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https://github.com/huggingface/datasets/issues/7225
| 7,225
|
Huggingface GIT returns null as Content-Type instead of application/x-git-receive-pack-result
|
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open
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[] | 2024-10-14T14:33:06Z
| 2024-10-14T14:33:06Z
| null |
NONE
| null | null |
### Describe the bug
We push changes to our datasets programmatically. Our git client jGit reports that the hf git server returns null as Content-Type after a push.
### Steps to reproduce the bug
A basic kotlin application:
```
val person = PersonIdent(
"padmalcom",
"padmalcom@sth.com"
)
val cp = UsernamePasswordCredentialsProvider(
"padmalcom",
"mysecrettoken"
)
val git =
KGit.cloneRepository {
setURI("https://huggingface.co/datasets/sth/images")
setTimeout(60)
setProgressMonitor(TextProgressMonitor())
setCredentialsProvider(cp)
}
FileOutputStream("./images/images.csv").apply { writeCsv(images) }
git.add {
addFilepattern("images.csv")
}
for (i in images) {
FileUtils.copyFile(
File("./files/${i.id}"),
File("./images/${i.id + File(i.fileName).extension }")
)
git.add {
addFilepattern("${i.id + File(i.fileName).extension }")
}
}
val revCommit = git.commit {
author = person
message = "Uploading images at " + LocalDateTime.now()
.format(DateTimeFormatter.ISO_DATE_TIME)
setCredentialsProvider(cp)
}
val push = git.push {
setCredentialsProvider(cp)
}
```
### Expected behavior
The git server is expected to return the Content-Type _application/x-git-receive-pack-result_.
### Environment info
It is independent from the datasets library.
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https://github.com/huggingface/datasets/pull/7224
| 7,224
|
fallback to default feature casting in case custom features not available during dataset loading
|
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[] | 2024-10-12T16:13:56Z
| 2024-10-12T16:13:56Z
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CONTRIBUTOR
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a fix for #7223 in case datasets is happy to support this kind of extensibility! seems cool / powerful for allowing sharing of datasets with potentially different feature types
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https://github.com/huggingface/datasets/issues/7223
| 7,223
|
Fallback to arrow defaults when loading dataset with custom features that aren't registered locally
|
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[] | 2024-10-12T16:08:20Z
| 2024-10-12T16:08:20Z
| null |
CONTRIBUTOR
| null | null |
### Describe the bug
Datasets allows users to create and register custom features.
However if datasets are then pushed to the hub, this means that anyone calling load_dataset without registering the custom Features in the same way as the dataset creator will get an error message.
It would be nice to offer a fallback in this case.
### Steps to reproduce the bug
```python
load_dataset("alex-hh/custom-features-example")
```
(Dataset creation process - must be run in separate session so that NewFeature isn't registered in session in which download is attempted:)
```python
from dataclasses import dataclass, field
import pyarrow as pa
from datasets.features.features import register_feature
from datasets import Dataset, Features, Value, load_dataset
from datasets import Feature
@dataclass
class NewFeature(Feature):
_type: str = field(default="NewFeature", init=False, repr=False)
def __call__(self):
return pa.int32()
def examples_generator():
for i in range(5):
yield {"feature": i}
ds = Dataset.from_generator(examples_generator, features=Features(feature=NewFeature()))
ds.push_to_hub("alex-hh/custom-features-example")
register_feature(NewFeature, "NewFeature")
```
### Expected behavior
It would be nice, and offer greater extensibility, if there was some kind of graceful fallback mechanism in place for cases where user-defined features are stored in the dataset but not available locally.
### Environment info
3.0.2
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https://github.com/huggingface/datasets/issues/7222
| 7,222
|
TypeError: Couldn't cast array of type string to null in long json
|
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[
"I am encountering this same issue. It seems that the library manages to recognise an optional column (but not **exclusively** null) if there is at least one non-null instance within the same file. For example, given a `test_0.jsonl` file:\r\n```json\r\n{\"a\": \"a1\", \"b\": \"b1\", \"c\": null, \"d\": null}\r\n{\"a\": \"a2\", \"b\": null, \"c\": \"c2\", \"d\": null}\r\n```\r\nthe data is correctly loaded, recognising that columns `b` & `c` are optional, while `d` is null.\r\n```python\r\n{'a': ['a1', 'a2'], 'b': ['b1', None], 'c': [None, 'c2'], 'd': [None, None]}\r\n```\r\n\r\nBut if the `config` has another file, say `test_1.jsonl` where `d` now has some non-null values:\r\n```json\r\n{\"a\": null, \"b\": \"b3\", \"c\": \"c3\", \"d\": \"d3\"}\r\n{\"a\": \"a4\", \"b\": \"b4\", \"c\": null, \"d\": null}\r\n```\r\nthen, an error is raised:\r\n```\r\nTypeError Traceback (most recent call last)\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\r\n 1869 try:\r\n-> 1870 writer.write_table(table)\r\n 1871 except CastError as cast_error:\r\n\r\n14 frames\r\n\r\nTypeError: Couldn't cast array of type string to null\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nDatasetGenerationError Traceback (most recent call last)\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\r\n 1895 if isinstance(e, DatasetGenerationError):\r\n 1896 raise\r\n-> 1897 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n 1898 \r\n 1899 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\r\n\r\nDatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\n---\r\n\r\nI have created a [sample repository](https://huggingface.co/datasets/KurtMica/optional_columns_mutiple_files) if that helps. Interestingly, the dataset viewer correctly shows the data across files, although it still indicates the above error.",
" Managed to find a workaround, by [specifying the features explicitly](https://huggingface.co/docs/datasets/main/en/loading#specify-features), which is also possible to do directly using the [YAML file configuration](https://discuss.huggingface.co/t/appropriate-yaml-for-dataset-info-list-float/74418).",
"I hit the same issue for `datasets 3.2.0`. Given the two jsonl files with the same content but different ordering, `load_dataset` worked for one but did not work for the other.\n\n```\nfrom datasets import load_dataset\n\nissues_dataset = load_dataset(\n \"json\", data_files=\"NeMo-issues-fixed.jsonl\", split=\"train\"\n)\nissues_dataset\n```\n\nFor [NeMo-issues.jsonl](https://github.com/renweizhukov/jupyter-lab-notebook/blob/main/hugging-face-nlp-course/NeMo-issues.jsonl), I got an exception:\n\n```\n---------------------------------------------------------------------------\nTypeError Traceback (most recent call last)\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py:1870](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py#line=1869), in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\n 1869 try:\n-> 1870 writer.write_table(table)\n 1871 except CastError as cast_error:\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/arrow_writer.py:622](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/arrow_writer.py#line=621), in ArrowWriter.write_table(self, pa_table, writer_batch_size)\n 621 pa_table = pa_table.combine_chunks()\n--> 622 pa_table = table_cast(pa_table, self._schema)\n 623 if self.embed_local_files:\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py:2292](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py#line=2291), in table_cast(table, schema)\n 2291 if table.schema != schema:\n-> 2292 return cast_table_to_schema(table, schema)\n 2293 elif table.schema.metadata != schema.metadata:\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py:2246](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py#line=2245), in cast_table_to_schema(table, schema)\n 2240 raise CastError(\n 2241 f\"Couldn't cast\\n{_short_str(table.schema)}\\nto\\n{_short_str(features)}\\nbecause column names don't match\",\n 2242 table_column_names=table.column_names,\n 2243 requested_column_names=list(features),\n 2244 )\n 2245 arrays = [\n-> 2246 cast_array_to_feature(\n 2247 table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),\n 2248 feature,\n 2249 )\n 2250 for name, feature in features.items()\n 2251 ]\n 2252 return pa.Table.from_arrays(arrays, schema=schema)\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py:1795](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py#line=1794), in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)\n 1794 if isinstance(array, pa.ChunkedArray):\n-> 1795 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\n 1796 else:\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py:2102](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py#line=2101), in cast_array_to_feature(array, feature, allow_primitive_to_str, allow_decimal_to_str)\n 2101 elif not isinstance(feature, (Sequence, dict, list, tuple)):\n-> 2102 return array_cast(\n 2103 array,\n 2104 feature(),\n 2105 allow_primitive_to_str=allow_primitive_to_str,\n 2106 allow_decimal_to_str=allow_decimal_to_str,\n 2107 )\n 2108 raise TypeError(f\"Couldn't cast array of type\\n{_short_str(array.type)}\\nto\\n{_short_str(feature)}\")\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py:1797](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py#line=1796), in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)\n 1796 else:\n-> 1797 return func(array, *args, **kwargs)\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py:1948](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/table.py#line=1947), in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)\n 1947 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):\n-> 1948 raise TypeError(f\"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}\")\n 1949 return array.cast(pa_type)\n\nTypeError: Couldn't cast array of type string to null\n\nThe above exception was the direct cause of the following exception:\n\nDatasetGenerationError Traceback (most recent call last)\nCell In[73], line 3\n 1 from datasets import load_dataset\n----> 3 issues_dataset = load_dataset(\n 4 \"json\", data_files=\"NeMo-issues.jsonl\", split=\"train\"\n 5 )\n 6 issues_dataset\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/load.py:2151](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/load.py#line=2150), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\n 2148 return builder_instance.as_streaming_dataset(split=split)\n 2150 # Download and prepare data\n-> 2151 builder_instance.download_and_prepare(\n 2152 download_config=download_config,\n 2153 download_mode=download_mode,\n 2154 verification_mode=verification_mode,\n 2155 num_proc=num_proc,\n 2156 storage_options=storage_options,\n 2157 )\n 2159 # Build dataset for splits\n 2160 keep_in_memory = (\n 2161 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\n 2162 )\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py:924](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py#line=923), in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\n 922 if num_proc is not None:\n 923 prepare_split_kwargs[\"num_proc\"] = num_proc\n--> 924 self._download_and_prepare(\n 925 dl_manager=dl_manager,\n 926 verification_mode=verification_mode,\n 927 **prepare_split_kwargs,\n 928 **download_and_prepare_kwargs,\n 929 )\n 930 # Sync info\n 931 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py:1000](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py#line=999), in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)\n 996 split_dict.add(split_generator.split_info)\n 998 try:\n 999 # Prepare split will record examples associated to the split\n-> 1000 self._prepare_split(split_generator, **prepare_split_kwargs)\n 1001 except OSError as e:\n 1002 raise OSError(\n 1003 \"Cannot find data file. \"\n 1004 + (self.manual_download_instructions or \"\")\n 1005 + \"\\nOriginal erro[r:\\n](file:///R:/n)\"\n 1006 + str(e)\n 1007 ) from None\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py:1741](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py#line=1740), in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)\n 1739 job_id = 0\n 1740 with pbar:\n-> 1741 for job_id, done, content in self._prepare_split_single(\n 1742 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args\n 1743 ):\n 1744 if done:\n 1745 result = content\n\nFile [~/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py:1897](http://localhost:8888/home/renwei/anaconda3/envs/llm/lib/python3.12/site-packages/datasets/builder.py#line=1896), in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)\n 1895 if isinstance(e, DatasetGenerationError):\n 1896 raise\n-> 1897 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\n 1899 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\n\nDatasetGenerationError: An error occurred while generating the dataset\n```\n\nFor [NeMo-issues-fixed.json](https://github.com/renweizhukov/jupyter-lab-notebook/blob/main/hugging-face-nlp-course/NeMo-issues-fixed.jsonl) which consists of the last 1000 lines and then the first 9000 lines of NeMo-issues.jsonl, I could load the data:\n\n```\nDataset({\n features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'sub_issues_summary', 'active_lock_reason', 'draft', 'pull_request', 'body', 'closed_by', 'reactions', 'timeline_url', 'performed_via_github_app', 'state_reason'],\n num_rows: 10000\n})\n```",
"having the same issue as well!",
"Is this fixed in the latest version?",
"@DronHazra @renweizhukov Is this fixed in the latest version?"
] | 2024-10-12T08:14:59Z
| 2025-09-26T11:54:48Z
| null |
NONE
| null | null |
### Describe the bug
In general, changing the type from string to null is allowed within a dataset — there are even examples of this in the documentation.
However, if the dataset is large and unevenly distributed, this allowance stops working. The schema gets locked in after reading a chunk.
Consequently, if all values in the first chunk of a field are, for example, null, the field will be locked as type null, and if a string appears in that field in the second chunk, it will trigger this error:
<details>
<summary>Traceback </summary>
```
TypeError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1868 try:
-> 1869 writer.write_table(table)
1870 except CastError as cast_error:
14 frames
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size)
579 pa_table = pa_table.combine_chunks()
--> 580 pa_table = table_cast(pa_table, self._schema)
581 if self.embed_local_files:
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema)
2291 if table.schema != schema:
-> 2292 return cast_table_to_schema(table, schema)
2293 elif table.schema.metadata != schema.metadata:
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema)
2244 )
-> 2245 arrays = [
2246 cast_array_to_feature(
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in <listcomp>(.0)
2245 arrays = [
-> 2246 cast_array_to_feature(
2247 table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in wrapper(array, *args, **kwargs)
1794 if isinstance(array, pa.ChunkedArray):
-> 1795 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1796 else:
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in <listcomp>(.0)
1794 if isinstance(array, pa.ChunkedArray):
-> 1795 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1796 else:
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_array_to_feature(array, feature, allow_primitive_to_str, allow_decimal_to_str)
2101 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 2102 return array_cast(
2103 array,
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in wrapper(array, *args, **kwargs)
1796 else:
-> 1797 return func(array, *args, **kwargs)
1798
[/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)
1947 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
-> 1948 raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
1949 return array.cast(pa_type)
TypeError: Couldn't cast array of type string to null
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[<ipython-input-353-e02f83980611>](https://localhost:8080/#) in <cell line: 1>()
----> 1 dd = load_dataset("json", data_files=["TEST.json"])
[/usr/local/lib/python3.10/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, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
2094
2095 # Download and prepare data
-> 2096 builder_instance.download_and_prepare(
2097 download_config=download_config,
2098 download_mode=download_mode,
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
922 if num_proc is not None:
923 prepare_split_kwargs["num_proc"] = num_proc
--> 924 self._download_and_prepare(
925 dl_manager=dl_manager,
926 verification_mode=verification_mode,
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
997 try:
998 # Prepare split will record examples associated to the split
--> 999 self._prepare_split(split_generator, **prepare_split_kwargs)
1000 except OSError as e:
1001 raise OSError(
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1738 job_id = 0
1739 with pbar:
-> 1740 for job_id, done, content in self._prepare_split_single(
1741 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1742 ):
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1894 if isinstance(e, DatasetGenerationError):
1895 raise
-> 1896 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1897
1898 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
</details>
### Steps to reproduce the bug
```python
import json
from datasets import load_dataset
with open("TEST.json", "w") as f:
row = {"ballast": "qwerty" * 1000, "b": None}
row_str = json.dumps(row) + "\n"
line_size = len(row_str)
chunk_size = 10 << 20
lines_in_chunk = chunk_size // line_size + 1
print(f"Writing {lines_in_chunk} lines")
for i in range(lines_in_chunk):
f.write(row_str)
null_row = {"ballast": "Gotcha", "b": "Not Null"}
f.write(json.dumps(null_row) + "\n")
load_dataset("json", data_files=["TEST.json"])
```
### Expected behavior
Concatenation of the chunks without errors
### Environment info
- `datasets` version: 3.0.1
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.24.7
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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https://github.com/huggingface/datasets/pull/7221
| 7,221
|
add CustomFeature base class to support user-defined features with encoding/decoding logic
|
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[
"@lhoestq would you be open to supporting this kind of extensibility?",
"I suggested a fix in https://github.com/huggingface/datasets/issues/7220 that would not necessarily require a parent class for custom features, lmk what you think"
] | 2024-10-11T20:10:27Z
| 2025-01-28T09:40:29Z
| 2025-01-28T09:40:29Z
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|
intended as fix for #7220 if this kind of extensibility is something that datasets is willing to support!
```python
from datasets.features.features import CustomFeature
class ListOfStrs(CustomFeature):
requires_encoding = True
def _encode_example(self, value):
if isinstance(value, str):
return [str]
else:
return value
feats = Features(strlist=ListOfStrs())
feats.encode_example({"strlist": "a"})["strlist"] == feats["strlist"].encode_example("a")
```
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| null | null | null | true
|
https://github.com/huggingface/datasets/issues/7220
| 7,220
|
Custom features not compatible with special encoding/decoding logic
|
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[
"I think you can fix this simply by replacing the line with hardcoded features with `hastattr(schema, \"encode_example\")` actually",
"#7284 "
] | 2024-10-11T19:20:11Z
| 2024-11-08T15:10:58Z
| null |
CONTRIBUTOR
| null | null |
### Describe the bug
It is possible to register custom features using datasets.features.features.register_feature (https://github.com/huggingface/datasets/pull/6727)
However such features are not compatible with Features.encode_example/decode_example if they require special encoding / decoding logic because encode_nested_example / decode_nested_example checks whether the feature is in a fixed list of encodable types:
https://github.com/huggingface/datasets/blob/16a121d7821a7691815a966270f577e2c503473f/src/datasets/features/features.py#L1349
This prevents the extensibility of features to complex cases
### Steps to reproduce the bug
```python
class ListOfStrs:
def encode_example(self, value):
if isinstance(value, str):
return [str]
else:
return value
feats = Features(strlist=ListOfStrs())
assert feats.encode_example({"strlist": "a"})["strlist"] = feats["strlist"].encode_example("a")}
```
### Expected behavior
Registered feature types should be encoded based on some property of the feature (e.g. requires_encoding)?
### Environment info
3.0.2
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https://github.com/huggingface/datasets/pull/7219
| 7,219
|
bump fsspec
|
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7219). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-11T15:56:36Z
| 2024-10-14T08:21:56Z
| 2024-10-14T08:21:55Z
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https://github.com/huggingface/datasets/issues/7217
| 7,217
|
ds.map(f, num_proc=10) is slower than df.apply
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[
"Hi ! `map()` reads all the columns and writes the resulting dataset with all the columns as well, while df.column_name.apply only reads and writes one column and does it in memory. So this is speed difference is actually expected.\r\n\r\nMoreover using multiprocessing on a dataset that lives in memory (from_pandas uses the same in-memory data as the pandas DataFrame while load_dataset or from_generator load from disk) requires to copy the data to each subprocess which can also be slow. Data loaded from disk don't need to be copied though since they work as a form of shared memory thanks to memory mapping.\r\n\r\nHowever you can make you map() call much faster by making it read and write only the column you want:\r\n\r\n```python\r\nhas_cover_ds = ds.map(lambda song_name: {'has_cover': has_cover(song_name)}, input_columns=[\"song_name\"], remove_columns=ds.column_names) # outputs a dataset with 1 column\r\nds = ds.concatenate_datasets([ds, has_cover_ds], axis=1)\r\n```\r\n\r\nand if your dataset is loaded from disk you can pass num_proc=10 and get a nice speed up as well (no need to copy the data to subprocesses)",
"Isn't there a way to do memory mapping with the in-memory dataset without saving it to disk?",
"Maybe saving it to a memory-mapped filesystem? It'd be like a trick to make datasets save to \"disk\" but actually it's memory. But it feels like there should be a better \"automatic\" way provided by `datasets`."
