 (-53.097764) | 2.067357 / 6.876477 (-4.809120) | 2.254954 / 2.142072 (0.112882) | 0.587016 / 4.805227 (-4.218211) | 0.133918 / 6.500664 (-6.366746) | 0.060311 / 0.075469 (-0.015158) |\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) | 1.250016 / 1.841788 (-0.591772) | 19.674333 / 8.074308 (11.600025) | 14.522764 / 10.191392 (4.331372) | 0.145741 / 0.680424 (-0.534683) | 0.018593 / 0.534201 (-0.515608) | 0.392833 / 0.579283 (-0.186450) | 0.408194 / 0.434364 (-0.026170) | 0.455164 / 0.540337 (-0.085174) | 0.622722 / 1.386936 (-0.764214) |\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) | 0.006583 / 0.011353 (-0.004770) | 0.004008 / 0.011008 (-0.007000) | 0.064688 / 0.038508 (0.026180) | 0.074969 / 0.023109 (0.051860) | 0.360504 / 0.275898 (0.084606) | 0.396926 / 0.323480 (0.073446) | 0.005190 / 0.007986 (-0.002796) | 0.003363 / 0.004328 (-0.000966) | 0.064372 / 0.004250 (0.060122) | 0.054428 / 0.037052 (0.017376) | 0.361204 / 0.258489 (0.102715) | 0.400917 / 0.293841 (0.107077) | 0.031117 / 0.128546 (-0.097429) | 0.008406 / 0.075646 (-0.067241) | 0.069655 / 0.419271 (-0.349617) | 0.048582 / 0.043533 (0.005049) | 0.365396 / 0.255139 (0.110257) | 0.381344 / 0.283200 (0.098145) | 0.023809 / 0.141683 (-0.117874) | 1.472926 / 1.452155 (0.020772) | 1.547298 / 1.492716 (0.054582) |\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) | 0.276912 / 0.018006 (0.258906) | 0.449096 / 0.000490 (0.448607) | 0.018921 / 0.000200 (0.018721) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030237 / 0.037411 (-0.007174) | 0.088610 / 0.014526 (0.074084) | 0.101529 / 0.176557 (-0.075027) | 0.154070 / 0.737135 (-0.583065) | 0.103471 / 0.296338 (-0.192867) |\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) | 0.416047 / 0.215209 (0.200838) | 4.152374 / 2.077655 (2.074719) | 2.111181 / 1.504120 (0.607061) | 1.943582 / 1.541195 (0.402387) | 2.031729 / 1.468490 (0.563239) | 0.486740 / 4.584777 (-4.098037) | 3.631547 / 3.745712 (-0.114165) | 3.251202 / 5.269862 (-2.018660) | 2.041272 / 4.565676 (-2.524405) | 0.057287 / 0.424275 (-0.366988) | 0.007303 / 0.007607 (-0.000304) | 0.491027 / 0.226044 (0.264982) | 4.906757 / 2.268929 (2.637829) | 2.581694 / 55.444624 (-52.862931) | 2.250996 / 6.876477 (-4.625481) | 2.441771 / 2.142072 (0.299698) | 0.600714 / 4.805227 (-4.204514) | 0.133233 / 6.500664 (-6.367431) | 0.060856 / 0.075469 (-0.014613) |\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) | 1.340062 / 1.841788 (-0.501725) | 19.973899 / 8.074308 (11.899591) | 14.347381 / 10.191392 (4.155989) | 0.166651 / 0.680424 (-0.513773) | 0.018691 / 0.534201 (-0.515510) | 0.393580 / 0.579283 (-0.185703) | 0.409425 / 0.434364 (-0.024939) | 0.474409 / 0.540337 (-0.065929) | 0.649423 / 1.386936 (-0.737514) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5da68102297c3639207a7901952d2765a4cdb8b \"CML watermark\")\n",
"_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) | 0.006593 / 0.011353 (-0.004760) | 0.004123 / 0.011008 (-0.006885) | 0.084424 / 0.038508 (0.045916) | 0.076867 / 0.023109 (0.053758) | 0.309149 / 0.275898 (0.033251) | 0.348572 / 0.323480 (0.025092) | 0.005463 / 0.007986 (-0.002523) | 0.003440 / 0.004328 (-0.000889) | 0.064604 / 0.004250 (0.060353) | 0.053920 / 0.037052 (0.016868) | 0.345221 / 0.258489 (0.086732) | 0.363209 / 0.293841 (0.069368) | 0.031209 / 0.128546 (-0.097337) | 0.008690 / 0.075646 (-0.066956) | 0.288851 / 0.419271 (-0.130421) | 0.052239 / 0.043533 (0.008707) | 0.308643 / 0.255139 (0.053504) | 0.346407 / 0.283200 (0.063207) | 0.023935 / 0.141683 (-0.117748) | 1.469207 / 1.452155 (0.017052) | 1.532855 / 1.492716 (0.040138) |\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) | 0.290885 / 0.018006 (0.272879) | 0.580561 / 0.000490 (0.580071) | 0.004698 / 0.000200 (0.004498) | 0.000286 / 0.000054 (0.000231) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028015 / 0.037411 (-0.009396) | 0.081172 / 0.014526 (0.066646) | 0.096822 / 0.176557 (-0.079735) | 0.151355 / 0.737135 (-0.585781) | 0.098017 / 0.296338 (-0.198321) |\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) | 0.384069 / 0.215209 (0.168859) | 3.828635 / 2.077655 (1.750980) | 1.829311 / 1.504120 (0.325192) | 1.672520 / 1.541195 (0.131325) | 1.743944 / 1.468490 (0.275453) | 0.481594 / 4.584777 (-4.103183) | 3.556204 / 3.745712 (-0.189509) | 3.279499 / 5.269862 (-1.990363) | 2.033243 / 4.565676 (-2.532434) | 0.056525 / 0.424275 (-0.367750) | 0.007717 / 0.007607 (0.000109) | 0.466815 / 0.226044 (0.240771) | 4.657022 / 2.268929 (2.388094) | 2.438600 / 55.444624 (-53.006024) | 2.097999 / 6.876477 (-4.778478) | 2.263122 / 2.142072 (0.121049) | 0.636001 / 4.805227 (-4.169226) | 0.147727 / 6.500664 (-6.352937) | 0.059293 / 0.075469 (-0.016176) |\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) | 1.243111 / 1.841788 (-0.598677) | 19.558379 / 8.074308 (11.484071) | 14.141017 / 10.191392 (3.949625) | 0.169840 / 0.680424 (-0.510583) | 0.017912 / 0.534201 (-0.516289) | 0.391325 / 0.579283 (-0.187958) | 0.417169 / 0.434364 (-0.017195) | 0.457129 / 0.540337 (-0.083209) | 0.629907 / 1.386936 (-0.757029) |\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) | 0.006687 / 0.011353 (-0.004666) | 0.004165 / 0.011008 (-0.006844) | 0.064738 / 0.038508 (0.026230) | 0.077286 / 0.023109 (0.054177) | 0.364236 / 0.275898 (0.088338) | 0.393228 / 0.323480 (0.069748) | 0.005451 / 0.007986 (-0.002535) | 0.003547 / 0.004328 (-0.000781) | 0.065761 / 0.004250 (0.061510) | 0.056526 / 0.037052 (0.019474) | 0.365523 / 0.258489 (0.107034) | 0.403331 / 0.293841 (0.109490) | 0.030900 / 0.128546 (-0.097646) | 0.008757 / 0.075646 (-0.066889) | 0.070961 / 0.419271 (-0.348311) | 0.048394 / 0.043533 (0.004861) | 0.365908 / 0.255139 (0.110769) | 0.381197 / 0.283200 (0.097998) | 0.022940 / 0.141683 (-0.118743) | 1.487909 / 1.452155 (0.035754) | 1.532931 / 1.492716 (0.040215) |\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) | 0.317506 / 0.018006 (0.299500) | 0.513391 / 0.000490 (0.512902) | 0.005464 / 0.000200 (0.005264) | 0.000214 / 0.000054 (0.000159) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032289 / 0.037411 (-0.005122) | 0.090157 / 0.014526 (0.075631) | 0.103514 / 0.176557 (-0.073043) | 0.158236 / 0.737135 (-0.578899) | 0.106554 / 0.296338 (-0.189784) |\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) | 0.406455 / 0.215209 (0.191246) | 4.061563 / 2.077655 (1.983908) | 2.082201 / 1.504120 (0.578081) | 1.914433 / 1.541195 (0.373238) | 2.039342 / 1.468490 (0.570852) | 0.478444 / 4.584777 (-4.106333) | 3.599755 / 3.745712 (-0.145957) | 3.294453 / 5.269862 (-1.975409) | 2.028519 / 4.565676 (-2.537158) | 0.056118 / 0.424275 (-0.368157) | 0.007325 / 0.007607 (-0.000282) | 0.493177 / 0.226044 (0.267132) | 4.926218 / 2.268929 (2.657289) | 2.605033 / 55.444624 (-52.839591) | 2.239933 / 6.876477 (-4.636544) | 2.454210 / 2.142072 (0.312137) | 0.571905 / 4.805227 (-4.233322) | 0.133251 / 6.500664 (-6.367413) | 0.062422 / 0.075469 (-0.013047) |\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) | 1.352752 / 1.841788 (-0.489036) | 20.265109 / 8.074308 (12.190801) | 14.293064 / 10.191392 (4.101672) | 0.169267 / 0.680424 (-0.511157) | 0.018607 / 0.534201 (-0.515594) | 0.393655 / 0.579283 (-0.185628) | 0.402132 / 0.434364 (-0.032232) | 0.477566 / 0.540337 (-0.062772) | 0.651773 / 1.386936 (-0.735163) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#80023f36b2b6678347979421ef973d8969d31306 \"CML watermark\")\n"
] | 2023-08-10T11:03:15 | 2023-08-10T11:31:45 | 2023-08-10T11:22:56 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6138",
"html_url": "https://github.com/huggingface/datasets/pull/6138",
"diff_url": "https://github.com/huggingface/datasets/pull/6138.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6138.patch",
"merged_at": "2023-08-10T11:22:56"
} | This PR ignores the violation of the lint rule E721 in `Pickler.memoize`.
