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https://api.github.com/repos/huggingface/datasets/issues/6728/comments | https://api.github.com/repos/huggingface/datasets/issues/6728/timeline | null | null | null | I_kwDODunzps6B2uek | open | [] | null | 6,728 | {
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} | Issue Downloading Certain Datasets After Setting Custom `HF_ENDPOINT` | https://api.github.com/repos/huggingface/datasets/issues/6728/events | null | https://api.github.com/repos/huggingface/datasets/issues/6728/labels{/name} | 2024-03-11T09:06:38Z | null | false | null | null | 2,178,607,012 | [] | https://api.github.com/repos/huggingface/datasets/issues/6728 | [
"Through debugging, I found a potential solution is to modify the code in the error handling module of `huggingface_hub`: https://github.com/huggingface/huggingface_hub/commit/56d6c798c44e83d2a3167e74c022737d8fcbe822 "
] | https://api.github.com/repos/huggingface/datasets | NONE | ### Describe the bug
This bug is triggered under the following conditions:
- datasets repo ids without organization names trigger errors, such as `bookcorpus`, `gsm8k`, `wikipedia`.
- If `HF_ENDPOINT` is set and the hostname is not in the form of `(hub-ci.)?huggingface.co`.
- specific versions of `datasets` and `huggingface-hub`. When installing their latest versions, `datasets==2.18.0` and `huggingface-hub==0.21.4`, The issue disappears when `datasets==2.14.6` or `huggingface-hub==0.19.4`.(I only tested this version; whether the behavior persists in other versions is unknown.
### Steps to reproduce the bug
the issue can be reproduced with the following code:
1. install specific datasets and huggingface_hub
```bash
pip install datasets==2.18.0
pip install huggingface_hub==0.21.4
```
2. execute python code.
```Python
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
from datasets import load_dataset
bookcorpus = load_dataset('bookcorpus', split='train')
```
console output:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 2228, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1879, in dataset_module_factory
raise e1 from None
File "/home/padeoe/.local/lib/python3.10/site-packages/datasets/load.py", line 1830, in dataset_module_factory
with fs.open(f"datasets/{path}/{filename}", "r", encoding="utf-8") as f:
File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1295, in open
self.open(
File "/home/padeoe/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1307, in open
f = self._open(
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 228, in _open
return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 615, in __init__
self.resolved_path = fs.resolve_path(path, revision=revision)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 180, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 117, in _repo_and_revision_exist
self._api.repo_info(repo_id, revision=revision, repo_type=repo_type)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2413, in repo_info
return method(
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2286, in dataset_info
hf_raise_for_status(r)
File "/home/padeoe/.local/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 362, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 401 Client Error: Unauthorized for url: https://hf-mirror.com/api/datasets/bookcorpus/bookcorpus.py (Request ID: Root=1-65ee8659-5ab10eec5960c63e71f2bb58;b00bdbea-fd6e-4a74-8fe0-bc4682ae090e)
```
### Expected behavior
The dataset was downloaded correctly without any errors.
### Environment info
datasets==2.18.0
huggingface-hub==0.21.4 | 2024-03-11T09:09:07Z | https://github.com/huggingface/datasets/issues/6728 | {
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https://api.github.com/repos/huggingface/datasets/issues/6727/comments | https://api.github.com/repos/huggingface/datasets/issues/6727/timeline | null | null | null | PR_kwDODunzps5pLJyE | open | [] | false | 6,727 | {
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} | Using a registry instead of calling globals for fetching feature types | https://api.github.com/repos/huggingface/datasets/issues/6727/events | null | https://api.github.com/repos/huggingface/datasets/issues/6727/labels{/name} | 2024-03-10T17:47:51Z | null | false | null | {
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} | 2,177,826,110 | [] | https://api.github.com/repos/huggingface/datasets/issues/6727 | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6727). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"looks like some files are missing in your google storage"
] | https://api.github.com/repos/huggingface/datasets | CONTRIBUTOR | Hello,
When working with bio-data, each feature often has metadata associated with it (e.g. species, lineage, snp position, etc). To store this, I like to use the feature classes with the added `metadata` attribute. However, when saving or loading with custom features, you get an error since that class doesn't exist in the global namespace in `datasets.features.features`. Take for example,
```python
from dataclasses import dataclass, field
from datasets import Dataset
from datasets.features.features import Value, Features
@dataclass
class FeatureA(Value):
metadata: dict = field(default=dict)
_type: str = field(default="FeatureA", init=False, repr=False)
@dataclass
class FeatureB(Value):
metadata: dict = field(default_factory=dict)
_type: str = field(default="FeatureB", init=False, repr=False)
test_data = {
"a": [1, 2, 3],
"b": [4, 5, 6],
}
test_data = Dataset.from_dict(
test_data,
features=Features({
"a": FeatureA("int32", metadata={"species": "lactobacillus acetotolerans"}),
"b": FeatureB("int32", metadata={"species": "lactobacillus iners"}),
})
)
# returns an error since FeatureA and FeatureB are not in the global namespace
test_data.save_to_disk('./test_data')
```
Saving the dataset (0/1 shards): 0%| | 0/3 [00:00<?, ? examples/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[2], line 28
19 test_data = Dataset.from_dict(
20 test_data,
21 features=Features({
(...)
24 })
25 )
27 # returns an error since FeatureA and FeatureB are not in the global namespace
---> 28 test_data.save_to_disk('./test_data')
...
File ~\Documents\datasets\src\datasets\features\features.py:1361, in generate_from_dict(obj)
1359 return {key: generate_from_dict(value) for key, value in obj.items()}
1360 obj = dict(obj)
-> 1361 class_type = globals()[obj.pop("_type")]
1363 if class_type == Sequence:
1364 return Sequence(feature=generate_from_dict(obj["feature"]), length=obj.get("length", -1))
KeyError: 'FeatureA'
We can avoid this by having a registry (like formatters) and doing
```python
from datasets.features.features import register_feature
register_feature(FeatureA, "FeatureA")
register_feature(FeatureB, "FeatureB")
test_data.save_to_disk('./test_data')
```
Saving the dataset (1/1 shards): 100%|------| 3/3 [00:00<00:00, 211.13 examples/s]
and loading from disk returns with all metadata information
```python
from datasets import load_from_disk
test_data = load_from_disk('./test_data')
test_data.features
```
{'a': FeatureA(dtype='int32', id=None, metadata={'species': 'lactobacillus acetotolerans'}),
'b': FeatureB(dtype='int32', id=None, metadata={'species': 'lactobacillus iners'})}
| 2024-03-10T18:25:01Z | https://github.com/huggingface/datasets/pull/6727 | {
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https://api.github.com/repos/huggingface/datasets/issues/6726/comments | https://api.github.com/repos/huggingface/datasets/issues/6726/timeline | null | null | null | I_kwDODunzps6Bw94Q | open | [] | null | 6,726 | {
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} | Profiling for HF Filesystem shows there are easy performance gains to be made | https://api.github.com/repos/huggingface/datasets/issues/6726/events | null | https://api.github.com/repos/huggingface/datasets/issues/6726/labels{/name} | 2024-03-09T07:08:45Z | null | false | null | null | 2,177,097,232 | [] | https://api.github.com/repos/huggingface/datasets/issues/6726 | [
"FWIW I debugged this while waiting for it to go",
"Oh I forgot to mention you can also cache resolve_pattern, and that seemed to also substantially improves things, if you want to load a dataset twice for whatever reason."
] | https://api.github.com/repos/huggingface/datasets | NONE | ### Describe the bug
# Let's make it faster
First, an evidence...
Figure 1: CProfile for loading 3 files from cerebras/SlimPajama-627B train split, and 3 files from test split using streaming=True. X axis is 1106 seconds long.
See? It's pretty slow.
What is resolve pattern doing?
```
resolve_pattern called with **/train/** and hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543
resolve_pattern took 20.815081119537354 seconds
```
Makes sense. How to improve it?
## Bigger project, biggest payoff
Databricks (and consequently, spark) store a compressed manifest file of the files contained in the remote filesystem.
Then, you download one tiny file, decompress it, and all the operations are local instead of this shenanigans.
It seems pretty straightforward to make dataset uploads compute a manifest and upload it alongside their data.
This would make resolution time so fast that nobody would ever think about it again.
It also means you either need to have the uploader compute it _every time_, or have a hook that computes it.
## Smaller project, immediate payoff: Be diligent in avoiding deepcopy
Revise the _ls_tree method to avoid deepcopy:
```
def _ls_tree(
self,
path: str,
recursive: bool = False,
refresh: bool = False,
revision: Optional[str] = None,
expand_info: bool = True,
):
..... omitted .....
for path_info in tree:
if isinstance(path_info, RepoFile):
cache_path_info = {
"name": root_path + "/" + path_info.path,
"size": path_info.size,
"type": "file",
"blob_id": path_info.blob_id,
"lfs": path_info.lfs,
"last_commit": path_info.last_commit,
"security": path_info.security,
}
else:
cache_path_info = {
"name": root_path + "/" + path_info.path,
"size": 0,
"type": "directory",
"tree_id": path_info.tree_id,
"last_commit": path_info.last_commit,
}
parent_path = self._parent(cache_path_info["name"])
self.dircache.setdefault(parent_path, []).append(cache_path_info)
out.append(cache_path_info)
return copy.deepcopy(out) # copy to not let users modify the dircache
```
Observe this deepcopy at the end. It is making a copy of a very simple data structure. We do not need to copy. We can simply generate the data structure twice instead. It will be much faster.
```
def _ls_tree(
self,
path: str,
recursive: bool = False,
refresh: bool = False,
revision: Optional[str] = None,
expand_info: bool = True,
):
..... omitted .....
def make_cache_path_info(path_info):
if isinstance(path_info, RepoFile):
return {
"name": root_path + "/" + path_info.path,
"size": path_info.size,
"type": "file",
"blob_id": path_info.blob_id,
"lfs": path_info.lfs,
"last_commit": path_info.last_commit,
"security": path_info.security,
}
else:
return {
"name": root_path + "/" + path_info.path,
"size": 0,
"type": "directory",
"tree_id": path_info.tree_id,
"last_commit": path_info.last_commit,
}
for path_info in tree:
cache_path_info = make_cache_path_info(path_info)
out_cache_path_info = make_cache_path_info(path_info) # copy to not let users modify the dircache
parent_path = self._parent(cache_path_info["name"])
self.dircache.setdefault(parent_path, []).append(cache_path_info)
out.append(out_cache_path_info)
return out
```
Note there is no longer a deepcopy in this method. We have replaced it with generating the output twice. This is substantially faster. For me, the entire resolution went from 1100s to 360s.
## Medium project, medium payoff
After the above change, we have this profile:
Figure 2: x-axis is 355 seconds. Note that globbing and _ls_tree deep copy is gone. No surprise there. It's much faster now, but we still spend ~187seconds in get_fs_token_paths.
Well get_fs_token_paths is part of fsspec. We don't need to fix that because we can trust their developers to write high performance code. Probably the caller has misconfigured something. Let's take a look at the storage_options being provided to the filesystem that is constructed during this call.