] | 2024-10-11T11:04:05Z
| 2025-02-28T21:21:01Z
| null |
NONE
| null | null |
### Describe the bug
pandas columns: song_id, song_name
ds = Dataset.from_pandas(df)
def has_cover(song_name):
if song_name is None or pd.isna(song_name):
return False
return 'cover' in song_name.lower()
df['has_cover'] = df.song_name.progress_apply(has_cover)
ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10)
time cost:
1. df.apply: 100%|██████████| 12500592/12500592 [00:13<00:00, 959825.47it/s]
2. ds.map: Map (num_proc=10): 31%
3899028/12500592 [00:28<00:38, 222532.89 examples/s]
### Steps to reproduce the bug
pandas columns: song_id, song_name
ds = Dataset.from_pandas(df)
def has_cover(song_name):
if song_name is None or pd.isna(song_name):
return False
return 'cover' in song_name.lower()
df['has_cover'] = df.song_name.progress_apply(has_cover)
ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10)
### Expected behavior
ds.map is ~num_proc faster than df.apply
### Environment info
pandas: 2.2.2
datasets: 2.19.1
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https://github.com/huggingface/datasets/issues/7215
| 7,215
|
Iterable dataset map with explicit features causes slowdown for Sequence features
|
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[] |
open
| false
|
[] | 2024-10-10T22:08:20Z
| 2024-10-10T22:10:32Z
| null |
CONTRIBUTOR
| null | null |
### Describe the bug
When performing map, it's nice to be able to pass the new feature type, and indeed required by interleave and concatenate datasets.
However, this can cause a major slowdown for certain types of array features due to the features being re-encoded.
This is separate to the slowdown reported in #7206
### Steps to reproduce the bug
```
from datasets import Dataset, Features, Array3D, Sequence, Value
import numpy as np
import time
features=Features(**{"array0": Sequence(feature=Value("float32"), length=-1), "array1": Sequence(feature=Value("float32"), length=-1)})
dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,), dtype=np.float32) for x in [5000,10000]*25] for i in range(2)}, features=features)
```
```
ds = dataset.to_iterable_dataset()
ds = ds.with_format("numpy").map(lambda x: x)
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~1.5 s on main
```
ds = dataset.to_iterable_dataset()
ds = ds.with_format("numpy").map(lambda x: x, features=features)
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~ 3 s on main
### Expected behavior
I'm not 100% sure whether passing new feature types to formatted outputs of map should be supported or not, but assuming it should, then there should be a cost-free way to specify the new feature type - knowing feature type is required by interleave_datasets and concatenate_datasets for example
### Environment info
3.0.2
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https://github.com/huggingface/datasets/issues/7214
| 7,214
|
Formatted map + with_format(None) changes array dtype for iterable datasets
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[
"possibly due to this logic:\r\n\r\n```python\r\n def _arrow_array_to_numpy(self, pa_array: pa.Array) -> np.ndarray:\r\n if isinstance(pa_array, pa.ChunkedArray):\r\n if isinstance(pa_array.type, _ArrayXDExtensionType):\r\n # don't call to_pylist() to preserve dtype of the fixed-size array\r\n zero_copy_only = _is_zero_copy_only(pa_array.type.storage_dtype, unnest=True)\r\n array: List = [\r\n row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only)\r\n ]\r\n else:\r\n zero_copy_only = _is_zero_copy_only(pa_array.type) and all(\r\n not _is_array_with_nulls(chunk) for chunk in pa_array.chunks\r\n )\r\n array: List = [\r\n row for chunk in pa_array.chunks for row in chunk.to_numpy(zero_copy_only=zero_copy_only)\r\n ]\r\n else:\r\n if isinstance(pa_array.type, _ArrayXDExtensionType):\r\n # don't call to_pylist() to preserve dtype of the fixed-size array\r\n zero_copy_only = _is_zero_copy_only(pa_array.type.storage_dtype, unnest=True)\r\n array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only)\r\n else:\r\n zero_copy_only = _is_zero_copy_only(pa_array.type) and not _is_array_with_nulls(pa_array)\r\n array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist()\r\n```"
] | 2024-10-10T12:45:16Z
| 2024-10-12T16:55:57Z
| null |
CONTRIBUTOR
| null | null |
### Describe the bug
When applying with_format -> map -> with_format(None), array dtypes seem to change, even if features are passed
### Steps to reproduce the bug
```python
features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32")})
dataset = Dataset.from_dict({f"array0": [np.zeros((100,10,10), dtype=np.float32)]*25}, features=features)
ds = dataset.to_iterable_dataset().with_format("numpy").map(lambda x: x, features=features)
ex_0 = next(iter(ds))
ds = dataset.to_iterable_dataset().with_format("numpy").map(lambda x: x, features=features).with_format(None)
ex_1 = next(iter(ds))
assert ex_1["array0"].dtype == ex_0["array0"].dtype, f"{ex_1['array0'].dtype} {ex_0['array0'].dtype}"
```
### Expected behavior
Dtypes should be preserved.
### Environment info
3.0.2
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https://github.com/huggingface/datasets/issues/7213
| 7,213
|
Add with_rank to Dataset.from_generator
|
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| false
|
[] | 2024-10-10T12:15:29Z
| 2024-10-10T12:17:11Z
| null |
NONE
| null | null |
### Feature request
Add `with_rank` to `Dataset.from_generator` similar to `Dataset.map` and `Dataset.filter`.
### Motivation
As for `Dataset.map` and `Dataset.filter`, this is useful when creating cache files using multi-GPU, where the rank can be used to select GPU IDs. For now, rank can be added in the `gen_kwars` argument; however, this, in turn, includes the rank when computing the fingerprint.
### Your contribution
Added #7199 which passes rank based on the `job_id` set by `num_proc`.
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https://github.com/huggingface/datasets/issues/7212
| 7,212
|
Windows do not supprot signal.alarm and singal.signal
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|
[] | 2024-10-10T12:00:19Z
| 2024-10-10T12:00:19Z
| null |
NONE
| null | null |
### Describe the bug
signal.alarm and signal.signal are used in the load.py module, but these are not supported by Windows.
### Steps to reproduce the bug
lighteval accelerate --model_args "pretrained=gpt2,trust_remote_code=True" --tasks "community|kinit_sts" --custom_tasks "community_tasks/kinit_evals.py" --output_dir "./evals"
### Expected behavior
proceed with input(..) method
### Environment info
Windows 11
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https://github.com/huggingface/datasets/issues/7211
| 7,211
|
Describe only selected fields in README
|
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| false
|
[] | 2024-10-09T16:25:47Z
| 2024-10-09T16:25:47Z
| null |
NONE
| null | null |
### Feature request
Hi Datasets team!
Is it possible to add the ability to describe only selected fields of the dataset files in `README.md`? For example, I have this open dataset ([open-llm-leaderboard/results](https://huggingface.co/datasets/open-llm-leaderboard/results?row=0)) and I want to describe only some fields in order not to overcomplicate the Dataset Preview and filter out some fields
### Motivation
The `Results` dataset for the Open LLM Leaderboard contains json files with a complex nested structure. I would like to add `README.md` there to use the SQL console, for example. But if I describe the structure of this dataset completely, it will overcomplicate the use of Dataset Preview and the total number of columns will exceed 50
### Your contribution
I'm afraid I'm not familiar with the project structure, so I won't be able to open a PR, but I'll try to help with something else if possible
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https://github.com/huggingface/datasets/issues/7210
| 7,210
|
Convert Array features to numpy arrays rather than lists by default
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|
[] | 2024-10-09T13:05:21Z
| 2024-10-09T13:05:21Z
| null |
CONTRIBUTOR
| null | null |
### Feature request
It is currently quite easy to cause massive slowdowns when using datasets and not familiar with the underlying data conversions by e.g. making bad choices of formatting.
Would it be more user-friendly to set defaults that avoid this as much as possible? e.g. format Array features as numpy arrays rather than python lists
### Motivation
Default array formatting leads to slow performance: e.g.
```python
import numpy as np
from datasets import Dataset, Features, Array3D
features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")})
dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features)
```
```python
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~1.4 s
```python
ds = dataset.to_iterable_dataset()
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~10s
```python
ds = dataset.with_format("numpy")
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~0.04s
```python
ds = dataset.to_iterable_dataset().with_format("numpy")
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
```
~0.04s
### Your contribution
May be able to contribute
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https://github.com/huggingface/datasets/pull/7209
| 7,209
|
Preserve features in iterable dataset.filter
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[
"Yes your assumption on concatenate/interleave is ok imo.\r\n\r\nIt seems the TypedExamplesIterable can slow down things, it should take formatting into account to not convert numpy arrays to python lists\r\n\r\nright now it's slow (unrelatedly to your PR):\r\n\r\n```python\r\n>>> ds = Dataset.from_dict({\"a\": np.zeros((1000, 32, 32))}).to_iterable_dataset().with_format(\"np\")\r\n>>> filtered_ds = ds.filter(lambda x: True)\r\n>>> %time sum(1 for _ in ds)\r\nCPU times: user 175 ms, sys: 8.1 ms, total: 183 ms\r\nWall time: 184 ms\r\n1000\r\n>>> %time sum(1 for _ in filtered_ds)\r\nCPU times: user 4.1 s, sys: 8.41 ms, total: 4.1 s\r\nWall time: 4.12 s\r\n1000\r\n```",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7209). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"> It seems the TypedExamplesIterable can slow down things, it should take formatting into account to not convert numpy arrays to python lists\r\n\r\nShould be fixed by updated #7207 I hope!"
] | 2024-10-09T10:42:05Z
| 2024-10-16T11:27:22Z
| 2024-10-09T16:04:07Z
|
CONTRIBUTOR
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Fixes example in #7208 - I'm not sure what other checks I should do? @lhoestq
I also haven't thought hard about the concatenate / interleaving example iterables but think this might work assuming that features are either all identical or None?
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https://github.com/huggingface/datasets/issues/7208
| 7,208
|
Iterable dataset.filter should not override features
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"closed by https://github.com/huggingface/datasets/pull/7209, thanks @alex-hh !"
] | 2024-10-09T10:23:45Z
| 2024-10-09T16:08:46Z
| 2024-10-09T16:08:45Z
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CONTRIBUTOR
| null | null |
### Describe the bug
When calling filter on an iterable dataset, the features get set to None
### Steps to reproduce the bug
import numpy as np
import time
from datasets import Dataset, Features, Array3D
```python
features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")})
dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features)
ds = dataset.to_iterable_dataset()
orig_column_names = ds.column_names
ds = ds.filter(lambda x: True)
assert ds.column_names == orig_column_names
```
### Expected behavior
Filter should preserve features information
### Environment info
3.0.2
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https://github.com/huggingface/datasets/pull/7207
| 7,207
|
apply formatting after iter_arrow to speed up format -> map, filter for iterable datasets
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[
"I think the problem is that the underlying ex_iterable will not use iter_arrow unless the formatting type is arrow, which leads to conversion from arrow -> python -> numpy in this case rather than arrow -> numpy.\r\n\r\nIdea of updated fix is to use the ex_iterable's iter_arrow in any case where it's available and any formatting is specified. The formatter then works directly on arrow tables; the outputs of the formatter get passed to the function to be mapped.\r\n\r\nWith updated version:\r\n\r\n```python\r\nimport numpy as np\r\nimport time\r\nfrom datasets import Dataset, Features, Array3D\r\n\r\nfeatures=Features(**{\"array0\": Array3D((None, 10, 10), dtype=\"float32\"), \"array1\": Array3D((None,10,10), dtype=\"float32\")})\r\ndataset = Dataset.from_dict({f\"array{i}\": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features)\r\n```\r\n\r\n```python\r\nds = dataset.to_iterable_dataset()\r\nds = ds.with_format(\"numpy\").map(lambda x: x, batched=True, batch_size=10)\r\nt0 = time.time()\r\nfor ex in ds:\r\n pass\r\nt1 = time.time()\r\n```\r\nTotal time: < 0.01s (~30s on main)\r\n\r\n```python\r\nds = dataset.to_iterable_dataset()\r\nds = ds.with_format(\"numpy\").map(lambda x: x, batched=False)\r\nt0 = time.time()\r\nfor ex in ds:\r\n pass\r\nt1 = time.time()\r\n```\r\nTime: ~0.02 s (~30s on main)\r\n\r\n```python\r\nds = dataset.to_iterable_dataset()\r\nds = ds.with_format(\"numpy\")\r\nt0 = time.time()\r\nfor ex in ds:\r\n pass\r\nt1 = time.time()\r\n```\r\nTime: ~0.02s",
"also now working for filter with similar performance improvements:\r\n\r\n```python\r\nfiltered_examples = []\r\nds = dataset.to_iterable_dataset()\r\nds = ds.with_format(\"numpy\").filter(lambda x: [arr.shape[0]==2000 for arr in x[\"array0\"]], batch_size=10, batched=True)\r\nt0 = time.time()\r\nfor ex in ds:\r\n filtered_examples.append(ex)\r\nt1 = time.time()\r\nassert len(filtered_examples) == 25\r\n```\r\n0.01s vs 50s on main\r\n\r\n\r\n```python\r\nfiltered_examples = []\r\nds = dataset.to_iterable_dataset()\r\nds = ds.with_format(\"numpy\").filter(lambda x: x[\"array0\"].shape[0]==2000, batched=False)\r\nt0 = time.time()\r\nfor ex in ds:\r\n filtered_examples.append(ex)\r\nt1 = time.time()\r\nassert len(filtered_examples) == 25\r\n```\r\n0.04s vs 50s on main\r\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7207). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"(the distributed tests failing in the CI are unrelated)",
"There also appears to be a separate? issue with chaining filter and map bc filter iter_arrow only returns _iter_arrow if arrow formatting is applied (and vv presumably)\r\n\r\nI don't have a good minimal example atm",
"> issue with chaining filter and map bc filter iter_arrow only returns _iter_arrow if arrow formatting is applied (and vv presumably)\r\n\r\nMaybe related to this issue ?\r\n\r\n```python\r\nds = Dataset.from_dict({\"a\": range(10)}).to_iterable_dataset()\r\nds = ds.with_format(\"arrow\").map(lambda x: x, features=Features({\"a\": Value(\"string\")})).with_format(None)\r\nprint(list(ds)) # yields integers instead of strings\r\n```",
"I feel like we could get rid of TypedExampleIterable altogether and apply formatting with feature conversion with `formatted_python_examples_iterator ` and `formatted_arrow_examples_iterator`\r\n\r\nbtw you can pass `features=` in `get_formatter()` to get a formatter that does the feature conversion at the same time as formatting\r\n\r\n(edit:\r\n\r\nexcept maybe the arrow formatter doesn't use `features` yet, we can fix it like this if it's really needed\r\n```diff\r\nclass ArrowFormatter(Formatter[pa.Table, pa.Array, pa.Table]):\r\n def format_row(self, pa_table: pa.Table) -> pa.Table:\r\n- return self.simple_arrow_extractor().extract_row(pa_table)\r\n+ pa_table = self.simple_arrow_extractor().extract_row(pa_table)\r\n+. return cast_table_to_features(pa_table, self.features) if self.features else pa_table\r\n \r\n```\r\n\r\n\r\n)",
"> I feel like we could get rid of TypedExampleIterable altogether and apply formatting with feature conversion with formatted_python_examples_iterator and formatted_arrow_examples_iterator\r\n\r\nOh nice didn't know about the feature support in get_formatter. Haven't thought through whether this works but would a FormattedExampleIterable (with feature conversion) be able to solve this and fit the API better?",
"> Oh nice didn't know about the feature support in get_formatter. Haven't thought through whether this works but would a FormattedExampleIterable (with feature conversion) be able to solve this and fit the API better?\r\n\r\nYes this is surely the way to go actually !",
"ok i've fixed the chaining issue with my last two commits.\r\n\r\nWill see if I can refactor into a FormattedExampleIterable\r\n\r\nThe other issue you posted seems to be unrelated (maybe something to do with feature decoding?)",
"updated with FormattedExamplesIterable.\r\n\r\nthere might be a few unnecessary format calls once the data is already formatted - doesn't seem like a big performance bottleneck but could maybe be fixed with e.g. an is_formatted property\r\n\r\nIt also might be possible to do a wider refactor and use FormattedExamplesIterable elsewhere. But I'd personally prefer not to try that rn.",
"Thinking about this in the context of #7210 - am wondering if it would make sense for Features to define their own extraction arrow->object logic? e.g. Arrays should *always* be extracted with NumpyArrowExtractor, not only in case with_format is set to numpy (which a user can easily forget or not know to do)\r\n",
"> Thinking about this in the context of https://github.com/huggingface/datasets/issues/7210 - am wondering if it would make sense for Features to define their own extraction arrow->object logic? e.g. Arrays should always be extracted with NumpyArrowExtractor, not only in case with_format is set to numpy (which a user can easily forget or not know to do)\r\n\r\nFor `ArrayND` they already implement `to_pylist` to decode arrow data and it can be updated to return a numpy array (see the `ArrayExtensionArray` class for more details)",
"@lhoestq im no longer sure my specific concern about with_format(None) was well-founded - I didn't appreciate that the python formatter tries to do nothing to python objects including numpy arrays, so the existing with_format(None) should I *think* do what I want. Do you think with_format(None) is ok as is after all? If so think this is hopefully ready for final review!",
"@lhoestq I've updated to make compatible with latest changes on main, and think the current with_format None behaviour is probably fine - please let me know if there's anything else I can do!",
"Hi Alex, I will be less available from today and for a week. I'll review your PR and play with it once I come back if you don't mind !",
"thanks for the reviews and extensions, happy to see this merged :)"
] | 2024-10-08T15:44:53Z
| 2025-01-14T18:36:03Z
| 2025-01-14T16:59:30Z
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I got to this by hacking around a bit but it seems to solve #7206
I have no idea if this approach makes sense or would break something else?
Could maybe work on a full pr if this looks reasonable @lhoestq ? I imagine the same issue might affect other iterable dataset methods?
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https://github.com/huggingface/datasets/issues/7206
| 7,206
|
Slow iteration for iterable dataset with numpy formatting for array data
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[
"The below easily eats up 32G of RAM. Leaving it for a while bricked the laptop with 16GB.\r\n\r\n```\r\ndataset = load_dataset(\"Voxel51/OxfordFlowers102\", data_dir=\"data\").with_format(\"numpy\")\r\nprocessed_dataset = dataset.map(lambda x: x)\r\n```\r\n\r\n\r\n\r\nSimilar problems occur if using a real transform function in `.map()`."
] | 2024-10-08T15:38:11Z
| 2024-10-17T17:14:52Z
| null |
CONTRIBUTOR
| null | null |
### Describe the bug
When working with large arrays, setting with_format to e.g. numpy then applying map causes a significant slowdown for iterable datasets.