The lint rule violation was introduced in this PR:
- #3182
@lhoestq is there a reason you did not use `isinstance` instead?
As a hotfix, we just ignore the violation of the lint rule.
Fix #6136. | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6138/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6138/timeline | null | null | true |
https://api.github.com/repos/huggingface/datasets/issues/6137 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6137/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6137/comments | https://api.github.com/repos/huggingface/datasets/issues/6137/events | https://github.com/huggingface/datasets/issues/6137 | 1,844,952,312 | I_kwDODunzps5t97z4 | 6,137 | (`from_spark()`) Unable to connect HDFS in pyspark YARN setting | {
"login": "kyoungrok0517",
"id": 1051900,
"node_id": "MDQ6VXNlcjEwNTE5MDA=",
"avatar_url": "https://avatars.githubusercontent.com/u/1051900?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/kyoungrok0517",
"html_url": "https://github.com/kyoungrok0517",
"followers_url": "https://api.github.com/users/kyoungrok0517/followers",
"following_url": "https://api.github.com/users/kyoungrok0517/following{/other_user}",
"gists_url": "https://api.github.com/users/kyoungrok0517/gists{/gist_id}",
"starred_url": "https://api.github.com/users/kyoungrok0517/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/kyoungrok0517/subscriptions",
"organizations_url": "https://api.github.com/users/kyoungrok0517/orgs",
"repos_url": "https://api.github.com/users/kyoungrok0517/repos",
"events_url": "https://api.github.com/users/kyoungrok0517/events{/privacy}",
"received_events_url": "https://api.github.com/users/kyoungrok0517/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [] | 2023-08-10T11:03:08 | 2023-08-10T11:03:08 | null | NONE | null | null | null | ### Describe the bug
related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613
---
Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using **huggingface's `datasets`** ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script.
Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process.
These are some peculiarities in my environment that might caused this issue.
* **Cluster requires kerberos authentication**
* But I think the error message implies that's not the problem in this case
* **The user that runs the worker process is different from that built the docker image**
* To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone
* **Pyspark-part of my code has no problem interacting with HDFS.**
* Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker)
* The problem occurs only when it runs as cluster's worker runtime
Hope I could get some help. Thanks.
```bash
2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
### Steps to reproduce the bug
Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path.
### Expected behavior
Work as described in document
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6137/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6137/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6136 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6136/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6136/comments | https://api.github.com/repos/huggingface/datasets/issues/6136/events | https://github.com/huggingface/datasets/issues/6136 | 1,844,887,866 | I_kwDODunzps5t9sE6 | 6,136 | CI check_code_quality error: E721 Do not compare types, use `isinstance()` | {
"login": "albertvillanova",
"id": 8515462,
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/albertvillanova",
"html_url": "https://github.com/albertvillanova",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"type": "User",
"site_admin": false
} | [
{
"id": 4296013012,
"node_id": "LA_kwDODunzps8AAAABAA_01A",
"url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance",
"name": "maintenance",
"color": "d4c5f9",
"default": false,
"description": "Maintenance tasks"
}
] | closed | false | {
"login": "albertvillanova",
"id": 8515462,
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/albertvillanova",
"html_url": "https://github.com/albertvillanova",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"type": "User",
"site_admin": false
} | [
{
"login": "albertvillanova",
"id": 8515462,
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/albertvillanova",
"html_url": "https://github.com/albertvillanova",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"type": "User",
"site_admin": false
}
] | null | [] | 2023-08-10T10:19:50 | 2023-08-10T11:22:58 | 2023-08-10T11:22:58 | MEMBER | null | null | null | After latest release of `ruff` (https://pypi.org/project/ruff/0.0.284/), we get the following CI error:
```
src/datasets/utils/py_utils.py:689:12: E721 Do not compare types, use `isinstance()`
``` | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6136/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6136/timeline | null | completed | false |
https://api.github.com/repos/huggingface/datasets/issues/6135 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6135/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6135/comments | https://api.github.com/repos/huggingface/datasets/issues/6135/events | https://github.com/huggingface/datasets/pull/6135 | 1,844,870,943 | PR_kwDODunzps5Xn2AT | 6,135 | Remove unused allowed_extensions param | {
"login": "albertvillanova",
"id": 8515462,
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/albertvillanova",
"html_url": "https://github.com/albertvillanova",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"type": "User",
"site_admin": false
} | [] | closed | false | null | [] | null | [
"_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) | 0.009055 / 0.011353 (-0.002298) | 0.008835 / 0.011008 (-0.002173) | 0.117048 / 0.038508 (0.078540) | 0.096268 / 0.023109 (0.073159) | 0.474678 / 0.275898 (0.198780) | 0.550509 / 0.323480 (0.227029) | 0.005552 / 0.007986 (-0.002434) | 0.004315 / 0.004328 (-0.000013) | 0.094336 / 0.004250 (0.090086) | 0.061945 / 0.037052 (0.024892) | 0.461422 / 0.258489 (0.202933) | 0.521271 / 0.293841 (0.227430) | 0.049116 / 0.128546 (-0.079430) | 0.015007 / 0.075646 (-0.060639) | 0.414351 / 0.419271 (-0.004920) | 0.137520 / 0.043533 (0.093987) | 0.465627 / 0.255139 (0.210488) | 0.537244 / 0.283200 (0.254044) | 0.068577 / 0.141683 (-0.073106) | 1.921373 / 1.452155 (0.469219) | 2.506653 / 1.492716 (1.013937) |\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) | 0.273970 / 0.018006 (0.255963) | 0.750295 / 0.000490 (0.749805) | 0.004241 / 0.000200 (0.004041) | 0.000128 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033793 / 0.037411 (-0.003618) | 0.105562 / 0.014526 (0.091037) | 0.131771 / 0.176557 (-0.044786) | 0.196890 / 0.737135 (-0.540245) | 0.119842 / 0.296338 (-0.176496) |\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) | 0.634881 / 0.215209 (0.419672) | 6.069221 / 2.077655 (3.991566) | 2.678765 / 1.504120 (1.174646) | 2.460309 / 1.541195 (0.919114) | 2.517579 / 1.468490 (1.049089) | 0.869558 / 4.584777 (-3.715219) | 5.407686 / 3.745712 (1.661974) | 4.920687 / 5.269862 (-0.349175) | 3.130066 / 4.565676 (-1.435611) | 0.100337 / 0.424275 (-0.323938) | 0.009615 / 0.007607 (0.002008) | 0.745275 / 0.226044 (0.519231) | 7.577890 / 2.268929 (5.308962) | 3.607887 / 55.444624 (-51.836738) | 2.922211 / 6.876477 (-3.954266) | 3.205592 / 2.142072 (1.063519) | 1.052298 / 4.805227 (-3.752929) | 0.218798 / 6.500664 (-6.281866) | 0.082137 / 0.075469 (0.006667) |\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) | 1.696551 / 1.841788 (-0.145237) | 24.946074 / 8.074308 (16.871766) | 23.114202 / 10.191392 (12.922810) | 0.220498 / 0.680424 (-0.459925) | 0.029388 / 0.534201 (-0.504813) | 0.494721 / 0.579283 (-0.084562) | 0.603085 / 0.434364 (0.168722) | 0.573093 / 0.540337 (0.032756) | 0.784937 / 1.386936 (-0.601999) |\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) | 0.009642 / 0.011353 (-0.001711) | 0.007551 / 0.011008 (-0.003457) | 0.085224 / 0.038508 (0.046716) | 0.099493 / 0.023109 (0.076384) | 0.503824 / 0.275898 (0.227926) | 0.546583 / 0.323480 (0.223103) | 0.006385 / 0.007986 (-0.001601) | 0.004751 / 0.004328 (0.000423) | 0.084699 / 0.004250 (0.080449) | 0.067875 / 0.037052 (0.030823) | 0.485313 / 0.258489 (0.226824) | 0.535808 / 0.293841 (0.241967) | 0.049935 / 0.128546 (-0.078611) | 0.014427 / 0.075646 (-0.061219) | 0.095531 / 0.419271 (-0.323741) | 0.068487 / 0.043533 (0.024954) | 0.502204 / 0.255139 (0.247065) | 0.514393 / 0.283200 (0.231193) | 0.037350 / 0.141683 (-0.104333) | 1.849380 / 1.452155 (0.397226) | 1.920151 / 1.492716 (0.427434) |\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) | 0.298363 / 0.018006 (0.280357) | 0.651555 / 0.000490 (0.651065) | 0.005910 / 0.000200 (0.005710) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039170 / 0.037411 (0.001758) | 0.106436 / 0.014526 (0.091910) | 0.129880 / 0.176557 (-0.046677) | 0.185401 / 0.737135 (-0.551734) | 0.125732 / 0.296338 (-0.170607) |\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) | 0.643248 / 0.215209 (0.428039) | 6.374807 / 2.077655 (4.297152) | 3.057296 / 1.504120 (1.553176) | 2.779534 / 1.541195 (1.238340) | 2.790165 / 1.468490 (1.321675) | 0.841580 / 4.584777 (-3.743197) | 5.371478 / 3.745712 (1.625766) | 4.973251 / 5.269862 (-0.296610) | 3.235817 / 4.565676 (-1.329860) | 0.097276 / 0.424275 (-0.326999) | 0.008840 / 0.007607 (0.001233) | 0.728678 / 0.226044 (0.502634) | 7.526382 / 2.268929 (5.257454) | 3.792550 / 55.444624 (-51.652074) | 3.439134 / 6.876477 (-3.437342) | 3.466626 / 2.142072 (1.324553) | 1.035894 / 4.805227 (-3.769333) | 0.211670 / 6.500664 (-6.288994) | 0.087596 / 0.075469 (0.012127) |\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) | 1.782755 / 1.841788 (-0.059033) | 25.704407 / 8.074308 (17.630099) | 23.799672 / 10.191392 (13.608280) | 0.233952 / 0.680424 (-0.446472) | 0.030810 / 0.534201 (-0.503391) | 0.505857 / 0.579283 (-0.073426) | 0.629331 / 0.434364 (0.194967) | 0.608530 / 0.540337 (0.068192) | 0.813688 / 1.386936 (-0.573248) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed4d6bb5f1331576c41b04acd9872a5349a0915c \"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) | 0.006401 / 0.011353 (-0.004952) | 0.003916 / 0.011008 (-0.007092) | 0.083976 / 0.038508 (0.045468) | 0.072583 / 0.023109 (0.049474) | 0.322747 / 0.275898 (0.046849) | 0.345159 / 0.323480 (0.021679) | 0.005366 / 0.007986 (-0.002620) | 0.003399 / 0.004328 (-0.000930) | 0.064232 / 0.004250 (0.059982) | 0.053313 / 0.037052 (0.016261) | 0.353127 / 0.258489 (0.094638) | 0.361398 / 0.293841 (0.067557) | 0.030604 / 0.128546 (-0.097942) | 0.008615 / 0.075646 (-0.067031) | 0.285806 / 0.419271 (-0.133466) | 0.050887 / 0.043533 (0.007354) | 0.312293 / 0.255139 (0.057154) | 0.349716 / 0.283200 (0.066516) | 0.024546 / 0.141683 (-0.117137) | 1.472318 / 1.452155 (0.020163) | 1.536063 / 1.492716 (0.043347) |\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) | 0.280012 / 0.018006 (0.262006) | 0.593574 / 0.000490 (0.593085) | 0.004083 / 0.000200 (0.003883) | 0.000195 / 0.000054 (0.000141) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027715 / 0.037411 (-0.009696) | 0.081392 / 0.014526 (0.066866) | 0.096445 / 0.176557 (-0.080112) | 0.152131 / 0.737135 (-0.585004) | 0.094825 / 0.296338 (-0.201514) |\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) | 0.380749 / 0.215209 (0.165540) | 3.806994 / 2.077655 (1.729339) | 1.842544 / 1.504120 (0.338424) | 1.682829 / 1.541195 (0.141635) | 1.701679 / 1.468490 (0.233189) | 0.484830 / 4.584777 (-4.099947) | 3.517359 / 3.745712 (-0.228353) | 3.231211 / 5.269862 (-2.038651) | 2.029371 / 4.565676 (-2.536306) | 0.057199 / 0.424275 (-0.367077) | 0.007653 / 0.007607 (0.000046) | 0.458572 / 0.226044 (0.232528) | 4.579835 / 2.268929 (2.310907) | 2.326467 / 55.444624 (-53.118157) | 1.939646 / 6.876477 (-4.936831) | 2.133150 / 2.142072 (-0.008922) | 0.596251 / 4.805227 (-4.208976) | 0.131979 / 6.500664 (-6.368686) | 0.059226 / 0.075469 (-0.016243) |\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) | 1.234833 / 1.841788 (-0.606955) | 19.475522 / 8.074308 (11.401214) | 14.102760 / 10.191392 (3.911368) | 0.159657 / 0.680424 (-0.520767) | 0.018292 / 0.534201 (-0.515909) | 0.391079 / 0.579283 (-0.188204) | 0.406736 / 0.434364 (-0.027628) | 0.459159 / 0.540337 (-0.081178) | 0.618159 / 1.386936 (-0.768777) |\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) | 0.006592 / 0.011353 (-0.004761) | 0.004052 / 0.011008 (-0.006957) | 0.064536 / 0.038508 (0.026028) | 0.075051 / 0.023109 (0.051942) | 0.379596 / 0.275898 (0.103698) | 0.412413 / 0.323480 (0.088933) | 0.005377 / 0.007986 (-0.002608) | 0.003466 / 0.004328 (-0.000863) | 0.064958 / 0.004250 (0.060708) | 0.055265 / 0.037052 (0.018213) | 0.391505 / 0.258489 (0.133016) | 0.425345 / 0.293841 (0.131504) | 0.030750 / 0.128546 (-0.097796) | 0.008652 / 0.075646 (-0.066994) | 0.072107 / 0.419271 (-0.347165) | 0.048340 / 0.043533 (0.004807) | 0.387714 / 0.255139 (0.132575) | 0.402602 / 0.283200 (0.119402) | 0.023492 / 0.141683 (-0.118191) | 1.528377 / 1.452155 (0.076222) | 1.574827 / 1.492716 (0.082110) |\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) | 0.316999 / 0.018006 (0.298993) | 0.528391 / 0.000490 (0.527901) | 0.005183 / 0.000200 (0.004983) | 0.000085 / 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) | 0.029670 / 0.037411 (-0.007741) | 0.087130 / 0.014526 (0.072604) | 0.099897 / 0.176557 (-0.076660) | 0.154074 / 0.737135 (-0.583062) | 0.104309 / 0.296338 (-0.192030) |\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) | 0.408804 / 0.215209 (0.193595) | 4.072248 / 2.077655 (1.994593) | 2.103333 / 1.504120 (0.599213) | 1.931972 / 1.541195 (0.390777) | 1.980132 / 1.468490 (0.511642) | 0.482623 / 4.584777 (-4.102154) | 3.532789 / 3.745712 (-0.212923) | 3.304962 / 5.269862 (-1.964899) | 2.036672 / 4.565676 (-2.529004) | 0.056944 / 0.424275 (-0.367331) | 0.007190 / 0.007607 (-0.000417) | 0.490650 / 0.226044 (0.264606) | 4.903604 / 2.268929 (2.634675) | 2.586247 / 55.444624 (-52.858377) | 2.227631 / 6.876477 (-4.648846) | 2.397286 / 2.142072 (0.255214) | 0.579167 / 4.805227 (-4.226060) | 0.132037 / 6.500664 (-6.368627) | 0.059971 / 0.075469 (-0.015498) |\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) | 