Ah yes, streaming_download_manager::_prepare_single_hop_path_and_storage_options. We know streaming download manager is not compatible with async right now, but we really need this specific part of the code to be async. We're spending so much time checking isDir on the remote filesystem, it's a huge waste.
We can make the call easily 20-30x faster by using async, removing this performance bottleneck almost entirely (and reducing the total time of this part of the code to <30s. There is no reason to block async isDir calls for streaming.
I'm not going to mess w/ this one myself; I didn't write the streaming impl, and I don't know how it works, but I know the isDir check can be async.
### Steps to reproduce the bug
```
with cProfile.Profile() as pr:
pr.enable()
# Begin Data
if not os.path.exists(data_cache_dir):
os.makedirs(data_cache_dir, exist_ok=True)
training_dataset = load_dataset(training_dataset_name, split=training_split, cache_dir=data_cache_dir, streaming=True).take(training_slice)
eval_dataset = load_dataset(eval_dataset_name, split=eval_split, cache_dir=data_cache_dir, streaming=True).take(eval_slice)
# End Data
pr.disable()
pr.create_stats()
if not os.path.exists(profiling_path):
os.makedirs(profiling_path, exist_ok=True)
pr.dump_stats(os.path.join(profiling_path, "cprofile.prof"))
```
run this code for "cerebras/SlimPajama-627B" and whatever other params
### Expected behavior
Something better.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.13
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0 | 2024-03-09T07:11:08Z | https://github.com/huggingface/datasets/issues/6726 | {
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} | Request for a comparison of huggingface datasets compared with other data format especially webdataset | https://api.github.com/repos/huggingface/datasets/issues/6725/events | null | https://api.github.com/repos/huggingface/datasets/issues/6725/labels{/name} | 2024-03-08T08:23:01Z | null | false | null | null | 2,175,527,530 | [
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] | https://api.github.com/repos/huggingface/datasets/issues/6725 | [] | https://api.github.com/repos/huggingface/datasets | NONE | ### Feature request
Request for a comparison of huggingface datasets compared with other data format especially webdataset
### Motivation
I see huggingface datasets uses Apache Arrow as its backend, it seems to be great, but I'm curious about how it is good compared with other dataset format, like webdataset, what's the pros/cons of them.
### Your contribution
More information | 2024-03-08T08:23:01Z | https://github.com/huggingface/datasets/issues/6725 | {
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https://api.github.com/repos/huggingface/datasets/issues/6724/comments | https://api.github.com/repos/huggingface/datasets/issues/6724/timeline | null | null | null | I_kwDODunzps6Bmq8T | open | [] | null | 6,724 | {
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} | Dataset with loading script does not work in renamed repos | https://api.github.com/repos/huggingface/datasets/issues/6724/events | null | https://api.github.com/repos/huggingface/datasets/issues/6724/labels{/name} | 2024-03-07T17:38:38Z | null | false | null | null | 2,174,398,227 | [] | https://api.github.com/repos/huggingface/datasets/issues/6724 | [] | https://api.github.com/repos/huggingface/datasets | CONTRIBUTOR | ### Describe the bug
My data repository was first called `BramVanroy/hplt-mono-v1-2` but I then renamed to use underscores instead of dashes. However, it seems that `datasets` retrieves the old repo name when it checks whether the repo contains data loading scripts in this line.
https://github.com/huggingface/datasets/blob/6fb6c834f008996c994b0a86c3808d0a33d44525/src/datasets/load.py#L1845
When I print `filename` it returns `hplt-mono-v1-2.py` but the files in the repo are of course `['.gitattributes', 'README.md', 'hplt_mono_v1_2.py']`. So the `filename` is the original reponame instead of the renamed one.
I am not sure if this is a caching issue or not or how I can resolve it.
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset(
"BramVanroy/hplt-mono-v1-2",
"ky",
trust_remote_code=True
)
```
### Expected behavior
That the most recent repo name is used when `filename` is generated.
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.10.0
| 2024-03-07T20:06:25Z | https://github.com/huggingface/datasets/issues/6724 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6723). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005658 / 0.011353 (-0.005694) | 0.003883 / 0.011008 (-0.007125) | 0.064007 / 0.038508 (0.025499) | 0.030370 / 0.023109 (0.007261) | 0.246677 / 0.275898 (-0.029221) | 0.270846 / 0.323480 (-0.052634) | 0.003102 / 0.007986 (-0.004884) | 0.002931 / 0.004328 (-0.001397) | 0.049446 / 0.004250 (0.045196) | 0.043555 / 0.037052 (0.006503) | 0.261810 / 0.258489 (0.003321) | 0.289705 / 0.293841 (-0.004136) | 0.028676 / 0.128546 (-0.099870) | 0.010778 / 0.075646 (-0.064868) | 0.210604 / 0.419271 (-0.208667) | 0.035987 / 0.043533 (-0.007546) | 0.248034 / 0.255139 (-0.007105) | 0.265019 / 0.283200 (-0.018181) | 0.018522 / 0.141683 (-0.123161) | 1.096364 / 1.452155 (-0.355791) | 1.152750 / 1.492716 (-0.339966) |\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) | 0.093987 / 0.018006 (0.075981) | 0.306143 / 0.000490 (0.305653) | 0.000218 / 0.000200 (0.000018) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018727 / 0.037411 (-0.018685) | 0.061983 / 0.014526 (0.047457) | 0.074254 / 0.176557 (-0.102303) | 0.121256 / 0.737135 (-0.615880) | 0.076756 / 0.296338 (-0.219582) |\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) | 0.278824 / 0.215209 (0.063615) | 2.815960 / 2.077655 (0.738305) | 1.472946 / 1.504120 (-0.031174) | 1.349722 / 1.541195 (-0.191473) | 1.327844 / 1.468490 (-0.140646) | 0.574964 / 4.584777 (-4.009813) | 2.403458 / 3.745712 (-1.342254) | 2.769293 / 5.269862 (-2.500569) | 1.736970 / 4.565676 (-2.828706) | 0.063144 / 0.424275 (-0.361131) | 0.004983 / 0.007607 (-0.002625) | 0.331212 / 0.226044 (0.105168) | 3.231496 / 2.268929 (0.962567) | 1.798487 / 55.444624 (-53.646138) | 1.523010 / 6.876477 (-5.353467) | 1.559973 / 2.142072 (-0.582099) | 0.657036 / 4.805227 (-4.148191) | 0.119084 / 6.500664 (-6.381580) | 0.042982 / 0.075469 (-0.032487) |\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) | 0.976433 / 1.841788 (-0.865355) | 11.475946 / 8.074308 (3.401638) | 9.339369 / 10.191392 (-0.852023) | 0.141761 / 0.680424 (-0.538662) | 0.014506 / 0.534201 (-0.519695) | 0.289944 / 0.579283 (-0.289340) | 0.273667 / 0.434364 (-0.160697) | 0.326682 / 0.540337 (-0.213655) | 0.458946 / 1.386936 (-0.927990) |\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) | 0.005194 / 0.011353 (-0.006159) | 0.003713 / 0.011008 (-0.007295) | 0.049297 / 0.038508 (0.010789) | 0.029723 / 0.023109 (0.006614) | 0.278664 / 0.275898 (0.002766) | 0.296387 / 0.323480 (-0.027093) | 0.004215 / 0.007986 (-0.003771) | 0.002680 / 0.004328 (-0.001648) | 0.048276 / 0.004250 (0.044025) | 0.044454 / 0.037052 (0.007402) | 0.290510 / 0.258489 (0.032021) | 0.319028 / 0.293841 (0.025187) | 0.029177 / 0.128546 (-0.099369) | 0.010361 / 0.075646 (-0.065285) | 0.056993 / 0.419271 (-0.362279) | 0.050765 / 0.043533 (0.007232) | 0.278234 / 0.255139 (0.023095) | 0.295848 / 0.283200 (0.012649) | 0.018776 / 0.141683 (-0.122906) | 1.134866 / 1.452155 (-0.317288) | 1.204083 / 1.492716 (-0.288634) |\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) | 0.094397 / 0.018006 (0.076391) | 0.304693 / 0.000490 (0.304203) | 0.000207 / 0.000200 (0.000007) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021322 / 0.037411 (-0.016090) | 0.075384 / 0.014526 (0.060859) | 0.086961 / 0.176557 (-0.089596) | 0.124424 / 0.737135 (-0.612711) | 0.087802 / 0.296338 (-0.208536) |\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) | 0.305542 / 0.215209 (0.090333) | 2.980678 / 2.077655 (0.903023) | 1.632348 / 1.504120 (0.128228) | 1.501466 / 1.541195 (-0.039728) | 1.517681 / 1.468490 (0.049191) | 0.579318 / 4.584777 (-4.005459) | 2.460734 / 3.745712 (-1.284978) | 2.650164 / 5.269862 (-2.619697) | 1.752061 / 4.565676 (-2.813615) | 0.064561 / 0.424275 (-0.359714) | 0.005097 / 0.007607 (-0.002510) | 0.359613 / 0.226044 (0.133569) | 3.518549 / 2.268929 (1.249620) | 1.962575 / 55.444624 (-53.482050) | 1.686108 / 6.876477 (-5.190369) | 1.787873 / 2.142072 (-0.354199) | 0.653715 / 4.805227 (-4.151512) | 0.117617 / 6.500664 (-6.383048) | 0.040359 / 0.075469 (-0.035110) |\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) | 1.021533 / 1.841788 (-0.820255) | 11.974817 / 8.074308 (3.900509) | 10.073530 / 10.191392 (-0.117862) | 0.141477 / 0.680424 (-0.538947) | 0.015081 / 0.534201 (-0.519120) | 0.292622 / 0.579283 (-0.286661) | 0.291043 / 0.434364 (-0.143321) | 0.347822 / 0.540337 (-0.192516) | 0.443647 / 1.386936 (-0.943289) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6fb6c834f008996c994b0a86c3808d0a33d44525 \"CML watermark\")\n"
] | https://api.github.com/repos/huggingface/datasets | MEMBER | fix https://github.com/huggingface/datasets/pull/6722 | 2024-03-07T17:27:29Z | https://github.com/huggingface/datasets/pull/6723 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6722). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | https://api.github.com/repos/huggingface/datasets | CONTRIBUTOR | see https://github.com/huggingface/datasets-server/pull/2554#discussion_r1516516867 | 2024-03-07T17:21:10Z | https://github.com/huggingface/datasets/pull/6722 | {
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} | Hi,do you know how to load the dataset from local file now? | https://api.github.com/repos/huggingface/datasets/issues/6721/events | null | https://api.github.com/repos/huggingface/datasets/issues/6721/labels{/name} | 2024-03-07T13:58:40Z | null | false | null | null | 2,173,931,714 | [] | https://api.github.com/repos/huggingface/datasets/issues/6721 | [] | https://api.github.com/repos/huggingface/datasets | NONE | Hi, if I want to load the dataset from local file, then how to specify the configuration name?
