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https://api.github.com/repos/huggingface/datasets/issues/5314 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5314/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5314/comments | https://api.github.com/repos/huggingface/datasets/issues/5314/events | https://github.com/huggingface/datasets/issues/5314 | 1,469,685,118 | I_kwDODunzps5XmZ1- | 5,314 | Datasets: classification_report() got an unexpected keyword argument 'suffix' | {
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"This seems similar to https://github.com/huggingface/datasets/issues/2512 Can you try to update seqeval ? ",
"@JonathanAlis also note that the metrics are deprecated in our `datasets` library.\r\n\r\nPlease, use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate"
] | "2022-11-30T14:01:03" | "2022-12-01T15:00:46" | null | NONE | null | https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py
> import datasets
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
seqeval = datasets.load_metric("seqeval")
results = seqeval.compute(predictions=predictions, references=references)
print(list(results.keys()))
print(results["overall_f1"])
print(results["PER"]["f1"])
It raises the error:
> TypeError: classification_report() got an unexpected keyword argument 'suffix'
For context, versions on my pip list -v
> datasets 1.12.1
seqeval 1.2.2 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-29T18:00:28" | "2022-12-01T14:55:30" | "2022-12-01T14:00:34" | CONTRIBUTOR | null | We say that "the data is being downloaded progressively" which is not true, it's just streamed, so I fixed it. Probably I missed some other places where it is written?
Also changed docstrings for `StreamingDownloadManager`'s `download` and `extract` to reflect the same, as these docstrings are displayed in the documentation cc @lhoestq | {
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"The current implementation is what I had in mind, i.e. concatenate all splits by default.\r\n\r\nHowever, I think most tabular datasets would come as a single split. So for that usecase, it wouldn't change UX if we raise when there are more than one splits.\r\n\r\nAnd for multiple splits, the user either passes a list, or they can pass `splits=\"all\"` to have all splits concatenated.",
"I think it's better to raise an error in cases when there are multiple splits but no split is specified so that users know for sure with which data they are working. I imagine a case when a user loads a dataset that they don't know much about (like what splits it has), and if they get a concatenation of everything, it might lead to incorrect processing or interpretations and it would be hard to notice it.\r\n(\"explicit is better than implicit\")",
"I just changed to raise an error if there are multiple splits. The error shows an example of how to choose a split to convert.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5312). All of your documentation changes will be reflected on that endpoint.",
"Thanks for the review, I've updated the type hint and added a line to raise an error on bad splits :)",
"Merging https://github.com/huggingface/datasets/pull/5301 would eliminate the need for this PR, no?\r\n\r\nIn the meantime, I find the current API cleaner.",
"This solution is simpler than https://github.com/huggingface/datasets/pull/5301 and covers most cases for tabular datasets, so I'm in favor of merging this one and put https://github.com/huggingface/datasets/pull/5301 on stand by",
"Let me know if it sounds good to you @mariosasko @albertvillanova :)",
"I'm still not convinced. If `DatasetDict` needs this method and there is no other way, then IMO it would make more sense to return a dictionary with the splits converted to `pd.DataFrame`. ",
"@mariosasko the issue we're dealing with is that in tabular scenarios, we often don't have splits in the dataset, and imposing that concept to people dealing with the library hampers adoption.",
"@adrinjalali This PR proposes a solution inconsistent with the existing API (in other words, a solution that clutters our API 🙂). Moreover, our library primarily focuses on larger-than-RAM datasets, and tabular datasets don't (directly) fall into this group.\r\n\r\nInstead of the temporary \"fix\" proposed here, it makes much more sense to align `load_dataset` with both tabular and DL workflows \"in a consistent way\", so I suggest we continue our discussion from https://github.com/huggingface/datasets/issues/5189 to have this resolved by version 3.0.",
"closing this one for now"
] | "2022-11-29T16:30:02" | "2023-01-25T17:33:43" | "2023-01-25T17:33:42" | MEMBER | null | From discussions in https://github.com/huggingface/datasets/issues/5189, for tabular data it doesn't really make sense to have to do
```python
df = load_dataset(...)["train"].to_pandas()
```
because many datasets are not split.
In this PR I added `to_pandas` to `DatasetDict` which returns the DataFrame:
If there's only one split, you don't need to specify the split name:
```python
df = load_dataset(...).to_pandas()
```
EDIT: and if a dataset has multiple splits:
```python
df = load_dataset(...).to_pandas(splits=["train", "test"])
# or
df = load_dataset(...).to_pandas(splits="all")
# raises an error because you need to select the split(s) to convert
load_dataset(...).to_pandas()
```
I do have one question though @merveenoyan @adrinjalali @mariosasko:
Should we raise an error if there are multiple splits and ask the user to choose one explicitly ?
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-29T11:08:34" | "2022-12-06T15:45:02" | "2022-12-06T15:42:04" | CONTRIBUTOR | null | ## Description
As suggested by @lhoestq in #3888, we should be adding the param `features` to `IterableDataset.map` so that the features can be preserved (not turned into `None` as that's the default behavior) whenever the user passes those as param, so as to be consistent with `Dataset.map`, as it provides the `features` param so that those are not inferred by default, but specified by the user, and later validated by `ArrowWriter`.
This is internally handled already by the functions relying on `IterableDataset.map` such as `rename_column`, `rename_columns`, and `remove_columns` as described in #5287.
## Usage Example
```python
from datasets import load_dataset, Features
ds = load_dataset("rotten_tomatoes", split="validation", streaming=True)
print(ds.info.features)
ds = ds.map(
lambda x: {"target": x["label"]},
features=Features(
{"target": ds.info.features["label"], "label": ds.info.features["label"], "text": ds.info.features["text"]}
),
)
print(ds.info.features)
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-29T09:20:47" | "2022-11-30T12:00:09" | "2022-11-30T11:57:16" | MEMBER | null | This PR implements a string representation of `xPath`, which is valid for local paths (also windows) and remote URLs.
Additionally, some `os.path` methods are fixed for remote URLs on Windows machines.
Now, on Windows machines:
```python
In [2]: str(xPath("C:\\dir\\file.txt"))
Out[2]: 'C:\\dir\\file.txt'
In [3]: str(xPath("http://domain.com/file.txt"))
Out[3]: 'http://domain.com/file.txt'
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-28T16:59:39" | "2022-12-07T12:55:20" | "2022-12-07T12:52:15" | CONTRIBUTOR | null | Ensure the file stream is closed in `ArrowWriter.finalize` before raising the `SchemaInferenceError` to avoid the `PermissionError` on Windows in `incomplete_dir`'s `shutil.rmtree`. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I like the `kwargs` approach, thanks!"
] | "2022-11-28T14:42:41" | "2022-12-09T12:58:55" | "2022-12-09T12:55:59" | CONTRIBUTOR | null | Add support for the `topdown` parameter in `xwalk` when `fsspec>=2022.11.0` is installed. | {
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https://api.github.com/repos/huggingface/datasets/issues/5307 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5307/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5307/comments | https://api.github.com/repos/huggingface/datasets/issues/5307/events | https://github.com/huggingface/datasets/pull/5307 | 1,466,477,427 | PR_kwDODunzps5Dzj8r | 5,307 | Use correct dataset type in `from_generator` docs | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-28T13:59:10" | "2022-11-28T15:30:37" | "2022-11-28T15:27:26" | CONTRIBUTOR | null | Use the correct dataset type in the `from_generator` docs (example with sharding). | {
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https://api.github.com/repos/huggingface/datasets/issues/5306 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5306/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5306/comments | https://api.github.com/repos/huggingface/datasets/issues/5306/events | https://github.com/huggingface/datasets/issues/5306 | 1,465,968,639 | I_kwDODunzps5XYOf_ | 5,306 | Can't use custom feature description when loading a dataset | {
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"Forgot to actually convert the feature dict to a Feature object. Closing."
] | "2022-11-28T07:55:44" | "2022-11-28T08:11:45" | "2022-11-28T08:11:44" | CONTRIBUTOR | null | ### Describe the bug
I have created a feature dictionary to describe my datasets' column types, to use when loading the dataset, following [the doc](https://huggingface.co/docs/datasets/main/en/about_dataset_features). It crashes at dataset load.
### Steps to reproduce the bug
```python
# Creating features
task_list = [f"motif_G{i}" for i in range(19, 53)]
features = {t: Sequence(feature=Value(dtype="float64")) for t in task_list}
for col_name in ["class_label"]:
features[col_name] = Sequence(feature=Value(dtype="int64"))
for col_name in ["num_nodes"]:
features[col_name] = Value(dtype="int64")
for col_name in ["num_bridges", "num_cycles", "avg_shortest_path_len"]:
features[col_name] = Sequence(feature=Value(dtype="float64"))
for col_name in ["edge_attr", "node_feat", "edge_index"]:
features[col_name] = Sequence(feature=Sequence(feature=Value(dtype="int64")))
print(features)
dataset = load_dataset(path=f"graphs-datasets/unbalanced-motifs-500K", split="train", features=features)
```
Last line will crash and say 'TypeError: argument of type 'Sequence' is not iterable'.
Full stack:
```
Traceback (most recent call last):
File "pretrain_tokengt.py", line 131, in <module>
main(output_folder = "../workspace/pretraining",
File "pretrain_tokengt.py", line 52, in main
dataset = load_dataset(path=f"graphs-datasets/{dataset_name}", split="train", features=features)
File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1718, in load_dataset
builder_instance = load_dataset_builder(
File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1514, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "huggingface_env/lib/python3.8/site-packages/datasets/builder.py", line 321, in __init__
info.update(self._info())
File "huggingface_env/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 62, in _info
return datasets.DatasetInfo(features=self.config.features)
File "<string>", line 20, in __init__
File "huggingface_env/lib/python3.8/site-packages/datasets/info.py", line 155, in __post_init__
self.features = Features.from_dict(self.features)
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1599, in from_dict
obj = generate_from_dict(dic)
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1281, in generate_from_dict
if "_type" not in obj or isinstance(obj["_type"], dict):
TypeError: argument of type 'Sequence' is not iterable
```
### Expected behavior
For it not to crash.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5305/comments | https://api.github.com/repos/huggingface/datasets/issues/5305/events | https://github.com/huggingface/datasets/issues/5305 | 1,465,627,826 | I_kwDODunzps5XW7Sy | 5,305 | Dataset joelito/mc4_legal does not work with multiple files | {
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"Thanks for reporting @JoelNiklaus.\r\n\r\nPlease note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/discussions\r\n\r\nI am also having a look at the bug in your script.",
"Issue transferred to: https://huggingface.co/datasets/joelito/mc4_legal/discussions/1"
] | "2022-11-28T00:16:16" | "2022-11-28T07:22:42" | "2022-11-28T07:22:42" | CONTRIBUTOR | null | ### Describe the bug
The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset.
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main) [1]> python test_mc4_legal.py (debug)
Found cached dataset mc4_legal (/Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/de/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f)
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 0
})
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main)> python test_mc4_legal.py (debug)
Downloading and preparing dataset mc4_legal/bg to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1240.55it/s]
Dataset mc4_legal downloaded and prepared to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f. Subsequent calls will reuse this data.
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 204
})
### Steps to reproduce the bug
import datasets
from datasets import load_dataset, get_dataset_config_names
language = "bg"
test = load_dataset("joelito/mc4_legal", language, split='train')
### Expected behavior
It should display the correct number of rows for the de dataset which should be a large number (thousands or more).
### Environment info
Package Version
------------------------ --------------
absl-py 1.3.0
aiohttp 3.8.1
aiosignal 1.2.0
astunparse 1.6.3
async-timeout 4.0.2
attrs 22.1.0
beautifulsoup4 4.11.1
blinker 1.4
blis 0.7.8
Bottleneck 1.3.4
brotlipy 0.7.0
cachetools 5.2.0
catalogue 2.0.7
certifi 2022.5.18.1
cffi 1.15.1
chardet 4.0.0
charset-normalizer 2.1.0
click 8.0.4
conllu 4.5.2
cryptography 38.0.1
cymem 2.0.6
datasets 2.6.1
dill 0.3.5.1
docker-pycreds 0.4.0
fasttext 0.9.2
fasttext-langdetect 1.0.3
filelock 3.0.12
flatbuffers 20210226132247
frozenlist 1.3.0
fsspec 2022.5.0
gast 0.4.0
gcloud 0.18.3
gitdb 4.0.9
GitPython 3.1.27
google-auth 2.9.0
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
googleapis-common-protos 1.57.0
grpcio 1.47.0
h5py 3.7.0
httplib2 0.21.0
huggingface-hub 0.8.1
idna 3.4
importlib-metadata 4.12.0
Jinja2 3.1.2
joblib 1.0.1
keras 2.9.0
Keras-Preprocessing 1.1.2
langcodes 3.3.0
lxml 4.9.1
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mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
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torch 1.13.0
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"The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:\r\n```python\r\nwav_paths = sorted(Path(data_dir).glob(f\"**/{split}/**/*.wav\"))\r\nwav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f\"**/{split.upper()}/**/*.WAV\"))\r\n```\r\n\r\nCan you check that there is a directory named \"test\" somewhere in your timit data directory ?"
] | "2022-11-26T10:18:22" | "2023-02-10T16:33:21" | "2023-02-10T16:33:21" | NONE | null | ### Describe the bug
When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split.
I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all.
```python
DatasetDict({
train: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 4620
})
test: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 0
})
})
```
The directory structure of both splits are same. (DIALECT_REGION / SPEAKER_CODE / DATA_FILES)
### Steps to reproduce the bug
1. just use ```timit = load_dataset('timit_asr', data_dir=data_dir)```
### Expected behavior
```python
DatasetDict({
train: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 4620
})
test: Dataset({
features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
num_rows: 1680
})
})
```
### Environment info
- ubuntu 20.04
- python 3.9.13
- datasets 2.7.1 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"100% agree that the checksum verification is overkill and not super useful. But I think this PR would also disable the check on num_examples no ?\r\n \r\nAs a user I would like to know if the dataset I'm loading changed significantly.\r\nAnd I also think it can be useful to make sure the metadata are up to date.\r\n\r\nWhat do you think ?\r\n\r\nWe could have a default `ignore_verifications=\"ignore_checksums\"`",
"> We could have a default `ignore_verifications=\"ignore_checksums\"`\r\n\r\nAccepting multiple types (booleans and strings) at the same time is not the best design. Maybe we could define an enum for this parameter?",
"Yes an enum sounds good !",
"so we can have three verification levels, - smth like \"ignore_all\" (to skip both checksums and all other info like num_examples verification), \"ignore_checksums\" (to skip only checksums verification), and \"verify_all\" (to perform all verification)?\r\nand deprecate `ignore_verifications` param.\r\n\r\n@mariosasko if you're not going to work on this PR in the coming days, I can take over it if you want (this PR will help me with [this issue](https://github.com/huggingface/datasets/issues/5315), not super urgent though).",
"Okay, I propose deprecating `ignore_verifications` in favor of `verification_mode` (`load_dataset` already has `download_mode`; some other projects use this name for verification control). `verification_mode` would accept the following enum (or strings in the same manner as `download_mode` does):\r\n\r\n```python\r\nclass VerificationMode(enum.Enum):\r\n FULL = \"full\" # runs all verification checks \r\n BASIC = \"basic\" # default, runs only the cheap ones (skips the checksum check)\r\n NONE = \"none\" # skips all the checks\r\n```\r\n\r\nWDTY?",
"(copy paste from my message on slack)\r\n\r\nWhat do you think of a config variable in config.py to switch from one verification mode to another ? This way we don’t deprecate anything\r\n\r\nMany users are familiar with ignore_verifications=True, it might be overkill to deprecate it",
"@lhoestq So we have \"basic\" verification mode in `config.py` and continue to have `False` as a default \r\nvalue for `ignore_verifications`? That way running all verifications including checksums would not be possible without switching the config var, right? \r\n\r\nI like having a `VerificationMode` enum because it's aligned with `DownloadMode` and sounds more natural to me (`ignore_verifications` feels a bit semantically reverted but this is probably just my feeling) and it's flexible (no need to worry about `config.py`, I'm not sure that users even know it exists, wdyt?).\r\n\r\nThe usage point seems also valid to me, but cases when users are stuck with NonMatchingX errors also happen from time to time and to figure out what's wrong is non-trivial here. \r\n\r\nAs a note aside - I suggest to add instructions to the NonMatchingX error message (how to use `ignore_verifications` / `verification_mode`), this would save users who don't know about this param a lot of time.",
"Ok I see. I'm fine with the new parameter then (even though I had a small pref for the config variable) :)",
"I like the idea of an enum and the `verification_mode` parameter. \r\n\r\nIn relation with the config parameter, we could additionally add a `DEFAULT_VERIFICATION_MODE`, maybe only if users require it. Note that until now there wasn't any config parameter for a default `ignore_verifications` value: I guess people are explicitly passing `ignore_verifications=True`...\r\n\r\nAs a note aside, I like the suggestion by @polinaeterna: we could give actionable messages when verifying checksums. This could be done in other PR.