1.336430 / 1.841788 (-0.505358) | 19.915846 / 8.074308 (11.841538) | 14.102781 / 10.191392 (3.911389) | 0.147956 / 0.680424 (-0.532468) | 0.018192 / 0.534201 (-0.516009) | 0.397949 / 0.579283 (-0.181334) | 0.408529 / 0.434364 (-0.025835) | 0.479382 / 0.540337 (-0.060955) | 0.659735 / 1.386936 (-0.727201) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98074122449bc031f7269f298f1c55f20e39b975 \"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) | 0.005880 / 0.011353 (-0.005473) | 0.003677 / 0.011008 (-0.007332) | 0.080022 / 0.038508 (0.041514) | 0.055554 / 0.023109 (0.032445) | 0.397449 / 0.275898 (0.121551) | 0.428346 / 0.323480 (0.104867) | 0.004613 / 0.007986 (-0.003373) | 0.002873 / 0.004328 (-0.001455) | 0.062226 / 0.004250 (0.057976) | 0.044721 / 0.037052 (0.007669) | 0.404792 / 0.258489 (0.146303) | 0.437467 / 0.293841 (0.143626) | 0.027166 / 0.128546 (-0.101381) | 0.008077 / 0.075646 (-0.067569) | 0.260469 / 0.419271 (-0.158803) | 0.043551 / 0.043533 (0.000018) | 0.401712 / 0.255139 (0.146573) | 0.427294 / 0.283200 (0.144094) | 0.021243 / 0.141683 (-0.120440) | 1.464553 / 1.452155 (0.012398) | 1.507112 / 1.492716 (0.014396) |\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) | 0.198415 / 0.018006 (0.180408) | 0.427940 / 0.000490 (0.427450) | 0.004236 / 0.000200 (0.004036) | 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) | 0.023759 / 0.037411 (-0.013652) | 0.073262 / 0.014526 (0.058736) | 0.677113 / 0.176557 (0.500557) | 0.194964 / 0.737135 (-0.542172) | 0.086121 / 0.296338 (-0.210217) |\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) | 0.401176 / 0.215209 (0.185967) | 4.028688 / 2.077655 (1.951034) | 2.026804 / 1.504120 (0.522685) | 1.887964 / 1.541195 (0.346770) | 2.008991 / 1.468490 (0.540501) | 0.498847 / 4.584777 (-4.085930) | 3.015920 / 3.745712 (-0.729792) | 2.837019 / 5.269862 (-2.432843) | 1.849976 / 4.565676 (-2.715701) | 0.057545 / 0.424275 (-0.366730) | 0.006645 / 0.007607 (-0.000962) | 0.470225 / 0.226044 (0.244180) | 4.720910 / 2.268929 (2.451982) | 2.473693 / 55.444624 (-52.970931) | 2.177525 / 6.876477 (-4.698952) | 2.374702 / 2.142072 (0.232630) | 0.588253 / 4.805227 (-4.216974) | 0.125512 / 6.500664 (-6.375152) | 0.061247 / 0.075469 (-0.014222) |\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) | 1.255829 / 1.841788 (-0.585959) | 18.251689 / 8.074308 (10.177381) | 13.690373 / 10.191392 (3.498981) | 0.146928 / 0.680424 (-0.533496) | 0.016534 / 0.534201 (-0.517667) | 0.335249 / 0.579283 (-0.244034) | 0.338940 / 0.434364 (-0.095424) | 0.382170 / 0.540337 (-0.158168) | 0.529570 / 1.386936 (-0.857366) |\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) | 0.005920 / 0.011353 (-0.005433) | 0.003557 / 0.011008 (-0.007451) | 0.062776 / 0.038508 (0.024267) | 0.058473 / 0.023109 (0.035364) | 0.358780 / 0.275898 (0.082882) | 0.394161 / 0.323480 (0.070682) | 0.004636 / 0.007986 (-0.003349) | 0.002865 / 0.004328 (-0.001463) | 0.062033 / 0.004250 (0.057782) | 0.047154 / 0.037052 (0.010101) | 0.367718 / 0.258489 (0.109229) | 0.400814 / 0.293841 (0.106973) | 0.026919 / 0.128546 (-0.101628) | 0.008071 / 0.075646 (-0.067575) | 0.067802 / 0.419271 (-0.351469) | 0.040894 / 0.043533 (-0.002638) | 0.358757 / 0.255139 (0.103618) | 0.384971 / 0.283200 (0.101771) | 0.020019 / 0.141683 (-0.121664) | 1.458578 / 1.452155 (0.006423) | 1.525059 / 1.492716 (0.032342) |\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) | 0.207795 / 0.018006 (0.189789) | 0.413201 / 0.000490 (0.412712) | 0.005199 / 0.000200 (0.004999) | 0.000085 / 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) | 0.025716 / 0.037411 (-0.011696) | 0.078434 / 0.014526 (0.063908) | 0.086920 / 0.176557 (-0.089637) | 0.138327 / 0.737135 (-0.598808) | 0.088120 / 0.296338 (-0.208219) |\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) | 0.434344 / 0.215209 (0.219135) | 4.343114 / 2.077655 (2.265459) | 2.384439 / 1.504120 (0.880319) | 2.253929 / 1.541195 (0.712735) | 2.306811 / 1.468490 (0.838321) | 0.497572 / 4.584777 (-4.087205) | 3.028794 / 3.745712 (-0.716919) | 2.833484 / 5.269862 (-2.436377) | 1.878918 / 4.565676 (-2.686759) | 0.057133 / 0.424275 (-0.367143) | 0.006357 / 0.007607 (-0.001251) | 0.508019 / 0.226044 (0.281975) | 5.076935 / 2.268929 (2.808007) | 2.745784 / 55.444624 (-52.698841) | 2.476291 / 6.876477 (-4.400186) | 2.677264 / 2.142072 (0.535191) | 0.587173 / 4.805227 (-4.218054) | 0.126373 / 6.500664 (-6.374291) | 0.062815 / 0.075469 (-0.012654) |\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) | 1.355482 / 1.841788 (-0.486305) | 18.818227 / 8.074308 (10.743919) | 13.954289 / 10.191392 (3.762896) | 0.143413 / 0.680424 (-0.537011) | 0.016844 / 0.534201 (-0.517357) | 0.338334 / 0.579283 (-0.240949) | 0.344559 / 0.434364 (-0.089805) | 0.400669 / 0.540337 (-0.139669) | 0.563835 / 1.386936 (-0.823101) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c02a44715c036b5261686669727394b1308a3a4b \"CML watermark\")\n"
] | 2023-08-10T10:09:54 | 2023-08-10T12:08:38 | 2023-08-10T12:00:02 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6135",
"html_url": "https://github.com/huggingface/datasets/pull/6135",
"diff_url": "https://github.com/huggingface/datasets/pull/6135.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6135.patch",
"merged_at": "2023-08-10T12:00:01"
} | This PR removes unused `allowed_extensions` parameter from `create_builder_configs_from_metadata_configs`. | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6135/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6135/timeline | null | null | true |
https://api.github.com/repos/huggingface/datasets/issues/6134 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6134/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6134/comments | https://api.github.com/repos/huggingface/datasets/issues/6134/events | https://github.com/huggingface/datasets/issues/6134 | 1,844,535,142 | I_kwDODunzps5t8V9m | 6,134 | `datasets` cannot be installed alongside `apache-beam` | {
"login": "boyleconnor",
"id": 6520892,
"node_id": "MDQ6VXNlcjY1MjA4OTI=",
"avatar_url": "https://avatars.githubusercontent.com/u/6520892?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/boyleconnor",
"html_url": "https://github.com/boyleconnor",
"followers_url": "https://api.github.com/users/boyleconnor/followers",
"following_url": "https://api.github.com/users/boyleconnor/following{/other_user}",
"gists_url": "https://api.github.com/users/boyleconnor/gists{/gist_id}",
"starred_url": "https://api.github.com/users/boyleconnor/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/boyleconnor/subscriptions",
"organizations_url": "https://api.github.com/users/boyleconnor/orgs",
"repos_url": "https://api.github.com/users/boyleconnor/repos",
"events_url": "https://api.github.com/users/boyleconnor/events{/privacy}",
"received_events_url": "https://api.github.com/users/boyleconnor/received_events",
"type": "User",
"site_admin": false
} | [] | closed | false | null | [] | null | [
"I noticed that this is actually covered by issue #5613, which for some reason I didn't see when I searched the issues in this repo the first time."