_Originally posted by @WHU-gentle in https://github.com/huggingface/datasets/issues/2976#issuecomment-1333455222_
| 2024-03-07T13:58:40Z | https://github.com/huggingface/datasets/issues/6721 | {
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"Hi ! I opened a PR to fix an issue in the Features defined in your code\r\n\r\nBasically changing\r\n```python\r\nSequence(\"float32\")\r\n```\r\n\r\nto\r\n```python\r\nSequence(Value(\"float32\"))\r\n```\r\n\r\n\r\nhttps://huggingface.co/datasets/BramVanroy/hplt_mono_v1_2/discussions/1",
"D'oh! Was wondering why the `str() is not callable` was in there. Glad the error is my end though, and not related to zstandard (which I had not used in the past).\r\n\r\nThanks a lot!"
] | https://api.github.com/repos/huggingface/datasets | CONTRIBUTOR | ### Describe the bug
I am trying to get the HPLT datasets on the hub. Downloading/re-uploading would be too time- and resource consuming so I wrote [a dataset loader script](https://huggingface.co/datasets/BramVanroy/hplt_mono_v1_2/blob/main/hplt_mono_v1_2.py). I think I am very close but for some reason I always get the error below. It happens during the clean-up phase where the directory cannot be removed because it is not empty.
My only guess would be that this may have to do with zstandard
```
Traceback (most recent call last):
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1744, in _prepare_split_single
writer.write(example, key)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write
self.write_examples_on_file()
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 434, in write_examples_on_file
if self.schema
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 409, in schema
else (pa.schema(self._features.type) if self._features is not None else None)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1643, in type
return get_nested_type(self)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in get_nested_type
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in <dictcomp>
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1221, in get_nested_type
value_type = get_nested_type(schema.feature)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1228, in get_nested_type
return schema()
TypeError: 'str' object is not callable
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1753, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 588, in finalize
self.write_examples_on_file()
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 434, in write_examples_on_file
if self.schema
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/arrow_writer.py", line 409, in schema
else (pa.schema(self._features.type) if self._features is not None else None)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1643, in type
return get_nested_type(self)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in get_nested_type
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1209, in <dictcomp>
{key: get_nested_type(schema[key]) for key in schema}
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1221, in get_nested_type
value_type = get_nested_type(schema.feature)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/features/features.py", line 1228, in get_nested_type
return schema()
TypeError: 'str' object is not callable
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 959, in incomplete_dir
yield tmp_dir
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1005, in download_and_prepare
self._download_and_prepare(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1767, in _download_and_prepare
super()._download_and_prepare(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1100, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1605, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1762, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/pricie/vanroy/.config/JetBrains/PyCharm2023.3/scratches/scratch_5.py", line 4, in <module>
ds = load_dataset(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/load.py", line 2549, in load_dataset
builder_instance.download_and_prepare(
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 985, in download_and_prepare
with incomplete_dir(self._output_dir) as tmp_output_dir:
File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/contextlib.py", line 153, in __exit__
self.gen.throw(typ, value, traceback)
File "/home/local/vanroy/dutch-instruction-datasets/.venv/lib/python3.10/site-packages/datasets/builder.py", line 966, in incomplete_dir
shutil.rmtree(tmp_dir)
File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/shutil.py", line 731, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/home/pricie/vanroy/.pyenv/versions/3.10.13/lib/python3.10/shutil.py", line 729, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/home/pricie/vanroy/.cache/huggingface/datasets/BramVanroy___hplt_mono_v1_2/ky/1.2.0/7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete'
```
Interestingly, though, this directory _does_ appear to be empty:
```shell
> cd /home/pricie/vanroy/.cache/huggingface/datasets/BramVanroy___hplt_mono_v1_2/ky/1.2.0/7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete
> ls -lah
total 0
drwxr-xr-x. 1 vanroy vanroy 0 Mar 7 12:01 .
drwxr-xr-x. 1 vanroy vanroy 304 Mar 7 11:52 ..
> cd ..
> ls
7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47_builder.lock 7ab138629fe7e9e29fe93ce63d809d5ef9d963273b829f61ab538e012dc9cc47.incomplete
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset(
"BramVanroy/hplt_mono_v1_2",
"ky",
trust_remote_code=True
)
```
### Expected behavior
No error.
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.10.0
| 2024-03-08T07:34:53Z | https://github.com/huggingface/datasets/issues/6720 | {
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https://api.github.com/repos/huggingface/datasets/issues/6719/comments | https://api.github.com/repos/huggingface/datasets/issues/6719/timeline | null | null | null | I_kwDODunzps6BUUA_ | open | [] | null | 6,719 | {
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} | Is there any way to solve hanging of IterableDataset using split by node + filtering during inference | https://api.github.com/repos/huggingface/datasets/issues/6719/events | null | https://api.github.com/repos/huggingface/datasets/issues/6719/labels{/name} | 2024-03-05T15:55:13Z | null | false | null | null | 2,169,585,727 | [] | https://api.github.com/repos/huggingface/datasets/issues/6719 | [] | https://api.github.com/repos/huggingface/datasets | NONE | ### Describe the bug
I am using an iterable dataset in a multi-node setup, trying to do training/inference while filtering the data on the fly. I usually do not use `split_dataset_by_node` but it is very slow using the IterableDatasetShard in `accelerate` and `transformers`. When I filter after applying `split_dataset_by_node`, it results in shards that are not equal sizes due to unequal samples filtered from each one.
The distributed process hangs when trying to accomplish this. Is there any way to resolve this or is it impossible to implement?
### Steps to reproduce the bug
Here is a toy example of what I am trying to do that reproduces the behavior
```
# torchrun --nproc-per-node 2 file.py
import os
import pandas as pd
import torch
from accelerate import Accelerator
from datasets import Features, Value, load_dataset
from datasets.distributed import split_dataset_by_node
from torch.utils.data import DataLoader
accelerator = Accelerator(device_placement=True, dispatch_batches=False)
if accelerator.is_main_process:
if not os.path.exists("scratch_data"):
os.mkdir("scratch_data")
n_shards = 4
for i in range(n_shards):
df = pd.DataFrame({"id": list(range(10 * i, 10 * (i + 1)))})
df.to_parquet(f"scratch_data/shard_{i}.parquet")
world_size = accelerator.num_processes
local_rank = accelerator.process_index
def collate_fn(examples):
input_ids = []
for example in examples:
input_ids.append(example["id"])
return torch.LongTensor(input_ids)
dataset = load_dataset(
"parquet", data_dir="scratch_data", split="train", streaming=True
)
dataset = (
split_dataset_by_node(dataset, rank=local_rank, world_size=world_size)
.filter(lambda x: x["id"] < 35)
.shuffle(seed=42, buffer_size=100)
)
batch_size = 2
train_dataloader = DataLoader(
dataset,
batch_size=batch_size,
collate_fn=collate_fn,
num_workers=2
)
for x in train_dataloader:
x = x.to(accelerator.device)
print({"rank": local_rank, "id": x})
y = accelerator.gather_for_metrics(x)
if accelerator.is_main_process:
print("gathered", y)
```
### Expected behavior
Is there any way to continue training/inference on the GPUs that have remaining data left without waiting for the others? Is it impossible to filter when
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-5.10.209-198.812.amzn2.x86_64-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2023.6.0 | 2024-03-05T15:55:13Z | https://github.com/huggingface/datasets/issues/6719 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6718). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005074 / 0.011353 (-0.006279) | 0.003505 / 0.011008 (-0.007503) | 0.063683 / 0.038508 (0.025175) | 0.029308 / 0.023109 (0.006199) | 0.246648 / 0.275898 (-0.029250) | 0.265546 / 0.323480 (-0.057933) | 0.004108 / 0.007986 (-0.003878) | 0.002683 / 0.004328 (-0.001646) | 0.048634 / 0.004250 (0.044383) | 0.043786 / 0.037052 (0.006733) | 0.262197 / 0.258489 (0.003708) | 0.291582 / 0.293841 (-0.002259) | 0.027472 / 0.128546 (-0.101074) | 0.010213 / 0.075646 (-0.065434) | 0.206744 / 0.419271 (-0.212527) | 0.036195 / 0.043533 (-0.007337) | 0.249090 / 0.255139 (-0.006049) | 0.280002 / 0.283200 (-0.003198) | 0.018568 / 0.141683 (-0.123115) | 1.124844 / 1.452155 (-0.327311) | 1.159358 / 1.492716 (-0.333359) |\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) | 0.093186 / 0.018006 (0.075180) | 0.302331 / 0.000490 (0.301842) | 0.000217 / 0.000200 (0.000017) | 0.000046 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018727 / 0.037411 (-0.018684) | 0.061730 / 0.014526 (0.047204) | 0.074330 / 0.176557 (-0.102226) | 0.119769 / 0.737135 (-0.617366) | 0.075611 / 0.296338 (-0.220727) |\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) | 0.285063 / 0.215209 (0.069854) | 2.824809 / 2.077655 (0.747155) | 1.481858 / 1.504120 (-0.022262) | 1.350193 / 1.541195 (-0.191002) | 1.358012 / 1.468490 (-0.110478) | 0.557842 / 4.584777 (-4.026935) | 2.380729 / 3.745712 (-1.364983) | 2.798891 / 5.269862 (-2.470970) | 1.719288 / 4.565676 (-2.846388) | 0.061705 / 0.424275 (-0.362570) | 0.005431 / 0.007607 (-0.002176) | 0.343233 / 0.226044 (0.117189) | 3.375223 / 2.268929 (1.106295) | 1.838188 / 55.444624 (-53.606436) | 1.570015 / 6.876477 (-5.306461) | 1.573157 / 2.142072 (-0.568915) | 0.650678 / 4.805227 (-4.154549) | 0.116412 / 6.500664 (-6.384252) | 0.041754 / 0.075469 (-0.033715) |\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) | 0.970431 / 1.841788 (-0.871357) | 11.317128 / 8.074308 (3.242819) | 9.691240 / 10.191392 (-0.500152) | 0.142260 / 0.680424 (-0.538164) | 0.014131 / 0.534201 (-0.520070) | 0.289910 / 0.579283 (-0.289373) | 0.265648 / 0.434364 (-0.168715) | 0.323130 / 0.540337 (-0.217208) | 0.447005 / 1.386936 (-0.939931) |\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) | 0.005322 / 0.011353 (-0.006031) | 0.003755 / 0.011008 (-0.007253) | 0.049646 / 0.038508 (0.011138) | 0.029669 / 0.023109 (0.006560) | 0.284151 / 0.275898 (0.008253) | 0.298351 / 0.323480 (-0.025128) | 0.004183 / 0.007986 (-0.003803) | 0.002683 / 0.004328 (-0.001645) | 0.048814 / 0.004250 (0.044563) | 0.045017 / 0.037052 (0.007965) | 0.287358 / 0.258489 (0.028869) | 0.317394 / 0.293841 (0.023553) | 0.030025 / 0.128546 (-0.098521) | 0.010854 / 0.075646 (-0.064793) | 0.058694 / 0.419271 (-0.360578) | 0.052287 / 0.043533 (0.008754) | 0.279038 / 0.255139 (0.023899) | 0.295442 / 0.283200 (0.012242) | 0.019413 / 0.141683 (-0.122270) | 1.146106 / 1.452155 (-0.306048) | 1.197777 / 1.492716 (-0.294939) |\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) | 0.092191 / 0.018006 (0.074184) | 0.302672 / 0.000490 (0.302182) | 0.000623 / 0.000200 (0.000423) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022067 / 0.037411 (-0.015345) | 0.081760 / 0.014526 (0.067235) | 0.087548 / 0.176557 (-0.089009) | 0.126405 / 0.737135 (-0.610730) | 0.089331 / 0.296338 (-0.207008) |\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) | 0.295821 / 0.215209 (0.080612) | 2.897930 / 2.077655 (0.820276) | 1.604500 / 1.504120 (0.100380) | 1.471502 / 1.541195 (-0.069692) | 1.497918 / 1.468490 (0.029428) | 0.576179 / 4.584777 (-4.008598) | 2.452103 / 3.745712 (-1.293609) | 2.668043 / 5.269862 (-2.601818) | 1.753544 / 4.565676 (-2.812133) | 0.064410 / 0.424275 (-0.359865) | 0.005027 / 0.007607 (-0.002580) | 0.351509 / 0.226044 (0.125465) | 3.479208 / 2.268929 (1.210280) | 1.990356 / 55.444624 (-53.454269) | 1.684920 / 6.876477 (-5.191556) | 1.794251 / 2.142072 (-0.347821) | 0.662692 / 4.805227 (-4.142535) | 0.118589 / 6.500664 (-6.382076) | 0.040813 / 0.075469 (-0.034656) |\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) | 1.002390 / 1.841788 (-0.839398) | 12.004617 / 8.074308 (3.930309) | 10.216005 / 10.191392 (0.024613) | 0.154354 / 0.680424 (-0.526070) | 0.015554 / 0.534201 (-0.518647) | 0.288741 / 0.579283 (-0.290542) | 0.276774 / 0.434364 (-0.157590) | 0.327055 / 0.540337 (-0.213282) | 0.435121 / 1.386936 (-0.951815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f45bc6caa25115a04c41b278671a5a89457eb66c \"CML watermark\")\n"
] | https://api.github.com/repos/huggingface/datasets | MEMBER | I added `lock_importable_file` in `get_dataset_builder_class` and `extend_dataset_builder_for_streaming` to fix the issue, and I also added a test
cc @clefourrier | 2024-03-07T14:05:53Z | https://github.com/huggingface/datasets/pull/6718 | {
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https://api.github.com/repos/huggingface/datasets/issues/6717/comments | https://api.github.com/repos/huggingface/datasets/issues/6717/timeline | null | null | null | I_kwDODunzps6BRCOg | open | [] | null | 6,717 | {
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} | `remove_columns` method used with a streaming enable dataset mode produces a LibsndfileError on multichannel audio | https://api.github.com/repos/huggingface/datasets/issues/6717/events | null | https://api.github.com/repos/huggingface/datasets/issues/6717/labels{/name} | 2024-03-05T09:33:26Z | null | false | null | null | 2,168,726,432 | [] | https://api.github.com/repos/huggingface/datasets/issues/6717 | [
"And it also works well with `dataset = dataset.select_columns([\"audio\"])`"
] | https://api.github.com/repos/huggingface/datasets | NONE | ### Describe the bug
When loading a HF dataset in streaming mode and removing some columns, it is impossible to load a sample if the audio contains more than one channel. I have the impression that the time axis and channels are swapped or concatenated.