### Steps to reproduce the bug
```python
import numpy as np
import time
from datasets import Dataset, Features, Array3D
features=Features(**{"array0": Array3D((None, 10, 10), dtype="float32"), "array1": Array3D((None,10,10), dtype="float32")})
dataset = Dataset.from_dict({f"array{i}": [np.zeros((x,10,10), dtype=np.float32) for x in [2000,1000]*25] for i in range(2)}, features=features)
```
Then
```python
ds = dataset.to_iterable_dataset()
ds = ds.with_format("numpy").map(lambda x: x)
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
print(t1-t0)
```
takes 27 s, whereas
```python
ds = dataset.to_iterable_dataset()
ds = ds.with_format("numpy")
ds = dataset.to_iterable_dataset()
t0 = time.time()
for ex in ds:
pass
t1 = time.time()
print(t1 - t0)
```
takes ~1s
### Expected behavior
Map should not introduce a slowdown when formatting is enabled.
### Environment info
3.0.2
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https://github.com/huggingface/datasets/pull/7205
| 7,205
|
fix ci benchmark
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] | 2024-10-08T15:06:18Z
| 2024-10-08T15:25:28Z
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we're not using the benchmarks anymore + they were not working anyway due to token permissions
I keep the code in case we ever want to re-run the benchmark manually
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https://github.com/huggingface/datasets/pull/7204
| 7,204
|
fix unbatched arrow map for iterable datasets
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] | 2024-10-08T13:54:09Z
| 2024-10-08T14:19:47Z
| 2024-10-08T14:19:47Z
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|
Fixes the bug when applying map to an arrow-formatted iterable dataset described here:
https://github.com/huggingface/datasets/issues/6833#issuecomment-2399903885
```python
from datasets import load_dataset
ds = load_dataset("rotten_tomatoes", split="train", streaming=True)
ds = ds.with_format("arrow").map(lambda x: x)
for ex in ds:
pass
```
@lhoestq
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https://github.com/huggingface/datasets/pull/7203
| 7,203
|
with_format docstring
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7203). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-08T13:05:19Z
| 2024-10-08T13:13:12Z
| 2024-10-08T13:13:05Z
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reported at https://github.com/huggingface/datasets/issues/3444
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https://github.com/huggingface/datasets/issues/7202
| 7,202
|
`from_parquet` return type annotation
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[] | 2024-10-08T09:08:10Z
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### Describe the bug
As already posted in https://github.com/microsoft/pylance-release/issues/6534, the correct type hinting fails when building a dataset using the `from_parquet` constructor.
Their suggestion is to comprehensively annotate the method's return type to better align with the docstring information.
### Steps to reproduce the bug
```python
from datasets import Dataset
dataset = Dataset.from_parquet(path_or_paths="file")
dataset.map(lambda x: {"new": x["old"]}, batched=True)
```
### Expected behavior
map is a [valid](https://huggingface.co/docs/datasets/v3.0.1/en/package_reference/main_classes#datasets.Dataset.map), no error should be thrown.
### Environment info
- `datasets` version: 3.0.1
- Platform: macOS-15.0.1-arm64-arm-64bit
- Python version: 3.12.6
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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https://github.com/huggingface/datasets/issues/7201
| 7,201
|
`load_dataset()` of images from a single directory where `train.png` image exists
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[] | 2024-10-07T09:14:17Z
| 2024-10-07T09:14:17Z
| null |
NONE
| null | null |
### Describe the bug
Hey!
Firstly, thanks for maintaining such framework!
I had a small issue, where I wanted to load a custom dataset of image+text captioning. I had all of my images in a single directory, and one of the images had the name `train.png`. Then, the loaded dataset had only this image.
I guess it's related to "train" as a split name, but it's definitely an unexpected behavior :)
Unfortunately I don't have time to submit a proper PR. I'm attaching a toy example to reproduce the issue.
Thanks,
Sagi
### Steps to reproduce the bug
All of the steps I'm attaching are in a fresh env :)
```
(base) sagipolaczek@Sagis-MacBook-Pro ~ % conda activate hf_issue_env
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python --version
Python 3.10.15
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % pip list | grep datasets
datasets 3.0.1
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % ls -la Documents/hf_datasets_issue
total 352
drwxr-xr-x 6 sagipolaczek staff 192 Oct 7 11:59 .
drwx------@ 23 sagipolaczek staff 736 Oct 7 11:46 ..
-rw-r--r--@ 1 sagipolaczek staff 72 Oct 7 11:59 metadata.csv
-rw-r--r--@ 1 sagipolaczek staff 160154 Oct 6 18:00 pika.png
-rw-r--r--@ 1 sagipolaczek staff 5495 Oct 6 12:02 pika_pika.png
-rw-r--r--@ 1 sagipolaczek staff 1753 Oct 6 11:50 train.png
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % cat Documents/hf_datasets_issue/metadata.csv
file_name,text
train.png,A train
pika.png,Pika
pika_pika.png,Pika Pika!
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python
Python 3.10.15 (main, Oct 3 2024, 02:33:33) [Clang 14.0.6 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> dataset = load_dataset("imagefolder", data_dir="Documents/hf_datasets_issue/")
>>> dataset
DatasetDict({
train: Dataset({
features: ['image', 'text'],
num_rows: 1
})
})
>>> dataset["train"][0]
{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=354x84 at 0x10B50FD90>, 'text': 'A train'}
### DELETING `train.png` sample ###
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % vim Documents/hf_datasets_issue/metadata.csv
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % rm Documents/hf_datasets_issue/train.png
(hf_issue_env) sagipolaczek@Sagis-MacBook-Pro ~ % python
Python 3.10.15 (main, Oct 3 2024, 02:33:33) [Clang 14.0.6 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> dataset = load_dataset("imagefolder", data_dir="Documents/hf_datasets_issue/")
Generating train split: 2 examples [00:00, 65.99 examples/s]
>>> dataset
DatasetDict({
train: Dataset({
features: ['image', 'text'],
num_rows: 2
})
})
>>> dataset["train"]
Dataset({
features: ['image', 'text'],
num_rows: 2
})
>>> dataset["train"][0],dataset["train"][1]
({'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=2356x1054 at 0x10DD11E70>, 'text': 'Pika'}, {'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=343x154 at 0x10E258C70>, 'text': 'Pika Pika!'})
```
### Expected behavior
My expected behavior would be to get a dataset with the sample `train.png` in it (along with the others data points).
### Environment info
I've attached it in the example:
Python 3.10.15
datasets 3.0.1
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https://github.com/huggingface/datasets/pull/7200
| 7,200
|
Fix the environment variable for huggingface cache
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[
"Hi ! yes now `datasets` uses `huggingface_hub` to download and cache files from the HF Hub so you need to use `HF_HOME` (or manually `HF_HUB_CACHE` and `HF_DATASETS_CACHE` if you want to separate HF Hub cached files and cached datasets Arrow files)\r\n\r\nSo in your change I guess it needs to be `HF_HOME` instead of `HF_CACHE` ?",
"Thank you for your comment. You are right. I am sorry for my mistake, I fixed it.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7200). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I just had this issue, and needed to move the setting the env code in the python file to top, before the import of the lib \r\nie. \r\n```python\r\nimport os\r\nLOCAL_DISK_MOUNT = '/mnt/data'\r\n\r\nos.environ['HF_HOME'] = f'{LOCAL_DISK_MOUNT}/hf_cache/'\r\nos.environ['HF_DATASETS_CACHE'] = f'{LOCAL_DISK_MOUNT}/datasets/'\r\n\r\nfrom datasets import load_dataset\r\nfrom datasets import load_dataset_builder\r\nfrom psutil._common import bytes2human\r\n\r\n\r\n```"
] | 2024-10-05T11:54:35Z
| 2024-10-30T23:10:27Z
| 2024-10-08T15:45:18Z
|
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Resolve #6256. As far as I tested, `HF_DATASETS_CACHE` was ignored and I could not specify the cache directory at all except for the default one by this environment variable. `HF_HOME` has worked. Perhaps the recent change on file downloading by `huggingface_hub` could affect this bug.
In my testing, I could not specify the cache directory even by `load_dataset("dataset_name" cache_dir="...")`. It might be another issue. I also welcome any advice to solve this issue.
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https://github.com/huggingface/datasets/pull/7199
| 7,199
|
Add with_rank to Dataset.from_generator
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[] | 2024-10-04T16:51:53Z
| 2024-10-04T16:51:53Z
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NONE
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Adds `with_rank` to `Dataset.from_generator`. As for `Dataset.map` and `Dataset.filter`, this is useful when creating cache files using multi-GPU.
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https://github.com/huggingface/datasets/pull/7198
| 7,198
|
Add repeat method to datasets
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[
"@lhoestq does this look reasonable?",
"updated and added test cases!",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7198). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"thanks for the fixes!"
] | 2024-10-04T10:45:16Z
| 2025-02-05T16:32:31Z
| 2025-02-05T16:32:31Z
|
CONTRIBUTOR
| false
|
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"patch_url": "https://github.com/huggingface/datasets/pull/7198.patch",
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|
Following up on discussion in #6623 and #7198 I thought this would be pretty useful for my case so had a go at implementing.
My main motivation is to be able to call iterable_dataset.repeat(None).take(samples_per_epoch) to safely avoid timeout issues in a distributed training setting. This would provide a straightforward workaround for several open issues related to this situation: https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719.
@lhoestq let me know if this looks on the right track!
|
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https://github.com/huggingface/datasets/issues/7197
| 7,197
|
ConnectionError: Couldn't reach 'allenai/c4' on the Hub (ConnectionError)数据集下不下来，怎么回事
|
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[
"Also cant download \"allenai/c4\", but with different error reported:\r\n```\r\nTraceback (most recent call last): \r\n File \"/***/lib/python3.10/site-packages/datasets/load.py\", line 2074, in load_dataset \r\n builder_instance = load_dataset_builder( \r\n File \"/***/lib/python3.10/site-packages/datasets/load.py\", line 1795, in load_dataset_builder \r\n dataset_module = dataset_module_factory( \r\n File \"/***/lib/python3.10/site-packages/datasets/load.py\", line 1659, in dataset_module_factory \r\n raise e1 from None \r\n File \"/***/lib/python3.10/site-packages/datasets/load.py\", line 1647, in dataset_module_factory \r\n ).get_module() \r\n File \"/***/lib/python3.10/site-packages/datasets/load.py\", line 1069, in get_module \r\n module_name, default_builder_kwargs = infer_module_for_data_files( \r\n File \"/***/lib/python3.10/site-packages/datasets/load.py\", line 594, in infer_module_for_data_files \r\n raise DataFilesNotFoundError(\"No (supported) data files found\" + (f\" in {path}\" if path else \"\")) \r\ndatasets.exceptions.DataFilesNotFoundError: No (supported) data files found in allenai/c4 \r\n```\r\n\r\n## Code to reproduce\r\n```\r\ndataset = load_dataset(\"allenai/c4\", \"en\", split=\"train\", streaming=True,trust_remote_code=True,\r\n cache_dir=\"dataset/en\",\r\n download_mode=\"force_redownload\")\r\n```\r\n\r\n## Environment\r\ndatasets 3.0.1 \r\nhuggingface_hub 0.25.1",
"应该是网络问题，无法访问外网？"
] | 2024-10-04T09:33:25Z
| 2025-02-26T02:26:16Z
| null |
NONE
| null | null |
### Describe the bug
from datasets import load_dataset
print("11")
traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train')
print("22")
valdata = load_dataset('ptb_text_only',
'penn_treebank',
split='validation')
### Steps to reproduce the bug
1
### Expected behavior
1
### Environment info
1
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https://github.com/huggingface/datasets/issues/7196
| 7,196
|
concatenate_datasets does not preserve shuffling state
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[
"It also does preserve `split_by_node`, so in the meantime you should call `shuffle` or `split_by_node` AFTER `interleave_datasets` or `concatenate_datasets`"
] | 2024-10-03T14:30:38Z
| 2025-03-18T10:56:47Z
| null |
CONTRIBUTOR
| null | null |
### Describe the bug
After concatenate datasets on an iterable dataset, the shuffling state is destroyed, similar to #7156
This means concatenation cant be used for resolving uneven numbers of samples across devices when using iterable datasets in a distributed setting as discussed in #6623
I also noticed that the number of shards is the same after concatenation, which I found surprising, but I don't understand the internals well enough to know whether this is actually surprising or not
### Steps to reproduce the bug
```python
import datasets
import torch.utils.data
def gen(shards):
yield {"shards": shards}
def main():
dataset1 = datasets.IterableDataset.from_generator(
gen, gen_kwargs={"shards": list(range(25))} # TODO: how to understand this?
)
dataset2 = datasets.IterableDataset.from_generator(
gen, gen_kwargs={"shards": list(range(25, 50))} # TODO: how to understand this?
)
dataset1 = dataset1.shuffle(buffer_size=1)
dataset2 = dataset2.shuffle(buffer_size=1)
print(dataset1.n_shards)
print(dataset2.n_shards)
dataset = datasets.concatenate_datasets(
[dataset1, dataset2]
)
print(dataset.n_shards)
# dataset = dataset1
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=0,
)
for i, batch in enumerate(dataloader):
print(batch)
print("\nNew epoch")
dataset = dataset.set_epoch(1)
for i, batch in enumerate(dataloader):
print(batch)
if __name__ == "__main__":
main()
```
### Expected behavior
Shuffling state should be preserved
### Environment info
Latest datasets
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https://github.com/huggingface/datasets/issues/7195
| 7,195
|
Add support for 3D datasets
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[
"maybe related: https://github.com/huggingface/datasets/issues/6388",
"Also look at https://github.com/huggingface/dataset-viewer/blob/f5fd117ceded990a7766e705bba1203fa907d6ad/services/worker/src/worker/job_runners/dataset/modalities.py#L241 which lists the 3D file formats that will assign the 3D modality to a dataset.",
"~~we can brainstorm about the UX maybe (i don't expect we should load all models on the page at once – IMO there should be a manual action from user to load + maybe load first couple of row by default) cc @gary149 @cfahlgren1~~\r\n\r\nit's more for the viewer issue (https://github.com/huggingface/dataset-viewer/issues/1003)"
] | 2024-10-03T13:27:44Z
| 2024-10-04T09:23:36Z
| null |
COLLABORATOR
| null | null |
See https://huggingface.co/datasets/allenai/objaverse for example
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https://github.com/huggingface/datasets/issues/7194
| 7,194
|
datasets.exceptions.DatasetNotFoundError for private dataset
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|
[
"Actually there is no such dataset available, that is why you are getting that error.",
"Fixed with @kdutia in Slack chat. Generating a new token fixed this issue. "
] | 2024-10-03T07:49:36Z
| 2024-10-03T10:09:28Z
| 2024-10-03T10:09:28Z
|
NONE
| null | null |
### Describe the bug
The following Python code tries to download a private dataset and fails with the error `datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.`. Downloading a public dataset doesn't work.
``` py
from datasets import load_dataset
_ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
```
This seems to be just an issue with my machine config as the code above works with a colleague's machine. So far I have tried:
- logging back out and in from the Huggingface CLI using `huggingface-cli logout`
- manually removing the token cache at `/Users/kalyan/.cache/huggingface/token` (found using `huggingface-cli env`)
- manually passing a token in `load_dataset`
My output of `huggingface-cli whoami`:
```
kdutia
orgs: ClimatePolicyRadar
```
### Steps to reproduce the bug
```
python
Python 3.12.2 (main, Feb 6 2024, 20:19:44) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
>>> _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 2074, in load_dataset
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1795, in load_dataset_builder
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1659, in dataset_module_factory
raise e1 from None
File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1597, in dataset_module_factory
raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e
datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.
>>>
```
### Expected behavior
The dataset downloads successfully.
### Environment info
From `huggingface-cli env`:
```
- huggingface_hub version: 0.25.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /Users/kalyan/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: kdutia
- Configured git credential helpers: osxkeychain
- FastAI: N/A
- Tensorflow: N/A
- Torch: N/A
- Jinja2: 3.1.4
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: N/A
- hf_transfer: N/A
- gradio: N/A
- tensorboard: N/A
- numpy: 2.1.1
- pydantic: N/A
- aiohttp: 3.10.8
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /Users/kalyan/.cache/huggingface/hub
- HF_ASSETS_CACHE: /Users/kalyan/.cache/huggingface/assets
- HF_TOKEN_PATH: /Users/kalyan/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10
```
from `datasets-cli env`:
```
- `datasets` version: 3.0.1
- Platform: macOS-14.2.1-arm64-arm-64bit
- Python version: 3.12.2
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
```
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https://github.com/huggingface/datasets/issues/7193
| 7,193
|
Support of num_workers (multiprocessing) in map for IterableDataset
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[
"I was curious about the same - since map is applied on the fly I was assuming that setting num_workers>1 in DataLoader would effectively do the map in parallel, have you tried that?"
] | 2024-10-02T18:34:04Z
| 2024-10-03T09:54:15Z
| null |
NONE
| null | null |
### Feature request
Currently, IterableDataset doesn't support setting num_worker in .map(), which results in slow processing here. Could we add support for it? As .map() can be run in the batch fashion (e.g., batch_size is default to 1000 in datasets), it seems to be doable for IterableDataset as the regular Dataset.
### Motivation
Improving data processing efficiency
### Your contribution
Testing
| null |
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https://github.com/huggingface/datasets/issues/7192
| 7,192
|
Add repeat() for iterable datasets
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[
"perhaps concatenate_datasets can already be used to achieve almost the same effect? ",
"`concatenate_datasets` does the job when there is a finite number of repetitions, but in case of `.repeat()` forever we need a new logic in `iterable_dataset.py`",
"done in https://github.com/huggingface/datasets/pull/7198"
] | 2024-10-02T17:48:13Z
| 2025-03-18T10:48:33Z
| 2025-03-18T10:48:32Z
|
CONTRIBUTOR
| null | null |
### Feature request
It would be useful to be able to straightforwardly repeat iterable datasets indefinitely, to provide complete control over starting and ending of iteration to the user.
An IterableDataset.repeat(n) function could do this automatically
### Motivation
This feature was discussed in this issue https://github.com/huggingface/datasets/issues/7147, and would resolve the need to use the hack of interleave datasets with probability 0 as a simple way to achieve this functionality.
An additional benefit might be the simplification of the use of iterable datasets in a distributed setting:
If the user can assume that datasets will repeat indefinitely, then issues around different numbers of samples appearing on different devices (e.g. https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719) can potentially be straightforwardly resolved by simply doing:
ids.repeat(None).take(n_samples_per_epoch)
### Your contribution
I'm not familiar enough with the codebase to assess how straightforward this would be to implement.
If it might be very straightforward, I could possibly have a go.
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| 7,191
|
Solution to issue: #7080 Modified load_dataset function, so that it prompts the user to select a dataset when subdatasets or splits (train, test) are available
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"I think the approach presented in https://github.com/huggingface/datasets/pull/6832 is the one we'll be taking.\r\n\r\nAsking user input is not a good idea since `load_dataset` is used a lot in server that don't have someone in front of them to select a split"
] | 2024-10-02T17:02:45Z
| 2024-11-10T08:48:21Z
| 2024-11-10T08:48:21Z
|
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# Feel free to give suggestions please..