] | 2023-08-10T06:54:32 | 2023-08-10T15:22:22 | 2023-08-10T15:22:10 | NONE | null | null | null | ### Describe the bug
If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to do something such as importing the `load_dataset` method from `datasets` results in a crashing error.
I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions.
### Steps to reproduce the bug
See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug.
In some environments, I have been able to reproduce the bug by running the following in Bash:
```bash
$ pip install datasets apache-beam
```
then the following in a Python shell:
```python
from datasets import load_dataset
```
Here is my stacktrace from running on Google Colab:
<details>
<summary>stacktrace</summary>
```
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.14.4"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
64
65 from . import config
---> 66 from .arrow_reader import ArrowReader
67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
68 from .data_files import sanitize_patterns
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module>
28 import pyarrow.parquet as pq
29
---> 30 from .download.download_config import DownloadConfig
31 from .naming import _split_re, filenames_for_dataset_split
32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables
[/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module>
7
8 from .download_config import DownloadConfig
----> 9 from .download_manager import DownloadManager, DownloadMode
10 from .streaming_download_manager import StreamingDownloadManager
[/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module>
33 from ..utils.info_utils import get_size_checksum_dict
34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm
---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str
36 from .download_config import DownloadConfig
37
[/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module>
38 import dill
39 import multiprocess
---> 40 import multiprocess.pool
41 import numpy as np
42 from packaging import version
[/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module>
607 #
608
--> 609 class ThreadPool(Pool):
610
611 from .dummy import Process
[/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool()
609 class ThreadPool(Pool):
610
--> 611 from .dummy import Process
612
613 def __init__(self, processes=None, initializer=None, initargs=()):
[/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module>
85 #
86
---> 87 class Condition(threading._Condition):
88 # XXX
89 if sys.version_info < (3, 0):
AttributeError: module 'threading' has no attribute '_Condition'
```
</details>
I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes pip to hang indefinitely.
### Expected behavior
I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`.
### Environment info
Google Colab | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6134/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6134/timeline | null | completed | false |
https://api.github.com/repos/huggingface/datasets/issues/6133 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6133/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6133/comments | https://api.github.com/repos/huggingface/datasets/issues/6133/events | https://github.com/huggingface/datasets/issues/6133 | 1,844,511,519 | I_kwDODunzps5t8QMf | 6,133 | Dataset is slower after calling `to_iterable_dataset` | {
"login": "npuichigo",
"id": 11533479,
"node_id": "MDQ6VXNlcjExNTMzNDc5",
"avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/npuichigo",
"html_url": "https://github.com/npuichigo",
"followers_url": "https://api.github.com/users/npuichigo/followers",
"following_url": "https://api.github.com/users/npuichigo/following{/other_user}",
"gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}",
"starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions",
"organizations_url": "https://api.github.com/users/npuichigo/orgs",
"repos_url": "https://api.github.com/users/npuichigo/repos",
"events_url": "https://api.github.com/users/npuichigo/events{/privacy}",
"received_events_url": "https://api.github.com/users/npuichigo/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [] | 2023-08-10T06:36:23 | 2023-08-10T06:36:23 | null | NONE | null | null | null | ### Describe the bug
Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset`
### Steps to reproduce the bug
Any dataset after converting to `IterableDataset`
### Expected behavior
Maybe it should be faster on big dataset? I only test on small dataset
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6133/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6133/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6132 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6132/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6132/comments | https://api.github.com/repos/huggingface/datasets/issues/6132/events | https://github.com/huggingface/datasets/issues/6132 | 1,843,491,020 | I_kwDODunzps5t4XDM | 6,132 | to_iterable_dataset is missing in document | {
"login": "npuichigo",
"id": 11533479,
"node_id": "MDQ6VXNlcjExNTMzNDc5",
"avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/npuichigo",
"html_url": "https://github.com/npuichigo",
"followers_url": "https://api.github.com/users/npuichigo/followers",
"following_url": "https://api.github.com/users/npuichigo/following{/other_user}",
"gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}",
"starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions",
"organizations_url": "https://api.github.com/users/npuichigo/orgs",
"repos_url": "https://api.github.com/users/npuichigo/repos",
"events_url": "https://api.github.com/users/npuichigo/events{/privacy}",
"received_events_url": "https://api.github.com/users/npuichigo/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [] | 2023-08-09T15:15:03 | 2023-08-09T15:15:03 | null | NONE | null | null | null | ### Describe the bug
to_iterable_dataset is missing in document
### Steps to reproduce the bug
to_iterable_dataset is missing in document
### Expected behavior
document enhancement
### Environment info
unrelated | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6132/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6132/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6131 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6131/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6131/comments | https://api.github.com/repos/huggingface/datasets/issues/6131/events | https://github.com/huggingface/datasets/issues/6131 | 1,843,448,643 | I_kwDODunzps5t4MtD | 6,131 | AttributeError: type object 'tqdm' has no attribute '_lock' | {
"login": "NielsRogge",
"id": 48327001,
"node_id": "MDQ6VXNlcjQ4MzI3MDAx",
"avatar_url": "https://avatars.githubusercontent.com/u/48327001?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/NielsRogge",
"html_url": "https://github.com/NielsRogge",
"followers_url": "https://api.github.com/users/NielsRogge/followers",
"following_url": "https://api.github.com/users/NielsRogge/following{/other_user}",
"gists_url": "https://api.github.com/users/NielsRogge/gists{/gist_id}",
"starred_url": "https://api.github.com/users/NielsRogge/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/NielsRogge/subscriptions",
"organizations_url": "https://api.github.com/users/NielsRogge/orgs",
"repos_url": "https://api.github.com/users/NielsRogge/repos",
"events_url": "https://api.github.com/users/NielsRogge/events{/privacy}",
"received_events_url": "https://api.github.com/users/NielsRogge/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [] | 2023-08-09T14:53:31 | 2023-08-09T14:54:36 | null | CONTRIBUTOR | null | null | null | ### Describe the bug
Getting a tqdm issue when writing a Dask dataframe to the hub.
Similar to #6066. Using latest Datasets version doesn't seem to resolve it
### Steps to reproduce the bug
This is a minimal reproducer:
```
import dask.dataframe as dd
import pandas as pd
import random
import huggingface_hub
data = {"number": [random.randint(0,10) for _ in range(1000)]}
df = pd.DataFrame.from_dict(data)
dataframe = dd.from_pandas(df, npartitions=1)
dataframe = dataframe.repartition(npartitions=2)
repo_id = "nielsr/test-dask"
repo_path = f"hf://datasets/{repo_id}"
huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
dd.to_parquet(dataframe, path=f"{repo_path}/data")
```
Note: I'm intentionally repartioning the Dask dataframe to 2 partitions, as it does work when only having one partition.
### Expected behavior
Would expect to write to the hub without any problem.
### Environment info
Datasets version 2.14.4 | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6131/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6131/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6130 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6130/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6130/comments | https://api.github.com/repos/huggingface/datasets/issues/6130/events | https://github.com/huggingface/datasets/issues/6130 | 1,843,158,846 | I_kwDODunzps5t3F8- | 6,130 | default config name doesn't work when config kwargs are specified. | {
"login": "npuichigo",
"id": 11533479,
"node_id": "MDQ6VXNlcjExNTMzNDc5",
"avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/npuichigo",
"html_url": "https://github.com/npuichigo",
"followers_url": "https://api.github.com/users/npuichigo/followers",
"following_url": "https://api.github.com/users/npuichigo/following{/other_user}",
"gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}",
"starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions",
"organizations_url": "https://api.github.com/users/npuichigo/orgs",
"repos_url": "https://api.github.com/users/npuichigo/repos",
"events_url": "https://api.github.com/users/npuichigo/events{/privacy}",
"received_events_url": "https://api.github.com/users/npuichigo/received_events",
"type": "User",
"site_admin": false
} | [] | open | false | null | [] | null | [] | 2023-08-09T12:43:15 | 2023-08-09T12:43:15 | null | NONE | null | null | null | ### Describe the bug
https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522
If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs.
### Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('/dataset/with/multiple/config'') # Ok
datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err
```
### Expected behavior
Default config behavior should be consistent.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6130/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6130/timeline | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/6129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6129/comments | https://api.github.com/repos/huggingface/datasets/issues/6129/events | https://github.com/huggingface/datasets/pull/6129 | 1,841,563,517 | PR_kwDODunzps5Xcmuw | 6,129 | Release 2.14.4 | {
"login": "albertvillanova",
"id": 8515462,
"node_id": "MDQ6VXNlcjg1MTU0NjI=",
"avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/albertvillanova",
"html_url": "https://github.com/albertvillanova",
"followers_url": "https://api.github.com/users/albertvillanova/followers",
"following_url": "https://api.github.com/users/albertvillanova/following{/other_user}",
"gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}",
"starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions",
"organizations_url": "https://api.github.com/users/albertvillanova/orgs",
"repos_url": "https://api.github.com/users/albertvillanova/repos",
"events_url": "https://api.github.com/users/albertvillanova/events{/privacy}",
"received_events_url": "https://api.github.com/users/albertvillanova/received_events",
"type": "User",
"site_admin": false
} | [] | closed | false | null | [] | null | [
"_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) | 0.006053 / 0.011353 (-0.005299) | 0.003532 / 0.011008 (-0.007476) | 0.081930 / 0.038508 (0.043422) | 0.059043 / 0.023109 (0.035934) | 0.322785 / 0.275898 (0.046887) | 0.378158 / 0.323480 (0.054678) | 0.004709 / 0.007986 (-0.003277) | 0.002907 / 0.004328 (-0.001421) | 0.061516 / 0.004250 (0.057266) | 0.047209 / 0.037052 (0.010157) | 0.346885 / 0.258489 (0.088396) | 0.381011 / 0.293841 (0.087170) | 0.027491 / 0.128546 (-0.101055) | 0.008014 / 0.075646 (-0.067632) | 0.260663 / 0.419271 (-0.158608) | 0.045427 / 0.043533 (0.001894) | 0.315277 / 0.255139 (0.060138) | 0.377902 / 0.283200 (0.094703) | 0.021371 / 0.141683 (-0.120311) | 1.416350 / 1.452155 (-0.035804) | 1.483345 / 1.492716 (-0.009372) |\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) | 0.203660 / 0.018006 (0.185654) | 0.569081 / 0.000490 (0.568591) | 0.002742 / 0.000200 (0.002542) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023456 / 0.037411 (-0.013955) | 0.073954 / 0.014526 (0.059428) | 0.082991 / 0.176557 (-0.093566) | 0.144781 / 0.737135 (-0.592354) | 0.083346 / 0.296338 (-0.212992) |\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) | 0.391542 / 0.215209 (0.176333) | 3.909505 / 2.077655 (1.831850) | 1.862234 / 1.504120 (0.358114) | 1.676076 / 1.541195 (0.134881) | 1.727595 / 1.468490 (0.259105) | 0.501769 / 4.584777 (-4.083008) | 3.083697 / 3.745712 (-0.662016) | 2.819751 / 5.269862 (-2.450111) | 1.867265 / 4.565676 (-2.698411) | 0.057575 / 0.424275 (-0.366700) | 0.006478 / 0.007607 (-0.001129) | 0.466684 / 0.226044 (0.240640) | 4.657982 / 2.268929 (2.389054) | 2.347052 / 55.444624 (-53.097573) | 1.964688 / 6.876477 (-4.911789) | 2.077821 / 2.142072 (-0.064252) | 0.590591 / 4.805227 (-4.214636) | 0.124585 / 6.500664 (-6.376079) | 0.059468 / 0.075469 (-0.016001) |\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) | 1.223484 / 1.841788 (-0.618304) | 18.104638 / 8.074308 (10.030330) | 13.755126 / 10.191392 (3.563734) | 0.143158 / 0.680424 (-0.537266) | 0.017147 / 0.534201 (-0.517054) | 0.337427 / 0.579283 (-0.241856) | 0.352270 / 0.434364 (-0.082094) | 0.383718 / 0.540337 (-0.156619) | 0.534973 / 1.386936 (-0.851963) |\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) | 0.006039 / 0.011353 (-0.005314) | 0.003735 / 0.011008 (-0.007274) | 0.061954 / 0.038508 (0.023446) | 0.061786 / 0.023109 (0.038677) | 0.429420 / 0.275898 (0.153522) | 0.457629 / 0.323480 (0.134149) | 0.004748 / 0.007986 (-0.003237) | 0.002843 / 0.004328 (-0.001485) | 0.061811 / 0.004250 (0.057560) | 0.048740 / 0.037052 (0.011687) | 0.430066 / 0.258489 (0.171577) | 0.465971 / 0.293841 (0.172130) | 0.027577 / 0.128546 (-0.100969) | 0.007981 / 0.075646 (-0.067665) | 0.067580 / 0.419271 (-0.351692) | 0.042058 / 0.043533 (-0.001475) | 0.428412 / 0.255139 (0.173273) | 0.451054 / 0.283200 (0.167855) | 0.020850 / 0.141683 (-0.120833) | 1.453907 / 1.452155 (0.001752) | 1.509914 / 1.492716 (0.017197) |\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) | 0.237713 / 0.018006 (0.219707) | 0.418064 / 0.000490 (0.417575) | 0.006411 / 0.000200 (0.006211) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024950 / 0.037411 (-0.012462) | 0.076806 / 0.014526 (0.062281) | 0.085237 / 0.176557 (-0.091320) | 0.137940 / 0.737135 (-0.599196) | 0.086266 / 0.296338 (-0.210072) |\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) | 0.418666 / 0.215209 (0.203457) | 4.160547 / 2.077655 (2.082893) | 2.135671 / 1.504120 (0.631551) | 1.964985 / 1.541195 (0.423790) | 2.009447 / 1.468490 (0.540957) | 0.501377 / 4.584777 (-4.083400) | 3.064293 / 3.745712 (-0.681419) | 2.827153 / 5.269862 (-2.442709) | 1.854698 / 4.565676 (-2.710978) | 0.057662 / 0.424275 (-0.366613) | 0.006829 / 0.007607 (-0.000778) | 0.496730 / 0.226044 (0.270686) | 4.964663 / 2.268929 (2.695735) | 2.583133 / 55.444624 (-52.861491) | 2.329700 / 6.876477 (-4.546776) | 2.415521 / 2.142072 (0.273449) | 0.591973 / 4.805227 (-4.213255) | 0.126801 / 6.500664 (-6.373863) | 0.062811 / 0.075469 (-0.012659) |\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) | 1.348575 / 1.841788 (-0.493212) | 18.282861 / 8.074308 (10.208553) | 13.734056 / 10.191392 (3.542664) | 0.154987 / 0.680424 (-0.525437) | 0.016996 / 0.534201 (-0.517205) | 0.335264 / 0.579283 (-0.244019) | 0.356907 / 0.434364 (-0.077456) | 0.399185 / 0.540337 (-0.141152) | 0.540209 / 1.386936 (-0.846727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887bef1217e0f4441d57bf0f4d1e806df12f2c50 \"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) | 0.006768 / 0.011353 (-0.004585) | 0.004250 / 0.011008 (-0.006758) | 0.086780 / 0.038508 (0.048272) | 0.080872 / 0.023109 (0.057762) | 0.309281 / 0.275898 (0.033383) | 0.352293 / 0.323480 (0.028814) | 0.005604 / 0.007986 (-0.002382) | 0.003544 / 0.004328 (-0.000784) | 0.066910 / 0.004250 (0.062659) | 0.055568 / 0.037052 (0.018516) | 0.314931 / 0.258489 (0.056442) | 0.366026 / 0.293841 (0.072185) | 0.031247 / 0.128546 (-0.097300) | 0.008860 / 0.075646 (-0.066786) | 0.293210 / 0.419271 (-0.126061) | 0.052868 / 0.043533 (0.009335) | 0.316769 / 0.255139 (0.061630) | 