### Steps to reproduce the bug
Minimal error code:
```python
from datasets import load_dataset
dataset_name = "zinc75/Vibravox_dummy"
config_name = "BWE_Larynx_microphone"
# if we use "ASR_Larynx_microphone" subset which is a monochannel audio, no error is thrown.
dataset = load_dataset(
path=dataset_name, name=config_name, split="train", streaming=True
)
dataset = dataset.remove_columns(["sensor_id"])
# dataset = dataset.map(lambda x:x, remove_columns=["sensor_id"])
# The commented version does not produce an error, but loses the dataset features.
sample = next(iter(dataset))
```
Error:
```
Traceback (most recent call last):
File "/home/julien/Bureau/github/vibravox/tmp.py", line 15, in <module>
sample = next(iter(dataset))
^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1392, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1080, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1889, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in <dictcomp>
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1300, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/audio.py", line 98, in encode_example
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 343, in write
with SoundFile(file, 'w', samplerate, channels,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7fd795d24680>: Format not recognised.
Process finished with exit code 1
```
### Expected behavior
I would expect this code to run without error.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-21-generic-x86_64-with-glibc2.35
- Python version: 3.11.0
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2023.10.0 | 2024-03-05T10:32:19Z | https://github.com/huggingface/datasets/issues/6717 | {
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https://api.github.com/repos/huggingface/datasets/issues/6716/comments | https://api.github.com/repos/huggingface/datasets/issues/6716/timeline | null | null | null | I_kwDODunzps6BQ9X- | open | [] | null | 6,716 | {
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} | Non-deterministic `Dataset.builder_name` value | https://api.github.com/repos/huggingface/datasets/issues/6716/events | null | https://api.github.com/repos/huggingface/datasets/issues/6716/labels{/name} | 2024-03-05T09:23:21Z | null | false | null | null | 2,168,706,558 | [] | https://api.github.com/repos/huggingface/datasets/issues/6716 | [
"When `rotten_tomatoes` is printed out, the following warning message is also printed out:\r\n\r\n```\r\nYou can avoid this message in future by passing the argument `trust_remote_code=True`.\r\nPassing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\r\n```"
] | https://api.github.com/repos/huggingface/datasets | NONE | ### Describe the bug
I'm not sure if this is a bug, but `print(ds.builder_name)` in the following code sometimes prints out `rotten_tomatoes` instead of `parquet`:
```python
import datasets
for _ in range(100):
ds = datasets.load_dataset("rotten_tomatoes", split="train")
print(ds.builder_name) # prints out "rotten_tomatoes" sometimes instead of "parquet"
```
Output:
```
...
parquet
parquet
parquet
rotten_tomatoes
parquet
parquet
parquet
...
```
Here's a reproduction using GitHub Actions:
https://github.com/mlflow/mlflow/actions/runs/8153247984/job/22284263613?pr=11329#step:12:241
One of our tests is flaky because `builder_name` is not deterministic.
### Steps to reproduce the bug
1. Run the code above.
### Expected behavior
Always prints out `parquet`?
### Environment info
```
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-1015-azure-x86_64-with-glibc2.34
- Python version: 3.8.18
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2024.2.0
``` | 2024-03-05T09:27:24Z | https://github.com/huggingface/datasets/issues/6716 | {
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https://api.github.com/repos/huggingface/datasets/issues/6715/comments | https://api.github.com/repos/huggingface/datasets/issues/6715/timeline | 2024-03-05T11:17:04Z | null | null | PR_kwDODunzps5oo36i | closed | [] | false | 6,715 | {
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} | Fix sliced ConcatenationTable pickling with mixed schemas vertically | https://api.github.com/repos/huggingface/datasets/issues/6715/events | null | https://api.github.com/repos/huggingface/datasets/issues/6715/labels{/name} | 2024-03-04T21:02:07Z | null | false | null | {
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} | 2,167,747,095 | [] | https://api.github.com/repos/huggingface/datasets/issues/6715 | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6715). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005294 / 0.011353 (-0.006059) | 0.003598 / 0.011008 (-0.007411) | 0.062798 / 0.038508 (0.024290) | 0.027479 / 0.023109 (0.004370) | 0.247146 / 0.275898 (-0.028752) | 0.272103 / 0.323480 (-0.051377) | 0.002979 / 0.007986 (-0.005007) | 0.002701 / 0.004328 (-0.001628) | 0.049384 / 0.004250 (0.045134) | 0.041562 / 0.037052 (0.004510) | 0.269924 / 0.258489 (0.011435) | 0.290749 / 0.293841 (-0.003092) | 0.028285 / 0.128546 (-0.100261) | 0.010464 / 0.075646 (-0.065183) | 0.207000 / 0.419271 (-0.212272) | 0.036186 / 0.043533 (-0.007347) | 0.254524 / 0.255139 (-0.000615) | 0.274843 / 0.283200 (-0.008356) | 0.020044 / 0.141683 (-0.121638) | 1.119223 / 1.452155 (-0.332931) | 1.156557 / 1.492716 (-0.336159) |\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) | 0.092014 / 0.018006 (0.074008) | 0.297349 / 0.000490 (0.296859) | 0.000205 / 0.000200 (0.000005) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018617 / 0.037411 (-0.018794) | 0.061879 / 0.014526 (0.047354) | 0.072877 / 0.176557 (-0.103680) | 0.121850 / 0.737135 (-0.615286) | 0.074686 / 0.296338 (-0.221653) |\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) | 0.281204 / 0.215209 (0.065995) | 2.728688 / 2.077655 (0.651033) | 1.469659 / 1.504120 (-0.034461) | 1.355306 / 1.541195 (-0.185889) | 1.350598 / 1.468490 (-0.117892) | 0.563669 / 4.584777 (-4.021108) | 2.377177 / 3.745712 (-1.368535) | 2.767402 / 5.269862 (-2.502460) | 1.720188 / 4.565676 (-2.845489) | 0.062594 / 0.424275 (-0.361681) | 0.005004 / 0.007607 (-0.002603) | 0.333017 / 0.226044 (0.106972) | 3.354543 / 2.268929 (1.085615) | 1.840031 / 55.444624 (-53.604593) | 1.545548 / 6.876477 (-5.330929) | 1.569858 / 2.142072 (-0.572214) | 0.642680 / 4.805227 (-4.162547) | 0.117463 / 6.500664 (-6.383201) | 0.042472 / 0.075469 (-0.032997) |\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) | 0.977436 / 1.841788 (-0.864351) | 11.285982 / 8.074308 (3.211673) | 9.441848 / 10.191392 (-0.749544) | 0.140773 / 0.680424 (-0.539650) | 0.013783 / 0.534201 (-0.520418) | 0.292304 / 0.579283 (-0.286979) | 0.275011 / 0.434364 (-0.159353) | 0.339094 / 0.540337 (-0.201244) | 0.447593 / 1.386936 (-0.939343) |\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) | 0.005258 / 0.011353 (-0.006095) | 0.003539 / 0.011008 (-0.007469) | 0.049920 / 0.038508 (0.011412) | 0.029789 / 0.023109 (0.006680) | 0.277187 / 0.275898 (0.001288) | 0.296817 / 0.323480 (-0.026663) | 0.004133 / 0.007986 (-0.003852) | 0.002679 / 0.004328 (-0.001649) | 0.048999 / 0.004250 (0.044749) | 0.044087 / 0.037052 (0.007034) | 0.290359 / 0.258489 (0.031870) | 0.319572 / 0.293841 (0.025731) | 0.030248 / 0.128546 (-0.098298) | 0.010453 / 0.075646 (-0.065194) | 0.058734 / 0.419271 (-0.360537) | 0.051216 / 0.043533 (0.007683) | 0.278667 / 0.255139 (0.023528) | 0.298792 / 0.283200 (0.015592) | 0.019131 / 0.141683 (-0.122552) | 1.131814 / 1.452155 (-0.320340) | 1.167208 / 1.492716 (-0.325508) |\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) | 0.088316 / 0.018006 (0.070309) | 0.297143 / 0.000490 (0.296653) | 0.000207 / 0.000200 (0.000007) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022457 / 0.037411 (-0.014954) | 0.075251 / 0.014526 (0.060726) | 0.086747 / 0.176557 (-0.089809) | 0.124975 / 0.737135 (-0.612161) | 0.087320 / 0.296338 (-0.209019) |\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) | 0.292339 / 0.215209 (0.077130) | 2.860196 / 2.077655 (0.782541) | 1.599058 / 1.504120 (0.094938) | 1.476104 / 1.541195 (-0.065091) | 1.509109 / 1.468490 (0.040619) | 0.564056 / 4.584777 (-4.020721) | 2.388870 / 3.745712 (-1.356842) | 2.582356 / 5.269862 (-2.687506) | 1.726033 / 4.565676 (-2.839644) | 0.061788 / 0.424275 (-0.362487) | 0.005021 / 0.007607 (-0.002586) | 0.345644 / 0.226044 (0.119600) | 3.384000 / 2.268929 (1.115071) | 1.946591 / 55.444624 (-53.498033) | 1.693485 / 6.876477 (-5.182992) | 1.790300 / 2.142072 (-0.351773) | 0.654637 / 4.805227 (-4.150590) | 0.116271 / 6.500664 (-6.384393) | 0.040710 / 0.075469 (-0.034759) |\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) | 1.007367 / 1.841788 (-0.834421) | 11.868065 / 8.074308 (3.793757) | 10.146212 / 10.191392 (-0.045180) | 0.128902 / 0.680424 (-0.551522) | 0.015259 / 0.534201 (-0.518942) | 0.288087 / 0.579283 (-0.291196) | 0.281516 / 0.434364 (-0.152848) | 0.325755 / 0.540337 (-0.214583) | 0.424814 / 1.386936 (-0.962122) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8247202a7ed1c3164c88f8f183513c5f003aa2af \"CML watermark\")\n"
] | https://api.github.com/repos/huggingface/datasets | MEMBER | A sliced + pickled ConcatenationTable could end up with a different schema than the original schema, if the slice only contains blocks with only a subset of the columns.