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.012891 / 0.011353 (0.001538) | 0.006474 / 0.011008 (-0.004535) | 0.144038 / 0.038508 (0.105530) | 0.036151 / 0.023109 (0.013042) | 0.404366 / 0.275898 (0.128468) | 0.479988 / 0.323480 (0.156508) | 0.010219 / 0.007986 (0.002233) | 0.005319 / 0.004328 (0.000990) | 0.099705 / 0.004250 (0.095455) | 0.046639 / 0.037052 (0.009586) | 0.398997 / 0.258489 (0.140508) | 0.478431 / 0.293841 (0.184590) | 0.069125 / 0.128546 (-0.059421) | 0.019603 / 0.075646 (-0.056043) | 0.400829 / 0.419271 (-0.018443) | 0.066549 / 0.043533 (0.023016) | 0.398343 / 0.255139 (0.143204) | 0.417928 / 0.283200 (0.134728) | 0.121124 / 0.141683 (-0.020559) | 1.751513 / 1.452155 (0.299358) | 1.821239 / 1.492716 (0.328523) |\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) | 0.251603 / 0.018006 (0.233597) | 0.579916 / 0.000490 (0.579427) | 0.003257 / 0.000200 (0.003058) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031502 / 0.037411 (-0.005909) | 0.134688 / 0.014526 (0.120162) | 0.152306 / 0.176557 (-0.024251) | 0.198943 / 0.737135 (-0.538192) | 0.142551 / 0.296338 (-0.153788) |\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) | 0.634672 / 0.215209 (0.419463) | 6.370215 / 2.077655 (4.292561) | 2.548123 / 1.504120 (1.044003) | 2.184263 / 1.541195 (0.643069) | 2.239026 / 1.468490 (0.770536) | 1.233340 / 4.584777 (-3.351437) | 5.791824 / 3.745712 (2.046112) | 5.093032 / 5.269862 (-0.176830) | 2.849833 / 4.565676 (-1.715844) | 0.143787 / 0.424275 (-0.280488) | 0.015279 / 0.007607 (0.007672) | 0.757984 / 0.226044 (0.531939) | 7.883604 / 2.268929 (5.614675) | 3.321591 / 55.444624 (-52.123033) | 2.671777 / 6.876477 (-4.204700) | 2.685215 / 2.142072 (0.543142) | 1.546709 / 4.805227 (-3.258519) | 0.247186 / 6.500664 (-6.253478) | 0.085117 / 0.075469 (0.009648) |\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) | 1.679809 / 1.841788 (-0.161979) | 18.528893 / 8.074308 (10.454585) | 23.168590 / 10.191392 (12.977198) | 0.277618 / 0.680424 (-0.402806) | 0.045109 / 0.534201 (-0.489092) | 0.568873 / 0.579283 (-0.010410) | 0.695017 / 0.434364 (0.260653) | 0.671024 / 0.540337 (0.130687) | 0.823817 / 1.386936 (-0.563119) |\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) | 0.009809 / 0.011353 (-0.001544) | 0.006890 / 0.011008 (-0.004118) | 0.099211 / 0.038508 (0.060703) | 0.035387 / 0.023109 (0.012278) | 0.507603 / 0.275898 (0.231705) | 0.535553 / 0.323480 (0.212073) | 0.007346 / 0.007986 (-0.000640) | 0.007559 / 0.004328 (0.003231) | 0.099132 / 0.004250 (0.094882) | 0.048048 / 0.037052 (0.010996) | 0.518096 / 0.258489 (0.259607) | 0.561134 / 0.293841 (0.267294) | 0.057580 / 0.128546 (-0.070966) | 0.023665 / 0.075646 (-0.051982) | 0.138409 / 0.419271 (-0.280862) | 0.061989 / 0.043533 (0.018456) | 0.510568 / 0.255139 (0.255429) | 0.552722 / 0.283200 (0.269522) | 0.115990 / 0.141683 (-0.025693) | 1.884900 / 1.452155 (0.432745) | 1.990604 / 1.492716 (0.497888) |\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) | 0.280638 / 0.018006 (0.262632) | 0.592837 / 0.000490 (0.592347) | 0.000465 / 0.000200 (0.000265) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030253 / 0.037411 (-0.007158) | 0.141580 / 0.014526 (0.127054) | 0.135114 / 0.176557 (-0.041443) | 0.190003 / 0.737135 (-0.547133) | 0.160230 / 0.296338 (-0.136109) |\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) | 0.699762 / 0.215209 (0.484553) | 6.632344 / 2.077655 (4.554689) | 2.718803 / 1.504120 (1.214683) | 2.485294 / 1.541195 (0.944099) | 2.579889 / 1.468490 (1.111399) | 1.268795 / 4.584777 (-3.315982) | 5.777745 / 3.745712 (2.032033) | 3.232551 / 5.269862 (-2.037311) | 2.127699 / 4.565676 (-2.437977) | 0.146570 / 0.424275 (-0.277705) | 0.015971 / 0.007607 (0.008364) | 0.803181 / 0.226044 (0.577137) | 8.377192 / 2.268929 (6.108264) | 3.551242 / 55.444624 (-51.893382) | 2.865228 / 6.876477 (-4.011249) | 2.774869 / 2.142072 (0.632797) | 1.553856 / 4.805227 (-3.251371) | 0.264510 / 6.500664 (-6.236154) | 0.087918 / 0.075469 (0.012449) |\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) | 1.653396 / 1.841788 (-0.188391) | 18.703863 / 8.074308 (10.629555) | 22.067331 / 10.191392 (11.875939) | 0.257424 / 0.680424 (-0.422999) | 0.026448 / 0.534201 (-0.507753) | 0.550100 / 0.579283 (-0.029183) | 0.647296 / 0.434364 (0.212932) | 0.657476 / 0.540337 (0.117138) | 0.781119 / 1.386936 (-0.605817) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c4a9cb95f8742a2850f11d59abbef71d6c1f60c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008889 / 0.011353 (-0.002464) | 0.004563 / 0.011008 (-0.006445) | 0.101627 / 0.038508 (0.063118) | 0.030526 / 0.023109 (0.007417) | 0.297175 / 0.275898 (0.021277) | 0.368454 / 0.323480 (0.044974) | 0.007246 / 0.007986 (-0.000740) | 0.003565 / 0.004328 (-0.000763) | 0.078644 / 0.004250 (0.074394) | 0.038616 / 0.037052 (0.001564) | 0.310521 / 0.258489 (0.052032) | 0.348014 / 0.293841 (0.054173) | 0.033463 / 0.128546 (-0.095083) | 0.011544 / 0.075646 (-0.064102) | 0.323281 / 0.419271 (-0.095990) | 0.040187 / 0.043533 (-0.003346) | 0.298015 / 0.255139 (0.042876) | 0.326392 / 0.283200 (0.043193) | 0.088730 / 0.141683 (-0.052952) | 1.503387 / 1.452155 (0.051233) | 1.548704 / 1.492716 (0.055988) |\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) | 0.185983 / 0.018006 (0.167977) | 0.451889 / 0.000490 (0.451400) | 0.001433 / 0.000200 (0.001233) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023396 / 0.037411 (-0.014015) | 0.118236 / 0.014526 (0.103710) | 0.124594 / 0.176557 (-0.051962) | 0.159089 / 0.737135 (-0.578047) | 0.129369 / 0.296338 (-0.166969) |\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) | 0.423161 / 0.215209 (0.207952) | 4.228211 / 2.077655 (2.150556) | 1.853862 / 1.504120 (0.349742) | 1.649471 / 1.541195 (0.108276) | 1.708631 / 1.468490 (0.240141) | 0.697456 / 4.584777 (-3.887321) | 3.473244 / 3.745712 (-0.272468) | 1.942586 / 5.269862 (-3.327275) | 1.291592 / 4.565676 (-3.274084) | 0.082758 / 0.424275 (-0.341517) | 0.012256 / 0.007607 (0.004649) | 0.528355 / 0.226044 (0.302311) | 5.277620 / 2.268929 (3.008691) | 2.299604 / 55.444624 (-53.145020) | 1.954940 / 6.876477 (-4.921537) | 2.055543 / 2.142072 (-0.086529) | 0.814723 / 4.805227 (-3.990505) | 0.149937 / 6.500664 (-6.350727) | 0.064529 / 0.075469 (-0.010941) |\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) | 1.266240 / 1.841788 (-0.575547) | 14.144016 / 8.074308 (6.069708) | 14.331733 / 10.191392 (4.140340) | 0.138963 / 0.680424 (-0.541461) | 0.029034 / 0.534201 (-0.505167) | 0.397325 / 0.579283 (-0.181958) | 0.405293 / 0.434364 (-0.029071) | 0.480745 / 0.540337 (-0.059592) | 0.573386 / 1.386936 (-0.813550) |\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) | 0.007214 / 0.011353 (-0.004139) | 0.004569 / 0.011008 (-0.006439) | 0.078718 / 0.038508 (0.040209) | 0.031104 / 0.023109 (0.007995) | 0.342562 / 0.275898 (0.066664) | 0.387802 / 0.323480 (0.064322) | 0.005378 / 0.007986 (-0.002608) | 0.003414 / 0.004328 (-0.000915) | 0.077249 / 0.004250 (0.072999) | 0.044337 / 0.037052 (0.007285) | 0.341397 / 0.258489 (0.082907) | 0.385536 / 0.293841 (0.091695) | 0.033257 / 0.128546 (-0.095289) | 0.011825 / 0.075646 (-0.063821) | 0.086723 / 0.419271 (-0.332549) | 0.045951 / 0.043533 (0.002418) | 0.340914 / 0.255139 (0.085775) | 0.367126 / 0.283200 (0.083926) | 0.096326 / 0.141683 (-0.045357) | 1.608612 / 1.452155 (0.156458) | 1.687251 / 1.492716 (0.194534) |\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) | 0.227595 / 0.018006 (0.209589) | 0.418502 / 0.000490 (0.418013) | 0.000392 / 0.000200 (0.000192) | 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) | 0.026232 / 0.037411 (-0.011179) | 0.101020 / 0.014526 (0.086494) | 0.110017 / 0.176557 (-0.066539) | 0.153497 / 0.737135 (-0.583639) | 0.110602 / 0.296338 (-0.185737) |\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) | 0.433789 / 0.215209 (0.218579) | 4.329350 / 2.077655 (2.251696) | 2.052136 / 1.504120 (0.548016) | 1.848457 / 1.541195 (0.307262) | 1.936791 / 1.468490 (0.468301) | 0.700609 / 4.584777 (-3.884168) | 3.391983 / 3.745712 (-0.353729) | 1.903220 / 5.269862 (-3.366642) | 1.179463 / 4.565676 (-3.386213) | 0.084025 / 0.424275 (-0.340250) | 0.012743 / 0.007607 (0.005136) | 0.536816 / 0.226044 (0.310772) | 5.420230 / 2.268929 (3.151302) | 2.507438 / 55.444624 (-52.937187) | 2.178907 / 6.876477 (-4.697570) | 2.228586 / 2.142072 (0.086514) | 0.812527 / 4.805227 (-3.992701) | 0.153382 / 6.500664 (-6.347282) | 0.069932 / 0.075469 (-0.005537) |\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) | 1.256861 / 1.841788 (-0.584927) | 14.309236 / 8.074308 (6.234928) | 13.740323 / 10.191392 (3.548931) | 0.142698 / 0.680424 (-0.537726) | 0.016998 / 0.534201 (-0.517203) | 0.385489 / 0.579283 (-0.193794) | 0.391515 / 0.434364 (-0.042849) | 0.472704 / 0.540337 (-0.067633) | 0.565042 / 1.386936 (-0.821894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b0713ddf2e2e7129d9ccda791d265684c96675c \"CML watermark\")\n",
"This is ready for review. \r\n\r\nIf `verification_mode` is None, it defaults to `VerificationMode.BASIC` instead of `VerificationMode.NONE`, so maybe we should find a better name for the latter to avoid confusion.\r\n\r\nPS: `ignore_verifications` is still present in the `test`/`run_beam` commands for simplicity. Let me know if you think these commands should support all three modes.",
"> I would also prefer to change the name for the NONE verification mode, but don't have really good ideas in mind. maybe smth like SKIP_ALL ?\r\n\r\nI decided to go with the following names:\r\n* `no_checks` (previously `none`)\r\n* `basic_checks` (previously `basic`)\r\n* `all_checks` (previously `full`)\r\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008900 / 0.011353 (-0.002453) | 0.004492 / 0.011008 (-0.006516) | 0.100957 / 0.038508 (0.062449) | 0.030145 / 0.023109 (0.007036) | 0.302531 / 0.275898 (0.026633) | 0.344072 / 0.323480 (0.020592) | 0.007032 / 0.007986 (-0.000953) | 0.004150 / 0.004328 (-0.000178) | 0.078272 / 0.004250 (0.074021) | 0.034142 / 0.037052 (-0.002910) | 0.310798 / 0.258489 (0.052308) | 0.350077 / 0.293841 (0.056236) | 0.034497 / 0.128546 (-0.094050) | 0.011417 / 0.075646 (-0.064230) | 0.323427 / 0.419271 (-0.095844) | 0.045664 / 0.043533 (0.002132) | 0.304688 / 0.255139 (0.049549) | 0.336591 / 0.283200 (0.053391) | 0.086116 / 0.141683 (-0.055567) | 1.519278 / 1.452155 (0.067123) | 1.576728 / 1.492716 (0.084011) |\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) | 0.242482 / 0.018006 (0.224476) | 0.403548 / 0.000490 (0.403058) | 0.001217 / 0.000200 (0.001017) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023466 / 0.037411 (-0.013945) | 0.095220 / 0.014526 (0.080694) | 0.104119 / 0.176557 (-0.072438) | 0.141107 / 0.737135 (-0.596029) | 0.107236 / 0.296338 (-0.189102) |\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) | 0.416290 / 0.215209 (0.201081) | 4.159068 / 2.077655 (2.081413) | 1.846014 / 1.504120 (0.341894) | 1.634789 / 1.541195 (0.093594) | 1.724687 / 1.468490 (0.256196) | 0.696887 / 4.584777 (-3.887890) | 3.313861 / 3.745712 (-0.431851) | 1.907239 / 5.269862 (-3.362622) | 1.266815 / 4.565676 (-3.298861) | 0.081660 / 0.424275 (-0.342615) | 0.012290 / 0.007607 (0.004683) | 0.522866 / 0.226044 (0.296822) | 5.237356 / 2.268929 (2.968428) | 2.294645 / 55.444624 (-53.149979) | 1.946407 / 6.876477 (-4.930069) | 1.995441 / 2.142072 (-0.146632) | 0.808340 / 4.805227 (-3.996887) | 0.149670 / 6.500664 (-6.350994) | 0.065162 / 0.075469 (-0.010307) |\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) | 1.219476 / 1.841788 (-0.622312) | 13.868709 / 8.074308 (5.794401) | 14.115783 / 10.191392 (3.924391) | 0.149403 / 0.680424 (-0.531021) | 0.028514 / 0.534201 (-0.505686) | 0.398194 / 0.579283 (-0.181089) | 0.410898 / 0.434364 (-0.023466) | 0.485763 / 0.540337 (-0.054574) | 0.574924 / 1.386936 (-0.812012) |\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) | 0.006906 / 0.011353 (-0.004447) | 0.004446 / 0.011008 (-0.006562) | 0.075936 / 0.038508 (0.037428) | 0.027693 / 0.023109 (0.004584) | 0.339505 / 0.275898 (0.063607) | 0.383315 / 0.323480 (0.059835) | 0.005138 / 0.007986 (-0.002847) | 0.004636 / 0.004328 (0.000308) | 0.074829 / 0.004250 (0.070578) | 0.040327 / 0.037052 (0.003274) | 0.340516 / 0.258489 (0.082027) | 0.388569 / 0.293841 (0.094729) | 0.031562 / 0.128546 (-0.096984) | 0.011585 / 0.075646 (-0.064061) | 0.084753 / 0.419271 (-0.334518) | 0.041310 / 0.043533 (-0.002223) | 0.338272 / 0.255139 (0.083133) | 0.367243 / 0.283200 (0.084043) | 0.092653 / 0.141683 (-0.049029) | 1.515973 / 1.452155 (0.063818) | 1.582869 / 1.492716 (0.090152) |\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) | 0.229366 / 0.018006 (0.211360) | 0.414404 / 0.000490 (0.413914) | 0.002922 / 0.000200 (0.002723) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026391 / 0.037411 (-0.011020) | 0.106754 / 0.014526 (0.092228) | 0.110718 / 0.176557 (-0.065839) | 0.145786 / 0.737135 (-0.591350) | 0.113180 / 0.296338 (-0.183159) |\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) | 0.446340 / 0.215209 (0.231131) | 4.499756 / 2.077655 (2.422101) | 2.071485 / 1.504120 (0.567365) | 1.873223 / 1.541195 (0.332029) | 1.931562 / 1.468490 (0.463071) | 0.699270 / 4.584777 (-3.885507) | 3.452383 / 3.745712 (-0.293329) | 2.970630 / 5.269862 (-2.299232) | 1.300859 / 4.565676 (-3.264817) | 0.083971 / 0.424275 (-0.340304) | 0.012489 / 0.007607 (0.004882) | 0.544190 / 0.226044 (0.318146) | 5.460097 / 2.268929 (3.191169) | 2.700244 / 55.444624 (-52.744380) | 2.396694 / 6.876477 (-4.479783) | 2.376334 / 2.142072 (0.234262) | 0.812845 / 4.805227 (-3.992382) | 0.154441 / 6.500664 (-6.346223) | 0.069510 / 0.075469 (-0.005959) |\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) | 1.278836 / 1.841788 (-0.562952) | 14.153158 / 8.074308 (6.078850) | 13.821290 / 10.191392 (3.629898) | 0.160464 / 0.680424 (-0.519960) | 0.016742 / 0.534201 (-0.517459) | 0.379840 / 0.579283 (-0.199443) | 0.391903 / 0.434364 (-0.042461) | 0.461646 / 0.540337 (-0.078691) | 0.550691 / 1.386936 (-0.836245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aeb637daab938d51b8b15ad4d175d06817e99512 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.009858 / 0.011353 (-0.001495) | 0.005383 / 0.011008 (-0.005625) | 0.100527 / 0.038508 (0.062019) | 0.037176 / 0.023109 (0.014067) | 0.295204 / 0.275898 (0.019306) | 0.364511 / 0.323480 (0.041031) | 0.008486 / 0.007986 (0.000500) | 0.004273 / 0.004328 (-0.000055) | 0.076538 / 0.004250 (0.072288) | 0.046250 / 0.037052 (0.009197) | 0.307102 / 0.258489 (0.048613) | 0.339313 / 0.293841 (0.045472) | 0.040783 / 0.128546 (-0.087763) | 0.012323 / 0.075646 (-0.063323) | 0.336216 / 0.419271 (-0.083055) | 0.050480 / 0.043533 (0.006947) | 0.293689 / 0.255139 (0.038550) | 0.315034 / 0.283200 (0.031834) | 0.113775 / 0.141683 (-0.027908) | 1.438738 / 1.452155 (-0.013416) | 1.499874 / 1.492716 (0.007157) |\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) | 0.202392 / 0.018006 (0.184386) | 0.442784 / 0.000490 (0.442295) | 0.003004 / 0.000200 (0.002804) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027792 / 0.037411 (-0.009620) | 0.110886 / 0.014526 (0.096360) | 0.121041 / 0.176557 (-0.055515) | 0.166803 / 0.737135 (-0.570333) | 0.127617 / 0.296338 (-0.168722) |\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) | 0.409762 / 0.215209 (0.194553) | 4.073297 / 2.077655 (1.995643) | 1.836375 / 1.504120 (0.332255) | 1.651507 / 1.541195 (0.110312) | 1.734134 / 1.468490 (0.265644) | 0.690900 / 4.584777 (-3.893877) | 3.812045 / 3.745712 (0.066333) | 2.101378 / 5.269862 (-3.168483) | 1.438242 / 4.565676 (-3.127434) | 0.083256 / 0.424275 (-0.341020) | 0.012436 / 0.007607 (0.004829) | 0.501702 / 0.226044 (0.275658) | 5.007679 / 2.268929 (2.738751) | 2.315158 / 55.444624 (-53.129466) | 2.003934 / 6.876477 (-4.872543) | 2.154658 / 2.142072 (0.012586) | 0.831749 / 4.805227 (-3.973478) | 0.165058 / 6.500664 (-6.335606) | 0.062166 / 0.075469 (-0.013303) |\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) | 1.212435 / 1.841788 (-0.629353) | 15.022673 / 8.074308 (6.948365) | 14.649631 / 10.191392 (4.458239) | 0.172121 / 0.680424 (-0.508303) | 0.028791 / 0.534201 (-0.505410) | 0.440290 / 0.579283 (-0.138993) | 0.437359 / 0.434364 (0.002995) | 0.543603 / 0.540337 (0.003265) | 0.643241 / 1.386936 (-0.743695) |\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) | 0.007572 / 0.011353 (-0.003781) | 0.005207 / 0.011008 (-0.005801) | 0.074427 / 0.038508 (0.035919) | 0.033384 / 0.023109 (0.010275) | 0.334538 / 0.275898 (0.058640) | 0.371556 / 0.323480 (0.048076) | 0.006453 / 0.007986 (-0.001532) | 0.004010 / 0.004328 (-0.000319) | 0.073488 / 0.004250 (0.069238) | 0.048082 / 0.037052 (0.011030) | 0.337325 / 0.258489 (0.078836) | 0.395143 / 0.293841 (0.101302) | 0.036714 / 0.128546 (-0.091832) | 0.012089 / 0.075646 (-0.063557) | 0.086008 / 0.419271 (-0.333263) | 0.049277 / 0.043533 (0.005744) | 0.333848 / 0.255139 (0.078709) | 0.354003 / 0.283200 (0.070803) | 0.105012 / 0.141683 (-0.036671) | 1.450769 / 1.452155 (-0.001386) | 1.554538 / 1.492716 (0.061821) |\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) | 0.208407 / 0.018006 (0.190400) | 0.438778 / 0.000490 (0.438288) | 0.000399 / 0.000200 (0.000199) | 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) | 0.030180 / 0.037411 (-0.007232) | 0.115432 / 0.014526 (0.100906) | 0.126106 / 0.176557 (-0.050451) | 0.167508 / 0.737135 (-0.569627) | 0.130566 / 0.296338 (-0.165772) |\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) | 0.421408 / 0.215209 (0.206198) | 4.208492 / 2.077655 (2.130838) | 2.024177 / 1.504120 (0.520057) | 1.834356 / 1.541195 (0.293161) | 1.923234 / 1.468490 (0.454744) | 0.699548 / 4.584777 (-3.885229) | 3.933775 / 3.745712 (0.188063) | 2.124526 / 5.269862 (-3.145336) | 1.360934 / 4.565676 (-3.204742) | 0.086568 / 0.424275 (-0.337707) | 0.012351 / 0.007607 (0.004744) | 0.517431 / 0.226044 (0.291387) | 5.175428 / 2.268929 (2.906499) | 2.471031 / 55.444624 (-52.973593) | 2.131529 / 6.876477 (-4.744948) | 2.202512 / 2.142072 (0.060440) | 0.849364 / 4.805227 (-3.955863) | 0.171505 / 6.500664 (-6.329159) | 0.065864 / 0.075469 (-0.009605) |\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) | 1.270054 / 1.841788 (-0.571734) | 15.254502 / 8.074308 (7.180194) | 13.874969 / 10.191392 (3.683577) | 0.144131 / 0.680424 (-0.536293) | 0.017743 / 0.534201 (-0.516458) | 0.421990 / 0.579283 (-0.157293) | 0.423924 / 0.434364 (-0.010439) | 0.522560 / 0.540337 (-0.017778) | 0.626159 / 1.386936 (-0.760777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05bd726a575a3c1c337022424fa7d226f1a2ebee \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\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) | 0.008643 / 0.011353 (-0.002710) | 0.004479 / 0.011008 (-0.006529) | 0.102372 / 0.038508 (0.063864) | 0.029703 / 0.023109 (0.006594) | 0.301479 / 0.275898 (0.025581) | 0.370970 / 0.323480 (0.047490) | 0.007044 / 0.007986 (-0.000942) | 0.004868 / 0.004328 (0.000540) | 0.079568 / 0.004250 (0.075318) | 0.035344 / 0.037052 (-0.001708) | 0.308091 / 0.258489 (0.049602) | 0.353812 / 0.293841 (0.059971) | 0.033406 / 0.128546 (-0.095140) | 0.011476 / 0.075646 (-0.064170) | 0.324343 / 0.419271 (-0.094929) | 0.040293 / 0.043533 (-0.003240) | 0.300007 / 0.255139 (0.044868) | 0.334410 / 0.283200 (0.051210) | 0.086553 / 0.141683 (-0.055130) | 1.463814 / 1.452155 (0.011659) | 1.501580 / 1.492716 (0.008864) |\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) | 0.198032 / 0.018006 (0.180025) | 0.409970 / 0.000490 (0.409480) | 0.001075 / 0.000200 (0.000875) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022941 / 0.037411 (-0.014471) | 0.097320 / 0.014526 (0.082794) | 0.106445 / 0.176557 (-0.070111) | 0.139073 / 0.737135 (-0.598063) | 0.108408 / 0.296338 (-0.187930) |\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) | 0.419315 / 0.215209 (0.204106) | 4.199273 / 2.077655 (2.121618) | 1.877689 / 1.504120 (0.373569) | 1.670442 / 1.541195 (0.129247) | 1.735034 / 1.468490 (0.266544) | 0.694691 / 4.584777 (-3.890086) | 3.323644 / 3.745712 (-0.422069) | 2.884349 / 5.269862 (-2.385513) | 1.518882 / 4.565676 (-3.046794) | 0.082390 / 0.424275 (-0.341886) | 0.012884 / 0.007607 (0.005277) | 0.525103 / 0.226044 (0.299058) | 5.277297 / 2.268929 (3.008369) | 2.328639 / 55.444624 (-53.115985) | 1.983210 / 6.876477 (-4.893267) | 2.037985 / 2.142072 (-0.104088) | 0.809520 / 4.805227 (-3.995707) | 0.150150 / 6.500664 (-6.350514) | 0.065578 / 0.075469 (-0.009891) |\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) | 1.221971 / 1.841788 (-0.619817) | 13.692361 / 8.074308 (5.618052) | 13.874582 / 10.191392 (3.683190) | 0.138182 / 0.680424 (-0.542242) | 0.028618 / 0.534201 (-0.505583) | 0.395104 / 0.579283 (-0.184179) | 0.397169 / 0.434364 (-0.037195) | 0.457509 / 0.540337 (-0.082829) | 0.537275 / 1.386936 (-0.849661) |\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) | 0.006835 / 0.011353 (-0.004518) | 0.004585 / 0.011008 (-0.006423) | 0.076877 / 0.038508 (0.038369) | 0.027305 / 0.023109 (0.004196) | 0.349085 / 0.275898 (0.073187) | 0.401416 / 0.323480 (0.077936) | 0.004912 / 0.007986 (-0.003074) | 0.003315 / 0.004328 (-0.001014) | 0.075676 / 0.004250 (0.071425) | 0.038960 / 0.037052 (0.001907) | 0.346196 / 0.258489 (0.087707) | 0.403185 / 0.293841 (0.109344) | 0.032054 / 0.128546 (-0.096493) | 0.011742 / 0.075646 (-0.063905) | 0.086631 / 0.419271 (-0.332640) | 0.041633 / 0.043533 (-0.001900) | 0.343519 / 0.255139 (0.088380) | 0.385413 / 0.283200 (0.102213) | 0.091430 / 0.141683 (-0.050253) | 1.478886 / 1.452155 (0.026731) | 1.546873 / 1.492716 (0.054156) |\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) | 0.167882 / 0.018006 (0.149876) | 0.396464 / 0.000490 (0.395974) | 0.003629 / 0.000200 (0.003429) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024829 / 0.037411 (-0.012583) | 0.099607 / 0.014526 (0.085081) | 0.106187 / 0.176557 (-0.070370) | 0.142379 / 0.737135 (-0.594756) | 0.109307 / 0.296338 (-0.187032) |\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) | 0.442276 / 0.215209 (0.227067) | 4.427099 / 2.077655 (2.349444) | 2.093407 / 1.504120 (0.589287) | 1.880973 / 1.541195 (0.339778) | 1.915592 / 1.468490 (0.447102) | 0.708196 / 4.584777 (-3.876581) | 3.417649 / 3.745712 (-0.328063) | 2.859953 / 5.269862 (-2.409909) | 1.528380 / 4.565676 (-3.037297) | 0.084054 / 0.424275 (-0.340221) | 0.012585 / 0.007607 (0.004978) | 0.537614 / 0.226044 (0.311569) | 5.409915 / 2.268929 (3.140987) | 2.555853 / 55.444624 (-52.888771) | 2.195075 / 6.876477 (-4.681402) | 2.232775 / 2.142072 (0.090703) | 0.814994 / 4.805227 (-3.990233) | 0.152882 / 6.500664 (-6.347782) | 0.067467 / 0.075469 (-0.008002) |\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) | 1.306007 / 1.841788 (-0.535780) | 13.923981 / 8.074308 (5.849673) | 13.385881 / 10.191392 (3.194489) | 0.150712 / 0.680424 (-0.529712) | 0.016731 / 0.534201 (-0.517470) | 0.376557 / 0.579283 (-0.202726) | 0.379396 / 0.434364 (-0.054968) | 0.456251 / 0.540337 (-0.084087) | 0.545731 / 1.386936 (-0.841205) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc637d107ef3e3b9948691379312a8099b6476aa \"CML watermark\")\n"
] | "2022-11-25T18:39:09" | "2023-02-13T16:50:42" | "2023-02-13T16:43:47" | CONTRIBUTOR | null | Skip the dataset verifications (split and checksum verifications, duplicate keys check) by default unless a dataset is being tested (`datasets-cli test/run_beam`). The main goal is to avoid running the checksum check in the default case due to how expensive it can be for large datasets.
PS: Maybe we should deprecate `ignore_verifications`, which is `True` now by default, and give it a different name? | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-25T17:09:21" | "2022-12-09T14:20:15" | "2022-12-09T14:17:20" | CONTRIBUTOR | null | Clarify in the docstrings what happens when `use_auth_token` is `None` and deprecate the `use_auth_token` param in `download_and_prepare`. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5301). All of your documentation changes will be reflected on that endpoint.",
"Just noticed that now we have to deal with indexed & split datasets. The remaining tests are failing because one should be able to get an indexed dataset when accessing the split of a dataset made of indexed splits (right now the index is just trashed)"
] | "2022-11-25T16:35:54" | "2023-02-21T13:13:13" | "2023-02-21T13:13:13" | MEMBER | null | ...instead of a DatasetDict.
```python
# now supported
ds = load_dataset("squad")
ds[0]
for example in ds:
pass
# still works
ds["train"]
ds["validation"]
# new
ds.splits # Dict[str, Dataset] | None
# soon to be supported (not in this PR)
ds = load_dataset("dataset_with_no_splits")
ds[0]
for example in ds:
pass
```
I implemented `Dataset.__getitem__` and `IterableDataset.__getitem__` to be able to get a split from a dataset.
The splits are defined by the `ds.info.splits` dictionary.
Therefore a dataset is a table that optionally has some splits defined in the dataset info. And a split dataset is the concatenation of all its splits.
I made as little breaking changes as possible. Notable breaking changes:
- `load_dataset("potato").keys() / .items() / .values() /` don't work anymore, since we don't return a dict
- same for `for split_name in load_dataset("potato")`, since we now iterate on the examples
- ..