0.352128 / 0.283200 (0.068929) | 0.025492 / 0.141683 (-0.116190) | 1.478379 / 1.452155 (0.026224) | 1.573652 / 1.492716 (0.080936) |\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) | 0.294975 / 0.018006 (0.276968) | 0.615093 / 0.000490 (0.614603) | 0.004279 / 0.000200 (0.004079) | 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) | 0.031557 / 0.037411 (-0.005855) | 0.085026 / 0.014526 (0.070500) | 0.101221 / 0.176557 (-0.075336) | 0.157432 / 0.737135 (-0.579703) | 0.102350 / 0.296338 (-0.193988) |\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) | 0.384158 / 0.215209 (0.168949) | 3.826656 / 2.077655 (1.749001) | 1.873510 / 1.504120 (0.369390) | 1.721913 / 1.541195 (0.180718) | 1.848779 / 1.468490 (0.380289) | 0.485128 / 4.584777 (-4.099649) | 3.656660 / 3.745712 (-0.089052) | 3.441964 / 5.269862 (-1.827898) | 2.150611 / 4.565676 (-2.415066) | 0.056869 / 0.424275 (-0.367406) | 0.007382 / 0.007607 (-0.000225) | 0.458751 / 0.226044 (0.232707) | 4.585028 / 2.268929 (2.316099) | 2.439538 / 55.444624 (-53.005086) | 2.116959 / 6.876477 (-4.759518) | 2.459220 / 2.142072 (0.317147) | 0.580907 / 4.805227 (-4.224321) | 0.134502 / 6.500664 (-6.366162) | 0.062528 / 0.075469 (-0.012941) |\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) | 1.251006 / 1.841788 (-0.590782) | 20.755849 / 8.074308 (12.681541) | 14.456950 / 10.191392 (4.265558) | 0.167074 / 0.680424 (-0.513350) | 0.018482 / 0.534201 (-0.515719) | 0.395867 / 0.579283 (-0.183416) | 0.415620 / 0.434364 (-0.018744) | 0.462247 / 0.540337 (-0.078090) | 0.645762 / 1.386936 (-0.741174) |\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) | 0.007050 / 0.011353 (-0.004303) | 0.004421 / 0.011008 (-0.006587) | 0.065312 / 0.038508 (0.026804) | 0.089790 / 0.023109 (0.066681) | 0.366318 / 0.275898 (0.090420) | 0.403542 / 0.323480 (0.080062) | 0.005695 / 0.007986 (-0.002290) | 0.003642 / 0.004328 (-0.000687) | 0.064540 / 0.004250 (0.060289) | 0.060933 / 0.037052 (0.023881) | 0.369004 / 0.258489 (0.110515) | 0.408056 / 0.293841 (0.114215) | 0.032124 / 0.128546 (-0.096422) | 0.008960 / 0.075646 (-0.066686) | 0.071267 / 0.419271 (-0.348005) | 0.049745 / 0.043533 (0.006212) | 0.367203 / 0.255139 (0.112064) | 0.383009 / 0.283200 (0.099809) | 0.025330 / 0.141683 (-0.116353) | 1.518290 / 1.452155 (0.066135) | 1.581738 / 1.492716 (0.089022) |\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) | 0.338281 / 0.018006 (0.320275) | 0.538195 / 0.000490 (0.537706) | 0.008498 / 0.000200 (0.008298) | 0.000121 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033279 / 0.037411 (-0.004133) | 0.093233 / 0.014526 (0.078707) | 0.106019 / 0.176557 (-0.070538) | 0.161262 / 0.737135 (-0.575874) | 0.109935 / 0.296338 (-0.186404) |\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) | 0.411563 / 0.215209 (0.196354) | 4.102149 / 2.077655 (2.024495) | 2.108513 / 1.504120 (0.604393) | 1.945344 / 1.541195 (0.404150) | 2.066964 / 1.468490 (0.598474) | 0.482771 / 4.584777 (-4.102006) | 3.659160 / 3.745712 (-0.086552) | 3.420833 / 5.269862 (-1.849029) | 2.147276 / 4.565676 (-2.418400) | 0.056957 / 0.424275 (-0.367318) | 0.007898 / 0.007607 (0.000290) | 0.482401 / 0.226044 (0.256357) | 4.821044 / 2.268929 (2.552115) | 2.567993 / 55.444624 (-52.876631) | 2.336165 / 6.876477 (-4.540312) | 2.545066 / 2.142072 (0.402994) | 0.580888 / 4.805227 (-4.224339) | 0.134092 / 6.500664 (-6.366572) | 0.062681 / 0.075469 (-0.012788) |\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) | 1.379124 / 1.841788 (-0.462664) | 21.627949 / 8.074308 (13.553641) | 15.064818 / 10.191392 (4.873426) | 0.169707 / 0.680424 (-0.510716) | 0.018671 / 0.534201 (-0.515530) | 0.400496 / 0.579283 (-0.178787) | 0.415542 / 0.434364 (-0.018822) | 0.484351 / 0.540337 (-0.055986) | 0.646046 / 1.386936 (-0.740890) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) | 0.007113 / 0.011353 (-0.004240) | 0.004436 / 0.011008 (-0.006572) | 0.087422 / 0.038508 (0.048914) | 0.085996 / 0.023109 (0.062887) | 0.311772 / 0.275898 (0.035873) | 0.353281 / 0.323480 (0.029801) | 0.004562 / 0.007986 (-0.003423) | 0.003840 / 0.004328 (-0.000488) | 0.066500 / 0.004250 (0.062250) | 0.061293 / 0.037052 (0.024241) | 0.328840 / 0.258489 (0.070351) | 0.365587 / 0.293841 (0.071746) | 0.031802 / 0.128546 (-0.096744) | 0.008881 / 0.075646 (-0.066765) | 0.289671 / 0.419271 (-0.129601) | 0.053348 / 0.043533 (0.009816) | 0.307822 / 0.255139 (0.052683) | 0.342559 / 0.283200 (0.059360) | 0.025760 / 0.141683 (-0.115923) | 1.509944 / 1.452155 (0.057789) | 1.556634 / 1.492716 (0.063918) |\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) | 0.282036 / 0.018006 (0.264029) | 0.608350 / 0.000490 (0.607860) | 0.004843 / 0.000200 (0.004643) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029810 / 0.037411 (-0.007601) | 0.086215 / 0.014526 (0.071689) | 0.102200 / 0.176557 (-0.074356) | 0.158051 / 0.737135 (-0.579084) | 0.103083 / 0.296338 (-0.193255) |\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) | 0.392119 / 0.215209 (0.176910) | 3.895796 / 2.077655 (1.818141) | 1.921118 / 1.504120 (0.416998) | 1.754271 / 1.541195 (0.213076) | 1.880991 / 1.468490 (0.412501) | 0.481158 / 4.584777 (-4.103618) | 3.609210 / 3.745712 (-0.136502) | 3.412018 / 5.269862 (-1.857843) | 2.131710 / 4.565676 (-2.433967) | 0.057122 / 0.424275 (-0.367153) | 0.007444 / 0.007607 (-0.000163) | 0.468880 / 0.226044 (0.242835) | 4.682441 / 2.268929 (2.413512) | 2.505613 / 55.444624 (-52.939012) | 2.149655 / 6.876477 (-4.726822) | 2.465904 / 2.142072 (0.323832) | 0.578877 / 4.805227 (-4.226350) | 0.133504 / 6.500664 (-6.367160) | 0.061422 / 0.075469 (-0.014047) |\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) | 1.269395 / 1.841788 (-0.572393) | 21.107558 / 8.074308 (13.033250) | 15.318502 / 10.191392 (5.127110) | 0.165273 / 0.680424 (-0.515151) | 0.018783 / 0.534201 (-0.515418) | 0.396259 / 0.579283 (-0.183024) | 0.412907 / 0.434364 (-0.021457) | 0.465723 / 0.540337 (-0.074615) | 0.638414 / 1.386936 (-0.748522) |\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) | 0.007083 / 0.011353 (-0.004270) | 0.004216 / 0.011008 (-0.006793) | 0.065362 / 0.038508 (0.026854) | 0.095454 / 0.023109 (0.072345) | 0.364220 / 0.275898 (0.088322) | 0.417650 / 0.323480 (0.094170) | 0.006114 / 0.007986 (-0.001872) | 0.003577 / 0.004328 (-0.000751) | 0.064830 / 0.004250 (0.060579) | 0.062535 / 0.037052 (0.025483) | 0.381844 / 0.258489 (0.123355) | 0.418996 / 0.293841 (0.125155) | 0.031386 / 0.128546 (-0.097160) | 0.008913 / 0.075646 (-0.066733) | 0.070860 / 0.419271 (-0.348411) | 0.049132 / 0.043533 (0.005599) | 0.360406 / 0.255139 (0.105267) | 0.392407 / 0.283200 (0.109207) | 0.024611 / 0.141683 (-0.117072) | 1.509051 / 1.452155 (0.056896) | 1.570288 / 1.492716 (0.077572) |\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) | 0.368611 / 0.018006 (0.350605) | 0.537587 / 0.000490 (0.537098) | 0.028056 / 0.000200 (0.027856) | 0.000317 / 0.000054 (0.000262) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031570 / 0.037411 (-0.005841) | 0.088985 / 0.014526 (0.074460) | 0.105268 / 0.176557 (-0.071288) | 0.156724 / 0.737135 (-0.580412) | 0.105266 / 0.296338 (-0.191073) |\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) | 0.413861 / 0.215209 (0.198652) | 4.127001 / 2.077655 (2.049347) | 2.112114 / 1.504120 (0.607994) | 1.945200 / 1.541195 (0.404005) | 2.083031 / 1.468490 (0.614540) | 0.488086 / 4.584777 (-4.096691) | 3.565584 / 3.745712 (-0.180128) | 3.380782 / 5.269862 (-1.889079) | 2.103481 / 4.565676 (-2.462195) | 0.058203 / 0.424275 (-0.366072) | 0.007996 / 0.007607 (0.000389) | 0.487986 / 0.226044 (0.261941) | 4.871023 / 2.268929 (2.602095) | 2.584632 / 55.444624 (-52.859992) | 2.240103 / 6.876477 (-4.636374) | 2.555165 / 2.142072 (0.413092) | 0.591950 / 4.805227 (-4.213278) | 0.134919 / 6.500664 (-6.365745) | 0.062868 / 0.075469 (-0.012601) |\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) | 1.369731 / 1.841788 (-0.472057) | 21.497888 / 8.074308 (13.423580) | 14.555054 / 10.191392 (4.363662) | 0.168768 / 0.680424 (-0.511656) | 0.018837 / 0.534201 (-0.515364) | 0.394512 / 0.579283 (-0.184771) | 0.405459 / 0.434364 (-0.028905) | 0.475479 / 0.540337 (-0.064858) | 0.631994 / 1.386936 (-0.754942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) | 0.009072 / 0.011353 (-0.002280) | 0.004894 / 0.011008 (-0.006114) | 0.108790 / 0.038508 (0.070282) | 0.081783 / 0.023109 (0.058674) | 0.381963 / 0.275898 (0.106064) | 0.450700 / 0.323480 (0.127220) | 0.006961 / 0.007986 (-0.001025) | 0.004035 / 0.004328 (-0.000293) | 0.081420 / 0.004250 (0.077169) | 0.058029 / 0.037052 (0.020976) | 0.437453 / 0.258489 (0.178964) | 0.472607 / 0.293841 (0.178766) | 0.048663 / 0.128546 (-0.079884) | 0.013512 / 0.075646 (-0.062134) | 0.406009 / 0.419271 (-0.013262) | 0.067616 / 0.043533 (0.024084) | 0.383641 / 0.255139 (0.128502) | 0.456734 / 0.283200 (0.173534) | 0.033391 / 0.141683 (-0.108292) | 1.753529 / 1.452155 (0.301375) | 1.859831 / 1.492716 (0.367115) |\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) | 0.215128 / 0.018006 (0.197122) | 0.538261 / 0.000490 (0.537771) | 0.005430 / 0.000200 (0.005230) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032664 / 0.037411 (-0.004748) | 0.093465 / 0.014526 (0.078939) | 0.106637 / 0.176557 (-0.069919) | 0.173642 / 0.737135 (-0.563494) | 0.113944 / 0.296338 (-0.182394) |\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) | 0.629212 / 0.215209 (0.414003) | 6.116729 / 2.077655 (4.039075) | 2.818000 / 1.504120 (1.313880) | 2.515317 / 1.541195 (0.974122) | 2.466588 / 1.468490 (0.998098) | 0.850815 / 4.584777 (-3.733962) | 5.051292 / 3.745712 (1.305579) | 4.472138 / 5.269862 (-0.797724) | 2.968317 / 4.565676 (-1.597360) | 0.100173 / 0.424275 (-0.324102) | 0.008407 / 0.007607 (0.000800) | 0.743972 / 0.226044 (0.517928) | 7.397619 / 2.268929 (5.128690) | 3.596681 / 55.444624 (-51.847943) | 2.854674 / 6.876477 (-4.021803) | 3.114274 / 2.142072 (0.972201) | 1.064879 / 4.805227 (-3.740348) | 0.215981 / 6.500664 (-6.284683) | 0.078159 / 0.075469 (0.002690) |\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) | 1.543291 / 1.841788 (-0.298497) | 23.244641 / 8.074308 (15.170333) | 20.784610 / 10.191392 (10.593218) | 0.222002 / 0.680424 (-0.458422) | 0.028584 / 0.534201 (-0.505617) | 0.478563 / 0.579283 (-0.100720) | 0.556101 / 0.434364 (0.121737) | 0.547446 / 0.540337 (0.007109) | 0.764318 / 1.386936 (-0.622618) |\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) | 0.008651 / 0.011353 (-0.002702) | 0.004925 / 0.011008 (-0.006083) | 0.078995 / 0.038508 (0.040487) | 0.092878 / 0.023109 (0.069769) | 0.485615 / 0.275898 (0.209717) | 0.532157 / 0.323480 (0.208677) | 0.008228 / 0.007986 (0.000243) | 0.004777 / 0.004328 (0.000449) | 0.076892 / 0.004250 (0.072642) | 0.066905 / 0.037052 (0.029853) | 0.465497 / 0.258489 (0.207008) | 0.520153 / 0.293841 (0.226312) | 0.047357 / 0.128546 (-0.081189) | 0.016870 / 0.075646 (-0.058776) | 0.090481 / 0.419271 (-0.328791) | 0.060774 / 0.043533 (0.017241) | 0.474368 / 0.255139 (0.219229) | 0.503981 / 0.283200 (0.220781) | 0.036025 / 0.141683 (-0.105658) | 1.769939 / 1.452155 (0.317784) | 1.851518 / 1.492716 (0.358802) |\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) | 0.265947 / 0.018006 (0.247941) | 0.532317 / 0.000490 (0.531828) | 0.004997 / 0.000200 (0.004797) | 0.000130 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034112 / 0.037411 (-0.003299) | 0.102290 / 0.014526 (0.087764) | 0.109989 / 0.176557 (-0.066567) | 0.182813 / 0.737135 (-0.554323) | 0.111774 / 0.296338 (-0.184565) |\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) | 0.584893 / 0.215209 (0.369684) | 6.138505 / 2.077655 (4.060850) | 2.925761 / 1.504120 (1.421641) | 2.607320 / 1.541195 (1.066125) | 2.655827 / 1.468490 (1.187337) | 0.871140 / 4.584777 (-3.713637) | 5.051171 / 3.745712 (1.305459) | 4.708008 / 5.269862 (-0.561854) | 3.027485 / 4.565676 (-1.538191) | 0.100970 / 0.424275 (-0.323305) | 0.009640 / 0.007607 (0.002033) | 0.747818 / 0.226044 (0.521774) | 7.539930 / 2.268929 (5.271001) | 3.611693 / 55.444624 (-51.832931) | 2.924087 / 6.876477 (-3.952390) | 3.141993 / 2.142072 (0.999920) | 1.062921 / 4.805227 (-3.742306) | 0.213185 / 6.500664 (-6.287479) | 0.077146 / 0.075469 (0.001677) |\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) | 1.669182 / 1.841788 (-0.172606) | 23.810242 / 8.074308 (15.735934) | 21.220649 / 10.191392 (11.029257) | 0.212639 / 0.680424 (-0.467785) | 0.026705 / 0.534201 (-0.507496) | 0.469231 / 0.579283 (-0.110053) | 0.551672 / 0.434364 (0.117308) | 0.575043 / 0.540337 (0.034706) | 0.767511 / 1.386936 (-0.619425) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"CML watermark\")\n"
] | 2023-08-08T15:43:56 | 2023-08-08T16:08:22 | 2023-08-08T15:49:06 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6129",
"html_url": "https://github.com/huggingface/datasets/pull/6129",
"diff_url": "https://github.com/huggingface/datasets/pull/6129.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6129.patch",
"merged_at": "2023-08-08T15:49:06"
} | null | {
"url": "https://api.github.com/repos/huggingface/datasets/issues/6129/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} | https://api.github.com/repos/huggingface/datasets/issues/6129/timeline | null | null | true |
End of preview. Expand
in Dataset Viewer.
- Downloads last month
- 30