This can lead to issues when saving datasets from a concatenation of datasets with mixed schemas
Reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595 | 2024-03-05T11:23:05Z | https://github.com/huggingface/datasets/pull/6715 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6714). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005237 / 0.011353 (-0.006116) | 0.003614 / 0.011008 (-0.007394) | 0.063349 / 0.038508 (0.024841) | 0.027297 / 0.023109 (0.004187) | 0.236203 / 0.275898 (-0.039695) | 0.260029 / 0.323480 (-0.063451) | 0.003096 / 0.007986 (-0.004889) | 0.003342 / 0.004328 (-0.000987) | 0.048703 / 0.004250 (0.044453) | 0.043121 / 0.037052 (0.006069) | 0.257491 / 0.258489 (-0.000998) | 0.282861 / 0.293841 (-0.010980) | 0.027701 / 0.128546 (-0.100845) | 0.010634 / 0.075646 (-0.065012) | 0.207369 / 0.419271 (-0.211903) | 0.035799 / 0.043533 (-0.007734) | 0.240445 / 0.255139 (-0.014694) | 0.261977 / 0.283200 (-0.021223) | 0.018175 / 0.141683 (-0.123508) | 1.143964 / 1.452155 (-0.308191) | 1.230057 / 1.492716 (-0.262659) |\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) | 0.096656 / 0.018006 (0.078650) | 0.303434 / 0.000490 (0.302944) | 0.000225 / 0.000200 (0.000025) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018454 / 0.037411 (-0.018957) | 0.061792 / 0.014526 (0.047266) | 0.073384 / 0.176557 (-0.103172) | 0.120148 / 0.737135 (-0.616988) | 0.074221 / 0.296338 (-0.222118) |\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) | 0.290291 / 0.215209 (0.075082) | 2.822908 / 2.077655 (0.745254) | 1.483139 / 1.504120 (-0.020981) | 1.349619 / 1.541195 (-0.191576) | 1.356588 / 1.468490 (-0.111902) | 0.571723 / 4.584777 (-4.013054) | 2.402696 / 3.745712 (-1.343016) | 2.832215 / 5.269862 (-2.437647) | 1.794962 / 4.565676 (-2.770714) | 0.062707 / 0.424275 (-0.361568) | 0.004997 / 0.007607 (-0.002610) | 0.343093 / 0.226044 (0.117049) | 3.383028 / 2.268929 (1.114100) | 1.818624 / 55.444624 (-53.626000) | 1.549859 / 6.876477 (-5.326618) | 1.667838 / 2.142072 (-0.474235) | 0.648574 / 4.805227 (-4.156653) | 0.119181 / 6.500664 (-6.381484) | 0.042074 / 0.075469 (-0.033395) |\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) | 0.982039 / 1.841788 (-0.859748) | 11.411759 / 8.074308 (3.337451) | 9.783405 / 10.191392 (-0.407987) | 0.129577 / 0.680424 (-0.550847) | 0.014091 / 0.534201 (-0.520110) | 0.297925 / 0.579283 (-0.281358) | 0.263884 / 0.434364 (-0.170480) | 0.346032 / 0.540337 (-0.194305) | 0.444806 / 1.386936 (-0.942130) |\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) | 0.005527 / 0.011353 (-0.005826) | 0.003677 / 0.011008 (-0.007332) | 0.050245 / 0.038508 (0.011737) | 0.030070 / 0.023109 (0.006961) | 0.272640 / 0.275898 (-0.003258) | 0.296555 / 0.323480 (-0.026925) | 0.004247 / 0.007986 (-0.003738) | 0.003833 / 0.004328 (-0.000495) | 0.049341 / 0.004250 (0.045091) | 0.046604 / 0.037052 (0.009552) | 0.282765 / 0.258489 (0.024276) | 0.314924 / 0.293841 (0.021084) | 0.029749 / 0.128546 (-0.098797) | 0.010524 / 0.075646 (-0.065122) | 0.057859 / 0.419271 (-0.361412) | 0.053172 / 0.043533 (0.009640) | 0.274906 / 0.255139 (0.019767) | 0.290566 / 0.283200 (0.007366) | 0.019299 / 0.141683 (-0.122384) | 1.164092 / 1.452155 (-0.288062) | 1.205074 / 1.492716 (-0.287642) |\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) | 0.093943 / 0.018006 (0.075936) | 0.298746 / 0.000490 (0.298256) | 0.000232 / 0.000200 (0.000032) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022098 / 0.037411 (-0.015313) | 0.075523 / 0.014526 (0.060997) | 0.086784 / 0.176557 (-0.089773) | 0.124610 / 0.737135 (-0.612525) | 0.087743 / 0.296338 (-0.208595) |\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) | 0.298555 / 0.215209 (0.083346) | 2.951493 / 2.077655 (0.873838) | 1.611448 / 1.504120 (0.107328) | 1.481503 / 1.541195 (-0.059692) | 1.497937 / 1.468490 (0.029447) | 0.580402 / 4.584777 (-4.004375) | 2.433308 / 3.745712 (-1.312404) | 2.712717 / 5.269862 (-2.557145) | 1.766286 / 4.565676 (-2.799391) | 0.063973 / 0.424275 (-0.360303) | 0.005006 / 0.007607 (-0.002601) | 0.354541 / 0.226044 (0.128497) | 3.486448 / 2.268929 (1.217519) | 1.972779 / 55.444624 (-53.471846) | 1.709018 / 6.876477 (-5.167458) | 1.864242 / 2.142072 (-0.277831) | 0.678213 / 4.805227 (-4.127014) | 0.119525 / 6.500664 (-6.381140) | 0.041387 / 0.075469 (-0.034082) |\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) | 1.021337 / 1.841788 (-0.820451) | 12.049563 / 8.074308 (3.975255) | 10.424701 / 10.191392 (0.233309) | 0.131444 / 0.680424 (-0.548980) | 0.015644 / 0.534201 (-0.518557) | 0.293712 / 0.579283 (-0.285571) | 0.279160 / 0.434364 (-0.155204) | 0.327991 / 0.540337 (-0.212346) | 0.435455 / 1.386936 (-0.951481) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fe9483acc1ccaf19f3c199b99391921a8526215 \"CML watermark\")\n"
] | https://api.github.com/repos/huggingface/datasets | CONTRIBUTOR | E.g., to have info about a dataset's number of examples for more informative TQDM bars. | 2024-03-04T20:28:30Z | https://github.com/huggingface/datasets/pull/6714 | {
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https://api.github.com/repos/huggingface/datasets/issues/6713/comments | https://api.github.com/repos/huggingface/datasets/issues/6713/timeline | 2024-03-04T18:06:05Z | null | null | PR_kwDODunzps5olmqh | closed | [] | false | 6,713 | {
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} | Bump huggingface-hub lower version to 0.21.2 | https://api.github.com/repos/huggingface/datasets/issues/6713/events | null | https://api.github.com/repos/huggingface/datasets/issues/6713/labels{/name} | 2024-03-04T13:00:52Z | null | false | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6713). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq if you agree, I could make a patch release tomorrow morning.",
"sure :)",
"<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) | 0.005086 / 0.011353 (-0.006267) | 0.003695 / 0.011008 (-0.007313) | 0.063430 / 0.038508 (0.024922) | 0.026798 / 0.023109 (0.003689) | 0.253761 / 0.275898 (-0.022138) | 0.301301 / 0.323480 (-0.022179) | 0.004160 / 0.007986 (-0.003825) | 0.002783 / 0.004328 (-0.001545) | 0.050698 / 0.004250 (0.046448) | 0.040899 / 0.037052 (0.003846) | 0.269024 / 0.258489 (0.010535) | 0.323467 / 0.293841 (0.029626) | 0.027756 / 0.128546 (-0.100791) | 0.010684 / 0.075646 (-0.064963) | 0.207128 / 0.419271 (-0.212144) | 0.035874 / 0.043533 (-0.007659) | 0.251620 / 0.255139 (-0.003519) | 0.268668 / 0.283200 (-0.014532) | 0.017387 / 0.141683 (-0.124296) | 1.139230 / 1.452155 (-0.312925) | 1.183613 / 1.492716 (-0.309103) |\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) | 0.096337 / 0.018006 (0.078331) | 0.305014 / 0.000490 (0.304524) | 0.000219 / 0.000200 (0.000019) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018086 / 0.037411 (-0.019325) | 0.061626 / 0.014526 (0.047100) | 0.072598 / 0.176557 (-0.103959) | 0.119944 / 0.737135 (-0.617192) | 0.074549 / 0.296338 (-0.221789) |\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) | 0.282661 / 0.215209 (0.067452) | 2.804473 / 2.077655 (0.726818) | 1.444602 / 1.504120 (-0.059517) | 1.313977 / 1.541195 (-0.227217) | 1.319426 / 1.468490 (-0.149064) | 0.570176 / 4.584777 (-4.014601) | 2.397895 / 3.745712 (-1.347818) | 2.760208 / 5.269862 (-2.509654) | 1.732457 / 4.565676 (-2.833220) | 0.062743 / 0.424275 (-0.361533) | 0.004950 / 0.007607 (-0.002657) | 0.338500 / 0.226044 (0.112456) | 3.287249 / 2.268929 (1.018320) | 1.777495 / 55.444624 (-53.667130) | 1.521255 / 6.876477 (-5.355222) | 1.517317 / 2.142072 (-0.624756) | 0.642202 / 4.805227 (-4.163025) | 0.116501 / 6.500664 (-6.384163) | 0.042418 / 0.075469 (-0.033052) |\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) | 0.968966 / 1.841788 (-0.872822) | 11.490531 / 8.074308 (3.416223) | 9.507803 / 10.191392 (-0.683589) | 0.141570 / 0.680424 (-0.538854) | 0.014000 / 0.534201 (-0.520201) | 0.284237 / 0.579283 (-0.295046) | 0.269341 / 0.434364 (-0.165022) | 0.321654 / 0.540337 (-0.218683) | 0.446914 / 1.386936 (-0.940022) |\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) | 0.005280 / 0.011353 (-0.006072) | 0.003794 / 0.011008 (-0.007214) | 0.050328 / 0.038508 (0.011820) | 0.029756 / 0.023109 (0.006647) | 0.273403 / 0.275898 (-0.002495) | 0.297346 / 0.323480 (-0.026133) | 0.004310 / 0.007986 (-0.003676) | 0.002858 / 0.004328 (-0.001470) | 0.048833 / 0.004250 (0.044583) | 0.045696 / 0.037052 (0.008644) | 0.291034 / 0.258489 (0.032545) | 0.318899 / 0.293841 (0.025058) | 0.029809 / 0.128546 (-0.098737) | 0.010710 / 0.075646 (-0.064936) | 0.058183 / 0.419271 (-0.361089) | 0.051761 / 0.043533 (0.008228) | 0.275022 / 0.255139 (0.019883) | 0.291614 / 0.283200 (0.008414) | 0.017975 / 0.141683 (-0.123708) | 1.148489 / 1.452155 (-0.303666) | 1.218111 / 1.492716 (-0.274605) |\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) | 0.091806 / 0.018006 (0.073799) | 0.299413 / 0.000490 (0.298923) | 0.000219 / 0.000200 (0.000019) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021506 / 0.037411 (-0.015905) | 0.075537 / 0.014526 (0.061011) | 0.087020 / 0.176557 (-0.089536) | 0.125270 / 0.737135 (-0.611865) | 0.088038 / 0.296338 (-0.208300) |\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) | 0.300401 / 0.215209 (0.085192) | 2.932571 / 2.077655 (0.854916) | 1.609502 / 1.504120 (0.105383) | 1.480078 / 1.541195 (-0.061117) | 1.514902 / 1.468490 (0.046412) | 0.575591 / 4.584777 (-4.009186) | 2.461873 / 3.745712 (-1.283839) | 2.728099 / 5.269862 (-2.541762) | 1.760054 / 4.565676 (-2.805622) | 0.064371 / 0.424275 (-0.359904) | 0.004990 / 0.007607 (-0.002617) | 0.350134 / 0.226044 (0.124090) | 3.453249 / 2.268929 (1.184321) | 1.979760 / 55.444624 (-53.464865) | 1.741128 / 6.876477 (-5.135348) | 1.825734 / 2.142072 (-0.316339) | 0.654902 / 4.805227 (-4.150325) | 0.116989 / 6.500664 (-6.383676) | 0.040800 / 0.075469 (-0.034669) |\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) | 1.033352 / 1.841788 (-0.808436) | 12.196711 / 8.074308 (4.122403) | 10.315114 / 10.191392 (0.123722) | 0.132541 / 0.680424 (-0.547882) | 0.016455 / 0.534201 (-0.517746) | 0.289025 / 0.579283 (-0.290258) | 0.281464 / 0.434364 (-0.152900) | 0.325302 / 0.540337 (-0.215036) | 0.428469 / 1.386936 (-0.958467) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7093b4b1a69f413e452119c87669af9e8ceaf749 \"CML watermark\")\n"