### This PR is raised because of issue: https://github.com/huggingface/datasets/issues/7080
### This PR gives solution to https://github.com/huggingface/datasets/issues/7080
1. Checking whether the dataset has splits or subdatasets.
2. Printing the available splits/subdatasets.
3. Asking the user to choose which one to load.
4. Loading only the selected dataset based on the user's input.
### Key Changes:
1. Available Splits/Subdatasets: The code checks for available splits/subdatasets using builder_instance.info.splits.keys().
2. User Prompt: If splits are found, it prints them out and prompts the user to select one.
3. Loading Based on User Input: The dataset is loaded based on the user's choice.
This way, the dataset loading function will interactively prompt the user to select which subdataset or split they want to load instead of automatically loading all of them.
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https://github.com/huggingface/datasets/issues/7190
| 7,190
|
Datasets conflicts with fsspec 2024.9
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[
"Yes, I need to use the latest version of fsspec and datasets for my usecase. \r\nhttps://github.com/fsspec/s3fs/pull/888#issuecomment-2404204606\r\nhttps://github.com/apache/arrow/issues/34363#issuecomment-2403553473\r\n\r\nlast version where things install without conflict is: 2.14.4\r\n\r\nSo this issue starts from:\r\nhttps://github.com/huggingface/datasets/releases/tag/2.14.5"
] | 2024-10-02T16:43:46Z
| 2024-10-10T07:33:18Z
| null |
NONE
| null | null |
### Describe the bug
Installing both in latest versions are not possible
`pip install "datasets==3.0.1" "fsspec==2024.9.0"`
But using older version of datasets is ok
`pip install "datasets==1.24.4" "fsspec==2024.9.0"`
### Steps to reproduce the bug
`pip install "datasets==3.0.1" "fsspec==2024.9.0"`
### Expected behavior
install both versions.
### Environment info
debian 11.
python 3.10.15
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https://github.com/huggingface/datasets/issues/7189
| 7,189
|
Audio preview in dataset viewer for audio array data without a path/filename
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[] | 2024-10-02T16:38:38Z
| 2024-10-02T17:01:40Z
| null |
NONE
| null | null |
### Feature request
Huggingface has quite a comprehensive set of guides for [audio datasets](https://huggingface.co/docs/datasets/en/audio_dataset). It seems, however, all these guides assume the audio array data to be decoded/inserted into a HF dataset always originates from individual files. The [Audio-dataclass](https://github.com/huggingface/datasets/blob/3.0.1/src/datasets/features/audio.py#L20) appears designed with this assumption in mind. Looking at its source code it returns a dictionary with the keys `path`, `array` and `sampling_rate`.
However, sometimes users may have different pipelines where they themselves decode the audio array. This feature request has to do with wishing some clarification in guides on whether it is possible, and in such case how users can insert already decoded audio array data into datasets (pandas DataFrame, HF dataset or whatever) that are later saved as parquet, and still get a functioning audio preview in the dataset viewer.
Do I perhaps need to write a tempfile of my audio array slice to wav and capture the bytes object with `io.BytesIO` and pass that to `Audio()`?
### Motivation
I'm working with large audio datasets, and my pipeline reads (decodes) audio from larger files, and slices the relevant portions of audio from that larger file based on metadata I have available.
The pipeline is designed this way to avoid having to store multiple copies of data, and to avoid having to store tens of millions of small files.
I tried [test-uploading parquet files](https://huggingface.co/datasets/Lauler/riksdagen_test) where I store the audio array data of decoded slices of audio in an `audio` column with a dictionary with the keys `path`, `array` and `sampling_rate`. But I don't know the secret sauce of what the Huggingface Hub expects and requires to be able to display audio previews correctly.
### Your contribution
I could contribute a tool agnostic guide of creating HF audio datasets directly as parquet to the HF documentation if there is an interest. Provided you help me figure out the secret sauce of what the dataset viewer expects to display the preview correctly.
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| 7,188
|
Pin multiprocess<0.70.1 to align with dill<0.3.9
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7188). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-02T05:40:18Z
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| 2024-10-02T06:08:23Z
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Pin multiprocess<0.70.1 to align with dill<0.3.9.
Note that multiprocess-0.70.1 requires dill-0.3.9: https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17
Fix #7186.
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https://github.com/huggingface/datasets/issues/7187
| 7,187
|
shard_data_sources() got an unexpected keyword argument 'worker_id'
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[] | 2024-10-02T01:26:35Z
| 2024-10-02T01:26:35Z
| null |
NONE
| null | null |
### Describe the bug
```
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 238, in __iter__
[rank0]: for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None):
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/packaged_modules/generator/generator.py", line 32, in _generate_examples
[rank0]: for idx, ex in enumerate(self.config.generator(**gen_kwargs)):
[rank0]: File "/home/qinghao/workdir/doremi/doremi/dataloader.py", line 337, in take_data_generator
[rank0]: for ex in ds:
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1791, in __iter__
[rank0]: yield from self._iter_pytorch()
[rank0]: File "/home/qinghao/miniconda3/envs/doremi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1704, in _iter_pytorch
[rank0]: ex_iterable = ex_iterable.shard_data_sources(worker_id=worker_info.id, num_workers=worker_info.num_workers)
[rank0]: TypeError: UpdatableRandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources() got an unexpected keyword argument 'worker_id'
```
### Steps to reproduce the bug
IterableDataset cannot use
### Expected behavior
can work on datasets==2.10, but will raise error for later versions.
### Environment info
datasets==3.0.1
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https://github.com/huggingface/datasets/issues/7186
| 7,186
|
pinning `dill<0.3.9` without pinning `multiprocess`
|
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[] | 2024-10-01T22:29:32Z
| 2024-10-02T06:08:24Z
| 2024-10-02T06:08:24Z
|
NONE
| null | null |
### Describe the bug
The [latest `multiprocess` release](https://github.com/uqfoundation/multiprocess/releases/tag/0.70.17) requires `dill>=0.3.9` which causes issues when installing `datasets` without backtracking during package version resolution. Is it possible to add a pin for multiprocess so something like `multiprocess<=0.70.16` so that the `dill` version is compatible?
### Steps to reproduce the bug
NA
### Expected behavior
NA
### Environment info
NA
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CI benchmarks are broken
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[
"Fixed by #7205"
] | 2024-10-01T08:16:08Z
| 2024-10-09T16:07:48Z
| 2024-10-09T16:07:48Z
|
MEMBER
| null | null |
Since Aug 30, 2024, CI benchmarks are broken: https://github.com/huggingface/datasets/actions/runs/11108421214/job/30861323975
```
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```
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Pin dill<0.3.9 to fix CI
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7184). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-30T14:26:25Z
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| 2024-09-30T14:38:57Z
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Pin dill<0.3.9 to fix CI for deps-latest.
Note that dill-0.3.9 was released yesterday Sep 29, 2024:
- https://pypi.org/project/dill/0.3.9/
- https://github.com/uqfoundation/dill/releases/tag/0.3.9
Fix #7183.
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https://github.com/huggingface/datasets/issues/7183
| 7,183
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CI is broken for deps-latest
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[] | 2024-09-30T14:02:07Z
| 2024-09-30T14:38:58Z
| 2024-09-30T14:38:58Z
|
MEMBER
| null | null |
See: https://github.com/huggingface/datasets/actions/runs/11106149906/job/30853879890
```
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_caching_on_disk - AssertionError: Lists differ: [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}] != [{'fi[44 chars] {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
First differing element 1:
{'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}
{'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}
[{'filename': '/tmp/tmp6xcyyjs4/dataset0.arrow'},
- {'filename': '/tmp/tmp6xcyyjs4/cache-9533fe2601cd3e48.arrow'}]
? ^^^^^ --------
+ {'filename': '/tmp/tmp6xcyyjs4/cache-e6e0a8b830976289.arrow'}]
? ++++++++++ ^^ +
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AssertionError: Lists differ: [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}] != [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
First differing element 0:
{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}
{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}
- [{'filename': '/tmp/tmp5gxrti_n/cache-e58d327daec8626f.arrow'}]
? ^^ -----------
+ [{'filename': '/tmp/tmp5gxrti_n/cache-d87234c5763e54a3.arrow'}]
? +++++++++++ ^^
FAILED tests/test_fingerprint.py::TokenizersHashTest::test_hash_regex - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ignores_line_definition_of_function - AssertionError: '52e56ee04ad92499' != '0a4f75cec280f634'
- 52e56ee04ad92499
+ 0a4f75cec280f634
FAILED tests/test_fingerprint.py::RecurseHashTest::test_hash_ipython_function - AssertionError: 'a6bd2041ca63d6c0' != '517bf36b7eecdef5'
- a6bd2041ca63d6c0
+ 517bf36b7eecdef5
FAILED tests/test_fingerprint.py::HashingTest::test_hash_tiktoken_encoding - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_compiled_module - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_generator - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_hash_torch_tensor - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_doesnt_depend_on_order - NameError: name 'log' is not defined
FAILED tests/test_fingerprint.py::HashingTest::test_set_stable - NameError: name 'log' is not defined
ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NameError: name 'log' is not defined
= 11 failed, 2850 passed, 3 skipped, 23 warnings, 1 error in 191.06s (0:03:11) =
```
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https://github.com/huggingface/datasets/pull/7182
| 7,182
|
Support features in metadata configs
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7182). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The CI issue is unrelated:\r\n- #7183"
] | 2024-09-30T11:14:53Z
| 2024-10-09T16:03:57Z
| 2024-10-09T16:03:54Z
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|
Support features in metadata configs, like:
```
configs:
- config_name: default
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: score
dtype: float64
```
This will allow to avoid inference of data types.
Currently, we allow passing this information in the `dataset_info` (instead of `configs`) field, but this is not intuitive and it is not properly documented.
TODO:
- [ ] Document usage
|
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https://github.com/huggingface/datasets/pull/7181
| 7,181
|
Fix datasets export to JSON
|
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[
"Linked Issue: #7037\r\nIdeas: #7039 ",
"@albertvillanova / @lhoestq any early feedback?\r\n\r\nAFAIK there is no param `orient` in `load_dataset()`. So for orientations other than \"records\", the loading isn't very accurate. Any thoughts?",
"`orient = \"split\"` can also be handled. I will add the changes soon",
"Thanks for diving into this ! I don't think we want the JSON export to be that complex though, especially if people can do `ds.to_pandas().to_json(orient=...)`. Maybe we can just raise an error and suggest users to use pandas ? And also note that it loads the full dataset in memory so it's mainly for small scale datasets. The only acceptable option for large scale datasets is probably just JSON Lines anyway since it enables streaming.",
"@lhoestq Simply doing `ds.to_pandas().to_json(orient=...)` is not going to give any batching or multiprocessing benefits right? Also, which function are you referring to - when you say that its meant for small scale datasets only?",
"Yes indeed. Though I think it's fine since using something else than orient=\"lines\" is only suitable/useful for small datasets. Or you know a case where a big dataset need to be in a format that is not orient=\"lines\" ?",
"@lhoestq Let me close this PR and open another one where I will add an error message, as suggested here.\r\n\r\n> Thanks for diving into this ! I don't think we want the JSON export to be that complex though, especially if people can do `ds.to_pandas().to_json(orient=...)`. Maybe we can just raise an error and suggest users to use pandas ? And also note that it loads the full dataset in memory so it's mainly for small scale datasets. The only acceptable option for large scale datasets is probably just JSON Lines anyway since it enables streaming.\r\n\r\n",
"Addressed here: #7273 \r\n@lhoestq "
] | 2024-09-29T12:45:20Z
| 2024-11-01T11:55:36Z
| 2024-11-01T11:55:36Z
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https://github.com/huggingface/datasets/issues/7180
| 7,180
|
Memory leak when wrapping datasets into PyTorch Dataset without explicit deletion
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"> I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.\r\n\r\nDatasets are memory mapped so they work like SWAP memory. In particular as long as you have RAM available the data will stay in RAM, and get paged out once your system needs RAM for something else (no OOM).\r\n\r\nrelated: https://github.com/huggingface/datasets/issues/4883"
] | 2024-09-28T14:00:47Z
| 2024-09-30T12:07:56Z
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NONE
| null | null |
### Describe the bug
I've encountered a memory leak when wrapping the HuggingFace dataset into a PyTorch Dataset. The RAM usage constantly increases during iteration if items are not explicitly deleted after use.
### Steps to reproduce the bug
Steps to reproduce:
Create a PyTorch Dataset wrapper for 'nebula/cc12m':
````
from torch.utils.data import Dataset
from tqdm import tqdm
from datasets import load_dataset
from torchvision import transforms
Image.MAX_IMAGE_PIXELS = None
class CC12M(Dataset):
def __init__(self, path_or_name='nebula/cc12m', split='train', transform=None, single_caption=True):
self.raw_dataset = load_dataset(path_or_name)[split]
if transform is None:
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711]
)
])
else:
self.transform = transforms.Compose(transform)
self.single_caption = single_caption
self.length = len(self.raw_dataset)
def __len__(self):
return self.length
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
# del item # Uncomment this line to prevent the memory leak
return image, caption
````
Iterate through the dataset without the del item line in __getitem__.
Observe RAM usage increasing constantly.
Add del item at the end of __getitem__:
```
def __getitem__(self, index):
item = self.raw_dataset[index]
caption = item['txt']
with io.BytesIO(item['webp']) as buffer:
image = Image.open(buffer).convert('RGB')
if self.transform:
image = self.transform(image)
del item # This line prevents the memory leak
return image, caption
```
Iterate through the dataset again and observe that RAM usage remains stable.
### Expected behavior
Expected behavior:
RAM usage should remain stable during iteration without needing to explicitly delete items.
Actual behavior:
RAM usage constantly increases unless items are explicitly deleted after use
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-4.18.0-513.5.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.12.4
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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Support Python 3.11
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7179). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-27T08:55:44Z
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Support Python 3.11.
Fix #7178.
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https://github.com/huggingface/datasets/issues/7178
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Support Python 3.11
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MEMBER
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Support Python 3.11: https://peps.python.org/pep-0664/
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https://github.com/huggingface/datasets/pull/7177
| 7,177
|
Fix release instructions
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] | 2024-09-27T08:47:01Z
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Fix release instructions.
During last release, I had to make this additional update.
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https://github.com/huggingface/datasets/pull/7176
| 7,176
|
fix grammar in fingerprint.py
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I see this error all the time and it was starting to get to me.
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https://github.com/huggingface/datasets/issues/7175
| 7,175
|
[FSTimeoutError] load_dataset
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[
"Is this `FSTimeoutError` due to download network issue from remote resource (from where it is being accessed)?",
"It seems to happen for all datasets, not just a specific one, and especially for versions after 3.0. (3.0.0, 3.0.1 have this problem)\r\n\r\nI had the same error on a different dataset, but after downgrading to datasets==2.21.0, the problem was solved.",
"Same as https://github.com/huggingface/datasets/issues/7164\r\n\r\nThis dataset is made of a python script that downloads data from elsewhere than HF, so availability depends on the original host. Ultimately it would be nice to host the files of this dataset on HF\r\n\r\nin `datasets` <3.0 there were lots of mechanisms that got removed after the decision to make datasets with python loading scripts legacy for security and maintenance reasons (we only do very basic support now)",
"@lhoestq Thank you for the clarification! Closing the issue.",
"I'm getting this too, and also at 5 minutes. But for `CSTR-Edinburgh/vctk`, so it's not just this dataset, it seems to be a timeout that was introduced and needs to be raised. The progress bar was moving along just fine before the timeout, and I get more or less of it depending on how fast the network is.",
"You can change the `aiohttp` timeout from 5min to 1h like this:\r\n\r\n```python\r\nimport datasets, aiohttp\r\ndataset = datasets.load_dataset(\r\n dataset_name,\r\n storage_options={'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=3600)}}\r\n)\r\n```",
"@JonasLoos Solution solved a download timeout error I received when downloading `\"HuggingFaceM4/VQAv2\"` 🎉 "
] | 2024-09-26T15:42:29Z
| 2025-02-01T09:09:35Z
| 2024-09-30T17:28:35Z
|
NONE
| null | null |
### Describe the bug
When using `load_dataset`to load [HuggingFaceM4/VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2), I am getting `FSTimeoutError`.
### Error
```
TimeoutError:
The above exception was the direct cause of the following exception:
FSTimeoutError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/fsspec/asyn.py](https://klh9mr78js-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240924-060116_RC00_678132060#) in sync(loop, func, timeout, *args, **kwargs)
99 if isinstance(return_result, asyncio.TimeoutError):
100 # suppress asyncio.TimeoutError, raise FSTimeoutError
--> 101 raise FSTimeoutError from return_result
102 elif isinstance(return_result, BaseException):
103 raise return_result
FSTimeoutError:
```
It usually fails around 5-6 GB.
<img width="847" alt="Screenshot 2024-09-26 at 9 10 19 PM" src="https://github.com/user-attachments/assets/ff91995a-fb55-4de6-8214-94025d6c8470">
### Steps to reproduce the bug
To reproduce it, run this in colab notebook:
```
!pip install -q -U datasets
from datasets import load_dataset
ds = load_dataset('HuggingFaceM4/VQAv2', split="train[:10%]")
```
### Expected behavior
It should download properly.
### Environment info
Using Colab Notebook.
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Set dev version
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https://github.com/huggingface/datasets/pull/7173
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Release: 3.0.1
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https://github.com/huggingface/datasets/pull/7172
| 7,172
|
Add torchdata as a regular test dependency
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] | 2024-09-26T07:45:55Z
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| 2024-09-26T08:05:40Z
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Add `torchdata` as a regular test dependency.
Note that previously, `torchdata` was installed from their repo and current main branch (0.10.0.dev) requires Python>=3.9.
Also note they made a recent release: 0.8.0 on Jul 31, 2024.
Fix #7171.
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CI is broken: No solution found when resolving dependencies
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[] | 2024-09-26T07:24:58Z
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MEMBER
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See: https://github.com/huggingface/datasets/actions/runs/11046967444/job/30687294297
```
Run uv pip install --system -r additional-tests-requirements.txt --no-deps
× No solution found when resolving dependencies:
╰─▶ Because the current Python version (3.8.18) does not satisfy Python>=3.9
and torchdata==0.10.0a0+1a98f21 depends on Python>=3.9, we can conclude
that torchdata==0.10.0a0+1a98f21 cannot be used.
And because only torchdata==0.10.0a0+1a98f21 is available and
you require torchdata, we can conclude that your requirements are
unsatisfiable.
Error: Process completed with exit code 1.
```
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https://github.com/huggingface/datasets/pull/7170
| 7,170
|
Support JSON lines with missing columns
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7170). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-25T05:08:15Z
| 2024-09-26T06:42:09Z
| 2024-09-26T06:42:07Z
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Support JSON lines with missing columns.
Fix #7169.