TODO:
- [x] Update push_to_hub
- [x] Update save_to_disk/load_from_disk
- [ ] check for other breaking changes
- [ ] fix existing tests
- [ ] add new tests
- [ ] docs
This is related to https://github.com/huggingface/datasets/issues/5189, to extend `load_dataset` to return datasets without splits | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I noticed this bug the other day and was going to look into it! \"Where are these processes coming from?\" ;-)"
] | "2022-11-25T15:37:42" | "2022-12-07T12:55:39" | "2022-12-07T12:52:51" | CONTRIBUTOR | null | Use the same `num_proc` value for data download and generation. Additionally, do not set `num_proc` to 16 in `DownloadManager` by default (`num_proc` now has to be specified explicitly). | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-25T15:35:28" | "2022-11-29T08:23:58" | "2022-11-29T08:21:24" | MEMBER | null | This PR fixes a bug in `xopen` function for Windows pathnames.
Fix #5298. | {
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] | null | [] | "2022-11-25T15:21:32" | "2022-11-29T08:21:25" | "2022-11-29T08:21:25" | MEMBER | null | Currently, `xopen` function has a bug with local Windows pathnames:
From its implementation:
```python
def xopen(file: str, mode="r", *args, **kwargs):
file = _as_posix(PurePath(file))
main_hop, *rest_hops = file.split("::")
if is_local_path(main_hop):
return open(file, mode, *args, **kwargs)
```
On a Windows machine, if we pass the argument:
```python
xopen("C:\\Users\\USERNAME\\filename.txt")
```
it returns
```python
open("C:/Users/USERNAME/filename.txt")
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-25T13:30:17" | "2022-11-29T08:07:39" | "2022-11-29T08:05:12" | MEMBER | null | This PR fixes a bug in `xjoin` function with Windows pathnames.
Fix #5296. | {
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] | null | [] | "2022-11-25T13:29:33" | "2022-11-29T08:05:13" | "2022-11-29T08:05:13" | MEMBER | null | Currently, `xjoin` function has a bug with local Windows pathnames: instead of returning the OS-dependent join pathname, it always returns it in POSIX format.
```python
from datasets.download.streaming_download_manager import xjoin
path = xjoin("C:\\Users\\USERNAME", "filename.txt")
```
Join path should be:
```python
"C:\\Users\\USERNAME\\filename.txt"
```
However it is:
```python
"C:/Users/USERNAME/filename.txt"
``` | {
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"Hi ! Thanks for reporting. Indeed the lock file should be placed in a directory with write permission (e.g. in the directory where the archive is extracted).",
"I opened https://github.com/huggingface/datasets/pull/5320 to fix this - it places the lock file in the cache directory instead of trying to put in next to the ZIP where it's read-only"
] | "2022-11-25T03:59:43" | "2022-12-01T13:56:40" | null | NONE | null | ### Describe the bug
Hi,
`load_dataset()` does not work .zip files located on a read-only directory. Looks like it's because Dataset creates a lock file in the [same directory](https://github.com/huggingface/datasets/blob/df4bdd365f2abb695f113cbf8856a925bc70901b/src/datasets/utils/extract.py) as the .zip file.
Encountered this when attempting `load_dataset()` on a datadir with SageMaker FastFile mode.
### Steps to reproduce the bug
```python
# Showing relevant lines only.
hyperparameters = {
"dataset_name": "ydshieh/coco_dataset_script",
"dataset_config_name": 2017,
"data_dir": "/opt/ml/input/data/coco",
"cache_dir": "/tmp/huggingface-cache", # Fix dataset complains out-of-space.
...
}
estimator = PyTorch(
base_job_name="clip",
source_dir="../src/sm-entrypoint",
entry_point="run_clip.py", # Transformers/src/examples/pytorch/contrastive-image-text/run_clip.py
framework_version="1.12",
py_version="py38",
hyperparameters=hyperparameters,
instance_count=1,
instance_type="ml.p3.16xlarge",
volume_size=100,
distribution={"smdistributed": {"dataparallel": {"enabled": True}}},
)
fast_file = lambda x: TrainingInput(x, input_mode='FastFile')
estimator.fit(
{
"pre-trained": fast_file("s3://vm-sagemakerr-us-east-1/clip/pre-trained-checkpoint/"),
"coco": fast_file("s3://vm-sagemakerr-us-east-1/clip/coco-zip-files/"),
}
)
```
Error message:
```text
ErrorMessage "OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock'
"""
The above exception was the direct cause of the following exception
Traceback (most recent call last)
File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.8/site-packages/mpi4py/__main__.py", line 7, in <module>
main()
File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 198, in main
run_command_line(args)
File "/opt/conda/lib/python3.8/site-packages/mpi4py/run.py", line 47, in run_command_line
run_path(sys.argv[0], run_name='__main__')
File "/opt/conda/lib/python3.8/runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "/opt/conda/lib/python3.8/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "run_clip_smddp.py", line 594, in <module>
File "run_clip_smddp.py", line 327, in main
dataset = load_dataset(
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare
super()._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 891, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/ydshieh--coco_dataset_script/e033205c0266a54c10be132f9264f2a39dcf893e798f6756d224b1ff5078998f/coco_dataset_script.py", line 123, in _split_generators
archive_path = dl_manager.download_and_extract(_DL_URLS)
File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 447, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/download/download_manager.py", line 419, in extract
extracted_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 472, in map_nested
mapped = pool.map(_single_map_nested, split_kwds)
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 364, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 771, in get
raise self._value
OSError: [Errno 30] Read-only file system: '/opt/ml/input/data/coco/image_info_test2017.zip.lock'"
```
### Expected behavior
`load_dataset()` to succeed, just like when .zip file is passed in SageMaker File mode.
### Environment info
* datasets-2.7.1
* transformers-4.24.0
* python-3.8
* torch-1.12
* SageMaker PyTorch DLC | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-24T18:04:38" | "2022-11-29T07:09:08" | "2022-11-29T07:06:32" | MEMBER | null | This PR extends the support in streaming mode for datasets that use `pathlib.Path.with_suffix`.
Fix #5293. | {
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This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension. | {
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"- Build docs for 2.6.2:\r\n - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044\r\n- Build docs for 2.7.1:\r\n - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539574442/jobs/5941636792"
] | "2022-11-24T09:42:10" | "2022-11-24T10:10:02" | "2022-11-24T10:10:02" | MEMBER | null | After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).
There was a fix by:
- #5291
However, both documentations were built from main branch, instead of their corresponding version branch.
We are rebuilding them. | {
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https://api.github.com/repos/huggingface/datasets/issues/5291 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5291/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5291/comments | https://api.github.com/repos/huggingface/datasets/issues/5291/events | https://github.com/huggingface/datasets/pull/5291 | 1,462,983,472 | PR_kwDODunzps5DoKNC | 5,291 | [build doc] for v2.7.1 & v2.6.2 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"doc versions are built https://huggingface.co/docs/datasets/index"
] | "2022-11-24T08:54:47" | "2022-11-24T09:14:10" | "2022-11-24T09:11:15" | CONTRIBUTOR | null | Do NOT merge. Using this PR to build docs for [v2.7.1](https://github.com/huggingface/datasets/pull/5291/commits/f4914af20700f611b9331a9e3ba34743bbeff934) & [v2.6.2](https://github.com/huggingface/datasets/pull/5291/commits/025f85300a0874eeb90a20393c62f25ac0accaa0) | {
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https://api.github.com/repos/huggingface/datasets/issues/5290 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5290/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5290/comments | https://api.github.com/repos/huggingface/datasets/issues/5290/events | https://github.com/huggingface/datasets/pull/5290 | 1,462,716,766 | PR_kwDODunzps5DnQsS | 5,290 | fix error where reading breaks when batch missing an assigned column feature | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5290). All of your documentation changes will be reflected on that endpoint."
] | "2022-11-24T03:53:46" | "2022-11-25T03:21:54" | null | CONTRIBUTOR | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5289 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5289/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5289/comments | https://api.github.com/repos/huggingface/datasets/issues/5289/events | https://github.com/huggingface/datasets/pull/5289 | 1,462,543,139 | PR_kwDODunzps5Dmrk9 | 5,289 | Added support for JXL images. | {
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"I'm fine with the addition of jxl in the list of known image extensions, this way users that have the plugin can work with their JXL datasets. WDYT @mariosasko ?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5289). All of your documentation changes will be reflected on that endpoint.",
"I think we should wait for official support from Pillow. Plus, the linked plugin doesn't support `Image.save`, which is one of the requirements for a format to be included in `IMAGE_EXTENSIONS`.\r\n\r\n@alexjc In the meantime, one option is to add these lines to the card:\r\n```python\r\nimport importlib\r\nimport datasets\r\n\r\nif \".jxl\" not in datasets.packaged_modules.imagefolder.IMAGE_EXTENSIONS:\r\n datasets.packaged_modules.imagefolder.IMAGE_EXTENSIONS.append(\".jxl\")\r\n\r\nif \"jxl\" not in datasets.packaged_modules._EXTENSION_TO_MODULE:\r\n datasets.packaged_modules._EXTENSION_TO_MODULE[\"jxl\"] = (\"imagefolder\", {})\r\n\r\nimportlib.reload(datasets.load)\r\nds = datasets.load_dataset(\"texturedesign/td01_natural-ground-textures\")\r\n```\r\nAnd you can add a note to the card that this dataset requires the \"jxlpy\" package to work. \r\n\r\nIn this case, you can also disable the viewer to avoid the discrepancy between the data displayed in the preview and the loaded data.\r\n\r\nAnother option is to define the loading script and add `jxlpy` to the list of dependencies [here](https://github.com/huggingface/datasets-server/blob/3012da62054a025467616abc14b0b46e1f11ea13/workers/first_rows/pyproject.toml#L8) to enable the viewer. This option requires more work, so let us know if you need help.",
"Thank you both for your thoughtful replies!\r\n\r\nOne questions and and update:\r\n* The jxlpy plugin does support saving, in the `_save` function of the JXLImagePlugin file. Did it not work? I'm working on the upgrade to the latest JXL, so it'd be good to know if it failed so I can fix it.\r\n* I wrote to the Pillow maintainer and the preferred solution would be to keep JXL as a separate plugin because they're a small team don't have the resources to maintain more code.\r\n\r\nWith that in mind, let me share the minimal set of features I'd need for this to work within the `datasets` library:\r\n1. Using `load_dataset()` with the HuggingFace dataset name correctly downloads the JXL files so they are available locally. Even if the `file_name` field is left intact and not loaded as a PIL image, this is the first step.\r\n2. With minimal monkey-patching, having the `load_dataset` correctly expand `file_name` into PIL `image` fields if JXL support is available.\r\n\r\nIf both of these work, then I can use HuggingFace's hub and the `datasets` library for an MVP even if not all features are there. I don't need automatic thumbnails or previews of the dataset on the server.\r\n\r\n\r\nGiven the reply from the Pillow maintainer, what solution can we come up with that works in a more permanent way than waiting for Pillow integration (which may not happen) — assuming users install the `jxlpy` plugin separately?",
"Link to my upgrade for the latest `libjxl`, pending review and merge. I tested load/save via Pillow extensively for this: https://github.com/olokelo/jxlpy/pull/13",
"After more research, here's my latest suggestion:\r\n* Depending on the build of pillow, the source (pip or conda), the platform even, certain formats may or may not be available — despite them being in the list. For example, webp support is not consistently available.\r\n* I'd suggest adding JXL to the list and simply catching the `PIL.UnidentifiedImageError` — printing a useful error message that sends them to a Wiki page to find out what to do.\r\n* On that page would be included instructions how to install support for the format and what to do for the dataset to load correctly on any platform, both with or without conda, etc.\r\n\r\nWhat do you think?",
"> The jxlpy plugin does support saving, in the _save function of the JXLImagePlugin file. Did it not work? I'm working on the upgrade to the latest JXL, so it'd be good to know if it failed so I can fix it.\r\n\r\nMy bad, I was referring to [this](https://github.com/google/brunsli/blob/2dd949e53ed05796eb44a31cc759fbf9e6c53e2f/contrib/py/jxl_library_patches/jxl_pillow.py) version of the plugin.\r\n\r\nI still think this involves too much work:\r\n* would require a new doc page\r\n* unofficial plugins have to be imported explicitly, leading to messier code on our side\r\n* etc.\r\n\r\nFor now, it seems more reasonable to create a loading script (faster than ImageFolder, as ImageFolder has to resolve the image files first) for this particular case and add `jxlpy` to the list of the `datasets-server`'s dependencies. Also, one additional advantage of this approach is that it reports if any of the modules imported in a script is missing, which is handy in your case for the plugin lib. WDYT?",
"OK, let me try it it and I'll report back.\r\n\r\nWill the JXL files (even if unknown format) be automatically downloaded if they are linked from the `.jsonl` file?\r\n\r\n(I had trouble getting that working before this patch.)",
"> Will the JXL files (even if unknown format) be automatically downloaded if they are linked from the .jsonl file?\r\n\r\nNo, they need to be downloaded explicitly.\r\n\r\nFeel free to use 🤗 Hub discussions in your dataset repo to ping us for help (our usernames are the same there)",
"Is it possible to add support for JXL files being downloaded without needing to add server-side rendering support?",
"In the loading script, data files are downloaded with `DownloadManager` (`dl_manager` in `_split_generators`), which doesn't have any requirements regarding the actual type of the downloaded files.\r\n\r\nPS: Let's use the forum or Hub discussions for further questions to avoid pinging other participants"
] | "2022-11-23T23:16:33" | "2022-11-29T18:49:46" | null | NONE | null | JPEG-XL is the most advanced of the next-generation of image codecs, supporting both lossless and lossy files — with better compression and quality than PNG and JPG respectively. It has reduced the disk sizes and bandwidth required for many of the datasets I use.
Pillow does not yet support JXL, but there's a plugin as a separate Python library that does (`pip install jxlpy`), and I've tested that this change works as expected when the plugin is imported.
Dataset used for testing, you must `git pull` as loading it from Python won't work until `datasets-server` is also changed to support JXL files:
https://huggingface.co/datasets/texturedesign/td01_natural-ground-textures
The case where the plugin is not imported first raises an error:
```
PIL.UnidentifiedImageError: cannot identify image file 'td01/train/set01/01_145523.jxl'
```
In order to enable support for JXL even before pillow supports this, should this exception be handled with a better error message? I'd expect/hope JXL support to follow in one of the pillow quarterly releases in the next 6-9 months. | {
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https://api.github.com/repos/huggingface/datasets/issues/5288 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5288/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5288/comments | https://api.github.com/repos/huggingface/datasets/issues/5288/events | https://github.com/huggingface/datasets/issues/5288 | 1,462,134,067 | I_kwDODunzps5XJmUz | 5,288 | Lossy json serialization - deserialization of dataset info | {
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"Hi ! JSON is a lossy format indeed. If you want to keep the feature types or other metadata I'd encourage you to store them as well. For example you can use `dataset.info.write_to_directory` and `DatasetInfo.from_directory` to store the feature types, split info, description, license etc."
] | "2022-11-23T17:20:15" | "2022-11-25T12:53:51" | null | NONE | null | ### Describe the bug
Saving a dataset to disk as json (using `to_json`) and then loading it again (using `load_dataset`) results in features whose labels are not type-cast correctly. In the code snippet below, `features.label` should have a label of type `ClassLabel` but has type `Value` instead.
### Steps to reproduce the bug
```
from datasets import load_dataset
def test_serdes_from_json(d):
dataset = load_dataset(d, split="train")
dataset.to_json('_test')
dataset_loaded = load_dataset("json", data_files='_test', split='train')
try:
assert dataset_loaded.info.features == dataset.info.features, "features unequal!"
except Exception as ex:
print(f'{ex}')
print(f'expected {dataset.info.features}, \nactual { dataset_loaded.info.features }')
test_serdes_from_json('rotten_tomatoes')
```
Output
```
features unequal!
expected {'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['neg', 'pos'], id=None)},
actual {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}
```
### Expected behavior
The deserialized `features.label` should have type `ClassLabel`.
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.144-127.601.amzn2.x86_64-x86_64-with-glibc2.17
- Python version: 3.7.13
- PyArrow version: 7.0.0
- Pandas version: 1.2.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5287 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5287/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5287/comments | https://api.github.com/repos/huggingface/datasets/issues/5287/events | https://github.com/huggingface/datasets/pull/5287 | 1,461,971,889 | PR_kwDODunzps5Dkttf | 5,287 | Fix methods using `IterableDataset.map` that lead to `features=None` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"_The documentation is not available anymore as the PR was closed or merged._",
"Maybe other options are:\r\n* Keep the `info.features` to `None` if those were initially `None`\r\n* Infer the features with pre-fetching just if the `info.features` is `None`\r\n* If the `info.features` are there, make sure that after `map` features is not `None`",
"Hi @lhoestq something that's still not clear to me is: should we infer the features always when applying a `map` if those are initially `None`, or just assume that if the features are initially `None` those should be left that way unless the user specifically sets those (or during iter)?\r\n\r\nIn this PR I'm using `from datasets.iterable_dataset import _infer_features_from_batch` to infer the features when those are `None` using pre-fetch of `self._head()`, but I'm not sure if that's the expected behavior.\r\n\r\nThanks in advance for your help!",
"Also, the PR still has some more work to do, but probably the most relevant thing to fix right now is that the `features` are being set to `None` in the functions `IterableDataset.rename_column`, `IterableDataset.rename_columns`, and `IterableDataset.remove_columns` when the `features` originally had a value. So once that's fixed maybe we can focus on improving the current `map`'s behavior, so as to avoid this from happening also when the user uses `map` directly and not through the functions mentioned above.",
"> Cool thank you ! Resolving the features can be expensive sometimes, so maybe we don't resolve the features and we can just rename/remove columns if the features are known (i.e. if they're not None). What do you think ?\r\n\r\nThanks for the feedback! Makes sense to me 👍🏻 I'll commit the comments now!",
"Already done @lhoestq, feel free to merge whenever you want! Also before merging, can you please link the following issues https://github.com/huggingface/datasets/issues/3888, https://github.com/huggingface/datasets/issues/5245, and https://github.com/huggingface/datasets/issues/5284, so that those are closed upon merge? Thanks!"
] | "2022-11-23T15:33:25" | "2022-11-28T15:43:14" | "2022-11-28T12:53:22" | CONTRIBUTOR | null | As currently `IterableDataset.map` is setting the `info.features` to `None` every time as we don't know the output of the dataset in advance, `IterableDataset` methods such as `rename_column`, `rename_columns`, and `remove_columns`. that internally use `map` lead to the features being `None`.
This PR is related to #3888, #5245, and #5284
## ✅ Current solution
The code in this PR is basically making sure that if the features were there since the beginning and a `rename_column`/`rename_columns` happens, those are kept and the rename is applied to the `Features` too. Also, if the features were not there before applying `rename_column`, `rename_columns` or `remove_columns`, a batch is prefetched and the features are being inferred (that could potentially be part of `IterableDataset.__init__` in case the `info.features` value is `None`).
## 💡 Ideas
Some ideas were proposed in https://github.com/huggingface/datasets/issues/3888, but probably the most consistent solution even though it may take some time is to actually do the type inferencing during the `IterableDataset.__init__` in case the provided `info.features` is `None`, otherwise, we can just use the provided features.
Additionally, as mentioned at https://github.com/huggingface/datasets/issues/3888, we could also include a `features` parameter to the `map` function, but that's probably more tedious.
Also thanks to @lhoestq for sharing some ideas in both https://github.com/huggingface/datasets/issues/3888 and https://github.com/huggingface/datasets/issues/5245 :hugs: | {
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https://api.github.com/repos/huggingface/datasets/issues/5286 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5286/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5286/comments | https://api.github.com/repos/huggingface/datasets/issues/5286/events | https://github.com/huggingface/datasets/issues/5286 | 1,461,908,087 | I_kwDODunzps5XIvJ3 | 5,286 | FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json | {
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"I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)"
] | "2022-11-23T14:54:15" | "2022-11-25T11:33:14" | "2022-11-25T11:33:14" | NONE | null | ### Describe the bug
I follow the steps provided on the website [https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia)
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
however this results in the following error:
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
If I then prompt the system with:
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
the following error occurs:
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
Here is the exact code:
Python 3.10.6 (main, Nov 2 2022, 18:53:38) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> load_dataset('wikipedia', '20220301.en')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 15.3k/15.3k [00:00<00:00, 22.2MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1879, in _download_and_prepare
raise MissingBeamOptions(
datasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
Downloading and preparing dataset wikipedia/20220301.en to /home/[EDITED]/.cache/huggingface/datasets/wikipedia/20220301.en/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████| 15.3k/15.3k [00:00<00:00, 18.8MB/s]
Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1909, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 891, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rorytol/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py", line 945, in _split_generators
downloaded_files = dl_manager.download_and_extract({"info": info_url})
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 447, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 311, in download
downloaded_path_or_paths = map_nested(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 444, in map_nested
mapped = [
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 445, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py", line 338, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 183, in cached_path
output_path = get_from_cache(
File "/usr/local/lib/python3.10/dist-packages/datasets/utils/file_utils.py", line 530, in get_from_cache
raise FileNotFoundError(f"Couldn't find file at {url}")
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
### Steps to reproduce the bug
$ pip install apache_beam mwparserfromhell
>>> from datasets import load_dataset
>>> load_dataset("wikipedia", "20220301.en")
>>> load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
### Expected behavior
Download the dataset
### Environment info
Running linux on a remote workstation operated through a macbook terminal
Python 3.10.6
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"_The documentation is not available anymore as the PR was closed or merged._",
"I updated the tests, met le know if it sounds good to you now :)"
] | "2022-11-23T10:55:54" | "2022-11-24T14:11:41" | "2022-11-24T14:08:37" | MEMBER | null | Having the file name is useful in case we need to check the extension of the file (e.g. mp3), or in general in case it includes some metadata information (track id, image id etc.)
Related to https://github.com/huggingface/datasets/issues/5276 | {
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"Related to https://github.com/huggingface/datasets/issues/5245",
"#self-assign",
"Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\r\n\r\n_c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377",
"> Thanks @lhoestq and @alvarobartt!\n> \n> \n> \n> This would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\n> \n> \n> \n> _c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377\n\nI'm almost done with at least a temporary fix to `rename_column`, `rename_columns`, and `remove_columns`, just trying to figure out how to extend it to the `map` function itself!\n\nI'll probably open the PR for review either tomorrow or Sunday hopefully! Glad I can help you and HuggingFace 🤗 ",
"Awesome - thank you so much for this PR @alvarobartt! Is much appreciated!",
"@sanchit-gandhi PR is ready and open for review at #5287, but there's still one issue I may need @lhoestq's input :hugs:",
"Let us know @sanchit-gandhi if you need a new release of `datasets` soon with this fix included :)",
"Thanks for the fix guys! We can direct people to install `datasets` from main if that's easier!",
"Hey guys, any update around this? I'm facing the same issue with a streamable dataset. ",
"Hi @asennoussi so this was already fixed and released as part of https://github.com/huggingface/datasets/releases/tag/2.8.0, so you should be able to install it as `pip install datasets==2.8.0` or just to use `pip install datasets --upgrade` to get the latest version, as of now, the https://github.com/huggingface/datasets/releases/tag/2.9.0 released last week! 🤗",
"Still facing the same issue though: \r\n```\r\nfrom datasets import IterableDatasetDict, load_dataset\r\n\r\nraw_datasets = vectorized_datasets = IterableDatasetDict()\r\n\r\n\r\nraw_datasets[\"train\"] = load_dataset(\"asennoussi/private\", split=\"train\", use_auth_token=True, streaming=True)\r\nraw_datasets[\"test\"] = load_dataset(\"asennoussi/private\", split=\"test\", use_auth_token=True, streaming=True)\r\n\r\nprint(\"Original features: \", raw_datasets['train'].features.keys())\r\n\r\n...\r\n\r\ndef prepare_dataset(batch):\r\n\r\n # load and (possibly) resample audio datato 16kHz\r\n audio = batch[\"audio\"]\r\n\r\n # compute log-Mel input features from input audio array \r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n # compute input length of audio sample in seconds\r\n batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\r\n \r\n # optional pre-processing steps\r\n transcription = batch[\"sentence\"]\r\n \r\n # encode target text to label ids\r\n batch[\"labels\"] = processor.tokenizer(transcription).input_ids\r\n batch[\"labels_length\"] = len(batch[\"labels\"])\r\n return batch\r\n...\r\nvectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\nprint(\"First sample:\", next(iter(vectorized_datasets['train'])))\r\n\r\n```\r\n\r\nOutput: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\n```",
"Hmm weird, could you try to print\r\n\r\n```python\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\n```\r\n\r\nagain after iterating over the `vectorized_datasets`? In the code above, should be last line :)",
"Didn't seem to fix it: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\nProcessed features: None\r\n```",
"Actually the culprit looks to be this one: \r\n`vectorized_datasets = raw_datasets.map(prepare_dataset).with_format(\"torch\")`\r\nWhen I remove this line: `vectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))`\r\n\r\nI still get \r\n```\r\nProcessed features: None\r\n```",
"The culprit is definitely `.map` \r\nJust validated it. \r\nAny idea please? ",
"> The culprit is definitely `.map` Just validated it. Any idea please?\r\n\r\nYes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue https://github.com/huggingface/datasets/issues/3888\r\n\r\nAnyway, now you can pass the `features` as a param to `.map` as follows:\r\n\r\n```python\r\nfrom datasets import Features\r\nvectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n features=Features(\r\n {\"path\": raw_datasets[\"train\"].info.features[\"path\"], \"audio\": raw_datasets[\"train\"].info.features[\"audio\"], \"sentence\": raw_datasets[\"train\"].info.features[\"sentence\"]}\r\n ),\r\n).with_format(\"torch\")\r\n```\r\n\r\nAlso, to let you know, when calling `.remove_columns` over an `IterableDataset`, the `features` are not lost, as well as `.rename_column` and `rename_columns` :)\r\n\r\nMore information about the latter at https://github.com/huggingface/datasets/pull/5287",
"@asennoussi alternatively you can just call `._resolve_features()` from your `IterableDataset` and it will pre-fetch the data to resolve the features, but note that feature-inference is not as accurate as if you manually specify which features and feature-types the `IterableDataset` has, as mentioned in the comment above, the alternative is to provide `features` param to `.map` :hugs:",
"Got it thanks a lot! "
] | "2022-11-23T10:54:59" | "2023-02-02T09:05:51" | "2022-11-28T12:53:24" | CONTRIBUTOR | null | ### Describe the bug
The `remove_column` method of the IterableDataset sets the dataset features to None.