] | https://api.github.com/repos/huggingface/datasets | MEMBER | This should fix the version compatibility issue when using `huggingface_hub` < 0.21.2 and latest fsspec (>=2023.12.0).
See my comment: https://github.com/huggingface/datasets/pull/6687#issuecomment-1976493336
>> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`
>
>Please note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`:
>- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86
>- https://github.com/huggingface/lighteval/pull/84
CC: @clefourrier
| 2024-03-04T18:14:03Z | https://github.com/huggingface/datasets/pull/6713 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6712). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005151 / 0.011353 (-0.006202) | 0.003813 / 0.011008 (-0.007196) | 0.062957 / 0.038508 (0.024449) | 0.028282 / 0.023109 (0.005173) | 0.246036 / 0.275898 (-0.029862) | 0.290024 / 0.323480 (-0.033456) | 0.004009 / 0.007986 (-0.003977) | 0.002749 / 0.004328 (-0.001580) | 0.049351 / 0.004250 (0.045101) | 0.041143 / 0.037052 (0.004090) | 0.264782 / 0.258489 (0.006293) | 0.290711 / 0.293841 (-0.003130) | 0.027248 / 0.128546 (-0.101298) | 0.010691 / 0.075646 (-0.064955) | 0.205926 / 0.419271 (-0.213345) | 0.035652 / 0.043533 (-0.007880) | 0.246357 / 0.255139 (-0.008782) | 0.267851 / 0.283200 (-0.015348) | 0.018498 / 0.141683 (-0.123185) | 1.135996 / 1.452155 (-0.316159) | 1.181841 / 1.492716 (-0.310875) |\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) | 0.094054 / 0.018006 (0.076048) | 0.305470 / 0.000490 (0.304980) | 0.000225 / 0.000200 (0.000025) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018842 / 0.037411 (-0.018569) | 0.061532 / 0.014526 (0.047006) | 0.073483 / 0.176557 (-0.103073) | 0.119426 / 0.737135 (-0.617709) | 0.075385 / 0.296338 (-0.220954) |\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) | 0.285544 / 0.215209 (0.070335) | 2.774256 / 2.077655 (0.696601) | 1.475719 / 1.504120 (-0.028401) | 1.353841 / 1.541195 (-0.187353) | 1.381891 / 1.468490 (-0.086599) | 0.570619 / 4.584777 (-4.014158) | 2.380300 / 3.745712 (-1.365412) | 2.788767 / 5.269862 (-2.481095) | 1.741790 / 4.565676 (-2.823886) | 0.061810 / 0.424275 (-0.362465) | 0.005004 / 0.007607 (-0.002603) | 0.334963 / 0.226044 (0.108918) | 3.286388 / 2.268929 (1.017459) | 1.831669 / 55.444624 (-53.612955) | 1.523372 / 6.876477 (-5.353105) | 1.581551 / 2.142072 (-0.560521) | 0.639642 / 4.805227 (-4.165585) | 0.117356 / 6.500664 (-6.383308) | 0.043277 / 0.075469 (-0.032192) |\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) | 0.973005 / 1.841788 (-0.868782) | 11.590148 / 8.074308 (3.515839) | 9.521262 / 10.191392 (-0.670130) | 0.143243 / 0.680424 (-0.537181) | 0.013529 / 0.534201 (-0.520672) | 0.285724 / 0.579283 (-0.293559) | 0.265642 / 0.434364 (-0.168721) | 0.366098 / 0.540337 (-0.174239) | 0.444410 / 1.386936 (-0.942526) |\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) | 0.005347 / 0.011353 (-0.006006) | 0.003797 / 0.011008 (-0.007212) | 0.050441 / 0.038508 (0.011933) | 0.032812 / 0.023109 (0.009703) | 0.281278 / 0.275898 (0.005379) | 0.304524 / 0.323480 (-0.018956) | 0.005039 / 0.007986 (-0.002946) | 0.002735 / 0.004328 (-0.001594) | 0.049184 / 0.004250 (0.044933) | 0.046751 / 0.037052 (0.009698) | 0.292093 / 0.258489 (0.033604) | 0.322087 / 0.293841 (0.028246) | 0.029775 / 0.128546 (-0.098771) | 0.010540 / 0.075646 (-0.065106) | 0.057927 / 0.419271 (-0.361345) | 0.054240 / 0.043533 (0.010707) | 0.281537 / 0.255139 (0.026398) | 0.298386 / 0.283200 (0.015186) | 0.019773 / 0.141683 (-0.121910) | 1.157161 / 1.452155 (-0.294994) | 1.210395 / 1.492716 (-0.282321) |\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) | 0.095098 / 0.018006 (0.077091) | 0.306952 / 0.000490 (0.306462) | 0.000211 / 0.000200 (0.000011) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022602 / 0.037411 (-0.014809) | 0.075242 / 0.014526 (0.060716) | 0.087134 / 0.176557 (-0.089422) | 0.127923 / 0.737135 (-0.609212) | 0.088645 / 0.296338 (-0.207693) |\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) | 0.304187 / 0.215209 (0.088978) | 2.977120 / 2.077655 (0.899465) | 1.663592 / 1.504120 (0.159473) | 1.527601 / 1.541195 (-0.013594) | 1.540121 / 1.468490 (0.071631) | 0.562492 / 4.584777 (-4.022285) | 2.473836 / 3.745712 (-1.271876) | 2.656782 / 5.269862 (-2.613080) | 1.754212 / 4.565676 (-2.811464) | 0.062330 / 0.424275 (-0.361945) | 0.005149 / 0.007607 (-0.002459) | 0.354905 / 0.226044 (0.128860) | 3.503587 / 2.268929 (1.234659) | 2.015682 / 55.444624 (-53.428943) | 1.744421 / 6.876477 (-5.132056) | 1.923120 / 2.142072 (-0.218952) | 0.652209 / 4.805227 (-4.153018) | 0.119406 / 6.500664 (-6.381258) | 0.042840 / 0.075469 (-0.032630) |\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) | 1.009164 / 1.841788 (-0.832624) | 12.379654 / 8.074308 (4.305346) | 10.408696 / 10.191392 (0.217304) | 0.141674 / 0.680424 (-0.538750) | 0.016815 / 0.534201 (-0.517386) | 0.292453 / 0.579283 (-0.286830) | 0.277577 / 0.434364 (-0.156787) | 0.325024 / 0.540337 (-0.215313) | 0.433181 / 1.386936 (-0.953755) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7a16a08c940e65397305aec5f1b484d91cee75a \"CML watermark\")\n"
] | https://api.github.com/repos/huggingface/datasets | MEMBER | reported in https://discuss.huggingface.co/t/datasetdict-save-to-disk-with-num-proc-1-seems-to-hang-with-error/75595 | 2024-03-04T20:23:47Z | https://github.com/huggingface/datasets/pull/6712 | {
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https://api.github.com/repos/huggingface/datasets/issues/6711/comments | https://api.github.com/repos/huggingface/datasets/issues/6711/timeline | null | null | null | PR_kwDODunzps5ohM1a | open | [] | false | 6,711 | {
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} | 3x Faster Text Preprocessing | https://api.github.com/repos/huggingface/datasets/issues/6711/events | null | https://api.github.com/repos/huggingface/datasets/issues/6711/labels{/name} | 2024-03-03T19:03:04Z | null | false | null | {
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} | 2,165,507,817 | [] | https://api.github.com/repos/huggingface/datasets/issues/6711 | [
"Unfortunately, that won't improve the performance. StringZilla repository has extensive benchmarks comparing against different built-in functionality of several programming languages. Using `re.finditer` for tokenization is practically the slowest anti-pattern I've encountered in any language. The gap between that and a SIMD-accelerated kernel can be as big as 10 MB/s vs 10 GB/s.\n\nI understand the need to keep the dependencies minimal. It helps the package remain small and portable. At this point, StringZilla provides 105 binaries for different OS and hardware versions (more portable than NumPy) and the [binary size generally ranges from 50 KB to 250 KB](https://pypi.org/project/stringzilla/), smaller than a single JPEG. \n",
"The `text` builder is not very popular, so I'm also not a fan of introducing a dependency for it.\r\n\r\nMoreover, I couldn't find any projects of this size/usage depending on StringZilla (with GitHub search), so we should at least wait for its greater adoption to merge this PR.\r\n"
] | https://api.github.com/repos/huggingface/datasets | NONE | I was preparing some datasets for AI training and noticed that `datasets` by HuggingFace uses the conventional `open` mechanism to read the file and split it into chunks. I thought it can be significantly accelerated, and [started with a benchmark](https://gist.github.com/ashvardanian/55c2052e9f78b05b8d614aa90cb12347):
```sh
$ pip install --upgrade --force-reinstall datasets
$ python benchmark_huggingface_datasets.py xlsum.csv
Generating train split: 1004598 examples [00:47, 21116.16 examples/s]
Time taken to load the dataset: 48.66838526725769 seconds
Time taken to chunk the dataset into parts of size 10000: 0.11466407775878906 seconds
Total time taken: 48.78304934501648 seconds
```
For benchmarks I've used a [large CSV file with mixed UTF-8 content](https://github.com/ashvardanian/StringZilla/blob/main/CONTRIBUTING.md#benchmarking-datasets), most common in modern large-scale pre-training pipelines. I've later patched the `datasets` library to use `stringzilla`, which resulted in significantly lower memory consumption and in 2.9x throughput improvement on the AWS `r7iz` instances. That's using slow SSDs mounted over the network. Performance on local SSDs on something like a DGX-H100 should be even higher:
```sh
$ pip install -e .