The implemented test raised:
```
datasets.table.CastError: Couldn't cast
age: int64
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
because column names don't match
```
Related to:
- #7160
- #7162
|
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https://github.com/huggingface/datasets/issues/7169
| 7,169
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JSON lines with missing columns raise CastError
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JSON lines with missing columns raise CastError:
> CastError: Couldn't cast ... to ... because column names don't match
Related to:
- #7159
- #7161
|
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https://github.com/huggingface/datasets/issues/7168
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|
sd1.5 diffusers controlnet training script gives new error
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[
"not sure why the issue is formatting oddly",
"I guess this is a dupe of\r\n\r\nhttps://github.com/huggingface/datasets/issues/7071",
"this turned out to be because of a bad image in dataset",
"@Night1099 could you spiecify what exactly was wrong with your image in the dataset? I think im facing the same issue.",
"\n> [@Night1099](https://github.com/Night1099) could you spiecify what exactly was wrong with your image in the dataset? I think im facing the same issue.\n\nOh boy I'm trying to remember that was many projects (a year) ago, I'm usually more informational now I apologize. I'm gonna say it was an img format problem but not sure\n\nHave ai write a script to verify all imgs are in rgb, and not a odd file extension. Or weird res\n"
] | 2024-09-25T01:42:49Z
| 2025-09-16T15:38:01Z
| 2024-09-30T05:24:02Z
|
NONE
| null | null |
### Describe the bug
This will randomly pop up during training now
```
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1192, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet.py", line 1041, in main
for step, batch in enumerate(train_dataloader):
File "/usr/local/lib/python3.11/dist-packages/accelerate/data_loader.py", line 561, in __iter__
next_batch = next(dataloader_iter)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py", line 673, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/torch/utils/data/_utils/fetch.py", line 50, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2746, in __getitems__
batch = self.__getitem__(keys)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2742, in __getitem__
return self._getitem(key)
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 2727, in _getitem
formatted_output = format_table(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 639, in format_table
return formatter(pa_table, query_type=query_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 407, in __call__
return self.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 521, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/formatting/formatting.py", line 228, in decode_batch
return self.features.decode_batch(batch) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2084, in decode_batch
[
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 2085, in <listcomp>
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.11/dist-packages/datasets/features/features.py", line 1403, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
```
### Steps to reproduce the bug
Train on diffusers sd1.5 controlnet example script
This will pop up randomly, you can see in wandb below when i manually resume run everytime this error appears
### Expected behavior
Training to continue without above error
### Environment info
- datasets version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- huggingface_hub version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- fsspec version: 2024.6.1
Training on 4090
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| 7,167
|
Error Mapping on sd3, sdxl and upcoming flux controlnet training scripts in diffusers
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[
"this is happening on large datasets, if anyone happens upon this i was able to fix by changing\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\r\n```\r\n\r\nto\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, new_fingerprint=new_fingerprint)\r\n```"
] | 2024-09-25T01:39:51Z
| 2024-09-30T05:28:15Z
| 2024-09-30T05:28:04Z
|
NONE
| null | null |
### Describe the bug
```
Map: 6%|██████ | 8000/138120 [19:27<5:16:36, 6.85 examples/s]
Traceback (most recent call last):
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1416, in <module>
main(args)
File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1132, in main
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 560, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3035, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3461, in _map_single
writer.write_batch(batch)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 567, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 579, in write_table
pa_table = pa_table.combine_chunks()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 4387, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 8, in <module>
sys.exit(main())
^^^^^^
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/accelerate_cli.py", line 48, in main
args.func(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 1174, in launch_command
simple_launcher(args)
File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 769, in simple_launcher
```
### Steps to reproduce the bug
The dataset has no problem training on sd1.5 controlnet train script
### Expected behavior
Script not randomly erroing with error above
### Environment info
- `datasets` version: 3.0.0
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
training on A100
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|
fix docstring code example for distributed shuffle
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7166). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-24T14:39:54Z
| 2024-09-24T14:42:41Z
| 2024-09-24T14:40:14Z
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close https://github.com/huggingface/datasets/issues/7163
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https://github.com/huggingface/datasets/pull/7165
| 7,165
|
fix increase_load_count
|
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7165). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I tested a few load_dataset and they do show up in download stats now",
"Thanks for having noticed and fixed."
] | 2024-09-24T10:14:40Z
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it was failing since 3.0 and therefore not updating download counts on HF or in our dashboard
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https://github.com/huggingface/datasets/issues/7164
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fsspec.exceptions.FSTimeoutError when downloading dataset
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[
"Hi ! If you check the dataset loading script [here](https://huggingface.co/datasets/openslr/librispeech_asr/blob/main/librispeech_asr.py) you'll see that it downloads the data from OpenSLR, and apparently their storage has timeout issues. It would be great to ultimately host the dataset on Hugging Face instead.\r\n\r\nIn the meantime I can only recommend to try again later :/",
"Ok, still many thanks!",
"I'm also getting this same error but for `CSTR-Edinburgh/vctk`, so I don't think it's the remote host that's timing out, since I also time out at exactly 5 minutes. It seems there is a universal fsspec timeout that's getting hit starting in v3.",
"in v3 we cleaned the download parts of the library to make it more robust for HF downloads and to simplify support of script-based datasets. As a side effect it's not the same code that is used for other hosts, maybe time out handling changed. Anyway it should be possible to tweak fsspec to use retries\r\n\r\nFor example using [aiohttp_retry](https://github.com/inyutin/aiohttp_retry) maybe (haven't tried) ?\r\n\r\n```python\r\nimport fsspec\r\nfrom aiohttp_retry import RetryClient\r\n\r\nfsspec.filesystem(\"http\")._session = RetryClient()\r\n```\r\n\r\nrelated topic : https://github.com/huggingface/datasets/issues/7175",
"Adding a timeout argument to the `fs.get_file` call in `fsspec_get` in `datasets/utils/file_utils.py` might fix this ([source code](https://github.com/huggingface/datasets/blob/65f6eb54aa0e8bb44cea35deea28e0e8fecc25b9/src/datasets/utils/file_utils.py#L330)):\r\n\r\n```python\r\nfs.get_file(path, temp_file.name, callback=callback, timeout=3600)\r\n```\r\n\r\nSetting `timeout=1` fails after about one second, so setting it to 3600 should give us 1h. Havn't really tested this though. I'm also not sure what implications this has and if it causes errors for other `fs` implementations/configurations.\r\n\r\nThis is using `datasets==3.0.1` and Python 3.11.6.\r\n\r\n---\r\n\r\nEdit: This doesn't seem to change the timeout time, but add a second timeout counter (probably in `fsspec/asyn.py/sync`). So one can reduce the time for downloading like this, but not expand.\r\n\r\n---\r\n\r\nEdit 2: `fs` is of type `fsspec.implementations.http.HTTPFileSystem` which initializes a `aiohttp.ClientSession` using `client_kwargs`. We can pass these when calling `load_dataset`.\r\n\r\n**TLDR; This fixes it:**\r\n\r\n```python\r\nimport datasets, aiohttp\r\ndataset = datasets.load_dataset(\r\n dataset_name,\r\n storage_options={'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=3600)}}\r\n)\r\n```",
"I've handled the issue like this to ensure smoother downloads when using the `datasets` library. \nIf modifying the library is not too inconvenient, this approach could be a good (but tentative) solution.\n\n### Changes Made\n\nModified `datasets.utils.file_utils.fsspec_get` to handle storage options and set a timeout:\n\n```python\ndef fsspec_get(url, temp_file, storage_options=None, desc=None, disable_tqdm=False):\n\n # ---> [ADD]\n if storage_options is None:\n storage_options = {}\n if \"client_kwargs\" not in storage_options:\n storage_options[\"client_kwargs\"] = {}\n storage_options[\"client_kwargs\"][\"timeout\"] = aiohttp.ClientTimeout(total=3600)\n # <---\n\n # The rest of the original code remains unchanged",
"Librispeech_asr is now hosted on HF, which fixes this issue 🎉\n\nclosing this one now :)"
] | 2024-09-24T08:45:05Z
| 2025-07-28T14:58:49Z
| 2025-07-28T14:58:49Z
|
NONE
| null | null |
### Describe the bug
I am trying to download the `librispeech_asr` `clean` dataset, which results in a `FSTimeoutError` exception after downloading around 61% of the data.
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("librispeech_asr", "clean")
```
The output is as follows:
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:06, 13.2MB/s]Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 56, in _runner
> result[0] = await coro
> ^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/implementations/http.py", line 262, in _get_file
> chunk = await r.content.read(chunk_size)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 393, in read
> await self._wait("read")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 311, in _wait
> with self._timer:
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/helpers.py", line 713, in __exit__
> raise asyncio.TimeoutError from None
> TimeoutError
>
> The above exception was the direct cause of the following exception:
>
> Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/load_dataset.py", line 3, in <module>
> datasets.load_dataset("librispeech_asr", "clean")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/load.py", line 2096, in load_dataset
> builder_instance.download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 924, in download_and_prepare
> self._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
> super()._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 977, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/2712a8f82f0d20807a56faadcd08734f9bdd24c850bb118ba21ff33ebff0432f/librispeech_asr.py", line 115, in _split_generators
> archive_path = dl_manager.download(_DL_URLS[self.config.name])
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 159, in download
> downloaded_path_or_paths = map_nested(
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 512, in map_nested
> _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 380, in _single_map_nested
> return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 216, in _download_batched
> self._download_single(url_or_filename, download_config=download_config)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 225, in _download_single
> out = cached_path(url_or_filename, download_config=download_config)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 205, in cached_path
> output_path = get_from_cache(
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 415, in get_from_cache
> fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 334, in fsspec_get
> fs.get_file(path, temp_file.name, callback=callback)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 118, in wrapper
> return sync(self.loop, func, *args, **kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 101, in sync
> raise FSTimeoutError from return_result
> fsspec.exceptions.FSTimeoutError
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:09, 13.0MB/s]
### Expected behavior
Complete the download
### Environment info
Python version 3.12.6
Dependencies:
> dependencies = [
> "accelerate>=0.34.2",
> "datasets[audio]>=3.0.0",
> "ipython>=8.18.1",
> "librosa>=0.10.2.post1",
> "torch>=2.4.1",
> "torchaudio>=2.4.1",
> "transformers>=4.44.2",
> ]
MacOS 14.6.1 (23G93)
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https://github.com/huggingface/datasets/issues/7163
| 7,163
|
Set explicit seed in iterable dataset ddp shuffling example
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[
"thanks for reporting !"
] | 2024-09-23T11:34:06Z
| 2024-09-24T14:40:15Z
| 2024-09-24T14:40:15Z
|
CONTRIBUTOR
| null | null |
### Describe the bug
In the examples section of the iterable dataset docs https://huggingface.co/docs/datasets/en/package_reference/main_classes#datasets.IterableDataset
the ddp example shuffles without seeding
```python
from datasets.distributed import split_dataset_by_node
ids = ds.to_iterable_dataset(num_shards=512)
ids = ids.shuffle(buffer_size=10_000) # will shuffle the shards order and use a shuffle buffer when you start iterating
ids = split_dataset_by_node(ds, world_size=8, rank=0) # will keep only 512 / 8 = 64 shards from the shuffled lists of shards when you start iterating
dataloader = torch.utils.data.DataLoader(ids, num_workers=4) # will assign 64 / 4 = 16 shards from this node's list of shards to each worker when you start iterating
for example in ids:
pass
```
This code would - I think - raise an error due to the lack of an explicit seed:
https://github.com/huggingface/datasets/blob/2eb4edb97e1a6af2ea62738ec58afbd3812fc66e/src/datasets/iterable_dataset.py#L1707-L1711
### Steps to reproduce the bug
Run example code
### Expected behavior
Add explicit seeding to example code
### Environment info
latest datasets
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Support JSON lines with empty struct
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Support JSON lines with empty struct.
Fix #7161.
Related to:
- #7160
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https://github.com/huggingface/datasets/issues/7161
| 7,161
|
JSON lines with empty struct raise ArrowTypeError
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JSON lines with empty struct raise ArrowTypeError: struct fields don't match or are in the wrong order
See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
> ArrowTypeError: struct fields don't match or are in the wrong order: Input fields: struct<> output fields: struct<pov_count: int64, update_count: int64, citation_needed_count: int64>
Related to:
- #7159
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Support JSON lines with missing struct fields
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Support JSON lines with missing struct fields.
Fix #7159.
The implemented test raised:
```
TypeError: Couldn't cast array of type
struct<age: int64>
to
{'age': Value(dtype='int32', id=None), 'name': Value(dtype='string', id=None)}
```
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https://github.com/huggingface/datasets/issues/7159
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JSON lines with missing struct fields raise TypeError: Couldn't cast array
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[
"Hello,\r\n\r\nI have still the same issue when loading the dataset with the new version:\r\n[https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5](https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5)\r\n\r\nI have downloaded and unzipped the wikimedia/structured-wikipedia dataset locally but when loading I have the same issue.\r\n\r\n```\r\nimport datasets\r\n\r\ndataset = datasets.load_dataset(\"/gpfsdsdir/dataset/HuggingFace/wikimedia/structured-wikipedia/20240916.fr\")\r\n```\r\n```\r\nTypeError: Couldn't cast array of type\r\nstruct<content_url: string, width: int64, height: int64, alternative_text: string>\r\nto\r\n{'content_url': Value(dtype='string', id=None), 'width': Value(dtype='int64', id=None), 'height': Value(dtype='int64', id=None)}\r\n\r\nThe above exception was the direct cause of the following exception:\r\n```\r\nMy version of datasets is 3.0.1"
] | 2024-09-23T07:57:58Z
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|
MEMBER
| null | null |
JSON lines with missing struct fields raise TypeError: Couldn't cast array of type.
See example: https://huggingface.co/datasets/wikimedia/structured-wikipedia/discussions/5
One would expect that the struct missing fields are added with null values.
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google colab ex
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Fix zero proba interleave datasets
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] | 2024-09-21T15:19:14Z
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fix https://github.com/huggingface/datasets/issues/7147
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https://github.com/huggingface/datasets/issues/7156
| 7,156
|
interleave_datasets resets shuffle state
|
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[
"It also does preserve `split_by_node`, so in the meantime you should call `shuffle` or `split_by_node` AFTER `interleave_datasets` or `concatenate_datasets`"
] | 2024-09-20T17:57:54Z
| 2025-03-18T10:56:25Z
| null |
NONE
| null | null |
### Describe the bug
```
import datasets
import torch.utils.data
def gen(shards):
yield {"shards": shards}
def main():
dataset = datasets.IterableDataset.from_generator(
gen,
gen_kwargs={'shards': list(range(25))}
)
dataset = dataset.shuffle(buffer_size=1)
dataset = datasets.interleave_datasets(
[dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=8,
)
for i, batch in enumerate(dataloader):
print(batch)
if i >= 10:
break
if __name__ == "__main__":
main()
```
### Steps to reproduce the bug
Run the script, it will output
```
{'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]}
{'shards': [tensor([ 1, 9, 17, 1, 9, 17, 1, 9])]}
{'shards': [tensor([ 2, 10, 18, 2, 10, 18, 2, 10])]}
{'shards': [tensor([ 3, 11, 19, 3, 11, 19, 3, 11])]}
{'shards': [tensor([ 4, 12, 20, 4, 12, 20, 4, 12])]}
{'shards': [tensor([ 5, 13, 21, 5, 13, 21, 5, 13])]}
{'shards': [tensor([ 6, 14, 22, 6, 14, 22, 6, 14])]}
{'shards': [tensor([ 7, 15, 23, 7, 15, 23, 7, 15])]}
{'shards': [tensor([ 0, 8, 16, 24, 0, 8, 16, 24])]}
{'shards': [tensor([17, 1, 9, 17, 1, 9, 17, 1])]}
{'shards': [tensor([18, 2, 10, 18, 2, 10, 18, 2])]}
```
### Expected behavior
The shards should be shuffled.
### Environment info
- `datasets` version: 3.0.0
- Platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.25.0
- PyArrow version: 17.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0
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https://github.com/huggingface/datasets/issues/7155
| 7,155
|
Dataset viewer not working! Failure due to more than 32 splits.
|
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[
"I have fixed it! But I would appreciate a new feature wheere I could iterate over and see what each file looks like. "
] | 2024-09-18T12:43:21Z
| 2024-09-18T13:20:03Z
| 2024-09-18T13:20:03Z
|
NONE
| null | null |
Hello guys,
I have a dataset and I didn't know I couldn't upload more than 32 splits. Now, my dataset viewer is not working. I don't have the dataset locally on my node anymore and recreating would take a week. And I have to publish the dataset coming Monday. I read about the practice, how I can resolve it and avoid this issue in the future. But, at the moment I need a hard fix for two of my datasets.
And I don't want to mess or change anything and allow everyone in public to see the dataset and interact with it. Can you please help me?
https://huggingface.co/datasets/laion/Wikipedia-X
https://huggingface.co/datasets/laion/Wikipedia-X-Full
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| 7,154
|
Support ndjson data files
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7154). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Thanks for your review, @severo.\r\n\r\nYes, I was aware of this. From internal conversation:\r\n> Please note that although NDJSON was planned to be submitted as an RFC standard spec, it is no longer maintained:\r\n> - See note from the author: https://github.com/ndjson/ndjson-spec/issues/35#issuecomment-1285673417\r\n> - See that their official website domain has expired: https://ndjson.org/ \r\n\r\nThe purpose of this PR is just supporting datasets with ndjson data files (e.g. Wikimedia Enterprise data files), but it should not imply any recommendation or endorsement of this format from our part."
] | 2024-09-18T06:10:10Z
| 2024-09-19T11:25:17Z
| 2024-09-19T11:25:14Z
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Support `ndjson` (Newline Delimited JSON) data files.
Fix #7153.
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https://github.com/huggingface/datasets/issues/7153
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Support data files with .ndjson extension
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[] | 2024-09-18T05:54:45Z
| 2024-09-19T11:25:15Z
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|
MEMBER
| null | null |
### Feature request
Support data files with `.ndjson` extension.
### Motivation
We already support data files with `.jsonl` extension.
### Your contribution
I am opening a PR.
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|
Align filename prefix splitting with WebDataset library
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[] | 2024-09-16T06:07:39Z
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| 2024-09-16T15:26:34Z
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Align filename prefix splitting with WebDataset library.
This PR uses the same `base_plus_ext` function as the one used by the `webdataset` library.
Fix #7150.
Related to #7144.
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https://github.com/huggingface/datasets/issues/7150
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|
WebDataset loader splits keys differently than WebDataset library
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MEMBER
| null | null |
As reported by @ragavsachdeva (see discussion here: https://github.com/huggingface/datasets/pull/7144#issuecomment-2348307792), our webdataset loader is not aligned with the `webdataset` library when splitting keys from filenames.