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# check original features
print("Original features: ", dataset.features.keys())
# define features to remove: we KEEP audio and text
COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id']
dataset = dataset.remove_columns(COLUMNS_TO_REMOVE)
# check processed features, uh-oh!
print("Processed features: ", dataset.features)
# streaming the first audio sample still works
print("First sample:", next(iter(ds)))
```
**Print Output:**
```
Original features: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
Processed features: None
First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157, 0.0005188 , 0.00024414, ..., -0.00097656,
-0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"}
```
### Expected behavior
The features should be those **not** removed by the `remove_column` method, i.e. audio and text.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
(Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml)
cc @polinaeterna @lhoestq | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-22T17:36:24" | "2022-11-22T17:50:12" | "2022-11-22T17:47:02" | MEMBER | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5281/comments | https://api.github.com/repos/huggingface/datasets/issues/5281/events | https://github.com/huggingface/datasets/issues/5281 | 1,459,930,271 | I_kwDODunzps5XBMSf | 5,281 | Support cloud storage in load_dataset | {
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"Or for example an archive on GitHub releases! Before I added support for JXL (locally only, PR still pending) I was considering hosting my files on GitHub instead...",
"+1 to this. I would like to use 'audiofolder' with a data_dir that's on S3, for example. I don't want to upload my dataset to the Hub, but I would find all the fingerprinting/caching features useful.",
"Adding to the conversation, Dask also uses `fsspec` for this feature.\r\n\r\n[Dask: How to connect to remote data](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html)\r\n\r\nHappy to help on this feature :D ",
"+1 to this feature request since I think it also tackles my use-case. I am collaborating with a team, working with a loading script which takes some time to generate the dataset artifacts. It would be very handy to use this as a cloud cache to avoid duplicating the effort. \r\n\r\nCurrently we could use `builder.download_and_prepare(path_to_cloud_storage, storage_options, ...)` to cache the artifacts to cloud storage, but then `builder.as_dataset()` yields `NotImplementedError: Loading a dataset cached in SomeCloudFileSystem is not supported`",
"Makes sense ! If you want to load locally a dataset that you download_and_prepared on a cloud storage, you would use `load_dataset(path_to_cloud_storage)` indeed. It would download the data from the cloud storage, cache them locally, and return a `Dataset`.",
"It seems currently the `cached_path` function handles all URLs by `get_from_cache` that only supports `ftp` and `http(s)` here:\r\nhttps://github.com/huggingface/datasets/blob/b5672a956d5de864e6f5550e493527d962d6ae55/src/datasets/utils/file_utils.py#L181\r\n\r\nI guess one can add another condition that handles `s3://` or `gs://` URLs via `fsspec` here.",
"I could use this functionality, so I put together a PR using @kyamagu's suggestion to use `fsspec` in `datasets.utils.file_utils`\r\n\r\nhttps://github.com/huggingface/datasets/pull/5580",
"Thanks @dwyatte for adding support for fsspec urls\r\n\r\nLet me just reopen this since the original issue is not resolved",
"I'm not yet understanding how to use https://github.com/huggingface/datasets/pull/5580 in order to use `load_dataset(data_files=\"s3://...\")`. Any help/example would be much appreciated :) thanks! ",
"It's still not officially supported x) But you can try to update `request_etag` in `file_utils.py` to use `fsspec_head` instead of `http_head`. It is responsible of getting the ETags of the remote files for caching. This change may do the trick for S3 urls",
"Thank you for your guys help on this and merging in #5580. I manually pulled the changes to my local datasets package (datasets.utils.file_utils.py) since it only seemed to be this file that was changed in the PR and I'm getting the error: \r\nInvalidSchema: No connection adapters were found for 's3://bucket/folder/'. I'm calling load_dataset using the S3 URI. When I use the S3 URL I get HTTPError: 403 Client Error. \r\nAm I not supposed to use the S3 URI? How do I pull in the changes from this merge? I'm running datasets 2.10.1. ",
"The current implementation depends on gcsfs/s3fs being able to authenticate through some other means e.g., environmental variables. For AWS, it looks like you can set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN`\r\n\r\nNote that while testing this just now, I did note a discrepancy between gcsfs and s3fs that we might want to address where gcsfs passes the timeout from `storage_options` [here](https://github.com/huggingface/datasets/blob/3e6269979fc80ae8939294d26298897f0db5b84d/src/datasets/utils/file_utils.py#L333) down into the `aiohttp.ClientSession.request`, but s3fs does not handle this (tries to pass to the `aiobotocore.session.AioSession` constructor raising `TypeError: __init__() got an unexpected keyword argument 'requests_timeout'`).\r\n\r\nIt seems like some work trying to unify kwargs across different fsspec implementations, so if the plan is to pass down `storage_options`, I wonder if we should just let users control the timeout (and other kwargs) using that and if not specified, use the default?",
"> Note that while testing this just now, I did note a discrepancy between gcsfs and s3fs that we might want to address where gcsfs passes the timeout from storage_options [here](https://github.com/huggingface/datasets/blob/3e6269979fc80ae8939294d26298897f0db5b84d/src/datasets/utils/file_utils.py#L333) down into the aiohttp.ClientSession.request, but s3fs does not handle this (tries to pass to the aiobotocore.session.AioSession constructor raising TypeError: __init__() got an unexpected keyword argument 'requests_timeout').\r\n\r\n> It seems like some work trying to unify kwargs across different fsspec implementations, so if the plan is to pass down storage_options, I wonder if we should just let users control the timeout (and other kwargs) and if not specified, use the default?\r\n\r\n@lhoestq here's a small PR for this: https://github.com/huggingface/datasets/pull/5673\r\n\r\n"
] | "2022-11-22T14:00:10" | "2023-04-05T09:01:23" | null | MEMBER | null | Would be nice to be able to do
```python
data_files=["s3://..."]
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```
or even
```python
load_dataset("gs://...")
```
The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.
This has been requested several times already. Some users want to use their data from private cloud storage to train models
related:
https://github.com/huggingface/datasets/issues/3490
https://github.com/huggingface/datasets/issues/5244
[forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2) | {
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https://api.github.com/repos/huggingface/datasets/issues/5280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5280/comments | https://api.github.com/repos/huggingface/datasets/issues/5280/events | https://github.com/huggingface/datasets/issues/5280 | 1,459,823,179 | I_kwDODunzps5XAyJL | 5,280 | Import error | {
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"Hi ! Can you \r\n```python\r\nimport platform\r\nprint(platform.python_version())\r\n```\r\nto see that it returns ?",
"Hi,\n\n3.8.13\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:37:02 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nHi ! Can you\n\nimport platform\nprint(platform.python_version())\n\nto see that it returns ?\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323691385>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F5YGG32W6WABYC25NJTWJTD75ANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n",
"Then it should work as expected if you use the same python when using `datasets`\r\n\r\nPlease make sure you're running your code in the right environment",
"It's the right environment. But in if statement I have\n\"3.8.13\" < 3.7\nAnd in the error message is Python>=3.7 which is true in my case (3.8.13 is greater then 3.7), so I don't understand my python should be below the 3.7 which case the if statement is right, but the message is wrong, or above 3.7 which case if statement is wrong, error message is right.\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:41:43 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nThen it should work as expected if you use the same python when using datasets\n\nPlease make sure you're running your code in the right environment\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323697094>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F54JURTAJJWWDO2QGI3WJTERPANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n",
"If you're having an error then you're not running your code in the right environment."
] | "2022-11-22T12:56:43" | "2022-12-15T19:57:40" | "2022-12-15T19:57:40" | NONE | null | https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I'm also in favor of disabling this by default - it's kinda impractical",
"Great, thanks for the quick turnaround on this!"
] | "2022-11-22T10:58:48" | "2022-11-23T11:43:50" | "2022-11-23T09:47:02" | MEMBER | null | It takes a lot of time on big datasets to compute the checksums, we should at least add a warning to notify the user about this step. I also mentioned how to disable it, and added a tqdm bar (delay=5 seconds)
cc @ola13 | {
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"Can you try to remove \"drop_labels=false\" ? It may force the loader to infer the labels instead of reading the metadata",
"Hi, thanks for responding. I tried that, but it does not change anything.",
"Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4",
"Probably the issue, will report back asap!",
"Okay, now it seems to actually load the metadata and create the train_split, but it still says only returns \"image\" and \"label\", which is always 0 since all images are from same folder",
"> Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4\r\n\r\nUpdate: This was the issue."
] | "2022-11-22T10:24:46" | "2023-02-14T14:48:16" | "2022-11-23T11:38:35" | NONE | null | ### Describe the bug
Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features.
Below is code to reproduce my exact example/problem.
### Steps to reproduce the bug
```ruby
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)
ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```
### Expected behavior
I would expect that it returned "image" and "text" columns from the code above.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Also note that this approach is valid when metadata keys are str, but also if they are int.\r\n- This will be helpful for any community dataset using old integer keys in their metadata",
"perfect !"
] | "2022-11-22T08:34:07" | "2022-11-22T13:58:26" | "2022-11-22T13:55:49" | MEMBER | null | Fix partially #5275. | {
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"Sounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?",
"Well I just sharded the original commonVoice dataset and pushed a small chunk of it in a private rep\n\nWhat did go wrong?\n\nHolen Sie sich Outlook für iOS<https://aka.ms/o0ukef>\n________________________________\nVon: Quentin Lhoest ***@***.***>\nGesendet: Tuesday, November 22, 2022 3:03:40 PM\nAn: huggingface/datasets ***@***.***>\nCc: capsabogdan ***@***.***>; Author ***@***.***>\nBetreff: Re: [huggingface/datasets] Bug in downloading common_voice data and snall chunk of it to one's own hub (Issue #5276)\n\n\nSounds like one of the file is not a valid one, can you make sure you uploaded valid mp3 files ?\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5276#issuecomment-1323727434>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ALSIFOAPAL2V4TBJTSPMAULWJTHDZANCNFSM6AAAAAASHQJ63U>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n",
"It should be all good then !\r\nCould you share a link to your repository for me to investigate what went wrong ?",
"https://huggingface.co/datasets/DTU54DL/common-voice-test16k\n\nAm Di., 22. Nov. 2022 um 16:43 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> It should be all good then !\n> Could you share a link to your repository for me to investigate what went\n> wrong ?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1323876682>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOEUJRZWXAM7DYA5VJDWJTS3NANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n",
"I see ! This is a bug with MP3 files.\r\n\r\nWhen we store audio data in parquet, we store the bytes and the file name. From the file name extension we know if it's a WAV, an MP3 or else. But here it looks like the paths are all None.\r\n\r\nIt looks like it comes from here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7feeb5648a63b6135a8259dedc3b1e19185ee4c7/src/datasets/features/audio.py#L212\r\n\r\nCc @polinaeterna maybe we should simply put the file name instead of None values ?",
"@lhoestq I remember we wanted to avoid storing redundant data but maybe it's not that crucial indeed to store one more string value. \r\nOr we can store paths only for mp3s, considering that for other formats we don't have such a problem with reading from bytes without format specified. ",
"It doesn't cost much to always store the file name IMO",
"thanks for the help!\n\ncan I do anything on my side? we are doing a DL project and we need the\ndata really quick.\n\nthanks\nbogdan\n\n> Message ID: ***@***.***>\n>\n",
"I opened a pull requests here: https://github.com/huggingface/datasets/pull/5285, we'll do a new release soon with this fix.\r\n\r\nOtherwise if you're really in a hurry you can install `datasets` from this PR",
"[image: image.png]\n\n> Message ID: ***@***.***>\n>\n",
"any idea on what's going wrong here?\n\nthanks\n\nAm So., 27. Nov. 2022 um 13:53 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> [image: image.png]\n>\n>> Message ID: ***@***.***>\n>>\n>\n",
"hi @capsabogdan! \r\ncould you please share more specifically what problem do you have now?",
"I have attached this screenshot above . can u pls help? So can not pip from pull request\r\n\r\n\r\n",
"The pull request has been merged on `main`.\r\nYou can install `datasets` from `main` using\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```",
"I've tried to load this dataset DTU54DL/common-voice-test16k, but am\ngetting the same error.\n\nSo the bug fix will fix only if I upload a new dataset, or also loading\npreviously uploaded datasets?\n\nthanks\n\nAm Mo., 28. Nov. 2022 um 19:51 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> The pull request has been merged on main.\n> You can install datasets from main using\n>\n> pip install git+https://github.com/huggingface/datasets.git\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1329587334>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOCNYYIGHM2EX3ZIO6DWKT5MXANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n",
"> So the bug fix will fix only if I upload a new dataset, or also loading\r\npreviously uploaded datasets?\r\n\r\nYou have to reupload the dataset, sorry for the inconvenience",
"thank you so much for the help! works like a charm!\n\nAm Di., 29. Nov. 2022 um 12:15 Uhr schrieb Quentin Lhoest <\n***@***.***>:\n\n> So the bug fix will fix only if I upload a new dataset, or also loading\n> previously uploaded datasets?\n>\n> You have to reupload the dataset, sorry for the inconvenience\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5276#issuecomment-1330468393>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOBKEFZO57BAKY4IGW3WKXQUZANCNFSM6AAAAAASHQJ63U>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n"
] | "2022-11-22T08:17:53" | "2022-11-30T16:59:49" | null | NONE | null | ### Describe the bug
I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset
Help please?
### Steps to reproduce the bug
So here is what I have done:
1. Download common_voice data
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.
### Expected behavior
There shouldn't be an error in downloading part of the data and publishing it to one's own repo
### Environment info
common_voice 11 | {
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"@huggingface/datasets if you agree, I can make the bulk edit on the Hub to fix integer keys into strings.",
"Ok for me, and we can merge (internal) https://github.com/huggingface/moon-landing/pull/4609",
"FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for class labels. And among those, some are using datasets with class labels like imdb (60 users), conllpp (40), msra_ner (40), peoples_daily_enr (40), weibo_ner (30), conll2003 (20), etc. And renaming to string would break these users code.",
"but isn't `datasets 2.6.1` downloading files from the Hub with the corresponding tag? I thought we had something like this before",
"We're using `main` as models do. Some datasets need to be updated from time to time, e.g. when a link to download the data is dead.\r\n\r\nBut yea a year ago we had those tags, we just ended up not using them",
"I opened https://github.com/huggingface/datasets/issues/5406 to communicate on this. Let me know what you think, and if it sounds good to you I can pin this issue",
"So, is it OK to make the bulk edit on the Hub now or should we wait longer? If the latter, how long?",
"I think we can do it. If you want to be extra cautious you can do it for all datasets except imdb and conllpp for now which are actively used by 2.6.1 users. For those two we can keep the YAML like this for some more time, or alternatively use the old dataset_infos.json file",
"The bulk edit of canonical datasets (except imdb and conllpp) is running. \r\n\r\nSee e.g.: https://huggingface.co/datasets/acronym_identification/discussions/3\r\n\r\nEDITED: \r\nDone, except for \"universal_morphologies\", where I get\r\n```\r\nHTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/validate-yaml\r\n```\r\n\r\nAlso not done for the datasets missing matadata \"dataset_info\":\r\n- mc4: https://huggingface.co/datasets/mc4/discussions/3\r\n- the_pile: https://huggingface.co/datasets/the_pile/discussions/6\r\n- timit_asr: https://huggingface.co/datasets/timit_asr/discussions/1",
"Thank you !",
"@lhoestq, there are 6 community datasets with YAML integer keys in their `dataset_info` `class_label`:\r\n- indonlp/indonlu\r\n- rcds/swiss_judgment_prediction\r\n- Jean-Baptiste/wikiner_fr\r\n- Bingsu/Cat_and_Dog\r\n- taskydata/tasky_or_not\r\n- RCC-MSU/collection3\r\n\r\nMaybe we could open a PR on them as well?",
"Let's do this then:\r\n\r\n- [x] [indonlp/indonlu](https://huggingface.co/datasets/indonlp/indonlu/discussions/3)\r\n- [x] rcds/swiss_judgment_prediction\r\n- [x] Jean-Baptiste/wikiner_fr\r\n- [x] Bingsu/Cat_and_Dog -> merged\r\n- [x] taskydata/tasky_or_not (was already using quotes)\r\n- [x] RCC-MSU/collection3\r\n\r\nEDIT: all done :)",
"@lhoestq I was not asking you to do it, but asking if you agree me to do it... :man_facepalming: \r\nAs I self-assigned this issue... :sweat_smile: "
] | "2022-11-22T08:14:47" | "2023-01-26T10:52:35" | "2023-01-26T10:40:21" | MEMBER | null | After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/5274 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5274/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5274/comments | https://api.github.com/repos/huggingface/datasets/issues/5274/events | https://github.com/huggingface/datasets/issues/5274 | 1,458,646,455 | I_kwDODunzps5W8S23 | 5,274 | load_dataset possibly broken for gated datasets? | {
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"@BradleyHsu",
"Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!",
"I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.\r\n\r\nhttps://github.com/poloclub/diffusiondb/issues/7",
"I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652\r\n\r\nI opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2",
"This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version 🙂. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.",
"Awesome, big thanks to both @xiaohk and @mariosasko!",
"so, if i reproduce the bug, what should i do ? with huggingface_hub0.13.3 dataset2.6.1"
] | "2022-11-21T21:59:53" | "2023-04-04T09:59:09" | "2022-11-28T02:50:42" | CONTRIBUTOR | null | ### Describe the bug
When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:
```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
165 if repo_id.count("/") > 1:
166 raise HFValidationError(
--> 167 "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
168 f" '{repo_id}'. Use `repo_type` argument if needed."
169 )
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```
### Steps to reproduce the bug
Install requirements:
```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```
Then:
```
from datasets import load_dataset
auth_token = "" # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```
### Expected behavior
Downloading of the datset
### Environment info
Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing | {
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} | [] | open | false | null | [] | null | [] | "2022-11-21T14:12:43" | "2022-11-21T14:13:03" | null | NONE | null | ### Describe the bug
`load_datasets` does not refresh dataset when features are imported from external file, even with `download_mode="force_redownload"`. The bug is not limited to nested fields, however it is more likely to occur with nested fields.
### Steps to reproduce the bug
To reproduce the bug 3 files are needed: `dataset.py` (contains dataset loading script), `schema.py` (contains features of dataset) and `main.py` (to run `load_datasets`)
`dataset.py`
```python
import datasets
from schema import features
class NewDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=features
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN
)
]
def _generate_examples(self):
data = [
{"id": 0, "nested": []},
{"id": 1, "nested": []}
]
for key, example in enumerate(data):
yield key, example
```
`schema.py`
```python
import datasets
features = datasets.Features(
{
"id": datasets.Value("int32"),
"nested": [
{"text": datasets.Value("string")}
]
}
)
```
`main.py`
```python
import datasets
a = datasets.load_dataset("dataset.py")
print(a["train"].info.features)
```
Now if `main.py` is run it prints the following correct output: `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`. However, if f.e. the label of the feature "text" is changed to something else, f.e. to
`schema.py`
```python
import datasets
features = datasets.Features(
{
"id": datasets.Value("int32"),
"nested": [
{"textfoo": datasets.Value("string")}
]
}
)
```
`main.py` still prints `{'id': Value(dtype='int32', id=None), 'nested': [{'text': Value(dtype='string', id=None)}]}`, even if run with `download_mode="force_redownload"`. The only fix is to delete the folder in the cache.
### Expected behavior
The cached dataset is deleted and refreshed when using `load_datasets` with `download_mode="force_redownload"`.
### Environment info
- `datasets` version: 2.7.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 10.0.0
- Pandas version: 1.3.5 | {
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"Hi ! We're using the Arrow format for the datasets, and PyArrow tensors are not part of the Arrow format AFAIK:\r\n\r\n> There is no direct support in the arrow columnar format to store Tensors as column values.\r\n\r\nsource: https://github.com/apache/arrow/issues/4802#issuecomment-508494694",
"@wesm @rok its been around three years. any updates, regarding dataset arrow tensor support? 🙏 I know you must be very busy, would appreciate to learn what is the state of art. I saw the PR is still open [#8510](https://github.com/apache/arrow/pull/8510)",
"Hey @franz101 & @lhoestq!\r\nThere is a plan and a PR to create an [ExtensionArray of Tensors](https://github.com/apache/arrow/pull/8510) of equal sizes as well as a plan to do the same for Tensors of different sizes [ARROW-8714](https://issues.apache.org/jira/browse/ARROW-8714).",
"The work stalled a little because it was not clear where TensorArray would live. However Arrow community recently agreed to make a [well-known-extension-type document](https://lists.apache.org/thread/sxd5fhc42hb6svs79t3fd79gkqj83pfh) and I would like https://github.com/apache/arrow/pull/8510 to land there and add an implementation to C++/Python + another language. Is that something you would find beneficial to you?",
"that is a great update, thank you.\r\nit looks like this feature would benefit datasets implementation of [ArrayExtensionArray](https://github.com/huggingface/datasets/blob/9f2ff14673cac1f1ad56d80221a793f5938b68c7/src/datasets/features/features.py#L585-L641). Is that correct @eladsegal @lhoestq?\r\n\r\n",
"TensorArray sounds great ! Looking forward to it :)\r\n\r\nWe've had our own ExtensionArray for fixed shape tensors for a while now, hoping to see something more standardized by the arrow community.\r\n\r\nAlso super interested in the extension array for tensors of different sizes cc @mariosasko ",
"[FixedShapeTensor ExtensionType](https://github.com/apache/arrow/pull/8510) was merged and will be in Arrow 12.0.0 (release is planned mid April).\r\n",
"@rok Thanks for keeping us updated! I think it's best to introduce a new feature type that would use this extension type under the hood. I'll create an issue to discuss the design with the community in the coming days.\r\n\r\nAlso, is there a tentative time frame for the variable-shape Tensor extension type?",
"@mariosasko please tag me in the discussion, perhaps I can contribute.\r\n\r\nAs for the [variable shape tensor array](https://github.com/apache/arrow/issues/24868) - I'd be interested in working on it but didn't see much interest in community yet. Are you saying `huggingface/datasets` could use it?"
] | "2022-11-20T15:18:41" | "2023-04-07T19:06:17" | null | NONE | null | ### Feature request
I was going the discussion of converting tensors to lists.