$ python benchmark_huggingface_datasets.py xlsum.csv
Generating train split: 1004598 examples [00:15, 64529.90 examples/s]
Time taken to load the dataset: 16.45028805732727 seconds
Time taken to chunk the dataset into parts of size 10000: 0.1291060447692871 seconds
Total time taken: 16.579394102096558 seconds
```
I've already [pushed the patches to my fork](https://github.com/ashvardanian/datasets/tree/faster-text-parsers), and would love to contribute them to the upstream repository.
---
All the tests pass, but they leave a couple of important questions open. The default Python `open(..., newline=None)` uses universal newlines, where `\n`, `\r`, and `\r\n` are all converted to `\n` on the fly. I am not sure if its a good idea for a general purpose dataset preparation pipeline?
I can simulate the same behavior (which I don't yet do) for `"line"` splitter. Adjusting it for `"paragraph"`-splitter would be harder. Should we stick exactly to the old Pythonic behavior or stay closer to how C and other programming languages do that? | 2024-03-04T15:15:51Z | https://github.com/huggingface/datasets/pull/6711 | {
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https://api.github.com/repos/huggingface/datasets/issues/6710/comments | https://api.github.com/repos/huggingface/datasets/issues/6710/timeline | null | null | null | PR_kwDODunzps5oe4ov | open | [] | false | 6,710 | {
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} | Persist IterableDataset epoch in workers | https://api.github.com/repos/huggingface/datasets/issues/6710/events | null | https://api.github.com/repos/huggingface/datasets/issues/6710/labels{/name} | 2024-03-02T12:08:50Z | null | false | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6710). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | https://api.github.com/repos/huggingface/datasets | MEMBER | Use shared memory for the IterableDataset epoch.
This way calling `ds.set_epoch()` in the main process will update the epoch in the DataLoader workers as well.
This is useful especially because the epoch is used to compute the `effective_seed` used for shuffling.
I used torch's shared memory in case users want to send dataset copies without shared memory using pickle. I also find it easier to use than using `multiprocessing.shared_memory` than requires unlinking only in the main process, or `mp.Value` that is not picklable.
close https://github.com/huggingface/datasets/issues/6673
cc @rwightman | 2024-03-06T14:41:54Z | https://github.com/huggingface/datasets/pull/6710 | {
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https://api.github.com/repos/huggingface/datasets/issues/6709/comments | https://api.github.com/repos/huggingface/datasets/issues/6709/timeline | 2024-03-01T21:01:23Z | null | null | PR_kwDODunzps5oc2Fg | closed | [] | false | 6,709 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6709). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005081 / 0.011353 (-0.006272) | 0.004182 / 0.011008 (-0.006826) | 0.063377 / 0.038508 (0.024869) | 0.027880 / 0.023109 (0.004770) | 0.247260 / 0.275898 (-0.028638) | 0.273580 / 0.323480 (-0.049900) | 0.002995 / 0.007986 (-0.004991) | 0.002804 / 0.004328 (-0.001524) | 0.049669 / 0.004250 (0.045418) | 0.042469 / 0.037052 (0.005417) | 0.268606 / 0.258489 (0.010117) | 0.292867 / 0.293841 (-0.000973) | 0.028077 / 0.128546 (-0.100469) | 0.011031 / 0.075646 (-0.064615) | 0.210225 / 0.419271 (-0.209047) | 0.035723 / 0.043533 (-0.007810) | 0.252131 / 0.255139 (-0.003008) | 0.272895 / 0.283200 (-0.010304) | 0.019809 / 0.141683 (-0.121874) | 1.138500 / 1.452155 (-0.313655) | 1.167752 / 1.492716 (-0.324964) |\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) | 0.094881 / 0.018006 (0.076875) | 0.300168 / 0.000490 (0.299678) | 0.000207 / 0.000200 (0.000007) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017917 / 0.037411 (-0.019494) | 0.061854 / 0.014526 (0.047328) | 0.074481 / 0.176557 (-0.102075) | 0.120075 / 0.737135 (-0.617061) | 0.074627 / 0.296338 (-0.221711) |\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) | 0.287888 / 0.215209 (0.072679) | 2.770165 / 2.077655 (0.692510) | 1.500071 / 1.504120 (-0.004049) | 1.374857 / 1.541195 (-0.166338) | 1.427291 / 1.468490 (-0.041200) | 0.558431 / 4.584777 (-4.026346) | 2.439352 / 3.745712 (-1.306361) | 2.787471 / 5.269862 (-2.482391) | 1.742636 / 4.565676 (-2.823041) | 0.061716 / 0.424275 (-0.362559) | 0.004961 / 0.007607 (-0.002646) | 0.345209 / 0.226044 (0.119164) | 3.360253 / 2.268929 (1.091325) | 1.847945 / 55.444624 (-53.596680) | 1.595733 / 6.876477 (-5.280744) | 1.642350 / 2.142072 (-0.499723) | 0.638639 / 4.805227 (-4.166588) | 0.116918 / 6.500664 (-6.383746) | 0.042132 / 0.075469 (-0.033338) |\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) | 0.980602 / 1.841788 (-0.861185) | 11.545402 / 8.074308 (3.471094) | 9.452471 / 10.191392 (-0.738921) | 0.129930 / 0.680424 (-0.550494) | 0.014143 / 0.534201 (-0.520058) | 0.290302 / 0.579283 (-0.288981) | 0.263785 / 0.434364 (-0.170579) | 0.339580 / 0.540337 (-0.200758) | 0.450355 / 1.386936 (-0.936581) |\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) | 0.005565 / 0.011353 (-0.005788) | 0.003764 / 0.011008 (-0.007244) | 0.050082 / 0.038508 (0.011574) | 0.030354 / 0.023109 (0.007245) | 0.250609 / 0.275898 (-0.025289) | 0.277200 / 0.323480 (-0.046280) | 0.004276 / 0.007986 (-0.003710) | 0.002805 / 0.004328 (-0.001523) | 0.048765 / 0.004250 (0.044514) | 0.045477 / 0.037052 (0.008425) | 0.267704 / 0.258489 (0.009215) | 0.303214 / 0.293841 (0.009373) | 0.029393 / 0.128546 (-0.099153) | 0.010623 / 0.075646 (-0.065023) | 0.058201 / 0.419271 (-0.361070) | 0.053131 / 0.043533 (0.009599) | 0.258682 / 0.255139 (0.003543) | 0.276069 / 0.283200 (-0.007131) | 0.018260 / 0.141683 (-0.123423) | 1.141542 / 1.452155 (-0.310613) | 1.185780 / 1.492716 (-0.306936) |\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) | 0.096857 / 0.018006 (0.078850) | 0.300656 / 0.000490 (0.300167) | 0.000450 / 0.000200 (0.000250) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022416 / 0.037411 (-0.014995) | 0.074781 / 0.014526 (0.060255) | 0.087299 / 0.176557 (-0.089257) | 0.127616 / 0.737135 (-0.609519) | 0.088382 / 0.296338 (-0.207957) |\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) | 0.298639 / 0.215209 (0.083430) | 2.940002 / 2.077655 (0.862347) | 1.709707 / 1.504120 (0.205587) | 1.556502 / 1.541195 (0.015307) | 1.592841 / 1.468490 (0.124351) | 0.570237 / 4.584777 (-4.014539) | 2.467576 / 3.745712 (-1.278137) | 2.741021 / 5.269862 (-2.528840) | 1.776526 / 4.565676 (-2.789151) | 0.063999 / 0.424275 (-0.360276) | 0.005068 / 0.007607 (-0.002539) | 0.360727 / 0.226044 (0.134682) | 3.535404 / 2.268929 (1.266476) | 2.035345 / 55.444624 (-53.409279) | 1.755916 / 6.876477 (-5.120561) | 1.889281 / 2.142072 (-0.252791) | 0.649025 / 4.805227 (-4.156202) | 0.118210 / 6.500664 (-6.382454) | 0.040815 / 0.075469 (-0.034654) |\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) | 1.005650 / 1.841788 (-0.836138) | 12.228314 / 8.074308 (4.154006) | 10.147363 / 10.191392 (-0.044029) | 0.159258 / 0.680424 (-0.521166) | 0.015288 / 0.534201 (-0.518913) | 0.288144 / 0.579283 (-0.291139) | 0.281319 / 0.434364 (-0.153045) | 0.323380 / 0.540337 (-0.216958) | 0.426887 / 1.386936 (-0.960049) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b04ccb486f3831b4b0d2474119823efa3815709 \"CML watermark\")\n"