For example, we get a different key splitting for filename `/some/path/22.0/1.1.png`:
- datasets library: `/some/path/22` and `0/1.1.png`
- webdataset library: `/some/path/22.0/1`, `1.png`
```python
import webdataset as wds
wds.tariterators.base_plus_ext("/some/path/22.0/1.1.png")
# ('/some/path/22.0/1', '1.png')
```
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https://github.com/huggingface/datasets/issues/7149
| 7,149
|
Datasets Unknown Keyword Argument Error - task_templates
|
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[
"Thanks, for reporting.\r\n\r\nWe have been fixing most Hub datasets to remove the deprecated (and now non-supported) task templates, but we missed the \"facebook/winoground\".\r\n\r\nIt is fixed now: https://huggingface.co/datasets/facebook/winoground/discussions/8\r\n\r\n",
"Hello @albertvillanova \r\n\r\nI got the same error while loading this dataset: https://huggingface.co/datasets/alaleye/aloresb...\r\n\r\nHow can I fix it ? \r\nThanks",
"I am getting the same error on the below code, any fix to this ?\n\n```\nfrom datasets import load_dataset\n\nminds = load_dataset(\"PolyAI/minds14\", name=\"en-AU\", split=\"train\")\nminds\n```"
] | 2024-09-13T10:30:57Z
| 2025-03-06T07:11:55Z
| 2024-09-13T14:10:48Z
|
NONE
| null | null |
### Describe the bug
Issue
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
Gives error
```
TypeError: DatasetInfo.__init__() got an unexpected keyword argument 'task_templates'
```
A simple downgrade to lower `datasets v 2.21.0` solves it.
### Steps to reproduce the bug
1. `pip install datsets`
2.
```python
from datasets import load_dataset
examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
### Expected behavior
Should load the dataset correctly.
### Environment info
- Datasets version `3.0.0`
- `transformers` version: 4.45.0.dev0
- Platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
- Python version: 3.12.4
- Huggingface_hub version: 0.24.6
- Safetensors version: 0.4.5
- Accelerate version: 0.35.0.dev0
- Accelerate config: not found
- PyTorch version (GPU?): 2.4.1+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
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https://github.com/huggingface/datasets/issues/7148
| 7,148
|
Bug: Error when downloading mteb/mtop_domain
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[
"Could you please try with `force_redownload` instead?\r\nEDIT:\r\n```python\r\ndata = load_dataset(\"mteb/mtop_domain\", \"en\", download_mode=\"force_redownload\")\r\n```",
"Seems the error is still there",
"I am not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: data = load_dataset(\"mteb/mtop_domain\", \"en\")\r\n\r\nIn [3]: data\r\nOut[3]: DatasetDict({\r\n train: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 15667\r\n })\r\n validation: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 2235\r\n })\r\n test: Dataset({\r\n features: ['id', 'text', 'label', 'label_text'],\r\n num_rows: 4386\r\n })\r\n})\r\n```",
"Just solved this by reinstall Huggingface Hub and datasets. Thanks for your help!"
] | 2024-09-13T04:09:39Z
| 2024-09-14T15:11:35Z
| 2024-09-14T15:11:35Z
|
NONE
| null | null |
### Describe the bug
When downloading the dataset "mteb/mtop_domain", ran into the following error:
```
Traceback (most recent call last):
File "/share/project/xzy/test/test_download.py", line 3, in <module>
data = load_dataset("mteb/mtop_domain", "en", trust_remote_code=True)
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2606, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2277, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1923, in dataset_module_factory
raise e1 from None
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1896, in dataset_module_factory
).get_module()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1507, in get_module
local_path = self.download_loading_script()
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1467, in download_loading_script
return cached_path(file_path, download_config=download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 211, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 689, in get_from_cache
fsspec_get(
File "/opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 395, in fsspec_get
fs.get_file(path, temp_file.name, callback=callback)
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 648, in get_file
http_get(
File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 578, in http_get
raise EnvironmentError(
OSError: Consistency check failed: file should be of size 2191 but has size 2190 ((…)ets/mteb/mtop_domain@main/mtop_domain.py).
We are sorry for the inconvenience. Please retry with `force_download=True`.
If the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub.
```
Try to download through HF datasets directly but got the same error as above.
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en")
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
data = load_dataset("mteb/mtop_domain", "en", force_download=True)
```
With and without `force_download=True` both ran into the same error.
### Expected behavior
Should download the dataset successfully.
### Environment info
- datasets version: 2.21.0
- huggingface-hub version: 0.24.6
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https://github.com/huggingface/datasets/issues/7147
| 7,147
|
IterableDataset strange deadlock
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[
"Yes `interleave_datasets` seems to have an issue with shuffling, could you open a new issue on this ?\r\n\r\nThen regarding the deadlock, it has to do with interleave_dataset with probabilities=[1, 0] with workers that may contain an empty dataset in first position (it can be empty since you distribute 1024 shard to 8 workers, so some workers may not have an example that satisfies your condition `if shard < 25`). It creates an infinite loop, trying to get samples from empty datasets with probability 1.",
"Opened https://github.com/huggingface/datasets/issues/7156\r\n\r\nCan the deadlock be fixed somehow? The point of IterableDataset is so we don't need to preload the entire dataset, which loses some meaning if we need to see how many examples are in the dataset in order to set shards correctly.",
"~~And it is kinda strange that `Commenting out the final shuffle avoids the issue` since if the infinite loop is inside interleave_datasets you'd expect that to happen regardless of the additional shuffle call?~~\r\n\r\nEdit: oh I guess without the shuffle it's guaranteed every worker gets something, but the shuffle makes it so some workers could have nothing\r\n\r\n~~Edit2: maybe the shuffle can be changed so initially it gives one example to each worker, and only starts the random shuffle after that~~ wait it's not about the workers not getting any shards, it's about a worker getting shards but all of the shards it gets are empty shards\r\n\r\nEdit3: If it's trying to get samples from empty datasets, it should be getting back a StopIteration -- and \"all_exhausted\" should mean it eventually discovers all its datasets are empty, and then it should just raise a StopIteration itself. So it seems like there is a reasonable behavior result for this?",
"well the second dataset passed to interleave_datasets is never exhausted, since it's never sampled. But we could also state that the stream of examples from the second dataset is empty if it has probability 0, so I opened https://github.com/huggingface/datasets/pull/7157 to fix the infinite loop issue by ignoring datasets with probability 0, let me know what you think !",
"Thanks for taking a look!\r\n\r\nI think you're right that this is ultimately an issue that the user opts into by specifying a dataset with probability 0, because the user is basically saying \"I want to force this `interleave_datasets` call to run forever\" and yet one of the workers can end up having only empty shards to mix...\r\n\r\nThat said it's probably not a good idea to randomly change the behavior of `interleave_datasets` with probability 0, I can't be the only one that uses it to repeat many different datasets (since there is no `datasets.repeat()` function). https://xkcd.com/1172/\r\n\r\nI think just the knowledge that filtering out probability 0 datasets fixes the deadlock is good enough for me. I can filter it out on my side and add a restart loop around the dataloader instead.\r\n\r\nThanks again for investigating.",
"Ok I see ! We can also add .repeat() as well"
] | 2024-09-12T18:59:33Z
| 2024-09-23T09:32:27Z
| 2024-09-21T17:37:34Z
|
NONE
| null | null |
### Describe the bug
```
import datasets
import torch.utils.data
num_shards = 1024
def gen(shards):
for shard in shards:
if shard < 25:
yield {"shard": shard}
def main():
dataset = datasets.IterableDataset.from_generator(
gen,
gen_kwargs={"shards": list(range(num_shards))},
)
dataset = dataset.shuffle(buffer_size=1)
dataset = datasets.interleave_datasets(
[dataset, dataset], probabilities=[1, 0], stopping_strategy="all_exhausted"
)
dataset = dataset.shuffle(buffer_size=1)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=8,
num_workers=8,
)
for i, batch in enumerate(dataloader):
print(batch)
if i >= 10:
break
print()
if __name__ == "__main__":
for _ in range(100):
main()
```
### Steps to reproduce the bug
Running the script above, at some point it will freeze.
- Changing `num_shards` from 1024 to 25 avoids the issue
- Commenting out the final shuffle avoids the issue
- Commenting out the interleave_datasets call avoids the issue
As an aside, if you comment out just the final shuffle, the output from interleave_datasets is not shuffled at all even though there's the shuffle before it. So something about that shuffle config is not being propagated to interleave_datasets.
### Expected behavior
The script should not freeze.
### Environment info
- `datasets` version: 3.0.0
- Platform: macOS-14.6.1-arm64-arm-64bit
- Python version: 3.12.5
- `huggingface_hub` version: 0.24.7
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
I observed this with 2.21.0 initially, then tried upgrading to 3.0.0 and could still repro.
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https://github.com/huggingface/datasets/pull/7146
| 7,146
|
Set dev version
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7146). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-11T13:53:27Z
| 2024-09-12T04:34:08Z
| 2024-09-12T04:34:06Z
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https://github.com/huggingface/datasets/pull/7145
| 7,145
|
Release: 3.0.0
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7145). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-11T13:41:47Z
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https://github.com/huggingface/datasets/pull/7144
| 7,144
|
Fix key error in webdataset
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[
"hi ! What version of `datasets` are you using ? Is this issue also happening with `datasets==3.0.0` ?\r\nAsking because we made sure to replicate the official webdataset logic, which is to use the latest dot as separator between the sample base name and the key",
"Hi, yes this is still a problem on `datasets==3.0.0`.\r\n\r\nI was using `datasets=2.20.0` and in that version you get the key error.\r\n\r\nI just upgraded to `datasets==3.0.0` and in this version, you do not get a key error because it sets all keys to none by default in `_generate_examples` function:\r\n\r\n```python\r\nif field_name not in example:\r\n example[field_name] = None\r\n```\r\n\r\nHowever, the behaviour is still incorrect. This `if` condition is triggered because the filename is not split properly and it returns the data as `None` when it shouldn't.\r\n\r\n> we made sure to replicate the official webdataset logic, which is to use the latest dot as separator\r\n \r\nAh, but that's not what `split(, 1)` does though. This is exactly why I'm suggesting to use `rsplit` instead. In general, using `rsplit` should not be a breaking change I believe.",
"Hi @ragavsachdeva,\r\n\r\nWe already had this discussion in the issue you have linked:\r\n- #6880\r\n- I even opened a PR with your proposed fix:\r\n - #6888\r\n\r\nHowever, we decided not to implement this feature because it is NOT aligned with the behavior of the `webdataset` library:\r\n> The prefix of a file is all directory components of the file plus the file name component up to the *first* “.” in the file name.\r\n```python\r\nIn [1]: import webdataset as wds\r\n\r\nIn [2]: wds.tariterators.base_plus_ext(\"22.05.png\")\r\nOut[2]: ('22', '05.png')\r\n```\r\n\r\n",
"Ah, my apologies I missed https://github.com/huggingface/datasets/pull/6888 (clearly didn't do my due diligence). It's such a weird convention to have though. My keys are `/some/path/22.0/1.1.png` and it splits them at `/some/path/22` and `.0/1.1.png`(!) I'm okay with this PR not being merged though. Thanks for your time.",
"Actually `datasets` is not behaving correctly in this case and should not split as `.0/1.1.png` - even webdataset handles this correctly via their regex `^((?:.*/|)[^.]+)[.]([^/]*)$` in `wds.tariterators.base_plus_ext` here:\r\n\r\nhttps://github.com/webdataset/webdataset/blob/87bd5aa41602d57f070f65a670893ee625702f2f/webdataset/tariterators.py#L36",
"Oh.. the intention with that regex is to capture \"multi-part\" extensions e.g. `.tar.gz`. Makes sense. So `rsplit` isn't the solution then and neither is `split`. This expression makes so much more sense. Nice find! I'm assuming you'll add a patch?",
"Issue addressed by:\r\n- #7151",
" Maybe the libray should suggest/warn users about **file naming**, in particular, file naming **with dots**. Indeed, otherwise a naive naming approach like mentioned above would lead to\r\n\r\n```python\r\nValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.\r\n```\r\n\r\nwith the following code\r\n\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: !tar tvf easy_trouble/001.tar\r\ndrwxr-xr-x phunc20/phunc20 0 2025-01-15 17:57 001/\r\n-rw-r--r-- phunc20/phunc20 20 2025-01-15 17:54 001/1.2.jpg\r\n-rw-r--r-- phunc20/phunc20 20 2025-01-15 17:53 001/1.1.jpg\r\n-rw-r--r-- phunc20/phunc20 20 2025-01-15 17:57 001/2.1.jpg\r\n-rw-r--r-- phunc20/phunc20 20 2025-01-15 17:57 001/2.2.jpg\r\n-rw-r--r-- phunc20/phunc20 20 2025-01-15 17:57 001/2.3.jpg\r\n\r\nIn [29]: dataset = load_dataset(\"webdataset\", data_dir=\"easy_trouble\")\r\n```\r\n\r\nBTW, @ragavsachdeva , why would you find including a `.tar.gz` inside a `.tar` makes sense? I personally find switching `base_plus_ext` into sth similar to the following makes more sense, though. (That is, no multi-part extension.)\r\n\r\n```python\r\nfrom pathlib import Path\r\n\r\ndef base_plus_ext(path):\r\n \"\"\"Split off all file extensions.\r\n\r\n Returns base, allext.\r\n \"\"\"\r\n p = Path(path)\r\n base = (p.parent / p.stem).as_posix()\r\n ext = p.suffix.split(\".\")[-1]\r\n return base, ext\r\n```"
] | 2024-09-11T10:50:17Z
| 2025-01-15T10:32:43Z
| 2024-09-13T04:31:37Z
|
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I was running into
```
example[field_name] = {"path": example["__key__"] + "." + field_name, "bytes": example[field_name]}
KeyError: 'png'
```
The issue is that a filename may have multiple "." e.g. `22.05.png`. Changing `split` to `rsplit` fixes it.
Related https://github.com/huggingface/datasets/issues/6880
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https://github.com/huggingface/datasets/pull/7143
| 7,143
|
Modify add_column() to optionally accept a FeatureType as param
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[
"Requesting review @lhoestq \r\nI will also update the docs if this looks good.",
"Cool ! maybe you can rename the argument `feature` and with type `FeatureType` ? This way it would work the same way as `.cast_column()` ?",
"@lhoestq Since there is no way to get a `pyarrow.Schema` from a `FeatureType`, I had to go via `Features`. How does this look?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7143). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq done!",
"@lhoestq anything pending on this?"
] | 2024-09-08T10:56:57Z
| 2024-09-17T06:01:23Z
| 2024-09-16T15:11:01Z
|
CONTRIBUTOR
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Fix #7142.
**Before (Add + Cast)**:
```
from datasets import load_dataset, Value
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]
ds = ds.add_column("new_col", lst)
# Assigns int64 to new_col by default
print(ds.features)
ds = ds.cast_column("new_col", Value(dtype="uint16", id=None))
print(ds.features)
```
**Before (Numpy Workaround)**:
```
from datasets import load_dataset
import numpy as np
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]
ds = ds.add_column("new_col", np.array(lst, dtype=np.uint16))
print(ds.features)
```
**After**:
```
from datasets import load_dataset, Value
ds = load_dataset("rotten_tomatoes", split="test")
lst = [i for i in range(len(ds))]
val = Value(dtype="uint16", id=None))
ds = ds.add_column("new_col", lst, feature=val)
print(ds.features)
```
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https://github.com/huggingface/datasets/issues/7142
| 7,142
|
Specifying datatype when adding a column to a dataset.
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[
"#self-assign"
] | 2024-09-08T07:34:24Z
| 2024-09-17T03:46:32Z
| 2024-09-17T03:46:32Z
|
CONTRIBUTOR
| null | null |
### Feature request
There should be a way to specify the datatype of a column in `datasets.add_column()`.
### Motivation
To specify a custom datatype, we have to use `datasets.add_column()` followed by `datasets.cast_column()` which is slow for large datasets. Another workaround is to pass a `numpy.array()` of desired type to the `datasets.add_column()` function.
IMO this functionality should be natively supported.
https://discuss.huggingface.co/t/add-column-with-a-particular-type-in-datasets/95674
### Your contribution
I can submit a PR for this.
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|
Older datasets throwing safety errors with 2.21.0
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[
"I am also getting this error with this dataset: https://huggingface.co/datasets/google/IFEval",
"Me too, didn't have this issue few hours ago.",
"same observation. I even downgraded `datasets==2.20.0` and `huggingface_hub==0.23.5` leading me to believe it's an issue on the server.\r\n\r\nany known workarounds?\r\n",
"Not a good idea, but commenting out the whole security block at `/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py` is a temporary workaround:\r\n\r\n```\r\n #security = kwargs.pop(\"security\", None)\r\n #if security is not None:\r\n # security = BlobSecurityInfo(\r\n # safe=security[\"safe\"], av_scan=security[\"avScan\"], pickle_import_scan=security[\"pickleImportScan\"]\r\n # )\r\n #self.security = security\r\n```\r\n",
"Uploading a dataset to Huggingface also results in the following error in the Dataset Preview:\r\n```\r\nThe full dataset viewer is not available (click to read why). Only showing a preview of the rows.\r\n'safe'\r\nError code: UnexpectedError\r\nNeed help to make the dataset viewer work? Make sure to review [how to configure the dataset viewer](link1), and [open a discussion](link2) for direct support.\r\n```\r\nI used jsonl format for the dataset in this case. Same exact dataset worked previously.",
"Same issue here. Even reverting to older version of `datasets` (e.g., `2.19.0`) results in same error:\r\n\r\n```python\r\n>>> datasets.load_dataset('allenai/ai2_arc', 'ARC-Easy')\r\n\r\nFile \"/Users/lucas/miniforge3/envs/oe-eval-internal/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 3048, in <listcomp>\r\n RepoFile(**path_info) if path_info[\"type\"] == \"file\" else RepoFolder(**path_info)\r\n File \"/Users/lucas/miniforge3/envs/oe-eval-internal/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 534, in __init__\r\n safe=security[\"safe\"], av_scan=security[\"avScan\"], pickle_import_scan=security[\"pickleImportScan\"]\r\nKeyError: 'safe'\r\n```",
"i just had this issue a few minutes ago, crawled the internet and found nothing. came here to open an issue and found this. it is really frustrating. anyone found a fix?",
"hi, me and my team have the same problem",
"Yeah, this just suddenly appeared without client-side code changes, within the last hours.\r\n\r\nHere's a patch to fix the issue temporarily:\r\n```python\r\nimport huggingface_hub\r\ndef patched_repofolder_init(self, **kwargs):\r\n self.path = kwargs.pop(\"path\")\r\n self.tree_id = kwargs.pop(\"oid\")\r\n last_commit = kwargs.pop(\"lastCommit\", None) or kwargs.pop(\"last_commit\", None)\r\n if last_commit is not None:\r\n last_commit = huggingface_hub.hf_api.LastCommitInfo(\r\n oid=last_commit[\"id\"],\r\n title=last_commit[\"title\"],\r\n date=huggingface_hub.utils.parse_datetime(last_commit[\"date\"]),\r\n )\r\n self.last_commit = last_commit\r\n\r\n\r\ndef patched_repo_file_init(self, **kwargs):\r\n self.path = kwargs.pop(\"path\")\r\n self.size = kwargs.pop(\"size\")\r\n self.blob_id = kwargs.pop(\"oid\")\r\n lfs = kwargs.pop(\"lfs\", None)\r\n if lfs is not None:\r\n lfs = huggingface_hub.hf_api.BlobLfsInfo(size=lfs[\"size\"], sha256=lfs[\"oid\"], pointer_size=lfs[\"pointerSize\"])\r\n self.lfs = lfs\r\n last_commit = kwargs.pop(\"lastCommit\", None) or kwargs.pop(\"last_commit\", None)\r\n if last_commit is not None:\r\n last_commit = huggingface_hub.hf_api.LastCommitInfo(\r\n oid=last_commit[\"id\"],\r\n title=last_commit[\"title\"],\r\n date=huggingface_hub.utils.parse_datetime(last_commit[\"date\"]),\r\n )\r\n self.last_commit = last_commit\r\n self.security = None\r\n\r\n # backwards compatibility\r\n self.rfilename = self.path\r\n self.lastCommit = self.last_commit\r\n\r\n\r\nhuggingface_hub.hf_api.RepoFile.__init__ = patched_repo_file_init\r\nhuggingface_hub.hf_api.RepoFolder.__init__ = patched_repofolder_init\r\n```\r\n",
"Also discussed here:\r\nhttps://discuss.huggingface.co/t/i-keep-getting-keyerror-safe-when-loading-my-datasets/105669/1",
"i'm thinking this should be a server issue, i mean no client code was changed on my end. so weird!",
"As far as I can tell, this seems to be happening with **all** datasets that use RepoFolder (probably represents most datasets on huggingface, right?)",
"> Here is a temporary fix for the problem: https://discuss.huggingface.co/t/i-keep-getting-keyerror-safe-when-loading-my-datasets/105669/12?u=mlscientist\r\n\r\nthis doesn't seem to work!",
"In case you are using Colab or similar, remember to restart your session after modyfing the hf_api.py file",
"No need to modify the file directly, just monkey-patch.\r\n\r\nI'm now more sure that the error appears because the backend expects the api code to look like it does on `main`. If `RepoFile` and `RepoFolder` look about like they look on main, they work again.\r\n\r\nIf not fixed like above, a secondary error that will appear is \r\n```\r\n return self.info(path, expand_info=False)[\"type\"] == \"directory\"\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n \"tree_id\": path_info.tree_id,\r\n ^^^^^^^^^^^^^^^^^\r\nAttributeError: 'RepoFolder' object has no attribute 'tree_id'\r\n```\r\n",
"We've reverted the deployment, please let us know if the issue still persists!",
"thanks @muellerzr!"