Is there a way to leverage pyarrow's Tensors for nested arrays / embeddings?
For example:
```python
import pyarrow as pa
import numpy as np
x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
```
[Apache docs](https://arrow.apache.org/docs/python/generated/pyarrow.Tensor.html)
Maybe this belongs into the pyarrow features / repo.
### Motivation
Working with big data, we need to make sure to use the best data structures and IO out there
### Your contribution
Can try to a PR if code changes necessary | {
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"See <https://github.com/huggingface/datasets/issues/5269>"
] | "2022-11-20T07:50:49" | "2022-11-21T15:07:19" | "2022-11-21T15:06:38" | NONE | null | ```
$ datasets-cli convert --datasets_directory <TAB>
datasets_directory
benchmarks/ docs/ metrics/ notebooks/ src/ templates/ tests/ utils/
```
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"It can fix the bug temporarily.\r\n```python\r\nfrom datasets import DownloadConfig\r\nconfig = DownloadConfig(num_proc=8)\r\nIn [5]: dataset = load_dataset('Freed-Wu/kodak', split='test', download_config=config)\r\nDownloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/6cf51f2b3d686d24a33fe86945f9e16802def212325f9345cf3cbb1b9f5f4a57...\r\nDownloading data files #4: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.39obj/s]\r\nDownloading data files #2: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.38obj/s]\r\nDownloading data files #3: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.13obj/s]\r\nDownloading data files #7: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.09obj/s]\r\nDownloading data files #5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.08obj/s]\r\nDownloading data files #0: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.08obj/s]\r\nDownloading data files #1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:10<00:00, 3.36s/obj]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 492k/492k [00:01<00:00, 253kB/s]\r\nDownloading data files #6: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:13<00:00, 4.63s/obj]\r\nExtracting data files #0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1407.17obj/s]\r\nExtracting data files #1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1325.91obj/s]\r\nExtracting data files #3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1524.46obj/s]\r\nExtracting data files #2: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1404.66obj/s]\r\nExtracting data files #4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1538.63obj/s]\r\nExtracting data files #6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1711.73obj/s]\r\nExtracting data files #7: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 2144.33obj/s]\r\nExtracting data files #5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1964.85obj/s]\r\nDataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/6cf51f2b3d686d24a33fe86945f9e16802def212325f9345cf3cbb1b9f5f4a57. Subsequent calls will reuse this data.\r\n```",
"Thanks for reporting ! This sounds like an issue with python multiprocessing. If we switch to multithreading for the downloads it should be much more robust - let me know if this is something you'd like to contribute, I'd be happy to help and give you some pointers",
"> an issue with python multiprocessing\r\n\r\nIf it is an issue with multiprocessing, should we report it to upstream?",
"Debugging this would require quite some work in my opinion, and I've often failed to make reproducible examples, since it's pretty correlated to one's environment + hardware. So I wouldn't spend too much time on this unless we manage to reproduce this on another machine consistently.\r\n\r\nInstead I'd encourage a more pragmatic fix that is: not create tons of processes (on regular machines it may slow things down anyway), and instead use multithreading by default.",
"I am not expert of python. I hear about python has GIL, which result in multi processing is worse than multi threading. So I am not sure if this change makes sense?\r\n\r\nAnd if this is a bug of multi processing, why not report to upstream and let them fix? And even if change it to multi threading, how can we make sure it can truly fix this problem?",
"Just my 2c. No offense.",
"> Just my 2c. No offense.\r\n\r\nsure np ^^\r\n\r\n> I hear about python has GIL, which result in multi processing is worse than multi threading. So I am not sure if this change makes sense?\r\n\r\nHere the bottleneck speed is the bandwidth used to download the files. When downloading, the GIL is released, so multithreading gives the same speed as multiprocessing.\r\n\r\n> And if this is a bug of multi processing, why not report to upstream and let them fix?\r\n\r\nUsually to fix a bug it's important to be able to reproduce it. This way you can share it, experiment with it, and then make sure it's fixed. Here I'm afraid it's not easy to reproduce. Though I think that spawning too many processes for your machine can lead to this kind of issues.\r\n\r\n> And even if change it to multi threading, how can we make sure it can truly fix this problem?\r\n\r\nMultithreading is more robust in python because IIRC there are less locks involved which are often the cause of code hanging for no reason."
] | "2022-11-19T14:27:41" | "2022-11-21T15:27:16" | null | NONE | null | ### Describe the bug
```python
In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 1.88MB/s]
[11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:02<00:00, 239kB/s]
#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.06s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 534k/534k [00:02<00:00, 193kB/s]
#14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.37s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 692k/692k [00:02<00:00, 269kB/s]
#12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.44s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 566k/566k [00:02<00:00, 210kB/s]
#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 613k/613k [00:02<00:00, 235kB/s]
#13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.53s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 786k/786k [00:02<00:00, 342kB/s]
#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.60s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 619k/619k [00:02<00:00, 254kB/s]
#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.68s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 737k/737k [00:02<00:00, 271kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 788k/788k [00:02<00:00, 285kB/s]
#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 618k/618k [00:04<00:00, 153kB/s]
#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:11<00:00, 5.69s/obj]
^CProcess ForkPoolWorker-47:
Process ForkPoolWorker-46:
Process ForkPoolWorker-36:
Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:05<00:00, 5.04s/obj]
Process ForkPoolWorker-37:
Process ForkPoolWorker-45:
Process ForkPoolWorker-39:
Process ForkPoolWorker-43:
Process ForkPoolWorker-33:
Process ForkPoolWorker-18:
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):
Traceback (most recent call last):
Traceback (most recent call last):
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get
res = self._reader.recv_bytes()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes
buf = self._recv_bytes(maxlength)
KeyboardInterrupt
KeyboardInterrupt
File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes
buf = self._recv(4)
File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker
task = get()
File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get
with self._rlock:
File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__
return self._semlock.__enter__()
KeyboardInterrupt
Process ForkPoolWorker-20:
Process ForkPoolWorker-44:
Process ForkPoolWorker-22:
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#1: 0%| | 0/2 [03:00<?, ?obj/s]
Traceback (most recent call last):
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache
http_get(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
KeyboardInterrupt
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#3: 0%| | 0/2 [03:00<?, ?obj/s]
#11: 0%| | 0/1 [00:49<?, ?obj/s]
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp>
history = [resp for resp in gen]
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects
resp = self.send(
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
#5: 0%| | 0/1 [03:00<?, ?obj/s]
KeyboardInterrupt
Process ForkPoolWorker-42:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path
output_path = get_from_cache(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache
response = http_head(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head
response = _request_with_retry(
File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send
resp = conn.urlopen(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect
self.sock = conn = self._new_conn()
File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection
for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
KeyboardInterrupt
#9: 0%| | 0/1 [00:51<?, ?obj/s]
```
### Steps to reproduce the bug
```python
"""Kodak.
Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import datasets
NUMBER = 17
_DESCRIPTION = """\
The pictures below link to lossless, true color (24 bits per pixel, aka "full
color") images. It is my understanding they have been released by the Eastman
Kodak Company for unrestricted usage. Many sites use them as a standard test
suite for compression testing, etc. Prior to this site, they were only
available in the Sun Raster format via ftp. This meant that the images could
not be previewed before downloading. Since their release, however, the lossless
PNG format has been incorporated into all the major browsers. Since PNG
supports 24-bit lossless color (which GIF and JPEG do not), it is now possible
to offer this browser-friendly access to the images.
"""
_HOMEPAGE = "https://r0k.us/graphics/kodak/"
_LICENSE = "GPLv3"
_URLS = [
f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png"
for i in range(1, 1 + NUMBER)
]
class Kodak(datasets.GeneratorBasedBuilder):
"""Kodak datasets."""
VERSION = datasets.Version("0.0.1")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Return SplitGenerators."""
file_paths = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_paths": file_paths,
},
),
]
def _generate_examples(self, file_paths):
"""Yield examples."""
for file_path in file_paths:
yield file_path, {"image": file_path}
```
### Expected behavior
When `len(_URLS) < 16`, it works.
```python
In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.53k/2.53k [00:00<00:00, 3.02MB/s]
[11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379
Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 593k/593k [00:00<00:00, 2.88MB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 621k/621k [00:03<00:00, 166kB/s]
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 531k/531k [00:01<00:00, 366kB/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:13<00:00, 1.18it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:00<00:00, 3832.38it/s]
Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data.
```
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | {
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"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] | closed | false | null | [] | null | [
"I don't think we need completion on the datasets-cli, since we're mainly developing huggingface-cli",
"I see."
] | "2022-11-19T13:48:59" | "2022-11-21T15:06:15" | "2022-11-21T15:06:14" | NONE | null | ### Feature request
Like <https://github.com/huggingface/huggingface_hub/issues/1197>, datasets-cli maybe need it, too.
### Motivation
See above.
### Your contribution
Maybe. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Added both num_shards and max_shard_size in push_to_hub/save_to_disk. Will take care of updating the tests later",
"It's ready for a final review @mariosasko and @albertvillanova, let me know what you think :)",
"Took your comments into account, and also changed `iflatmap_unordered` to take an iterable of kwargs to make the code more redable :)"
] | "2022-11-18T18:50:01" | "2022-12-14T18:25:52" | "2022-12-14T18:22:58" | MEMBER | null | Added `num_shards=` and `num_proc=` to `save_to_disk()`
EDIT: also added `max_shard_size=` to `save_to_disk()`, and also `num_shards=` to `push_to_hub`
I also:
- deprecated the fs parameter in favor of storage_options (for consistency with the rest of the lib) in save_to_disk and load_from_disk
- always embed the image/audio data in arrow when doing `save_to_disk`
- added a tqdm bar in `save_to_disk`
- Use the MockFileSystem in tests for `save_to_disk` and `load_from_disk`
- removed the unused integration tests with S3, since we can now test with `mockfs` instead of `s3fs`
TODO:
- [x] implem save_to_disk for dataset dict
- [x] save_to_disk for dataset dict tests
- [x] deprecate fs in dataset dict load_from_disk as well
- [x] update docs
Close #5263
Close https://github.com/huggingface/datasets/issues/4196
Close https://github.com/huggingface/datasets/issues/4351 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-18T16:55:22" | "2022-11-18T17:28:58" | "2022-11-18T17:25:27" | MEMBER | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5266 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5266/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5266/comments | https://api.github.com/repos/huggingface/datasets/issues/5266/events | https://github.com/huggingface/datasets/pull/5266 | 1,455,281,310 | PR_kwDODunzps5DN9BT | 5,266 | Specify arguments as keywords in librosa.reshape to avoid future errors | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-18T14:58:47" | "2022-11-21T15:45:02" | "2022-11-21T15:41:57" | CONTRIBUTOR | null | Fixes a warning and future deprecation from `librosa.reshape`:
```
FutureWarning: Pass orig_sr=16000, target_sr=48000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
``` | {
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"I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.\r\n\r\nPS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf_dataset` to the API for consistency and deprecate `to_tf_dataset`."
] | "2022-11-18T14:54:40" | "2023-02-01T16:36:03" | "2023-02-01T16:36:03" | MEMBER | null | This is useful to leverage iterable datasets specific features like:
- fast approximate shuffling
- lazy map, filter etc.
Iterating over the resulting iterable dataset should be at least as fast at iterating over the map-style dataset.
Here are some ideas regarding the API:
```python
# 1.
# - consistency with load_dataset(..., streaming=True)
# - gives intuition that map/filter/etc. are done on-the-fly
ids = ds.stream()
# 2.
# - more explicit on the output type
# - but maybe sounds like a conversion tool rather than a step in a processing pipeline
ids = ds.as_iterable_dataset()
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5264 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5264/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5264/comments | https://api.github.com/repos/huggingface/datasets/issues/5264/events | https://github.com/huggingface/datasets/issues/5264 | 1,455,252,906 | I_kwDODunzps5WvWWq | 5,264 | `datasets` can't read a Parquet file in Python 3.9.13 | {
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"Could you share the full stack trace please ?\r\n\r\n\r\nCan you also try running this code ? It can be useful to determine if the issue comes from `datasets` or `fsspec` (streaming) or `pyarrow` (parquet reading):\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=True)\r\n```",
"Here's the full trace\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/loubna_huggingface_co/load.py\", line 15, in <module>\r\n ds_all = load_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\",use_auth_token=True, split=\"train\", revision=\"v1.1.a1\")\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1742, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 814, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 905, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 1502, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 67, in _generate_tables\r\n parquet_file = pq.ParquetFile(f)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py\", line 286, in __init__\r\n self.reader.open(\r\n File \"pyarrow/_parquet.pyx\", line 1227, in pyarrow._parquet.ParquetReader.open\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```\r\n\r\nwhen running\r\n```python\r\nds = load_dataset(\"parquet\", data_files=\"https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/java/data_0000.parquet\", use_auth_token=True)\r\n```\r\nI get 401 error, but that's the case for the python subset too which I can load properly\r\n```\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1719, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1497, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1134, in dataset_module_factory\r\n return PackagedDatasetModuleFactory(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 707, in get_module\r\n data_files = DataFilesDict.from_local_or_remote(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 795, in from_local_or_remote\r\n DataFilesList.from_local_or_remote(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 764, in from_local_or_remote\r\n origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 710, in _get_origin_metadata_locally_or_by_urls\r\n return thread_map(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py\", line 94, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py\", line 76, in _executor_map\r\n return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1183, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 609, in result_iterator\r\n yield fs.pop().result()\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 446, in result\r\n return self.__get_result()\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 391, in __get_result\r\n raise self._exception\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 701, in _get_single_origin_metadata_locally_or_by_urls\r\n return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 411, in request_etag\r\n response.raise_for_status()\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/requests/models.py\", line 960, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/python/data_0000.parquet```",
"Can you check you used the right token ? You shouldn't get a 401 using your token",
"I checked it’s the right token, when loading the full dataset I get the error after data extraction so I can access the files. \r\n```\r\nDownloading and preparing dataset parquet/bigcode--the-stack-dedup-pjj to /home/loubna_huggingface_co/.cache/huggingface/datasets/bigcode___parquet/bigcode--the-stack-dedup-pjj-872ffac7f4bb46ca/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\r\nDownloading data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22.38it/s]\r\nExtracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 49.91it/s]\r\nTraceback (most recent call last):\r\n File \"/home/loubna_huggingface_co/load_ds.py\", line 5, in <module>\r\n ds = load_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\", use_auth_token=True,split=\"train\", revision=\"v1.1.a1\")\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1742, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 814, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 905, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 1502, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 67, in _generate_tables\r\n parquet_file = pq.ParquetFile(f)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py\", line 286, in __init__\r\n self.reader.open(\r\n File \"pyarrow/_parquet.pyx\", line 1227, in pyarrow._parquet.ParquetReader.open\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```\r\nCould it be that I'm using a wrong url, I just copied it from the address bar",
"The URL is wrong indeed, the right one is the one with \"resolve\" (the one you get when clicking on \"download\")- otherwise you try to download an html page ;)\r\n```\r\nhttps://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/resolve/v1.1.a1/data/java/data_0000.parquet\r\n```",
"Ah thanks! So I tried it with the first parquet file and it works, is there a way to know which parquet file was causing the issue since there are a lot of shards?",
"I think you have to try them all :/\r\n\r\nAlternatively you can add a try/catch in `parquet.py` in `datasets` to raise the name of the file that fails at doing `parquet_file = pq.ParquetFile(f)` when you run your initial code\r\n```python\r\nload_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\", split=\"train\", revision=\"v1.1.a1\", use_auth_token=True)\r\n```\r\nbut it will still iterate on all the files until it fails",
"Ok I will do that",
"I did find the file, and I get the same error as before \r\n```\r\nDownloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 8160.12it/s]\r\nExtracting data files: 100%|████████████████████| 1/1 [00:00<00:00, 1447.81it/s]\r\n \r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\nInput In [22], in <cell line: 7>()\r\n 4 data_features = (data[\"train\"].features)\r\n 6 url = \"/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7\"\r\n----> 7 data = load_dataset(\"parquet\", \r\n 8 data_files=url,\r\n 9 split=\"train\",\r\n 10 features=data_features,\r\n 11 use_auth_token=True)\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py:1742, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n 1739 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1741 # Download and prepare data\r\n-> 1742 builder_instance.download_and_prepare(\r\n 1743 download_config=download_config,\r\n 1744 download_mode=download_mode,\r\n 1745 ignore_verifications=ignore_verifications,\r\n 1746 try_from_hf_gcs=try_from_hf_gcs,\r\n 1747 use_auth_token=use_auth_token,\r\n 1748 )\r\n 1750 # Build dataset for splits\r\n 1751 keep_in_memory = (\r\n 1752 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1753 )\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:814, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)\r\n 808 if not downloaded_from_gcs:\r\n 809 prepare_split_kwargs = {\r\n 810 \"file_format\": file_format,\r\n 811 \"max_shard_size\": max_shard_size,\r\n 812 **download_and_prepare_kwargs,\r\n 813 }\r\n--> 814 self._download_and_prepare(\r\n 815 dl_manager=dl_manager,\r\n 816 verify_infos=verify_infos,\r\n 817 **prepare_split_kwargs,\r\n 818 **download_and_prepare_kwargs,\r\n 819 )\r\n 820 # Sync info\r\n 821 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:905, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 901 split_dict.add(split_generator.split_info)\r\n 903 try:\r\n 904 # Prepare split will record examples associated to the split\r\n--> 905 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 906 except OSError as e:\r\n 907 raise OSError(\r\n 908 \"Cannot find data file. \"\r\n 909 + (self.manual_download_instructions or \"\")\r\n 910 + \"\\nOriginal error:\\n\"\r\n 911 + str(e)\r\n 912 ) from None\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:1502, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, max_shard_size)\r\n 1500 total_num_examples, total_num_bytes = 0, 0\r\n 1501 try:\r\n-> 1502 for key, table in logging.tqdm(\r\n 1503 generator,\r\n 1504 unit=\" tables\",\r\n 1505 leave=False,\r\n 1506 disable=not logging.is_progress_bar_enabled(),\r\n 1507 ):\r\n 1508 if max_shard_size is not None and writer._num_bytes > max_shard_size:\r\n 1509 num_examples, num_bytes = writer.finalize()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py:1195, in tqdm.__iter__(self)\r\n 1192 time = self._time\r\n 1194 try:\r\n-> 1195 for obj in iterable:\r\n 1196 yield obj\r\n 1197 # Update and possibly print the progressbar.\r\n 1198 # Note: does not call self.update(1) for speed optimisation.\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py:67, in Parquet._generate_tables(self, files)\r\n 65 for file_idx, file in enumerate(itertools.chain.from_iterable(files)):\r\n 66 with open(file, \"rb\") as f:\r\n---> 67 parquet_file = pq.ParquetFile(f)\r\n 68 try:\r\n 69 for batch_idx, record_batch in enumerate(\r\n 70 parquet_file.iter_batches(batch_size=self.config.batch_size, columns=self.config.columns)\r\n 71 ):\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py:286, in ParquetFile.__init__(self, source, metadata, common_metadata, read_dictionary, memory_map, buffer_size, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit)\r\n 280 def __init__(self, source, *, metadata=None, common_metadata=None,\r\n 281 read_dictionary=None, memory_map=False, buffer_size=0,\r\n 282 pre_buffer=False, coerce_int96_timestamp_unit=None,\r\n 283 decryption_properties=None, thrift_string_size_limit=None,\r\n 284 thrift_container_size_limit=None):\r\n 285 self.reader = ParquetReader()\r\n--> 286 self.reader.open(\r\n 287 source, use_memory_map=memory_map,\r\n 288 buffer_size=buffer_size, pre_buffer=pre_buffer,\r\n 289 read_dictionary=read_dictionary, metadata=metadata,\r\n 290 coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,\r\n 291 decryption_properties=decryption_properties,\r\n 292 thrift_string_size_limit=thrift_string_size_limit,\r\n 293 thrift_container_size_limit=thrift_container_size_limit,\r\n 294 )\r\n 295 self.common_metadata = common_metadata\r\n 296 self._nested_paths_by_prefix = self._build_nested_paths()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/_parquet.pyx:1227, in pyarrow._parquet.ParquetReader.open()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```",
"Can you check the JSON file associated to `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` ? In the JSON file we can know from where it was downloaded\r\n\r\nYou can find it at `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json`",
"It's this file `https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/resolve/f48656daa9f3a3607dacf8b57a65810a6a7a7f73/data/java/data_0022.parquet` loading it gives the same error",
"I'm able to load it properly using\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=token)\r\n```\r\n\r\nMy guess is that your download was corrupted. Please delete `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` and `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json` locally and try again",
"That worked, thanks! But I thought if something went wrong with a download `datasets` creates new cache for all the files, that's not the case? (at some point I even changed dataset versions so it was still using that cache?)",
"Cool !\r\n\r\n> But I thought if something went wrong with a download datasets creates new cache for all the files\r\n\r\nWe don't perform integrity verifications if we don't know in advance the hash of the file to download.\r\n\r\n> at some point I even changed dataset versions so it was still using that cache?\r\n\r\n`datasets` caches the files by URL and ETag. If the content of a file changes, then the ETag changes and so it redownloads the file",
"I see, thank you!\r\n"
] | "2022-11-18T14:44:01" | "2022-11-22T11:18:08" | "2022-11-22T11:18:08" | NONE | null | ### Describe the bug
I have an error when trying to load this [dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj) (it's private but I can add you to the bigcode org). `datasets` can't read one of the parquet files in the Java subset
```python
from datasets import load_dataset
ds = load_dataset("bigcode/the-stack-dedup-pjj", data_dir="data/java", split="train", revision="v1.1.a1", use_auth_token=True)
````
```
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
It seems to be an issue with new Python versions, Because it works in these two environements:
```
- `datasets` version: 2.6.1
- Platform: Linux-5.4.0-131-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
But not in this:
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
```
### Steps to reproduce the bug
Load the dataset in python 3.9.13
### Expected behavior
Load the dataset without the pyarrow error.
### Environment info
```
- `datasets` version: 2.6.1
- Platform: Linux-4.19.0-22-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.4
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https://api.github.com/repos/huggingface/datasets/issues/5263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5263/comments | https://api.github.com/repos/huggingface/datasets/issues/5263/events | https://github.com/huggingface/datasets/issues/5263 | 1,455,252,626 | I_kwDODunzps5WvWSS | 5,263 | Save a dataset in a determined number of shards | {
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] | null | [] | "2022-11-18T14:43:54" | "2022-12-14T18:22:59" | "2022-12-14T18:22:59" | MEMBER | null | This is useful to distribute the shards to training nodes.
This can be implemented in `save_to_disk` and can also leverage multiprocessing to speed up the process | {
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https://api.github.com/repos/huggingface/datasets/issues/5262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5262/comments | https://api.github.com/repos/huggingface/datasets/issues/5262/events | https://github.com/huggingface/datasets/issues/5262 | 1,455,171,100 | I_kwDODunzps5WvCYc | 5,262 | AttributeError: 'Value' object has no attribute 'names' | {
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"Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_names = [...]\r\n# OR get them from the dataset\r\nlabel_names = sorted(set(label for labels in raw_datasets[\"train\"][\"isDif\"] for label in labels))\r\n\r\n# Cast to ClassLabel\r\nraw_datasets = raw_datasets.cast_column(\"isDif\", Sequence(ClassLabel(names=label_names)))\r\n```\r\n",
"thank you \r\nit works 💯 "
] | "2022-11-18T13:58:42" | "2022-11-22T10:09:24" | "2022-11-22T10:09:23" | NONE | null | Hello
I'm trying to build a model for custom token classification
I already followed the token classification course on huggingface
while adapting the code to my work, this message occures :
'Value' object has no attribute 'names'
Here's my code:
`raw_datasets`
generates
DatasetDict({
train: Dataset({
features: ['isDisf', 'pos', 'tokens', 'id'],
num_rows: 14
})
})
`raw_datasets["train"][3]["isDisf"]`
generates
['B_RM', 'I_RM', 'I_RM', 'B_RP', 'I_RP', 'O', 'O']
`dis_feature = raw_datasets["train"].features["isDisf"]
dis_feature`
generates
Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)
and
`label_names = dis_feature.feature.names
label_names`
generates
AttributeError Traceback (most recent call last)
[<ipython-input-28-972fd54a869a>](https://localhost:8080/#) in <module>
----> 1 label_names = dis_feature.feature.names
2 label_names
AttributeError: 'Value' object has
AttributeError: 'Value' object has no attribute 'names'
Thank you for your help | {
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https://api.github.com/repos/huggingface/datasets/issues/5261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5261/comments | https://api.github.com/repos/huggingface/datasets/issues/5261/events | https://github.com/huggingface/datasets/issues/5261 | 1,454,647,861 | I_kwDODunzps5WtCo1 | 5,261 | Add PubTables-1M | {
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"cc @albertvillanova the author would like to add this dataset to the hub: https://github.com/microsoft/table-transformer/issues/68#issuecomment-1319114621. Could you help him out?"