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6708). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005442 / 0.011353 (-0.005910) | 0.003796 / 0.011008 (-0.007213) | 0.063663 / 0.038508 (0.025155) | 0.028901 / 0.023109 (0.005792) | 0.256742 / 0.275898 (-0.019156) | 0.279555 / 0.323480 (-0.043925) | 0.004128 / 0.007986 (-0.003858) | 0.002789 / 0.004328 (-0.001539) | 0.049463 / 0.004250 (0.045213) | 0.043461 / 0.037052 (0.006409) | 0.272975 / 0.258489 (0.014486) | 0.299057 / 0.293841 (0.005216) | 0.029030 / 0.128546 (-0.099516) | 0.010453 / 0.075646 (-0.065193) | 0.207611 / 0.419271 (-0.211660) | 0.037200 / 0.043533 (-0.006332) | 0.258327 / 0.255139 (0.003188) | 0.279746 / 0.283200 (-0.003454) | 0.018940 / 0.141683 (-0.122743) | 1.150379 / 1.452155 (-0.301776) | 1.217621 / 1.492716 (-0.275095) |\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) | 0.095115 / 0.018006 (0.077109) | 0.299393 / 0.000490 (0.298903) | 0.000223 / 0.000200 (0.000023) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018972 / 0.037411 (-0.018439) | 0.061669 / 0.014526 (0.047143) | 0.075605 / 0.176557 (-0.100951) | 0.125695 / 0.737135 (-0.611440) | 0.076654 / 0.296338 (-0.219684) |\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) | 0.286431 / 0.215209 (0.071222) | 2.763554 / 2.077655 (0.685899) | 1.489902 / 1.504120 (-0.014218) | 1.375082 / 1.541195 (-0.166113) | 1.418903 / 1.468490 (-0.049587) | 0.555646 / 4.584777 (-4.029131) | 2.410578 / 3.745712 (-1.335134) | 2.827453 / 5.269862 (-2.442408) | 1.764381 / 4.565676 (-2.801295) | 0.062937 / 0.424275 (-0.361339) | 0.004989 / 0.007607 (-0.002619) | 0.342115 / 0.226044 (0.116071) | 3.354660 / 2.268929 (1.085732) | 1.858418 / 55.444624 (-53.586206) | 1.586403 / 6.876477 (-5.290074) | 1.625762 / 2.142072 (-0.516311) | 0.643678 / 4.805227 (-4.161550) | 0.116764 / 6.500664 (-6.383900) | 0.042198 / 0.075469 (-0.033271) |\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) | 0.974953 / 1.841788 (-0.866835) | 11.748419 / 8.074308 (3.674111) | 9.753700 / 10.191392 (-0.437692) | 0.131330 / 0.680424 (-0.549094) | 0.018876 / 0.534201 (-0.515325) | 0.290078 / 0.579283 (-0.289205) | 0.264676 / 0.434364 (-0.169688) | 0.340285 / 0.540337 (-0.200052) | 0.445340 / 1.386936 (-0.941596) |\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) | 0.005513 / 0.011353 (-0.005840) | 0.003665 / 0.011008 (-0.007344) | 0.049368 / 0.038508 (0.010860) | 0.032045 / 0.023109 (0.008936) | 0.280955 / 0.275898 (0.005057) | 0.299804 / 0.323480 (-0.023675) | 0.004391 / 0.007986 (-0.003594) | 0.002896 / 0.004328 (-0.001432) | 0.048914 / 0.004250 (0.044663) | 0.045448 / 0.037052 (0.008396) | 0.298779 / 0.258489 (0.040289) | 0.322012 / 0.293841 (0.028171) | 0.029449 / 0.128546 (-0.099097) | 0.010410 / 0.075646 (-0.065236) | 0.057867 / 0.419271 (-0.361405) | 0.053944 / 0.043533 (0.010411) | 0.278139 / 0.255139 (0.023000) | 0.297453 / 0.283200 (0.014254) | 0.018746 / 0.141683 (-0.122937) | 1.137890 / 1.452155 (-0.314264) | 1.206109 / 1.492716 (-0.286607) |\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) | 0.091741 / 0.018006 (0.073735) | 0.300415 / 0.000490 (0.299925) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022097 / 0.037411 (-0.015314) | 0.076853 / 0.014526 (0.062327) | 0.088440 / 0.176557 (-0.088116) | 0.127176 / 0.737135 (-0.609959) | 0.088976 / 0.296338 (-0.207363) |\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) | 0.300754 / 0.215209 (0.085545) | 2.917713 / 2.077655 (0.840058) | 1.619338 / 1.504120 (0.115218) | 1.501543 / 1.541195 (-0.039652) | 1.506032 / 1.468490 (0.037542) | 0.579481 / 4.584777 (-4.005296) | 2.458917 / 3.745712 (-1.286795) | 2.754621 / 5.269862 (-2.515241) | 1.796440 / 4.565676 (-2.769237) | 0.067547 / 0.424275 (-0.356728) | 0.005001 / 0.007607 (-0.002606) | 0.351030 / 0.226044 (0.124985) | 3.466282 / 2.268929 (1.197353) | 1.954661 / 55.444624 (-53.489964) | 1.688737 / 6.876477 (-5.187740) | 1.836762 / 2.142072 (-0.305311) | 0.656441 / 4.805227 (-4.148786) | 0.118258 / 6.500664 (-6.382406) | 0.041608 / 0.075469 (-0.033861) |\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) | 0.999696 / 1.841788 (-0.842092) | 12.383471 / 8.074308 (4.309162) | 10.338488 / 10.191392 (0.147096) | 0.150214 / 0.680424 (-0.530210) | 0.014997 / 0.534201 (-0.519204) | 0.288949 / 0.579283 (-0.290334) | 0.272012 / 0.434364 (-0.162352) | 0.327253 / 0.540337 (-0.213084) | 0.427594 / 1.386936 (-0.959342) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca8409a8bec4508255b9c3e808d0751eb1005260 \"CML watermark\")\n"
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6707). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.004728 / 0.011353 (-0.006624) | 0.002941 / 0.011008 (-0.008067) | 0.058270 / 0.038508 (0.019762) | 0.027418 / 0.023109 (0.004309) | 0.224993 / 0.275898 (-0.050905) | 0.243103 / 0.323480 (-0.080377) | 0.004668 / 0.007986 (-0.003318) | 0.002499 / 0.004328 (-0.001829) | 0.045020 / 0.004250 (0.040770) | 0.038006 / 0.037052 (0.000953) | 0.240807 / 0.258489 (-0.017682) | 0.264554 / 0.293841 (-0.029287) | 0.027018 / 0.128546 (-0.101529) | 0.009866 / 0.075646 (-0.065780) | 0.196578 / 0.419271 (-0.222694) | 0.034536 / 0.043533 (-0.008997) | 0.236535 / 0.255139 (-0.018604) | 0.248879 / 0.283200 (-0.034321) | 0.017140 / 0.141683 (-0.124543) | 1.046927 / 1.452155 (-0.405228) | 1.121209 / 1.492716 (-0.371507) |\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) | 0.088267 / 0.018006 (0.070261) | 0.279774 / 0.000490 (0.279284) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.016521 / 0.037411 (-0.020890) | 0.056499 / 0.014526 (0.041974) | 0.067264 / 0.176557 (-0.109293) | 0.117270 / 0.737135 (-0.619865) | 0.069284 / 0.296338 (-0.227055) |\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) | 0.260679 / 0.215209 (0.045470) | 2.608971 / 2.077655 (0.531316) | 1.363139 / 1.504120 (-0.140981) | 1.262128 / 1.541195 (-0.279067) | 1.273619 / 1.468490 (-0.194871) | 0.523417 / 4.584777 (-4.061360) | 2.291145 / 3.745712 (-1.454567) | 2.540603 / 5.269862 (-2.729258) | 1.599090 / 4.565676 (-2.966586) | 0.058170 / 0.424275 (-0.366105) | 0.004556 / 0.007607 (-0.003051) | 0.308361 / 0.226044 (0.082316) | 3.069269 / 2.268929 (0.800340) | 1.698064 / 55.444624 (-53.746560) | 1.426631 / 6.876477 (-5.449846) | 1.463913 / 2.142072 (-0.678160) | 0.595234 / 4.805227 (-4.209993) | 0.107202 / 6.500664 (-6.393462) | 0.038183 / 0.075469 (-0.037286) |\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) | 0.905999 / 1.841788 (-0.935789) | 10.828492 / 8.074308 (2.754184) | 8.705635 / 10.191392 (-1.485757) | 0.121203 / 0.680424 (-0.559221) | 0.013789 / 0.534201 (-0.520412) | 0.268172 / 0.579283 (-0.311111) | 0.254277 / 0.434364 (-0.180086) | 0.310280 / 0.540337 (-0.230057) | 0.410490 / 1.386936 (-0.976446) |\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) | 0.004696 / 0.011353 (-0.006657) | 0.002982 / 0.011008 (-0.008026) | 0.045333 / 0.038508 (0.006825) | 0.027483 / 0.023109 (0.004374) | 0.253438 / 0.275898 (-0.022460) | 0.272657 / 0.323480 (-0.050823) | 0.004060 / 0.007986 (-0.003926) | 0.002574 / 0.004328 (-0.001754) | 0.045462 / 0.004250 (0.041212) | 0.041260 / 0.037052 (0.004208) | 0.267919 / 0.258489 (0.009430) | 0.290935 / 0.293841 (-0.002906) | 0.026674 / 0.128546 (-0.101873) | 0.009370 / 0.075646 (-0.066276) | 0.053543 / 0.419271 (-0.365729) | 0.047390 / 0.043533 (0.003857) | 0.255774 / 0.255139 (0.000635) | 0.273909 / 0.283200 (-0.009291) | 0.017252 / 0.141683 (-0.124431) | 1.064298 / 1.452155 (-0.387857) | 1.125374 / 1.492716 (-0.367342) |\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) | 0.091506 / 0.018006 (0.073499) | 0.298570 / 0.000490 (0.298080) | 0.000741 / 0.000200 (0.000541) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020232 / 0.037411 (-0.017179) | 0.069640 / 0.014526 (0.055114) | 0.081360 / 0.176557 (-0.095197) | 0.116955 / 0.737135 (-0.620180) | 0.080920 / 0.296338 (-0.215418) |\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) | 0.283000 / 0.215209 (0.067791) | 2.802526 / 2.077655 (0.724871) | 1.534631 / 1.504120 (0.030511) | 1.407260 / 1.541195 (-0.133935) | 1.409111 / 1.468490 (-0.059379) | 0.534892 / 4.584777 (-4.049885) | 2.350516 / 3.745712 (-1.395196) | 2.550444 / 5.269862 (-2.719418) | 1.661747 / 4.565676 (-2.903930) | 0.060978 / 0.424275 (-0.363297) | 0.005300 / 0.007607 (-0.002308) | 0.367418 / 0.226044 (0.141373) | 3.338046 / 2.268929 (1.069117) | 1.883914 / 55.444624 (-53.560710) | 1.638561 / 6.876477 (-5.237916) | 1.751547 / 2.142072 (-0.390526) | 0.633318 / 4.805227 (-4.171910) | 0.114971 / 6.500664 (-6.385693) | 0.040202 / 0.075469 (-0.035267) |\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) | 0.962053 / 1.841788 (-0.879735) | 11.600643 / 8.074308 (3.526334) | 9.526461 / 10.191392 (-0.664931) | 0.123909 / 0.680424 (-0.556515) | 0.015944 / 0.534201 (-0.518257) | 0.271542 / 0.579283 (-0.307741) | 0.254366 / 0.434364 (-0.179998) | 0.300499 / 0.540337 (-0.239838) | 0.409122 / 1.386936 (-0.977814) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0acb27347a3c03efde612023235201a777e08e72 \"CML watermark\")\n"
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