] | 2024-09-06T16:26:30Z
| 2024-09-06T21:14:14Z
| 2024-09-06T19:09:29Z
|
NONE
| null | null |
### Describe the bug
The dataset loading was throwing some safety errors for this popular dataset `wmt14`.
[in]:
```
import datasets
# train_data = datasets.load_dataset("wmt14", "de-en", split="train")
train_data = datasets.load_dataset("wmt14", "de-en", split="train")
val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]")
```
[out]:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-9-445f0ecc4817>](https://localhost:8080/#) in <cell line: 4>()
2
3 # train_data = datasets.load_dataset("wmt14", "de-en", split="train")
----> 4 train_data = datasets.load_dataset("wmt14", "de-en", split="train")
5 val_data = datasets.load_dataset("wmt14", "de-en", split="validation[:10%]")
12 frames
[/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py](https://localhost:8080/#) in __init__(self, **kwargs)
636 if security is not None:
637 security = BlobSecurityInfo(
--> 638 safe=security["safe"], av_scan=security["avScan"], pickle_import_scan=security["pickleImportScan"]
639 )
640 self.security = security
KeyError: 'safe'
```
### Steps to reproduce the bug
See above.
### Expected behavior
Dataset properly loaded.
### Environment info
version: 2.21.0
|
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https://github.com/huggingface/datasets/issues/7139
| 7,139
|
Use load_dataset to load imagenet-1K But find a empty dataset
|
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open
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|
[
"Imagenet-1k is a gated dataset which means you’ll have to agree to share your contact info to access it. Have you tried this yet? Once you have, you can sign in with your user token (you can find this in your Hugging Face account settings) when prompted by running.\r\n\r\n```\r\nhuggingface-cli login\r\ntrain_set = load_dataset('imagenet-1k', split='train', use_auth_token=True)\r\n``` ",
"Thanks a lot! It helps me"
] | 2024-09-05T15:12:22Z
| 2024-10-09T04:02:41Z
| null |
NONE
| null | null |
### Describe the bug
```python
def get_dataset(data_path, train_folder="train", val_folder="val"):
traindir = os.path.join(data_path, train_folder)
valdir = os.path.join(data_path, val_folder)
def transform_val_examples(examples):
transform = Compose([
Resize(256),
CenterCrop(224),
ToTensor(),
])
examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]]
return examples
def transform_train_examples(examples):
transform = Compose([
RandomResizedCrop(224),
RandomHorizontalFlip(),
ToTensor(),
])
examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]]
return examples
# @fengsicheng: This way is very slow for big dataset like ImageNet-1K (but can pass the network problem using local dataset)
# train_set = load_dataset("imagefolder", data_dir=traindir, num_proc=4)
# test_set = load_dataset("imagefolder", data_dir=valdir, num_proc=4)
train_set = load_dataset("imagenet-1K", split="train", trust_remote_code=True)
test_set = load_dataset("imagenet-1K", split="test", trust_remote_code=True)
print(train_set["label"])
train_set.set_transform(transform_train_examples)
test_set.set_transform(transform_val_examples)
return train_set, test_set
```
above the code, but output of the print is a list of None:
<img width="952" alt="image" src="https://github.com/user-attachments/assets/c4e2fdd8-3b8f-481e-8f86-9bbeb49d79fb">
### Steps to reproduce the bug
1. just ran the code
2. see the print
### Expected behavior
I do not know how to fix this, can anyone provide help or something? It is hurry for me
### Environment info
- `datasets` version: 2.21.0
- Platform: Linux-5.4.0-190-generic-x86_64-with-glibc2.31
- Python version: 3.10.14
- `huggingface_hub` version: 0.24.6
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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https://github.com/huggingface/datasets/issues/7138
| 7,138
|
Cache only changed columns?
|
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|
[
"so I guess a workaround to this is to simply remove all columns except the ones to cache and then add them back with `concatenate_datasets(..., axis=1)`.",
"yes this is the right workaround. We're keeping the cache like this to make it easier for people to delete intermediate cache files"
] | 2024-09-05T12:56:47Z
| 2024-09-20T13:27:20Z
| null |
CONTRIBUTOR
| null | null |
### Feature request
Cache only the actual changes to the dataset i.e. changed columns.
### Motivation
I realized that caching actually saves the complete dataset again.
This is especially problematic for image datasets if one wants to only change another column e.g. some metadata and then has to save 5 TB again.
### Your contribution
Is this even viable in the current architecture of the package?
I quickly looked into it and it seems it would require significant changes.
I would spend some time looking into this but maybe somebody could help with the feasibility and some plan to implement before spending too much time on it?
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https://github.com/huggingface/datasets/issues/7137
| 7,137
|
[BUG] dataset_info sequence unexpected behavior in README.md YAML
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[
"The non-sequence case works well (`dict[str, str]` instead of `list[dict[str, str]]`), which makes me believe it shall be a bug for `sequence` and my proposed behavior shall be expected.\r\n```\r\ndataset_info:\r\n- config_name: default\r\n features:\r\n - name: answers\r\n dtype:\r\n - name: text\r\n dtype: string\r\n - name: label\r\n dtype: string\r\n\r\n\r\n# data\r\n{\"answers\": {\"text\": \"ADDRESS\", \"label\": \"abc\"}}\r\n```",
"According to https://github.com/huggingface/datasets/issues/7590#issuecomment-3035647354\nreplacing `sequence` to `list` will solve this issue.\n\n```\ndataset_info:\n- config_name: default\n features:\n - name: answers\n list:\n - name: text\n sequence: string\n - name: label\n sequence: string\n```",
"Btw you can use `list` instead of `sequence` everywhere for consistency:\n\n```\ndataset_info:\n- config_name: default\n features:\n - name: answers\n list:\n - name: text\n list: string\n - name: label\n list: string\n```"
] | 2024-09-05T06:06:06Z
| 2025-07-07T09:20:29Z
| 2025-07-04T19:50:59Z
|
NONE
| null | null |
### Describe the bug
When working on `dataset_info` yaml, I find my data column with format `list[dict[str, str]]` cannot be coded correctly.
My data looks like
```
{"answers":[{"text": "ADDRESS", "label": "abc"}]}
```
My `dataset_info` in README.md is:
```
dataset_info:
- config_name: default
features:
- name: answers
sequence:
- name: text
dtype: string
- name: label
dtype: string
```
**Error log**:
```
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from list<item: struct<text: string, label: string>> to struct using function cast_struct
```
## Potential Reason
After some analysis, it turns out that my yaml config is requiring `dict[str, list[str]]` instead of `list[dict[str, str]]`. It would work if I change my data to
```
{"answers":{"text": ["ADDRESS"], "label": ["abc", "def"]}}
```
These following 2 different `dataset_info` are actually equivalent.
```
dataset_info:
- config_name: default
features:
- name: answers
dtype:
- name: text
sequence: string
- name: label
sequence: string
dataset_info:
- config_name: default
features:
- name: answers
sequence:
- name: text
dtype: string
- name: label
dtype: string
```
### Steps to reproduce the bug
```
# README.md
---
dataset_info:
- config_name: default
features:
- name: answers
sequence:
- name: text
dtype: string
- name: label
dtype: string
configs:
- config_name: default
default: true
data_files:
- split: train
path:
- "test.jsonl"
---
# test.jsonl
# expected but not working
{"answers":[{"text": "ADDRESS", "label": "abc"}]}
# unexpected but working
{"answers":{"text": ["ADDRESS"], "label": ["abc", "def"]}}
```
### Expected behavior
```
dataset_info:
- config_name: default
features:
- name: answers
sequence:
- name: text
dtype: string
- name: label
dtype: string
```
Should work on following data format:
```
{"answers":[{"text":"ADDRESS", "label": "abc"}]}
```
### Environment info
- `datasets` version: 2.21.0
- Platform: macOS-14.6.1-arm64-arm-64bit
- Python version: 3.12.4
- `huggingface_hub` version: 0.24.5
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.6.1
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https://github.com/huggingface/datasets/pull/7136
| 7,136
|
Do not consume unnecessary memory during sharding
|
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[] | 2024-09-04T19:26:06Z
| 2024-09-04T19:28:23Z
| null |
NONE
| false
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When sharding `IterableDataset`s, a temporary list is created that is then indexed. There is no need to create a temporary list of a potentially very large step/world size, with standard `islice` functionality, so we avoid it.
```shell
pytest tests/test_distributed.py -k iterable
```
Runs successfully.
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https://github.com/huggingface/datasets/issues/7135
| 7,135
|
Bug: Type Mismatch in Dataset Mapping
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[
"By the way, following code is working. This show the inconsistentcy.\r\n```python\r\nfrom datasets import Dataset\r\n\r\n# Original data\r\ndata = {\r\n 'text': ['Hello', 'world', 'this', 'is', 'a', 'test'],\r\n 'label': [0, 1, 0, 1, 1, 0]\r\n}\r\n\r\n# Creating a Dataset object\r\ndataset = Dataset.from_dict(data)\r\n\r\n# Mapping function to convert label to string\r\ndef add_one(example):\r\n example['label'] += 1\r\n return example\r\n\r\n# Applying the mapping function\r\ndataset = dataset.map(add_one)\r\n\r\n# Iterating over the dataset to show results\r\nfor item in dataset:\r\n print(item)\r\n print(type(item['label']))\r\n```",
"Hello, thanks for submitting an issue.\r\n\r\nFWIU, the issue is that `datasets` tries to limit casting [ref](https://github.com/huggingface/datasets/blob/ca58154bba185c1916ca5eea4e33b27258642044/src/datasets/arrow_writer.py#L526) and as such will try to convert your strings back to int to preserve the `Features`. \r\n\r\nA quick solution would be to use `dataset.cast` or to supply `features` when calling `dataset.map`.\r\n\r\n\r\n```python\r\n# using Dataset.cast\r\ndataset = dataset.cast_column('label', Value('string'))\r\n\r\n# Alternative, supply features\r\ndataset = dataset.map(add_one, features=Features({**dataset.features, 'label': Value('string')}))\r\n```",
"LGTM! Thanks for the review.\r\n\r\nJust to clarify, is this intended behavior, or is it something that might be addressed in a future update?\r\nI'll leave this issue open until it's fixed if this is not the intended behavior."
] | 2024-09-03T16:37:01Z
| 2024-09-05T14:09:05Z
| null |
NONE
| null | null |
# Issue: Type Mismatch in Dataset Mapping
## Description
There is an issue with the `map` function in the `datasets` library where the mapped output does not reflect the expected type change. After applying a mapping function to convert an integer label to a string, the resulting type remains an integer instead of a string.
## Reproduction Code
Below is a Python script that demonstrates the problem:
```python
from datasets import Dataset
# Original data
data = {
'text': ['Hello', 'world', 'this', 'is', 'a', 'test'],
'label': [0, 1, 0, 1, 1, 0]
}
# Creating a Dataset object
dataset = Dataset.from_dict(data)
# Mapping function to convert label to string
def add_one(example):
example['label'] = str(example['label'])
return example
# Applying the mapping function
dataset = dataset.map(add_one)
# Iterating over the dataset to show results
for item in dataset:
print(item)
print(type(item['label']))
```
## Expected Output
After applying the mapping function, the expected output should have the `label` field as strings:
```plaintext
{'text': 'Hello', 'label': '0'}
<class 'str'>
{'text': 'world', 'label': '1'}
<class 'str'>
{'text': 'this', 'label': '0'}
<class 'str'>
{'text': 'is', 'label': '1'}
<class 'str'>
{'text': 'a', 'label': '1'}
<class 'str'>
{'text': 'test', 'label': '0'}
<class 'str'>
```
## Actual Output
The actual output still shows the `label` field values as integers:
```plaintext
{'text': 'Hello', 'label': 0}
<class 'int'>
{'text': 'world', 'label': 1}
<class 'int'>
{'text': 'this', 'label': 0}
<class 'int'>
{'text': 'is', 'label': 1}
<class 'int'>
{'text': 'a', 'label': 1}
<class 'int'>
{'text': 'test', 'label': 0}
<class 'int'>
```
## Why necessary
In the case of Image process we often need to convert PIL to tensor with same column name.
Thank for every dev who review this issue. 🤗
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https://github.com/huggingface/datasets/issues/7134
| 7,134
|
Attempting to return a rank 3 grayscale image from dataset.map results in extreme slowdown
|
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[] | 2024-09-01T13:55:41Z
| 2024-09-02T10:34:53Z
| null |
NONE
| null | null |
### Describe the bug
Background: Digital images are often represented as a (Height, Width, Channel) tensor. This is the same for huggingface datasets that contain images. These images are loaded in Pillow containers which offer, for example, the `.convert` method.
I can convert an image from a (H,W,3) shape to a grayscale (H,W) image and I have no problems with this. But when attempting to return a (H,W,1) shaped matrix from a map function, it never completes and sometimes even results in an OOM from the OS.
I've used various methods to expand a (H,W) shaped array to a (H,W,1) array. But they all resulted in extremely long map operations consuming a lot of CPU and RAM.
### Steps to reproduce the bug
Below is a minimal example using two methods to get the desired output. Both of which don't work
```py
import tensorflow as tf
import datasets
import numpy as np
ds = datasets.load_dataset("project-sloth/captcha-images")
to_gray_pillow = lambda sample: {'image': np.expand_dims(sample['image'].convert("L"), axis=-1)}
ds_gray = ds.map(to_gray_pillow)
# Alternatively
ds = datasets.load_dataset("project-sloth/captcha-images").with_format("tensorflow")
to_gray_tf = lambda sample: {'image': tf.expand_dims(tf.image.rgb_to_grayscale(sample['image']), axis=-1)}
ds_gray = ds.map(to_gray_tf)
```
### Expected behavior
I expect the map operation to complete and return a new dataset containing grayscale images in a (H,W,1) shape.
### Environment info
datasets 2.21.0
python tested with both 3.11 and 3.12
host os : linux
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https://github.com/huggingface/datasets/pull/7133
| 7,133
|
remove filecheck to enable symlinks
|
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7133). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The CI is failing, looks like it breaks imagefolder loading.\r\n\r\nI just checked fsspec internals and maybe instead we can detect symlink by checking `islink` and `size` to make sure it's a file\r\n```python\r\nif info[\"type\"] == \"file\" or (info.get(\"islink\") and info[\"size\"])\r\n```\r\n",
"hmm actually `size` doesn't seem to filter symlinked directories, we need another way",
"Does fsspec perhaps allow resolving symlinks? Something like https://docs.python.org/3/library/pathlib.html#pathlib.Path.resolve",
"there is `info[\"destination\"]` in case of a symlink, so maybe\r\n\r\n\r\n```python\r\nif info[\"type\"] == \"file\" or (info.get(\"islink\") and info.get(\"destination\") and os.path.isfile(info[\"destination\"]))\r\n```",
"I've added a fix which works with some temporary test files locally \r\n\r\n`(info[\"type\"] == \"file\" or (info.get(\"islink\") and os.path.isfile(os.path.realpath(filepath))))`"
] | 2024-08-30T07:36:56Z
| 2024-12-24T14:25:22Z
| 2024-12-24T14:25:22Z
|
CONTRIBUTOR
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Enables streaming from local symlinks #7083
@lhoestq
|
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https://github.com/huggingface/datasets/pull/7132
| 7,132
|
Fix data file module inference
|
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[
"Hi ! datasets saved using `save_to_disk` should be loaded with `load_from_disk` ;)",
"It is convienient to just pass in a path to a local dataset or one from the hub and use the same function to load it. Is it not possible to get this fix merged in to allow this? ",
"We can modify `save_to_disk` to write the dataset in a structure supported by the Hub in this case, it's kind of a legacy function anyway"
] | 2024-08-29T13:48:16Z
| 2024-09-02T19:52:13Z
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I saved a dataset with two splits to disk with `DatasetDict.save_to_disk`. The train is bigger and ended up in 10 shards, whereas the test split only resulted in 1 split.
Now when trying to load the dataset, an error is raised that not all splits have the same data format:
> ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}
This is not expected because both splits are saved as arrow files.
I did some debugging and found that this is the case because the list of data_files includes a `state.json` file.
Now this means for train split I get 10 ".arrow" and 1 ".json" file. Since datasets picks based on the most common extension this is correctly inferred as "arrow". In the test split, there is 1 .arrow and 1 .json file. Given the function description:
> It picks the module based on the most common file extension.
In case of a draw ".parquet" is the favorite, and then alphabetical order.
This is not quite true though, because in a tie the extensions are actually based on reverse-alphabetical order:
```
for (ext, _), _ in sorted(extensions_counter.items(), key=sort_key, *reverse=True*):
```
Which thus leads to the module wrongly inferred as "json", whereas it should be "arrow", matching the train split.
I first thought about adding "state.json" in the list of excluded files for the inference: https://github.com/huggingface/datasets/blob/main/src/datasets/load.py#L513. However, I think from digging into the code it looks like the right thing to do is to exclude it in the list of `data_files` to start with, because it is more of a metadata than a data file.