] | "2022-11-18T07:56:36" | "2022-11-18T08:02:18" | null | CONTRIBUTOR | null | ### Name
PubTables-1M
### Paper
https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html
### Data
https://github.com/microsoft/table-transformer
### Motivation
Table Transformer is now available in 🤗 Transformer, and it was trained on PubTables-1M. It's a large dataset for table extraction and structure recognition in unstructured documents. | {
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https://api.github.com/repos/huggingface/datasets/issues/5260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5260/comments | https://api.github.com/repos/huggingface/datasets/issues/5260/events | https://github.com/huggingface/datasets/issues/5260 | 1,453,921,697 | I_kwDODunzps5WqRWh | 5,260 | consumer-finance-complaints dataset not loading | {
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"Thanks for reporting, @adiprasad.\r\n\r\nWe are having a look at it.",
"I have opened an issue in that dataset Community tab on the Hub: https://huggingface.co/datasets/consumer-finance-complaints/discussions/1\r\n\r\nPlease note that in the meantime, you can load the dataset by passing `ignore_verifications=True`:\r\n```python\r\n>>> ds = load_dataset(\"consumer-finance-complaints\", ignore_verifications=True)\r\n>>> ds\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['Date Received', 'Product', 'Sub Product', 'Issue', 'Sub Issue', 'Complaint Text', 'Company Public Response', 'Company', 'State', 'Zip Code', 'Tags', 'Consumer Consent Provided', 'Submitted via', 'Date Sent To Company', 'Company Response To Consumer', 'Timely Response', 'Consumer Disputed', 'Complaint ID'],\r\n num_rows: 3079747\r\n })\r\n})\r\n```",
"PR fixing this issue: https://huggingface.co/datasets/consumer-finance-complaints/discussions/2"
] | "2022-11-17T20:10:26" | "2022-11-18T10:16:53" | null | NONE | null | ### Describe the bug
Error during dataset loading
### Steps to reproduce the bug
```
>>> import datasets
>>> cf_raw = datasets.load_dataset("consumer-finance-complaints")
Downloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8.42k/8.42k [00:00<00:00, 3.33MB/s]
Downloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.60k/5.60k [00:00<00:00, 2.90MB/s]
Downloading readme: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16.6k/16.6k [00:00<00:00, 510kB/s]
Downloading and preparing dataset consumer-finance-complaints/default to /root/.cache/huggingface/datasets/consumer-finance-complaints/default/0.0.0/30e483d37fb4b25bb98cad1bfd2dc48f6ed6d1f3371eb4568c625a61d1a79b69...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 511M/511M [00:04<00:00, 103MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/load.py", line 1741, in load_dataset
builder_instance.download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare
self._download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare
super()._download_and_prepare(
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/builder.py", line 931, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/skunk-pod-storage-lee-2emartie-40ibm-2ecom-pvc/anaconda3/envs/datasets/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=1605177353, num_examples=2455765, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=2043641693, num_examples=3079747, shard_lengths=[721000, 656000, 788000, 846000, 68747], dataset_name='consumer-finance-complaints')}]
```
### Expected behavior
dataset should load
### Environment info
>>> datasets.__version__
'2.7.0'
Python 3.8.10
"Ubuntu 20.04.4 LTS" | {
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https://api.github.com/repos/huggingface/datasets/issues/5259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5259/comments | https://api.github.com/repos/huggingface/datasets/issues/5259/events | https://github.com/huggingface/datasets/issues/5259 | 1,453,555,923 | I_kwDODunzps5Wo4DT | 5,259 | datasets 2.7 introduces sharding error | {
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"I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to have a useful error message of an argument that could be passed to avoid the error.",
"Sorry for the inconvenience, I opened a PR in your repo to fix this: https://huggingface.co/datasets/sil-ai/bloom-speech/discussions/2\r\n\r\nBasically we've always considered lists in `gen_kwargs` to be a shard list that we can split and pass into different workers to generate the dataset (e.g. if you pass `num_proc=` in `load_dataset()` to generate the dataset in parallel), but it was documented only recently",
"@lhoestq Thanks for the help. It looks like that took care of it."
] | "2022-11-17T15:36:52" | "2022-12-24T01:44:02" | "2022-11-18T12:52:05" | NONE | null | ### Describe the bug
dataset fails to load with runtime error
`RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.`
### Steps to reproduce the bug
With datasets[audio] 2.7 loaded, and logged into hugging face,
`data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)`
creates the error.
Full stack trace:
```---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-7-8cb9ca0f79f0>](https://localhost:8080/#) in <module>
----> 1 data = datasets.load_dataset('sil-ai/bloom-speech', 'bis', use_auth_token=True)
5 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1745 try_from_hf_gcs=try_from_hf_gcs,
1746 use_auth_token=use_auth_token,
-> 1747 num_proc=num_proc,
1748 )
1749
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
824 verify_infos=verify_infos,
825 **prepare_split_kwargs,
--> 826 **download_and_prepare_kwargs,
827 )
828 # Sync info
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1554 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
1555 super()._download_and_prepare(
-> 1556 dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs
1557 )
1558
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
911 try:
912 # Prepare split will record examples associated to the split
--> 913 self._prepare_split(split_generator, **prepare_split_kwargs)
914 except OSError as e:
915 raise OSError(
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1362 fpath = path_join(self._output_dir, fname)
1363
-> 1364 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1365 if num_input_shards <= 1 and num_proc is not None:
1366 logger.warning(
[/usr/local/lib/python3.7/dist-packages/datasets/utils/sharding.py](https://localhost:8080/#) in _number_of_shards_in_gen_kwargs(gen_kwargs)
16 + "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items())
17 + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
---> 18 + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
19 )
20 )
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_files has length 46
- key data has length 0
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.```
### Expected behavior
the dataset loads in datasets version 2.6.1 and should load with datasets 2.7
### Environment info
- `datasets` version: 2.7.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 6.0.1
- Pandas version: 1.3.5 | {
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"The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1",
"TODO: Add \"dataset_info\" YAML metadata to:\r\n- [x] \"chr_en\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n - Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1 \r\n- [x] \"conll2000\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"crime_and_punish\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"dart\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"iwslt2017\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [ ] \"mc4\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"the_pile\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"timit_asr\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card",
"The bulk edit is finished."
] | "2022-11-17T15:13:15" | "2023-02-16T09:49:05" | "2022-11-19T06:51:37" | MEMBER | null | After a bulk edit of canonical datasets to create the YAML `dataset_info` metadata, the split names were accidentally sorted alphabetically. See for example:
- https://huggingface.co/datasets/bc2gm_corpus/commit/2384629484401ecf4bb77cd808816719c424e57c
Note that this order is the one appearing in the preview of the datasets.
I'm making a bulk edit to align the order of the splits appearing in the metadata info with the order appearing in the loading script.
Related to:
- #5202 | {
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"Also cc @mariosasko and @lhoestq ",
"Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?",
"> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, and then we can transfer. Meanwhile, if it's recommended to loop the dataset author in here, let me know. \r\n\r\nAlso, the NYU Depth dataset seems big. Any example scripts for creating image datasets that I could refer? ",
"You can check the imagenet-1k one.\r\n\r\nPS: If the licenses allows it, it'b be nice to host the dataset as sharded TAR archives (like imagenet-1k) instead of the ZIP format they use:\r\n- it will make streaming much faster\r\n- ZIP compression is not well suited for images\r\n- it will allow parallel processing of the dataset (you can pass a subset of shards to each worker)\r\n\r\n> if it's recommended to loop the dataset author in here, let me know.\r\n\r\nIt's recommended indeed, you can send them an email once you have the dataset ready and invite them to the org on the Hub",
"> You can check the imagenet-1k one.\r\n\r\nWhere can I find the script? Are you referring to https://huggingface.co/docs/datasets/image_process ? Or is there anything more specific? ",
"You can find it here: https://huggingface.co/datasets/imagenet-1k/blob/main/imagenet-1k.py",
"Update: started working on it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. \r\n\r\nI am facing an issue and I have detailed it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/1\r\n\r\nEdit: The issue is gone. \r\n\r\nHowever, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive? \r\n\r\n@lhoestq \r\n\r\n",
"A Colab Notebook demonstrating the dataset loading part: \r\n\r\nhttps://colab.research.google.com/gist/sayakpaul/aa0958c8d4ad8518d52a78f28044d871/scratchpad.ipynb\r\n\r\n@osanseviero @lhoestq \r\n\r\nI will work on a notebook to work with the dataset including data visualization.",
"@osanseviero @lhoestq things seem to work fine with the current version of the dataset [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2). Here's a notebook I developed to help with visualization: https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing. \r\n\r\n@lhoestq I need your help with the following:\r\n\r\n> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\n@osanseviero @lhoestq question for you:\r\n\r\nWhere should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly. We could then reach out to Diana Wofk (author of [Fast Depth](https://github.com/dwofk/fast-depth) and the owner of the repo on which TFDS NYU Depth V2 is based) for a review. WDYT? ",
"> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\nFirst you can separate the train data and the validation data.\r\n\r\nThen since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nAlso no need to gzip the TAR archives, the images are already compressed in png or jpeg.",
"> Then since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nYes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards? \r\n\r\n> Also no need to gzip the TAR archives, the images are already compressed in png or jpeg.\r\n\r\nI was not going to do that. Not sure what brought it up. ",
"> Yes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards?\r\n\r\nI don't, but I agree it'd be nice to have a script for that !\r\n\r\n> I was not going to do that. Not sure what brought it up.\r\n\r\nThe original dataset is gzipped for some reason",
"Oh, I am using this URL for the download: https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py#L24. ",
"> Where should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly.\r\n\r\nMaybe you can create an org for NYU Courant (this is the institute of the lab of the main author of the dataset if I'm not mistaken), and invite the authors to join.\r\n\r\nWe don't add datasets without namespace anymore",
"Updates: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/5\r\n\r\nThe entire process (preparing multiple archives, preparing data loading script, etc.) was fun and engaging, thanks to the documentation. I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say? \r\n\r\nCc: @lhoestq @osanseviero ",
"> I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say?\r\n\r\n@polinaeterna already mentioned it would be nice to present this process for audio (it's exactly the same), I believe it can be useful to many people",
"Cool. Let's work on that after the NYU Depth Dataset is fully in on Hub (under the appropriate org). 🤗",
"@lhoestq need to discuss something while I am adding the dataset card to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/. \r\n\r\nAs per [Papers With Code](https://paperswithcode.com/dataset/nyuv2), NYU Depth v2 is used for many different tasks:\r\n\r\n* Monocular depth estimation\r\n* Depth estimation \r\n* Semantic segmentation\r\n* Plane instance segmentation \r\n* ...\r\n\r\nSo, while writing the supported task part of the dataset card, should we focus on all these? IMO, we could focus on just depth estimation and semantic segmentation for now since we have supported models for these two. WDYT?\r\n\r\nAlso, I am getting: \r\n\r\n\r\n```\r\nremote: Your push was accepted, but with warnings:\r\nremote: - Warning: The task_ids \"depth-estimation\" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering\r\nremote: ----------------------------------------------------------\r\nremote: Please find the documentation at:\r\nremote: https://huggingface.co/docs/hub/model-cards#model-card-metadata\r\n```\r\n\r\nWhat should be the plan of action for this?\r\n\r\nCc: @osanseviero \r\n\r\n",
"> What should be the plan of action for this?\r\n\r\nWhen you merged https://github.com/huggingface/hub-docs/pull/488, there is a JS Interfaces GitHub Actions workflow that runs https://github.com/huggingface/hub-docs/actions/workflows/js-interfaces-tests.yml. It has a step called [export-task scripts](https://github.com/huggingface/hub-docs/actions/runs/3622479064/jobs/6107238948) which exports an interface you can use in `dataset`. If you look at the logs, it prints out a map. This map can replace https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/tasks.json (tasks.json was generated with this script), which should add depth estimation\r\n",
"Thanks @osanseviero. \r\n\r\nhttps://github.com/huggingface/datasets/pull/5335",
"Closing the issue as the dataset has been successfully added: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2"
] | "2022-11-17T03:22:22" | "2022-12-17T12:20:38" | "2022-12-17T12:20:37" | MEMBER | null | ### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5252). All of your documentation changes will be reflected on that endpoint.",
"Yes, if the image column is the first in the batch keys, it will decode the images because it reads the actual values. We could avoid this by checking the batch type, and if it's `LazyDict`, `num_examples` is equal to `len(batch.pa_table)`, which doesn't lead to decoding.",
"Good idea. This can be done in a subsequent PR btw, since it's out of scope of the original goal of this PR",
"Just fixed a small bug where it would show the pyarrow 10 warning about None -> empty lists conversions even with an Array2D with no nulls",
"Fixed another bug when your map function returns a mix of LazyDict or regular dict and added some tests"
] | "2022-11-16T15:02:13" | "2022-12-13T17:01:54" | "2022-12-13T16:59:04" | CONTRIBUTOR | null | Add support for decoding the `Image`/`Audio` types in `map` for the formats (Numpy, TF, Jax, PyTorch) other than the default one (Python).
Additional improvements:
* make `Dataset`'s "iter" API cleaner by removing `_iter` and replacing `_iter_batches` with `iter(batch_size)` (also implemented for `IterableDataset`)
* iterate over arrow tables in `map` to avoid `_getitem` calls, which are much slower than `__iter__`/`iter(batch_size)`, when the `format_type` is not Python
* fix `_iter_batches` (now named `iter`) when `drop_last_batch=True` and `pyarrow<=8.0.0` is installed
* lazily extract and decode arrow data in the default format
TODO:
* [x] update the `iter` benchmark in the docs (the `BeamBuilder` cannot load the preprocessed datasets from our bucket, so wait for this to be fixed (cc @lhoestq))
Fix https://github.com/huggingface/datasets/issues/3992, fix https://github.com/huggingface/datasets/issues/3756 | {
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"After a discussion with @mishig25:\r\n- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does\r\n- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main\r\n - Indeed the release tag is not yet created when we make the release PR (not event when this is merged to main), but when we make the Release itself.\r\n\r\nI was thinking that maybe we could change the triggering event: use `release` instead of `push`.\r\n\r\nWhat do you think, @huggingface/datasets?",
"Why is it an issue if our branch is temporary ?",
"He says not; but the branch has no tag yet; does the doc building require the tag? Or just the version number in `__init__.py` or setup.py?",
"It uses `module.__version__` (i.e. the one defined in `__init__.py`) - no need to have a tag\r\n\r\nhttps://github.com/huggingface/doc-builder/blob/81575cf081964c30ea5fd39450f4820db963f18e/src/doc_builder/commands/build.py#L69",
"Thanks, @lhoestq.\r\n\r\n@mishig25 has manually forced the generation of the docs, that are live for 2.7.0 version: https://huggingface.co/docs/datasets/v2.7.0/en/index ",
"Cool ! this can be closed then ?",
"I was waiting for #5250 to be merged to close this.",
"just to confirm, is there anything I need to do from my side ? Or is everything good here ?"
] | "2022-11-16T14:59:31" | "2022-11-22T16:27:50" | "2022-11-22T16:27:50" | MEMBER | null | After the latest `datasets` release version 0.7.0, the docs were not generated.
As we have changed the release procedure (so that now we do not push directly to main branch), maybe we should also change the corresponding GitHub action:
https://github.com/huggingface/datasets/blob/edf1902f954c5568daadebcd8754bdad44b02a85/.github/workflows/build_documentation.yml#L3-L8
Related to:
- #5250
CC: @mishig25 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5250). All of your documentation changes will be reflected on that endpoint.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5250). All of your documentation changes will be reflected on that endpoint.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5250). All of your documentation changes will be reflected on that endpoint.",
"Little recap:\r\n- The release-conda GH action was properly triggered by push-tag event: therefore I guess this event is also created when we publish a release and create a tag within it (as it is the case in the new procedure)\r\n - However, the package was only uploaded to huggingface channel and not to conda-forge channel\r\n - [x] Why? Need to address this.\r\n - Reply by @lhoestq: https://github.com/huggingface/datasets/pull/5250#discussion_r1025047531\r\n - We only maintain the huggingface channel\r\n - The conda-forge channel is maintained by the community; the 2.7.0 has been finally added as well to this channel \r\n- The generate-documentation GH action will be triggered by the push-to-branch event if we align the name of the release branch with the expected regex `v*-release`\r\n - [x] The naming has been aligned in the new procedure\r\n - [ ] Question: why do we have different triggering events for generate-doc and release-conda? Maybe we could set the same for both: either push-tag (when publishing the release), or push-to-branch\r\n - I think it will be better to use the push-tag event because in the new release procedure this happens later (when we publish the release), once we have already tested that everything works using the test-PyPI; on the contrary, the push-to-branch event happens before, even before opening the release PR: we could see afterwards that there is an issue, and cancel the Pull Request, but the docs and conda-package will already be published.\r\n- For the naming of the dev-version branch/PR, instead of having a complicated version naming, I'm proposing:\r\n - Using always the same branch name `dev-version`\r\n - Just include a step to delete this branch locally if it exists: `git branch -D dev-version`\r\n - The remote version will not exist because it is deleted once the PR is merged\r\n - This approach is approved by @lhoestq: https://github.com/huggingface/datasets/pull/5250#discussion_r1025048300",
"Just one question to be addressed: why do we have different triggering events for generate-doc and release-conda? Maybe we could set the same for both: either push-tag (when publishing the release), or push-to-branch\r\n\r\nI think it will be better to use the push-tag event because in the new release procedure this happens later (when we publish the release), once we have already tested that everything works using the test-PyPI; on the contrary, the push-to-branch event happens before, even before opening the release PR: we could see afterwards that there is an issue, and cancel the Pull Request, but the docs and conda-package will already be published.\r\n\r\nWe could even use the release-published event instead: [8694901](https://github.com/huggingface/datasets/pull/5250/commits/86949013c9dc59a07b55fad5b78104b8a03f60cd)\r\n",
"@lhoestq now that we have push-tag event for both build_documentation and release-conda, we have no constraint on the naming of the release branch:\r\n- we could name it simpler: maybe as you suggested above: https://github.com/huggingface/datasets/pull/5250#discussion_r1024119018\r\n `release-VERSION` instead of `vVERSION-release` (we do not use the prefix \"v\" anywhere in our repo)"
] | "2022-11-16T14:35:32" | "2022-11-22T16:30:58" | "2022-11-22T16:27:48" | MEMBER | null | This PR changes the release procedure so that:
- it only make changes to main branch via pull requests
- it is no longer necessary to directly commit/push to main branch
Close #5251.
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] | null | [] | "2022-11-16T14:19:03" | "2022-11-16T14:36:14" | null | MEMBER | null | We have decided to implement a protection mechanism in this repository, so that nobody (not even administrators) can inadvertently push accidentally directly to the main branch.
See context here:
- d7c942228b8dcf4de64b00a3053dce59b335f618
To do:
- [x] Protect main branch
- Settings > Branches > Branch protection rules > main > Edit
- [x] Check: Do not allow bypassing the above settings
- The above settings will apply to administrators and custom roles with the "bypass branch protections" permission.
- [x] Additionally, uncheck: Require approvals [under "Require a pull request before merging", which was already checked]
- Before, we could exceptionally merge a non-approved PR, using Administrator bypass
- Now that Administrator bypass is no longer possible, we would always need an approval to be able to merge; and pull request authors cannot approve their own pull requests. This could be an inconvenient in some exceptional circumstances when an urgent fix is needed
- Nevertheless, although it is no longer enforced, it is strongly recommended to merge PRs only if they have at least one approval
- [ ] #5250
- So that direct pushes to main branch are no longer necessary | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5248). All of your documentation changes will be reflected on that endpoint.",
"Thanks for the fix @mishig25.\r\n\r\nI guess this is the reason why the docs are not generated for the latest release version 2.7.0? https://huggingface.co/docs/datasets/index "
] | "2022-11-16T10:41:04" | "2022-11-16T15:06:50" | "2022-11-16T10:41:10" | CONTRIBUTOR | null | Reverts huggingface/datasets#5214
Everything is handled on the doc-builder side now 😊 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5247). All of your documentation changes will be reflected on that endpoint."
] | "2022-11-16T10:17:31" | "2022-11-16T10:22:20" | "2022-11-16T10:17:50" | MEMBER | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-16T09:32:44" | "2022-11-16T09:39:42" | "2022-11-16T09:37:03" | MEMBER | null | null | {
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"Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!",
"Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also tried keeping the 'info.features' and then adding a modified version back after the remove/rename. Unforutunately that leads to a dataset that is not possible to iterate over.",
"So if you iterate over the `IterableDataset` as `next(iter(ds))` and then run `rename_columns` when checking that data it will work, but in the end, it's just renaming the column one example/batch at a time, not renaming the column name for all the entries in the dataset, which is the ideal.",
"@alvarobartt Thanks. My use case was that I wanted to do multiple things, ie removing all unnecessary columns, renaming some valid columns, and then using cast (in my case checking if the audio is not 16K and casting it). It is just convenient to look into the info.features between each of these operations. Alternatively, I will just plan ahead...;) To me it seems like all the operations are working.\r\n\r\nThanks for the advice. It was very useful.",
"If we know the features before renaming, then we know the features after renaming, so we can pass the new features to the returned dataset in `rename_column` indeed ! If anyone is interested in contributing, feel free to open a PR and I'd be happy to help / give some pointers :)",
"Sure @lhoestq thanks! I’ll try to work on that",
"#self-assign"
] | "2022-11-15T21:04:41" | "2022-11-28T12:53:24" | "2022-11-28T12:53:24" | NONE | null | ### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1 | {
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"Hi ! What kind of private source ? We're exploring adding support for cloud storage and URIs like s3://, gs:// etc. with authentication in the download manager",
"Hello! It's a google cloud storage, so gs://, but I'm using it with https.\r\nBeing able to provide a file system like [here](https://huggingface.co/docs/datasets/main/filesystems#load-serialized-datasets) would be even more practical indeed.\r\nI've found a quite complicated workaround which consists of monkey patching all of the functions in streaming_download_manager.py to use my own _get_authentication_headers_for_url_ . \r\n\r\nA support for this use case would be greatly appreciated!\r\n\r\nFor reference my _get_authentication_headers_for_url_ looks like this:\r\n```\r\nimport os\r\nfrom typing import Optional, Union\r\n\r\nfrom datasets import config\r\nfrom huggingface_hub import HfFolder\r\nfrom gcsfs.credentials import GoogleCredentials\r\n\r\nDEFAULT_PROJECT = os.environ.get(\"GCSFS_DEFAULT_PROJECT\", \"\")\r\naccess = \"full_control\"\r\ngcs_token = os.environ.get(\"GCS_TOKEN\")\r\n\r\n\r\ndef get_authentication_headers_for_url(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> dict:\r\n \"\"\"Handle the HF authentication\"\"\"\r\n headers = {}\r\n if url.startswith(config.HF_ENDPOINT):\r\n if use_auth_token is False:\r\n token = None\r\n elif isinstance(use_auth_token, str):\r\n token = use_auth_token\r\n else:\r\n token = HfFolder.get_token()\r\n elif url.startswith(\"https://storage.googleapis.com\"):\r\n credentials = GoogleCredentials(DEFAULT_PROJECT, access, gcs_token)\r\n credentials.maybe_refresh()\r\n token = credentials.credentials.token\r\n else:\r\n token = None\r\n if token:\r\n headers[\"authorization\"] = f\"Bearer {token}\"\r\n return headers\r\n```",
"I would be a big fan of this feature! @Hubert-Bonisseur if this doesn't become a supported feature, would you mind sharing your code? Thanks!",
"> I would be a big fan of this feature! @Hubert-Bonisseur if this doesn't become a supported feature, would you mind sharing your code? Thanks!\r\n\r\nI published it here:\r\nhttps://github.com/Hubert-Bonisseur/private-dataset-hub\r\n\r\nI modified the names of a lot of functions for privacy and I don't have time to test it again so you may get import errors, but you have the code. The custom_load_dataset is the function you are interested in I think.\r\n\r\nIt relies a lot on patching, if you find a better way to do this, I'd be interested.",
"Given the amount of patching it does, this is likely to break at one point. I'd encourage you to wait for a proper support in `datasets` directly if you can wait."
] | "2022-11-15T16:02:10" | "2022-11-23T14:02:30" | null | CONTRIBUTOR | null | ### Feature request
Add arguments to the function _get_authentication_headers_for_url_ like custom_endpoint and custom_token in order to add flexibility when downloading files from a private source.
It should also be possible to provide these arguments from the dataset loading script, maybe giving them to the dl_manager
### Motivation
It is possible to share a dataset hosted on another platform by writing a dataset loading script. It works perfectly for publicly available resources.
For resources that require authentication, you can provide a [download_custom](https://huggingface.co/docs/datasets/package_reference/builder_classes#datasets.DownloadManager) method to the download_manager.
Unfortunately, this function doesn't work with **dataset streaming**.
A solution so as to allow dataset streaming from private sources would be a more flexible _get_authentication_headers_for_url_ function.
### Your contribution
Would you be interested in this improvement ?
If so I could provide a PR. I've got something working locally, but it's not very clean, I'd need some guidance regarding integration. | {
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"Hi @capsabogdan! Unfortunately, it's hard to implement because quite often datasets data is being hosted in a single archive for all splits :( So we have to download the whole archive to split it into splits. This is the case for CommonVoice too. \r\n\r\nHowever, for cases when data is distributed in separate archives ащк different splits I suppose it can (and will) be implemented someday. \r\n\r\n\r\nBtw for quick check of the dataset you can use [streaming](https://huggingface.co/docs/datasets/stream):\r\n```python\r\ncv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)\r\ncv = iter(cv)\r\nprint(next(cv))\r\n\r\n>> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',\r\n>> 'path': None,\r\n>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',\r\n>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,\r\n>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),\r\n>> ...}\r\n\r\n```",
"thank you for the answer but am not sure if this will not be helpful, as we\nneed maybe just 10% of the datasets for some experiment\n\ncan we get just a portion of the dataset with stream?\n\n\nis there really no solution? :(\n\nAm Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n***@***.***>:\n\n> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n> hard to implement because quite often datasets data is being hosted in a\n> single archive for all splits :( So we have to download the whole archive\n> to split it into splits. This is the case for CommonVoice too.\n>\n> However, for cases when data is distributed in separate archives in\n> different splits I suppose it can be implemented someday.\n>\n> Btw for quick check of the dataset you can use streaming\n> <https://huggingface.co/docs/datasets/stream>:\n>\n> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n",
"maybe it would be nice if you guys ould do some sort of shard before\nloading the dataset, so users can download just chunks of data :)\n\nI think this would be very helpful\n\nAm Di., 15. Nov. 2022 um 19:24 Uhr schrieb Bogdan Capsa <\n***@***.***>:\n\n> thank you for the answer but am not sure if this will not be helpful, as\n> we need maybe just 10% of the datasets for some experiment\n>\n> can we get just a portion of the dataset with stream?\n>\n>\n> is there really no solution? :(\n>\n> Am Di., 15. Nov. 2022 um 16:55 Uhr schrieb Polina Kazakova <\n> ***@***.***>:\n>\n>> Hi @capsabogdan <https://github.com/capsabogdan>! Unfortunately, it's\n>> hard to implement because quite often datasets data is being hosted in a\n>> single archive for all splits :( So we have to download the whole archive\n>> to split it into splits. This is the case for CommonVoice too.\n>>\n>> However, for cases when data is distributed in separate archives in\n>> different splits I suppose it can be implemented someday.\n>>\n>> Btw for quick check of the dataset you can use streaming\n>> <https://huggingface.co/docs/datasets/stream>:\n>>\n>> cv = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"en\", split=\"test\", streaming=True)cv = iter(cv)print(next(cv))\n>> >> {'client_id': 'a07b17f8234ded5e847443ea6f423cef745cbbc7537fb637d58326000aa751e829a21c4fd0a35fc17fb833aa7e95ebafce5efd19beeb8d843887b85e4eb35f5b',>> 'path': None,>> 'audio': {'path': 'cv-corpus-11.0-2022-09-21/en/clips/common_voice_en_100363.mp3',>> 'array': array([ 0.0000000e+00, 1.1748125e-14, 1.5450088e-14, ...,>> 1.3011958e-06, -6.3548953e-08, -9.9098514e-08], dtype=float32),>> ...}\n>>\n>> —\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/datasets/issues/5243#issuecomment-1315512887>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/ALSIFOC3JYRCTH54OBRUJULWIOW6PANCNFSM6AAAAAASAYO2LY>\n>> .\n>> You are receiving this because you were mentioned.Message ID:\n>> ***@***.***>\n>>\n>\n",
"+1 on this feature request - I am running into the same problem, where I only need the test set for a dataset that has a huge training set"
] | "2022-11-15T10:15:54" | "2023-01-05T09:01:07" | null | NONE | null | ### Feature request
Is it possible to download only the data that I am requesting and not the entire dataset? I run out of disk spaceas it seems to download the entire dataset, instead of only the part needed.
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test",
cache_dir="cache/path...",
use_auth_token=True,
download_config=DownloadConfig(delete_extracted='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')
)
### Motivation
efficiency improvement
### Your contribution
n/a | {
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https://api.github.com/repos/huggingface/datasets/issues/5242 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5242/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5242/comments | https://api.github.com/repos/huggingface/datasets/issues/5242/events | https://github.com/huggingface/datasets/issues/5242 | 1,449,069,382 | I_kwDODunzps5WXwtG | 5,242 | Failed Data Processing upon upload with zip file full of images | {
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"cc @abhishekkrthakur @SBrandeis "
] | "2022-11-15T02:47:52" | "2022-11-15T17:59:23" | null | NONE | null | I went to autotrain and under image classification arrived where it was time to prepare my dataset. Screenshot below
I chose the method 2 option. I have a csv file with two columns. ~23,000 files.
I uploaded this and chose the image_relpath, and target columns.
The image uploader said that I could only upload 10,000 singular images at a time so the 2nd option was to zip the images up and upload a zip archive which I did.
That all uploaded.
Now I have the message below. It appears the zip archive does just uncompress on the Hugging Face end?
What am I missing here?
| {
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https://api.github.com/repos/huggingface/datasets/issues/5241 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5241/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5241/comments | https://api.github.com/repos/huggingface/datasets/issues/5241/events | https://github.com/huggingface/datasets/pull/5241 | 1,448,510,407 | PR_kwDODunzps5C3MTG | 5,241 | Support hfh rc version | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-14T18:05:47" | "2022-11-15T16:11:30" | "2022-11-15T16:09:31" | MEMBER | null | otherwise the code doesn't work for hfh 0.11.0rc0
following #5237 | {
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https://api.github.com/repos/huggingface/datasets/issues/5240 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5240/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5240/comments | https://api.github.com/repos/huggingface/datasets/issues/5240/events | https://github.com/huggingface/datasets/pull/5240 | 1,448,478,617 | PR_kwDODunzps5C3Fe6 | 5,240 | Cleaner error tracebacks for dataset script errors | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq Good catch! This currently leads to an AttributeError (due to `writer` being None) on this line:\r\nhttps://github.com/huggingface/datasets/blob/fed1628d49a91f9ae259ddf6edbb252c7972d9a3/src/datasets/builder.py#L1552\r\n"
] | "2022-11-14T17:42:02" | "2022-11-15T18:26:48" | "2022-11-15T18:24:38" | CONTRIBUTOR | null | Make the traceback of the errors raised in `_generate_examples` cleaner for easier debugging. Additionally, initialize the `writer` in the for-loop to avoid the `ValueError` from `ArrowWriter.finalize` raised in the `finally` block when no examples are yielded before the `_generate_examples` error.
<details>
<summary>
The full traceback of the "SQLAlchemy ImportError" error that gets printed with these changes:
</summary>
```bash
ImportError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split_single(self, arg)
1759 _time = time.time()
-> 1760 for _, table in generator:
1761 # Only initialize the writer when we have the first record (to avoid having to do the clean-up if an error occurs before that)
9 frames
/usr/local/lib/python3.7/dist-packages/datasets/packaged_modules/sql/sql.py in _generate_tables(self)
112 sql_reader = pd.read_sql(
--> 113 self.config.sql, self.config.con, chunksize=chunksize, **self.config.pd_read_sql_kwargs
114 )
/usr/local/lib/python3.7/dist-packages/pandas/io/sql.py in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)
598 """
--> 599 pandas_sql = pandasSQL_builder(con)
600
/usr/local/lib/python3.7/dist-packages/pandas/io/sql.py in pandasSQL_builder(con, schema, meta, is_cursor)
789 elif isinstance(con, str):
--> 790 raise ImportError("Using URI string without sqlalchemy installed.")
791 else:
ImportError: Using URI string without sqlalchemy installed.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
<ipython-input-4-5af11af4737b> in <module>
----> 1 ds = Dataset.from_sql('''SELECT * from states WHERE state=="New York";''', "sqlite:///us_covid_data.db")
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in from_sql(sql, con, features, cache_dir, keep_in_memory, **kwargs)
1152 cache_dir=cache_dir,
1153 keep_in_memory=keep_in_memory,
-> 1154 **kwargs,
1155 ).read()
1156
/usr/local/lib/python3.7/dist-packages/datasets/io/sql.py in read(self)
47 # try_from_hf_gcs=try_from_hf_gcs,
48 base_path=base_path,
---> 49 use_auth_token=use_auth_token,
50 )
51
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
825 verify_infos=verify_infos,
826 **prepare_split_kwargs,
--> 827 **download_and_prepare_kwargs,
828 )
829 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
912 try:
913 # Prepare split will record examples associated to the split
--> 914 self._prepare_split(split_generator, **prepare_split_kwargs)
915 except OSError as e:
916 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1652 job_id = 0
1653 for job_id, done, content in self._prepare_split_single(
-> 1654 {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args}
1655 ):
1656 if done:
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split_single(self, arg)
1789 raise DatasetGenerationError(
1790 f"An error occured while generating the dataset"
-> 1791 ) from e
1792 finally:
1793 yield job_id, False, num_examples_progress_update
DatasetGenerationError: An error occurred while generating the dataset
```
</details>
PS: I've also considered raising the error as follows:
```python
tb = sys.exc_info()[2]
raise DatasetGenerationError(f"An error occurred while generating the dataset: {type(e).__name__}: {e}").with_traceback(tb) from None # this raises the DatasetGenerationError with "e"'s traceback
```
But it seems like "from e" is now the [preferred](https://docs.python.org/3/library/exceptions.html#BaseException.with_traceback) way to chain exceptions.
Fix https://github.com/huggingface/datasets/issues/5186
cc @nateraw
| {
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https://api.github.com/repos/huggingface/datasets/issues/5239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5239/comments | https://api.github.com/repos/huggingface/datasets/issues/5239/events | https://github.com/huggingface/datasets/pull/5239 | 1,448,211,373 | PR_kwDODunzps5C2L_P | 5,239 | Add num_proc to from_csv/generator/json/parquet/text | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5239). All of your documentation changes will be reflected on that endpoint.",
"I ended up moving `num_proc` to `AbstractDatasetReader.__init__` :)\r\n\r\nLet me know if it sounds good to you now"
] | "2022-11-14T14:53:00" | "2022-12-06T15:39:10" | "2022-12-06T15:39:09" | MEMBER | null | Allow multiprocessing to from_* methods | {
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https://api.github.com/repos/huggingface/datasets/issues/5238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5238/comments | https://api.github.com/repos/huggingface/datasets/issues/5238/events | https://github.com/huggingface/datasets/pull/5238 | 1,448,211,251 | PR_kwDODunzps5C2L9h | 5,238 | Make `Version` hashable | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-14T14:52:55" | "2022-11-14T15:30:02" | "2022-11-14T15:27:35" | CONTRIBUTOR | null | Add `__hash__` to the `Version` class to make it hashable (and remove the unneeded methods), as `Version("0.0.0")` is the default value of `BuilderConfig.version` and the default fields of a dataclass need to be hashable in Python 3.11.
Fix https://github.com/huggingface/datasets/issues/5230 | {
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https://api.github.com/repos/huggingface/datasets/issues/5237 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5237/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5237/comments | https://api.github.com/repos/huggingface/datasets/issues/5237/events | https://github.com/huggingface/datasets/pull/5237 | 1,448,202,491 | PR_kwDODunzps5C2KGz | 5,237 | Encode path only for old versions of hfh | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-14T14:46:57" | "2022-11-14T17:38:18" | "2022-11-14T17:35:59" | MEMBER | null | Next version of `huggingface-hub` 0.11 does encode the `path`, and we don't want to encode twice | {
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https://api.github.com/repos/huggingface/datasets/issues/5236 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5236/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5236/comments | https://api.github.com/repos/huggingface/datasets/issues/5236/events | https://github.com/huggingface/datasets/pull/5236 | 1,448,190,801 | PR_kwDODunzps5C2Hnj | 5,236 | Handle ArrowNotImplementedError caused by try_type being Image or Audio in cast | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Not sure how we can have a test that is relevant for this though - feel free to add one if you have ideas\r\n\r\nYes, this was my reasoning for not adding a test. This change is pretty simple, so I think it's OK not to have a test for it."
] | "2022-11-14T14:38:59" | "2022-11-14T16:04:29" | "2022-11-14T16:01:48" | CONTRIBUTOR | null | Handle the `ArrowNotImplementedError` thrown when `try_type` is `Image` or `Audio` and the input array cannot be converted to their storage formats.
Reproducer:
```python
from datasets import Dataset
from PIL import Image
import requests
ds = Dataset.from_dict({"image": [Image.open(requests.get("https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", stream=True).raw)]})
ds.map(lambda x: {"image": True}) # ArrowNotImplementedError
```
PS: This could also be fixed by raising `TypeError` in `{Image, Audio}.cast_storage` for unsupported types instead of passing the array to `array_cast.` | {
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https://api.github.com/repos/huggingface/datasets/issues/5235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5235/comments | https://api.github.com/repos/huggingface/datasets/issues/5235/events | https://github.com/huggingface/datasets/pull/5235 | 1,448,052,660 | PR_kwDODunzps5C1pjc | 5,235 | Pin `typer` version in tests to <0.5 to fix Windows CI | {
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} | [] | closed | false | null | [] | null | [] | "2022-11-14T13:17:02" | "2022-11-14T15:43:01" | "2022-11-14T13:41:12" | CONTRIBUTOR | null | Otherwise `click` fails on Windows:
```
Traceback (most recent call last):
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\__main__.py", line 4, in <module>
setup_cli()
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\spacy\cli\_util.py", line 71, in setup_cli
command(prog_name=COMMAND)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\click\core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 785, in main
**extra,
File "C:\hostedtoolcache\windows\Python\3.7.9\x64\lib\site-packages\typer\core.py", line 190, in _main
args = click.utils._expand_args(args)
AttributeError: module 'click.utils' has no attribute '_expand_args'
```
See https://github.com/tiangolo/typer/issues/427 | {
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https://api.github.com/repos/huggingface/datasets/issues/5234 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5234/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5234/comments | https://api.github.com/repos/huggingface/datasets/issues/5234/events | https://github.com/huggingface/datasets/pull/5234 | 1,447,999,062 | PR_kwDODunzps5C1diq | 5,234 | fix: dataset path should be absolute | {
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"Good catch thanks ! Have you tried to use the absolue path in `MemoryMappedTable.__init__` in `table.py`?\r\n\r\nI think it can fix issues with relative paths at more levels than just fixing it `load_from_disk`. If it works I think it would be a more robust fix to this issue",
"@lhoestq right, that actually fixed it indeed. I've pushed the changes (one-liner). lemme know if there's anything else you need for this fix",
"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-14T12:47:40" | "2022-12-07T23:49:22" | "2022-12-07T23:46:34" | CONTRIBUTOR | null | cache_file_name depends on dataset's path.
A simple way where this could cause a problem:
```
import os
import datasets
def add_prefix(example):
example["text"] = "Review: " + example["text"]
return example
ds = datasets.load_from_disk("a/relative/path")
os.chdir("/tmp")
ds_1 = ds.map(add_prefix)
```
while it may feel that the `chdir` is quite constructed, there are many scenarios when the current working dir can/will change... | {
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https://api.github.com/repos/huggingface/datasets/issues/5233 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5233/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5233/comments | https://api.github.com/repos/huggingface/datasets/issues/5233/events | https://github.com/huggingface/datasets/pull/5233 | 1,447,906,868 | PR_kwDODunzps5C1JVh | 5,233 | Fix shards in IterableDataset.from_generator | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-14T11:42:09" | "2022-11-14T14:16:03" | "2022-11-14T14:13:22" | MEMBER | null | Allow to define a sharded iterable dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/5232 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5232/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5232/comments | https://api.github.com/repos/huggingface/datasets/issues/5232/events | https://github.com/huggingface/datasets/issues/5232 | 1,446,294,165 | I_kwDODunzps5WNLKV | 5,232 | Incompatible dill versions in datasets 2.6.1 | {
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"Thanks for reporting, @vinaykakade.\r\n\r\nWe are discussing about making a release early this week.\r\n\r\nPlease note that in the meantime, in your specific case (as we also pointed out here: https://github.com/huggingface/datasets/issues/5162#issuecomment-1291720293), you can circumvent the issue by pinning `multiprocess` to 0.70.13 version (instead of using latest 0.70.14).\r\n\r\nDuplicate of:\r\n- https://github.com/huggingface/datasets/issues/5162",
"You can also make `pip-compile` work by using the backtracking resolver (instead of the legacy one): https://pip-tools.readthedocs.io/en/latest/#a-note-on-resolvers\r\n```\r\npip-compile --resolver=backtracking requirements.in\r\n```\r\nThis resolver will automatically use `multiprocess` 0.70.13 version.\r\n"
] | "2022-11-12T06:46:23" | "2022-11-14T08:24:43" | "2022-11-14T08:07:59" | NONE | null | ### Describe the bug
datasets version 2.6.1 has a dependency on dill<0.3.6. This causes a conflict with dill>=0.3.6 used by multiprocess dependency in datasets 2.6.1
This issue is already fixed in https://github.com/huggingface/datasets/pull/5166/files, but not yet been released. Please release a new version of the datasets library to fix this.
### Steps to reproduce the bug
1. Create requirements.in with only dependency being datasets (or datasets[s3])
2. Run pip-compile
3. The output is as follows:
```
Could not find a version that matches dill<0.3.6,>=0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
Tried: 0.2, 0.2, 0.2.1, 0.2.1, 0.2.2, 0.2.2, 0.2.3, 0.2.3, 0.2.4, 0.2.4, 0.2.5, 0.2.5, 0.2.6, 0.2.7, 0.2.7.1, 0.2.8, 0.2.8.1, 0.2.8.2, 0.2.9, 0.3.0, 0.3.1, 0.3.1.1, 0.3.2, 0.3.3, 0.3.3, 0.3.4, 0.3.4, 0.3.5, 0.3.5, 0.3.5.1, 0.3.5.1, 0.3.6, 0.3.6
Skipped pre-versions: 0.1a1, 0.2a1, 0.2a1, 0.2b1, 0.2b1
There are incompatible versions in the resolved dependencies:
dill<0.3.6 (from datasets[s3]==2.6.1->-r requirements.in (line 1))
dill>=0.3.6 (from multiprocess==0.70.14->datasets[s3]==2.6.1->-r requirements.in (line 1))
```
### Expected behavior
pip-compile produces requirements.txt without any conflicts
### Environment info
datasets version 2.6.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5231 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5231/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5231/comments | https://api.github.com/repos/huggingface/datasets/issues/5231/events | https://github.com/huggingface/datasets/issues/5231 | 1,445,883,267 | I_kwDODunzps5WLm2D | 5,231 | Using `set_format(type='torch', columns=columns)` makes Array2D/3D columns stop formatting correctly | {
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"In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types"
] | "2022-11-11T18:54:36" | "2022-11-11T20:42:29" | "2022-11-11T18:59:50" | NONE | null | I have a Dataset with two Features defined as follows:
```
'image': Array3D(dtype="int64", shape=(3, 224, 224)),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
```
On said dataset, if I `dataset.set_format(type='torch')` and then use the dataset in a dataloader, these columns are correctly cast to Tensors of (batch_size, 3, 224, 244) for example.
However, if I `dataset.set_format(type='torch', columns=['image', 'bbox'])` these columns are cast to Lists of tensors and miss the batch size completely (the 3 dimension is the list length).
I'm currently digging through datasets formatting code to try and find out why, but was curious if someone knew an immediate solution for this. | {
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https://api.github.com/repos/huggingface/datasets/issues/5230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5230/comments | https://api.github.com/repos/huggingface/datasets/issues/5230/events | https://github.com/huggingface/datasets/issues/5230 | 1,445,507,580 | I_kwDODunzps5WKLH8 | 5,230 | dataclasses error when importing the library in python 3.11 | {
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"I opened [this issue](https://github.com/python/cpython/issues/99401).\r\nPython's maintainers say that the issue is caused by [this change](https://docs.python.org/3.11/whatsnew/3.11.html#dataclasses).\r\nI believe adding a `__hash__` method to `datasets.utils.version.Version` should solve (at least partially) this issue.",
"Has this been fixed? I am running into this issue now. \r\n\r\nIf this has been fixed, could have a new release with this?\r\n"
] | "2022-11-11T13:53:49" | "2022-11-14T20:51:44" | "2022-11-14T15:27:37" | NONE | null | ### Describe the bug
When I import datasets using python 3.11 the dataclasses standard library raises the following error:
`ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory`
When I tried to import the library using the following jupyter notebook:
```
%%bash
# create python 3.11 conda env
conda create --yes --quiet -n myenv -c conda-forge python=3.11
# activate is
source activate myenv
# install pyarrow
/opt/conda/envs/myenv/bin/python -m pip install --quiet --extra-index-url https://pypi.fury.io/arrow-nightlies/ \
--prefer-binary --pre pyarrow
# install datasets
/opt/conda/envs/myenv/bin/python -m pip install --quiet datasets
```
```
# create a python file that only imports datasets
with open("import_datasets.py", 'w') as f:
f.write("import datasets")
# run it with the env
!/opt/conda/envs/myenv/bin/python import_datasets.py
```
I get the following error:
```
Traceback (most recent call last):
File "/kaggle/working/import_datasets.py", line 1, in <module>
import datasets
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/__init__.py", line 45, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/opt/conda/envs/myenv/lib/python3.11/site-packages/datasets/builder.py", line 91, in <module>
@dataclass
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1221, in dataclass
return wrap(cls)
^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 1211, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 959, in _process_class
cls_fields.append(_get_field(cls, name, type, kw_only))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/envs/myenv/lib/python3.11/dataclasses.py", line 816, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'datasets.utils.version.Version'> for field version is not allowed: use default_factory
```
This is probably due to one of the following changes in the [dataclasses standard library](https://docs.python.org/3/library/dataclasses.html) in version 3.11:
1. Changed in version 3.11: Instead of looking for and disallowing objects of type list, dict, or set, unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.
2. fields may optionally specify a default value, using normal Python syntax:
```
@dataclass
class C:
a: int # 'a' has no default value
b: int = 0 # assign a default value for 'b'
In this example, both a and b will be included in the added __init__() method, which will be defined as:
def __init__(self, a: int, b: int = 0):
```
3. Changed in version 3.11: If a field name is already included in the __slots__ of a base class, it will not be included in the generated __slots__ to prevent [overriding them](https://docs.python.org/3/reference/datamodel.html#datamodel-note-slots). Therefore, do not use __slots__ to retrieve the field names of a dataclass. Use [fields()](https://docs.python.org/3/library/dataclasses.html#dataclasses.fields) instead. To be able to determine inherited slots, base class __slots__ may be any iterable, but not an iterator.
4. weakref_slot: If true (the default is False), add a slot named “__weakref__”, which is required to make an instance weakref-able. It is an error to specify weakref_slot=True without also specifying slots=True.
[TypeError](https://docs.python.org/3/library/exceptions.html#TypeError) will be raised if a field without a default value follows a field with a default value. This is true whether this occurs in a single class, or as a result of class inheritance.
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. go to [the notebook in kaggle](https://www.kaggle.com/yonikremer/repreducing-issue)
2. rub both of the cells
### Expected behavior
I'm expecting no issues.
This error should not occur.
### Environment info
kaggle kernels, with default settings:
pin to original environment, no accelerator. | {
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https://api.github.com/repos/huggingface/datasets/issues/5229 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5229/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5229/comments | https://api.github.com/repos/huggingface/datasets/issues/5229/events | https://github.com/huggingface/datasets/issues/5229 | 1,445,121,028 | I_kwDODunzps5WIswE | 5,229 | Type error when calling `map` over dataset containing 0-d tensors | {
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"Hi! \r\n\r\nWe could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side (Arrow is the underlying format we use to store datasets on disk/in memory). WDYT @lhoestq?",
"I think we can just convert the item to a numpy typed scalar using `.numpy()` ?\r\n\r\nFor example this works:\r\n```python\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\nassert pa.array([np.float64(1.0)]).type == pa.float64()\r\nassert pa.array([np.float32(1.0)]).type == pa.float32()\r\nassert pa.array([np.int32(1)]).type == pa.int32()\r\nassert pa.array([np.int64(1)]).type == pa.int64()\r\n```\r\n\r\nAnd therefore it would work the same as for PyTorch N-D Tensors: convert to Numpy Array to keep the type in `_cast_to_python_objects`, then convert to Arrow"
] | "2022-11-11T08:27:28" | "2023-01-13T16:00:53" | "2023-01-13T16:00:53" | NONE | null | ### Describe the bug
0-dimensional tensors in a dataset lead to `TypeError: iteration over a 0-d array` when calling `map`. It is easy to generate such tensors by using `.with_format("...")` on the whole dataset.
### Steps to reproduce the bug
```
ds = datasets.Dataset.from_list([{"a": 1}, {"a": 1}]).with_format("torch")
ds.map(None)
```
### Expected behavior
Getting back `ds` without errors.
### Environment info
Python 3.10.8
datasets 2.6.
torch 1.13.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5228 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5228/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5228/comments | https://api.github.com/repos/huggingface/datasets/issues/5228/events | https://github.com/huggingface/datasets/issues/5228 | 1,444,763,105 | I_kwDODunzps5WHVXh | 5,228 | Loading a dataset from the hub fails if you happen to have a folder of the same name | {
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"`load_dataset` first checks for a local directory before checking for the Hub.\r\n\r\nTo make it explicit that it has to fetch the Hub, we could support the `hffs` syntax:\r\n```python\r\nload_dataset(\"hf://datasets/glue\")\r\n```\r\n\r\nwould that work for you ? Also cc @mariosasko who's leading the `hffs` project",
"yeah, that would be a fine solution."
] | "2022-11-11T00:51:54" | "2022-11-14T18:17:34" | null | NONE | null | ### Describe the bug
I'm not 100% sure this should be considered a bug, but it was certainly annoying to figure out the cause of. And perhaps I am just missing a specific argument needed to avoid this conflict. Basically I had a situation where multiple workers were downloading different parts of the glue dataset and then training on them. Additionally, they were writing their checkpoints to a folder called `glue`. This meant that once one worker had created the `glue` folder to write checkpoints to, the next worker to try to load a glue dataset would fail as shown in the minimal repro below. I'm not sure what the solution would be since I'm not super familiar with the `datasets` code, but I would expect `load_dataset` to not crash just because i have a local folder with the same name as a dataset from the hub.
### Steps to reproduce the bug
```
In [1]: import datasets
In [2]: rte = datasets.load_dataset('glue', 'rte')
Downloading and preparing dataset glue/rte to /Users/danielking/.cache/huggingface/datasets/glue/rte/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...
Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 697k/697k [00:00<00:00, 6.08MB/s]
Dataset glue downloaded and prepared to /Users/danielking/.cache/huggingface/datasets/glue/rte/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 773.81it/s]
In [3]: import os
In [4]: os.mkdir('glue')
In [5]: rte = datasets.load_dataset('glue', 'rte')
---------------------------------------------------------------------------
EmptyDatasetError Traceback (most recent call last)
<ipython-input-5-0d6b9ad8bbd0> in <cell line: 1>()
----> 1 rte = datasets.load_dataset('glue', 'rte')
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1717
1718 # Create a dataset builder
-> 1719 builder_instance = load_dataset_builder(
1720 path=path,
1721 name=name,
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1495 download_config = download_config.copy() if download_config else DownloadConfig()
1496 download_config.use_auth_token = use_auth_token
-> 1497 dataset_module = dataset_module_factory(
1498 path,
1499 revision=revision,
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1152 ).get_module()
1153 elif os.path.isdir(path):
-> 1154 return LocalDatasetModuleFactoryWithoutScript(
1155 path, data_dir=data_dir, data_files=data_files, download_mode=download_mode
1156 ).get_module()
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/load.py in get_module(self)
624 base_path = os.path.join(self.path, self.data_dir) if self.data_dir else self.path
625 patterns = (
--> 626 sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns_locally(base_path)
627 )
628 data_files = DataFilesDict.from_local_or_remote(
~/miniconda3/envs/composer/lib/python3.9/site-packages/datasets/data_files.py in get_data_patterns_locally(base_path)
458 return _get_data_files_patterns(resolver)
459 except FileNotFoundError:
--> 460 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
461
462
EmptyDatasetError: The directory at glue doesn't contain any data files
```
### Expected behavior
Dataset is still able to be loaded from the hub even if I have a local folder with the same name.
### Environment info
datasets version: 2.6.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5227 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5227/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5227/comments | https://api.github.com/repos/huggingface/datasets/issues/5227/events | https://github.com/huggingface/datasets/issues/5227 | 1,444,620,094 | I_kwDODunzps5WGyc- | 5,227 | datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files | {
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"Fixed. Please close."
] | "2022-11-10T21:57:06" | "2022-11-10T22:05:43" | "2022-11-10T22:05:43" | NONE | null | ### Describe the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Steps to reproduce the bug
From these lines:
from datasets import list_datasets, load_dataset
dataset = load_dataset("wikisql","binary")
I get error message:
datasets.data_files.EmptyDatasetError: The directory at wikisql doesn't contain any data files
And yet the 'wikisql' is reported to exist via the list_datasets().
Any help appreciated.
### Expected behavior
Dataset should load. This same code used to work.
### Environment info
Mac OS | {
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"Hi ! Datasets are memory mapped from your disk, i.e. they're not loaded in RAM. This is possible thanks to the Arrow data format.\r\n\r\nTherefore the column you remove is not in RAM, so removing it doesn't cause the RAM to decrease.",
"Thanks for the explanation! @lhoestq \r\nI wonder since it is memory mapped, can we reduce or remove this memory map?",
"Yes you can `del common_voice` for example or wait for it to be garbage collected"
] | "2022-11-10T18:35:27" | "2022-11-29T15:10:10" | "2022-11-29T15:10:10" | NONE | null | ### Describe the bug
How do I release memory when I use methods like `.remove_columns()` or `clear()` in notebooks?
```python
from datasets import load_dataset
common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "ja", use_auth_token=True)
# check memory -> RAM Used (GB): 0.704 / Total (GB) 33.670
common_voice = common_voice.remove_columns(column_names=common_voice.column_names['train'])
common_voice.clear()
# check memory -> RAM Used (GB): 0.705 / Total (GB) 33.670
```
I tried `gc.collect()` but did not help
### Steps to reproduce the bug
1. load dataset
2. remove all the columns
3. check memory is reduced or not
[link to reproduce](https://www.kaggle.com/code/bayartsogtya/huggingface-dataset-memory-issue/notebook?scriptVersionId=110630567)
### Expected behavior
Memory released when I remove the column
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5225 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5225/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5225/comments | https://api.github.com/repos/huggingface/datasets/issues/5225/events | https://github.com/huggingface/datasets/issues/5225 | 1,444,305,183 | I_kwDODunzps5WFlkf | 5,225 | Add video feature | {
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] | open | false | null | [] | null | [
"@NielsRogge @rwightman may have additional requirements regarding this feature.\r\n\r\nWhen adding a new (decodable) type, the hardest part is choosing the right decoding library. What I mean by \"right\" here is that it has all the features we need and is easy to install (with GPU support?).\r\n\r\nSome candidates/options:\r\n* [`decord`](https://github.com/dmlc/decord): no longer [maintained](https://github.com/dmlc/decord/issues/214), not trivial to install with GPU support\r\n* [`pyAV`](https://github.com/PyAV-Org/PyAV): used for CPU decoding in `torchvision`, GPU decoding not supported if I'm not mistaken, otherwise the best candidate probably\r\n* [`video_reader`](https://github.com/pytorch/vision/blob/de350bc01ad2193ea2888f0ce8a6a346d3cba5a9/torchvision/csrc/io/video_reader/video_reader.cpp): used for GPU decoding in `torchvision`, depends on `torch'\r\n* OpenCV: uses `ffmpeg` for video decoding under the hood\r\n* ...\r\n\r\nAnd the last resort is building our own library, which is the most flexible solution but also requires the most work.\r\n\r\nPS: I'm adding a link to an article that compares various video decoding libraries: https://towardsdatascience.com/lightning-fast-video-reading-in-python-c1438771c4e6",
"@mariosasko is GPU decoding a hard requirement here? Do we really need it? (I don't know)\r\n\r\nSomething to consider with `decord` is that it doesn't (AFAIK) support writing videos, so you'd still need something else for that. also I've noticed [issues](https://github.com/dmlc/decord/issues/242) with decord's ability to decode stereo audio streams along side the video (which you don't run into with PyAV).\r\n\r\n---\r\n\r\nI think PyAV should be able to do the job just fine to start. If we write the video io utilities as their own functions, we can hot swap them later if we find/write a different solution that's faster/better.",
"Video is still a bit of a mess, but I'd say pyAV is likely the best approach (or supporting all three via pytorchvideo, but that adds a middle man dependency).\r\n\r\nBeing able to decode on the GPU, into memory that could be passed off to a Tensor in whatever framework is being used would be the dream, I don't think there is any interop of that nature working right now. Number of decoder instances per GPU is limited so it's not clear if balancing load btw GPU decoders and CPUs would be needed in say large scale video training.\r\n\r\nAny of these solutions is less than ideal due to the nature of video, having a simple Python interface video / start -> end results in lots of extra memory (you need to decode whole range of the clips into a buffer before using anything). Any scalable video system would be streaming on the fly (issuing frames via callbacks as soon as the stream is far enough along to have re-ordered the frames and synced audio+video+other metadata (sensors, CC, etc).\r\n\r\n",
"For standalone usage, decoding on GPU could be ideal but isn't async processing of inputs on CPUs while letting the accelerator busy for training the de-facto? Of course, I am aware of other advanced mechanisms such as CPU offloading, but I think my point is conveyed. ",
"Here's a minimal implementation of the helper functions we'd need from PyAV, a lot of which I borrowed from `pytorchvideo`, stripping out the `torch` specific stuff:\r\n\r\n[](https://colab.research.google.com/gist/nateraw/c327cb6ff6b074e6ddc8068d19c0367d/pyav-io.ipynb)\r\n \r\nIt's not too much code...@mariosasko we could probably just maintain these helper fns within the `datasets` library, right? ",
"Also wanted to note I added a PR for video classification in `transformers` here, which uses `decord`. It's still open...should we make a decision now to align the libraries we are using between `datasets` and `transformers`? (CC @Narsil )\r\n\r\nhttps://github.com/huggingface/transformers/pull/20151",
"Fully agree on at least trying to unite things.\r\n\r\nMaking clear function boundaries to help us change dependency if needed seems like a good idea since there doesn't seem to be a clear winner.\r\n\r\nI also happen to like directly calling ffmpeg. For some reason it was a lot faster than pyav. "
] | "2022-11-10T17:36:11" | "2022-12-02T15:13:15" | null | CONTRIBUTOR | null | ### Feature request
Add a `Video` feature to the library so folks can include videos in their datasets.
### Motivation
Being able to load Video data would be quite helpful. However, there are some challenges when it comes to videos:
1. Videos, unlike images, can end up being extremely large files
2. Often times when training video models, you need to do some very specific sampling. Videos might end up needing to be broken down into X number of clips used for training/inference
3. Videos have an additional audio stream, which must be accounted for
4. The feature needs to be able to encode/decode videos (with right video settings) from bytes.
### Your contribution
I did work on this a while back in [this (now closed) PR](https://github.com/huggingface/datasets/pull/4532). It used a library I made called [encoded_video](https://github.com/nateraw/encoded-video), which is basically the utils from [pytorchvideo](https://github.com/facebookresearch/pytorchvideo), but without the `torch` dep. It included the ability to read/write from bytes, as we need to do here. We don't want to be using a sketchy library that I made as a dependency in this repo, though.
Would love to use this issue as a place to:
- brainstorm ideas on how to do this right
- list ways/examples to work around it for now
CC @sayakpaul @mariosasko @fcakyon | {
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https://api.github.com/repos/huggingface/datasets/issues/5224 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5224/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5224/comments | https://api.github.com/repos/huggingface/datasets/issues/5224/events | https://github.com/huggingface/datasets/issues/5224 | 1,443,640,867 | I_kwDODunzps5WDDYj | 5,224 | Seems to freeze when loading audio dataset with wav files from local folder | {
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"I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.",
"I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uploaded (or that is what I understood, might be wrong!!).\r\n\r\nNow, including the `drop_metadata` flag I can load the dataset normally (at least with colab notebook):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"audiofolder\", data_dir=\"../archive/Dataset\", , drop_metadata=True)\r\n```\r\n\r\nI'll close the issue."
] | "2022-11-10T10:29:31" | "2022-11-22T11:24:19" | "2022-11-22T11:24:19" | NONE | null | ### Describe the bug
I'm following the instructions in [https://huggingface.co/docs/datasets/audio_load#audiofolder-with-metadata](url) to be able to load a dataset from a local folder.
I have everything into a folder, into a train folder and then the audios and csv. When I try to load the dataset and run from terminal, seems to work but then freezes with no apparent reason.
The metadata.csv file contains a few columns but the important ones, `file_name` with the filename and `transcription` with the transcription are okay.
The audios are `.wav` files, I don't know if that might be the problem (I will proceed to try to change them all to `.mp3` and try again).
### Steps to reproduce the bug
The code I'm using:
```python
from datasets import load_dataset
dataset = load_dataset("audiofolder", data_dir="../archive/Dataset")
dataset[0]["audio"]
```
The output I obtain:
```
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 311135.43it/s]
Using custom data configuration default-38d4546ffd010f3e
Downloading and preparing dataset audiofolder/default to /Users/mine/.cache/huggingface/datasets/audiofolder/default-38d4546ffd010f3e/0.0.0/6cbdd16f8688354c63b4e2a36e1585d05de285023ee6443ffd71c4182055c0fc...
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 166467.72it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 187772.74it/s]
Using custom data configuration default-38d4546ffd010f3e
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Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 162343.45it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 101881.23it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 60145.67it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 80890.02it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 54036.67it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 95851.09it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 155897.00it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 137656.96it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 131230.81it/s]
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
```
And then here it just freezes and nothing more happens.
### Expected behavior
Load the dataset.
### Environment info
Datasets version:
datasets 2.6.1 pypi_0 pypi
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https://api.github.com/repos/huggingface/datasets/issues/5223 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5223/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5223/comments | https://api.github.com/repos/huggingface/datasets/issues/5223/events | https://github.com/huggingface/datasets/pull/5223 | 1,442,610,658 | PR_kwDODunzps5CjT9Z | 5,223 | Add SQL guide | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5223). All of your documentation changes will be reflected on that endpoint.",
"I think we may want more content on this page that's not SQL related. Some of that content probably already lives in the main `load` docs page, but might be bad to remove major things like csv/pandas from there...WDYT we should do @lhoestq ?",
"Maybe the main load page can only show one example and redirect to this page for more details ?\r\n\r\nWe can do the same for pandas stuff: have one example in load, and redirect to this page for more details",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5223). All of your documentation changes will be reflected on that endpoint."
] | "2022-11-09T19:10:27" | "2022-11-15T17:40:25" | "2022-11-15T17:40:21" | MEMBER | null | This PR adapts @nateraw's awesome SQL notebook as a guide for the docs! | {
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"cc @McPatate maybe you know what's happening ?",
"Yes I think I know what is happening. We check in zips for pickles, and the UI must display the pickle jar when a scan has an associated list of imports, even when empty.\r\n~I'll fix ASAP !~",
"> I'll fix ASAP !\r\n\r\nActually I'd rather leave it like that for now, as it indicates that we checked for pickles and nothing dangerous appeared :)",
"Closing the issue with the typical \"feature not a bug\" "
] | "2022-11-09T16:41:16" | "2022-11-09T18:10:46" | "2022-11-09T18:06:57" | NONE | null | ### Describe the bug
HuggingFace is incorrectly reporting that my datasets are pickled. They are not picked, they are simple ZIP files containing PNG images.
Hopefully this is the right location to report this bug.
### Steps to reproduce the bug
Inspect my dataset respository here: https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images
### Expected behavior
They should not be reported as being pickled.
### Environment info
N/A | {
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https://api.github.com/repos/huggingface/datasets/issues/5221 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5221/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5221/comments | https://api.github.com/repos/huggingface/datasets/issues/5221/events | https://github.com/huggingface/datasets/issues/5221 | 1,442,309,094 | I_kwDODunzps5V9-Pm | 5,221 | Cannot push | {
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"Did you run `huggingface-cli lfs-enable-largefiles` before committing or before adding ? Maybe you can try before adding\r\n\r\nAnyway I'd encourage you to split your data into several TAR archives if possible, this way the dataset can loaded faster using multiprocessing (by giving each process a subset of shards to process)",
"@lhoestq \r\nThanks for the help!\r\n> Maybe you can try before adding\r\n\r\nIt did not help\r\n\r\nBut I totally got your point about split into multiple TAR archives. It really helped!"
] | "2022-11-09T15:32:05" | "2022-11-10T18:11:21" | "2022-11-10T18:11:11" | NONE | null | ### Describe the bug
I am facing the issue when I try to push the tar.gz file around 11G to HUB.
```
(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ du -sh *
4.0K README.md
13G data
516K test.jsonl
18M train.jsonl
4.0K ulaanbal_v0.py
11G ulaanbal_v0.tar.gz
452K validation.jsonl
(venv) ╭─laptop@laptop~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ git add ulaanbal_v0.tar.gz && git commit -m 'large version'
(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ git push
EOFoading LFS objects: 0% (0/1), 0 B | 0 B/s
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
error: failed to push some refs to 'https://huggingface.co/datasets/bayartsogt/ulaanbal_v0'
```
I have already tried pushing a small version of this and it was working fine. So my guess it is probably because of the big file.
Following I run before the commit:
```
╰─$ git lfs install
╰─$ huggingface-cli lfs-enable-largefiles .
```
### Steps to reproduce the bug
Create a private dataset on huggingface and push 12G tar.gz file
### Expected behavior
To be pushed with no issue
### Environment info
- `datasets` version: 2.6.1
- Platform: Darwin-21.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 10.0.0
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/5220 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5220/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5220/comments | https://api.github.com/repos/huggingface/datasets/issues/5220/events | https://github.com/huggingface/datasets/issues/5220 | 1,441,664,377 | I_kwDODunzps5V7g15 | 5,220 | Implicit type conversion of lists in to_pandas | {
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"I think this behavior comes from PyArrow:\r\n```python\r\nimport pyarrow as pa\r\nt = pa.table({\"a\": [[0]]})\r\nt.to_pandas().a.values[0]\r\n# array([0])\r\n```\r\n\r\nI believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy arrays.",
"That's interesting, I guess not much to do here then."
] | "2022-11-09T08:40:18" | "2022-11-10T16:12:26" | "2022-11-10T16:12:26" | CONTRIBUTOR | null | ### Describe the bug
```
ds = Dataset.from_list([{'a':[1,2,3]}])
ds.to_pandas().a.values[0]
```
Results in `array([1, 2, 3])` -- a rather unexpected conversion of types which made downstream tools expecting lists not happy.
### Steps to reproduce the bug
See snippet
### Expected behavior
Keep the original type
### Environment info
datasets 2.6.1
python 3.8.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/5219 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5219/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5219/comments | https://api.github.com/repos/huggingface/datasets/issues/5219/events | https://github.com/huggingface/datasets/issues/5219 | 1,441,255,910 | I_kwDODunzps5V59Hm | 5,219 | Delta Tables usage using Datasets Library | {
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"Hi ! Interesting :) Can you provide concrete examples of cases where it can be useful ?",
"Few example blogs and posts that might help on this - \r\n\r\n1. https://hevodata.com/learn/databricks-delta-tables/\r\n2. https://docs.databricks.com/delta/index.html\r\n\r\nBasically, we are looking at utility of Datasets library with Delta Lake Tables.\r\n",
"`datasets` can already read/write from parquet from/to a cloud storage using fsspec, if I understand correctly it's should be possible to load parquet files as delat lake tables no ? :) Or is there someting missing ?",
"@lhoestq Per my understanding, delta lake table is a bunch of paruqet files together with the meta to support ACID. For example file 1 contains v0.1 of record A while file 2 contains v0.2 of record A. I am assuming the Hugging face dataset would delegate the read/write delta table to 3rd party lib, maybe pyarrow. Correct me if I was wrong @reichenbch \r\n\r\nAnd I am assuming, people are asking the versioning of Hugging face datasets. But I am assuming Hugging face delegate this function to github and it is not the key requirement for Public Data set. It actually the key function of ML Ops, I am not sure whether hugging face would like expand to that area."
] | "2022-11-09T02:43:56" | "2023-03-02T19:29:12" | null | NONE | null | ### Feature request
Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.
### Motivation
We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.
This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.
### Your contribution
Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature. | {
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] | closed | false | null | [] | null | [] | "2022-11-09T02:42:18" | "2022-11-09T02:42:36" | "2022-11-09T02:42:36" | NONE | null | ### Feature request
Adding compatibility of Datasets library with Delta Format. Elevating the utilities of Datasets library from Machine Learning Scope to Data Engineering Scope as well.
### Motivation
We know datasets library can absorb csv, json, parquet, etc. file formats but it would be great if Datasets library could work with Delta Tables (with delta format) as it has different features such as time travelling, layout optimization, query performance, aids in Data Engineering.
This will help and enhance Datasets library from Machine Learning utility to Data Engineering utilities and expand horizons thereafter. I am totally using Datasets library in all my usecases and as my role expands so does the work, compatibility with Datasets library is something I don't want to lose.
### Your contribution
Would love to work on this feature, even if this has to picked up from scratch, including design paradigms and patterns.
I have basic idea about Delta Live Tables, would brush it easily for this feature. | {
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https://api.github.com/repos/huggingface/datasets/issues/5217 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5217/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5217/comments | https://api.github.com/repos/huggingface/datasets/issues/5217/events | https://github.com/huggingface/datasets/pull/5217 | 1,441,252,740 | PR_kwDODunzps5CetXs | 5,217 | Reword E2E training and inference tips in the vision guides | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2022-11-09T02:40:01" | "2022-11-10T01:38:09" | "2022-11-10T01:36:09" | MEMBER | null | Reference: https://github.com/huggingface/datasets/pull/5188#discussion_r1012148730 | {
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"Hi ! I think there exist tools to dump and reload an index in your elastic search but I'm not super familiar with it.\r\n\r\nAnyway after reloading an index in elastic search you can call `ds.load_elasticsearch_index` which will connect the index to the dataset without re-indexing"
] | "2022-11-08T23:06:52" | "2022-11-09T13:16:45" | null | NONE | null | Hi,
I am new to Dataset and elasticsearch. I was wondering is there any equivalent approach to save elasticsearch index as of save_faiss_index locally for later use, to remove the need to re-index a dataset? | {
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https://api.github.com/repos/huggingface/datasets/issues/5214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5214/comments | https://api.github.com/repos/huggingface/datasets/issues/5214/events | https://github.com/huggingface/datasets/pull/5214 | 1,440,334,978 | PR_kwDODunzps5CbmWE | 5,214 | Update github pr docs actions | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5214). All of your documentation changes will be reflected on that endpoint."
] | "2022-11-08T14:43:37" | "2022-11-08T15:39:58" | "2022-11-08T15:39:57" | CONTRIBUTOR | null | null | {
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