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|
https://github.com/huggingface/datasets/issues/7129
| 7,129
|
Inconsistent output in documentation example: `num_classes` not displayed in `ClassLabel` output
|
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[] | 2024-08-28T12:27:48Z
| 2024-12-06T11:32:02Z
| 2024-12-06T11:32:02Z
|
MEMBER
| null | null |
In the documentation for [ClassLabel](https://huggingface.co/docs/datasets/v2.21.0/en/package_reference/main_classes#datasets.ClassLabel), there is an example of usage with the following code:
````
from datasets import Features
features = Features({'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'])})
features
````
which expects to output (as stated in the documentation):
````
{'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'], id=None)}
````
but it generates the following
````
{'label': ClassLabel(names=['bad', 'ok', 'good'], id=None)}
````
If my understanding is correct, this happens because although num_classes is used during the init of the object, it is afterward ignored:
https://github.com/huggingface/datasets/blob/be5cff059a2a5b89d7a97bc04739c4919ab8089f/src/datasets/features/features.py#L975
I would like to work on this issue if this is something needed 😄
|
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https://github.com/huggingface/datasets/issues/7128
| 7,128
|
Filter Large Dataset Entry by Entry
|
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[
"Hi ! you can do\r\n\r\n```python\r\nfiltered_dataset = dataset.filter(filter_function)\r\n```\r\n\r\non a subset:\r\n\r\n```python\r\nfiltered_subset = dataset.select(range(10_000)).filter(filter_function)\r\n```\r\n",
"Jumping on this as it seems relevant - when I use the `filter` method, it often results in an OOM (or at least unacceptably high memory usage).\r\n\r\nFor example in the [this notebook](https://colab.research.google.com/drive/1N_rWko6jzGji3j_ayDR7ngT5lf4P8at_), we load an object detection dataset from HF and imagine I want to filter such that I only have images which contain a single annotation class. Each row has a JSON field that contains MS-COCO annotations for the image, so we could load that field and filter on it.\r\n\r\nThe test dataset is only about 440 images, probably less than 1GB, but running the following filter crashes the VM (over 12 GB RAM):\r\n\r\n```python\r\nimport json\r\ndef filter_single_class(example, target_class_id):\r\n \"\"\"Filters examples based on whether they contain annotations from a single class.\r\n\r\n Args:\r\n example: A dictionary representing a single example from the dataset.\r\n target_class_id: The target class ID to filter for.\r\n\r\n Returns:\r\n True if the example contains only annotations from the target class, False otherwise.\r\n \"\"\"\r\n if not example['coco_annotations']:\r\n return False\r\n\r\n annotation_category_ids = set([annotation['category_id'] for annotation in json.loads(example['coco_annotations'])])\r\n\r\n return len(annotation_category_ids) == 1 and target_class_id in annotation_category_ids\r\n\r\ntarget_class_id = 1 \r\nfiltered_dataset = dataset['test'].filter(lambda example: filter_single_class(example, target_class_id))\r\n```\r\n\r\n<img width=\"255\" alt=\"image\" src=\"https://github.com/user-attachments/assets/be475f15-5b6b-4df2-b5b5-a1f60ae2b05c\">\r\n\r\nIterating over the dataset works fine:\r\n\r\n```python\r\nfiltered_dataset = []\r\nfor example in dataset['test']:\r\n if filter_single_class(example, target_class_id):\r\n filtered_dataset.append(example)\r\n```\r\n\r\n<img width=\"129\" alt=\"image\" src=\"https://github.com/user-attachments/assets/34fa5612-0394-4c46-9f34-e94650f05d65\">\r\n\r\nIt would be great if there was guidance in the documentation on how to use filters efficiently, or if this is some performance bug that could be addressed. At the very least I would expect a filter operation to use at most 2x the footprint of the database plus some overhead for the lambda (i.e. worst case would be a duplicate copy with all entries retained). Even if the operation is parallelised, each thread/worker should only take a subset of the dataset - so I'm not sure where this ballooning in memory usage comes from.\r\n\r\nFrom some other comments there seems to be a workaround with `writer_batch_size` or caching to file, but in the [docs](https://huggingface.co/docs/datasets/v3.0.0/en/package_reference/main_classes#datasets.Dataset.filter) at least, `keep_in_memory` defaults to `False`.",
"You can try passing input_columns=[\"coco_annotations\"] to only load this column instead of all the columns. In that case your function should take coco_annotations as input instead of example",
"If your filter_function is large and computationally intensive, consider using multi-processing or multi-threading with concurrent.futures to filter the dataset. This approach allows you to process multiple tables concurrently, reducing overall processing time, especially for CPU-bound tasks. Use ThreadPoolExecutor for I/O-bound operations and ProcessPoolExecutor for CPU-bound operations.\r\n"
] | 2024-08-27T20:31:09Z
| 2024-10-07T23:37:44Z
| null |
NONE
| null | null |
### Feature request
I am not sure if this is a new feature, but I wanted to post this problem here, and hear if others have ways of optimizing and speeding up this process.
Let's say I have a really large dataset that I cannot load into memory. At this point, I am only aware of `streaming=True` to load the dataset. Now, the dataset consists of many tables. Ideally, I would want to have some simple filtering criterion, such that I only see the "good" tables. Here is an example of what the code might look like:
```
dataset = load_dataset(
"really-large-dataset",
streaming=True
)
# And let's say we process the dataset bit by bit because we want intermediate results
dataset = islice(dataset, 10000)
# Define a function to filter the data
def filter_function(table):
if some_condition:
return True
else:
return False
# Use the filter function on your dataset
filtered_dataset = (ex for ex in dataset if filter_function(ex))
```
And then I work on the processed dataset, which would be magnitudes faster than working on the original. I would love to hear if the problem setup + solution makes sense to people, and if anyone has suggestions!
### Motivation
See description above
### Your contribution
Happy to make PR if this is a new feature
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https://github.com/huggingface/datasets/issues/7127
| 7,127
|
Caching shuffles by np.random.Generator results in unintiutive behavior
|
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[
"I first thought this was a mistake of mine, and also posted on stack overflow. https://stackoverflow.com/questions/78913797/iterating-a-huggingface-dataset-from-disk-using-generator-seems-broken-how-to-d \r\n\r\nIt seems to me the issue is the caching step in \r\n\r\nhttps://github.com/huggingface/datasets/blob/be5cff059a2a5b89d7a97bc04739c4919ab8089f/src/datasets/arrow_dataset.py#L4306-L4316\r\n\r\nbecause the shuffle happens after checking the cache, the rng state won't advance if the cache is used. This is VERY confusing. Also not documented.\r\n\r\nMy proposal is that you remove the API for using a Generator, and only keep the seed-based API since that is functional and cache-compatible.",
"Second that, its very confusing."
] | 2024-08-26T10:29:48Z
| 2025-07-28T11:00:00Z
| null |
NONE
| null | null |
### Describe the bug
Create a dataset. Save it to disk. Load from disk. Shuffle, usning a `np.random.Generator`. Iterate. Shuffle again. Iterate. The iterates are different since the supplied np.random.Generator has progressed between the shuffles.
Load dataset from disk again. Shuffle and Iterate. See same result as before. Shuffle and iterate, and this time it does not have the same shuffling as ion previous run.
The motivation is I have a deep learning loop with
```
for epoch in range(10):
for batch in dataset.shuffle(generator=generator).iter(batch_size=32):
.... # do stuff
```
where I want a new shuffling at every epoch. Instead I get the same shuffling.
### Steps to reproduce the bug
Run the code below two times.
```python
import datasets
import numpy as np
generator = np.random.default_rng(0)
ds = datasets.Dataset.from_dict(mapping={"X":range(1000)})
ds.save_to_disk("tmp")
print("First loop: ", end="")
for _ in range(10):
print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ")
print("")
print("Second loop: ", end="")
ds = datasets.Dataset.load_from_disk("tmp")
for _ in range(10):
print(next(ds.shuffle(generator=generator).iter(batch_size=1))['X'], end=", ")
print("")
```
The output is:
```
$ python main.py
Saving the dataset (1/1 shards): 100%|███████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 495019.95 examples/s]
First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334,
Second loop: 741, 847, 944, 795, 483, 842, 717, 865, 231, 840,
$ python main.py
Saving the dataset (1/1 shards): 100%|████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 22243.40 examples/s]
First loop: 459, 739, 72, 943, 241, 181, 845, 830, 896, 334,
Second loop: 741, 741, 741, 741, 741, 741, 741, 741, 741, 741,
```
The second loop, on the second run, only spits out "741, 741, 741...." which is *not* the desired output
### Expected behavior
I want the dataset to shuffle at every epoch since I provide it with a generator for shuffling.
### Environment info
Datasets version 2.21.0
Ubuntu linux.
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https://github.com/huggingface/datasets/pull/7126
| 7,126
|
Disable implicit token in CI
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7126). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005232 / 0.011353 (-0.006121) | 0.003428 / 0.011008 (-0.007580) | 0.062673 / 0.038508 (0.024164) | 0.030111 / 0.023109 (0.007002) | 0.238017 / 0.275898 (-0.037881) | 0.262655 / 0.323480 (-0.060825) | 0.003015 / 0.007986 (-0.004971) | 0.002664 / 0.004328 (-0.001665) | 0.050010 / 0.004250 (0.045759) | 0.045620 / 0.037052 (0.008567) | 0.251800 / 0.258489 (-0.006689) | 0.278829 / 0.293841 (-0.015011) | 0.029838 / 0.128546 (-0.098709) | 0.011703 / 0.075646 (-0.063943) | 0.204503 / 0.419271 (-0.214768) | 0.036173 / 0.043533 (-0.007359) | 0.242850 / 0.255139 (-0.012289) | 0.263811 / 0.283200 (-0.019389) | 0.019027 / 0.141683 (-0.122656) | 1.168028 / 1.452155 (-0.284126) | 1.208975 / 1.492716 (-0.283742) |\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) | 0.091309 / 0.018006 (0.073303) | 0.299583 / 0.000490 (0.299093) | 0.000215 / 0.000200 (0.000015) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018451 / 0.037411 (-0.018960) | 0.062516 / 0.014526 (0.047991) | 0.073983 / 0.176557 (-0.102573) | 0.120952 / 0.737135 (-0.616184) | 0.075275 / 0.296338 (-0.221063) |\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) | 0.286870 / 0.215209 (0.071661) | 2.810498 / 2.077655 (0.732843) | 1.490028 / 1.504120 (-0.014092) | 1.362249 / 1.541195 (-0.178946) | 1.368939 / 1.468490 (-0.099551) | 0.736643 / 4.584777 (-3.848134) | 2.414237 / 3.745712 (-1.331475) | 2.898911 / 5.269862 (-2.370951) | 1.840630 / 4.565676 (-2.725047) | 0.077872 / 0.424275 (-0.346403) | 0.005087 / 0.007607 (-0.002520) | 0.337054 / 0.226044 (0.111009) | 3.390734 / 2.268929 (1.121806) | 1.844174 / 55.444624 (-53.600451) | 1.532741 / 6.876477 (-5.343736) | 1.551650 / 2.142072 (-0.590422) | 0.778642 / 4.805227 (-4.026585) | 0.131899 / 6.500664 (-6.368765) | 0.041801 / 0.075469 (-0.033668) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958362 / 1.841788 (-0.883425) | 11.323330 / 8.074308 (3.249022) | 9.396199 / 10.191392 (-0.795193) | 0.131154 / 0.680424 (-0.549270) | 0.014705 / 0.534201 (-0.519496) | 0.302424 / 0.579283 (-0.276859) | 0.261870 / 0.434364 (-0.172494) | 0.340788 / 0.540337 (-0.199550) | 0.433360 / 1.386936 (-0.953576) |\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) | 0.005571 / 0.011353 (-0.005782) | 0.003388 / 0.011008 (-0.007621) | 0.050366 / 0.038508 (0.011858) | 0.032633 / 0.023109 (0.009524) | 0.261847 / 0.275898 (-0.014051) | 0.292197 / 0.323480 (-0.031283) | 0.005070 / 0.007986 (-0.002916) | 0.002753 / 0.004328 (-0.001575) | 0.048613 / 0.004250 (0.044363) | 0.040272 / 0.037052 (0.003219) | 0.275441 / 0.258489 (0.016952) | 0.309175 / 0.293841 (0.015334) | 0.032403 / 0.128546 (-0.096143) | 0.011734 / 0.075646 (-0.063912) | 0.059532 / 0.419271 (-0.359740) | 0.033886 / 0.043533 (-0.009647) | 0.263453 / 0.255139 (0.008314) | 0.281997 / 0.283200 (-0.001203) | 0.018522 / 0.141683 (-0.123161) | 1.150364 / 1.452155 (-0.301791) | 1.204090 / 1.492716 (-0.288627) |\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) | 0.093129 / 0.018006 (0.075123) | 0.303691 / 0.000490 (0.303201) | 0.000231 / 0.000200 (0.000031) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022084 / 0.037411 (-0.015327) | 0.076354 / 0.014526 (0.061828) | 0.087710 / 0.176557 (-0.088847) | 0.128907 / 0.737135 (-0.608228) | 0.088603 / 0.296338 (-0.207735) |\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) | 0.301161 / 0.215209 (0.085952) | 2.954780 / 2.077655 (0.877125) | 1.601366 / 1.504120 (0.097246) | 1.477225 / 1.541195 (-0.063970) | 1.482355 / 1.468490 (0.013865) | 0.722461 / 4.584777 (-3.862315) | 0.981439 / 3.745712 (-2.764273) | 2.927006 / 5.269862 (-2.342856) | 1.884444 / 4.565676 (-2.681233) | 0.079044 / 0.424275 (-0.345231) | 0.005530 / 0.007607 (-0.002077) | 0.347082 / 0.226044 (0.121037) | 3.491984 / 2.268929 (1.223056) | 1.944317 / 55.444624 (-53.500307) | 1.645792 / 6.876477 (-5.230685) | 1.649506 / 2.142072 (-0.492567) | 0.800822 / 4.805227 (-4.004405) | 0.133936 / 6.500664 (-6.366729) | 0.041198 / 0.075469 (-0.034271) |\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) | 1.029764 / 1.841788 (-0.812024) | 11.928840 / 8.074308 (3.854532) | 10.021390 / 10.191392 (-0.170002) | 0.141608 / 0.680424 (-0.538816) | 0.014921 / 0.534201 (-0.519280) | 0.302050 / 0.579283 (-0.277233) | 0.124151 / 0.434364 (-0.310213) | 0.347143 / 0.540337 (-0.193195) | 0.467649 / 1.386936 (-0.919287) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4c87a6bf57b3aa094c28895c5b89b91b3509c58 \"CML watermark\")\n"
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Disable implicit token in CI.
This PR allows running CI tests locally without implicitly using the local user HF token. For example, run locally the tests in:
- #7124
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https://github.com/huggingface/datasets/pull/7125
| 7,125
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Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7125). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005741 / 0.011353 (-0.005612) | 0.004011 / 0.011008 (-0.006998) | 0.063962 / 0.038508 (0.025454) | 0.031512 / 0.023109 (0.008403) | 0.242249 / 0.275898 (-0.033649) | 0.269601 / 0.323480 (-0.053879) | 0.004502 / 0.007986 (-0.003483) | 0.002835 / 0.004328 (-0.001494) | 0.049878 / 0.004250 (0.045628) | 0.048012 / 0.037052 (0.010959) | 0.250454 / 0.258489 (-0.008035) | 0.283266 / 0.293841 (-0.010575) | 0.030752 / 0.128546 (-0.097794) | 0.012655 / 0.075646 (-0.062991) | 0.211043 / 0.419271 (-0.208229) | 0.037165 / 0.043533 (-0.006367) | 0.246815 / 0.255139 (-0.008324) | 0.264306 / 0.283200 (-0.018893) | 0.018343 / 0.141683 (-0.123340) | 1.140452 / 1.452155 (-0.311702) | 1.214849 / 1.492716 (-0.277867) |\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) | 0.098048 / 0.018006 (0.080042) | 0.292201 / 0.000490 (0.291712) | 0.000217 / 0.000200 (0.000017) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018732 / 0.037411 (-0.018679) | 0.062887 / 0.014526 (0.048361) | 0.074353 / 0.176557 (-0.102204) | 0.120794 / 0.737135 (-0.616341) | 0.077066 / 0.296338 (-0.219272) |\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) | 0.276335 / 0.215209 (0.061126) | 2.722905 / 2.077655 (0.645250) | 1.423080 / 1.504120 (-0.081040) | 1.305443 / 1.541195 (-0.235752) | 1.342142 / 1.468490 (-0.126348) | 0.741899 / 4.584777 (-3.842878) | 2.407567 / 3.745712 (-1.338145) | 3.070263 / 5.269862 (-2.199599) | 1.935732 / 4.565676 (-2.629944) | 0.081371 / 0.424275 (-0.342904) | 0.005207 / 0.007607 (-0.002401) | 0.328988 / 0.226044 (0.102943) | 3.240771 / 2.268929 (0.971842) | 1.801028 / 55.444624 (-53.643597) | 1.490593 / 6.876477 (-5.385884) | 1.521317 / 2.142072 (-0.620756) | 0.794051 / 4.805227 (-4.011176) | 0.136398 / 6.500664 (-6.364266) | 0.042902 / 0.075469 (-0.032567) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974186 / 1.841788 (-0.867602) | 12.280011 / 8.074308 (4.205703) | 9.453389 / 10.191392 (-0.738003) | 0.132627 / 0.680424 (-0.547797) | 0.014608 / 0.534201 (-0.519593) | 0.309298 / 0.579283 (-0.269985) | 0.275911 / 0.434364 (-0.158452) | 0.348261 / 0.540337 (-0.192077) | 0.439031 / 1.386936 (-0.947905) |\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) | 0.006248 / 0.011353 (-0.005105) | 0.004369 / 0.011008 (-0.006639) | 0.050588 / 0.038508 (0.012080) | 0.032880 / 0.023109 (0.009771) | 0.268979 / 0.275898 (-0.006919) | 0.294714 / 0.323480 (-0.028766) | 0.004518 / 0.007986 (-0.003467) | 0.002995 / 0.004328 (-0.001333) | 0.048776 / 0.004250 (0.044525) | 0.041696 / 0.037052 (0.004644) | 0.283413 / 0.258489 (0.024924) | 0.322137 / 0.293841 (0.028296) | 0.032809 / 0.128546 (-0.095737) | 0.012559 / 0.075646 (-0.063087) | 0.060456 / 0.419271 (-0.358815) | 0.034564 / 0.043533 (-0.008968) | 0.267263 / 0.255139 (0.012124) | 0.292633 / 0.283200 (0.009434) | 0.019011 / 0.141683 (-0.122672) | 1.199820 / 1.452155 (-0.252335) | 1.251829 / 1.492716 (-0.240887) |\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) | 0.097615 / 0.018006 (0.079609) | 0.313764 / 0.000490 (0.313274) | 0.000220 / 0.000200 (0.000020) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024365 / 0.037411 (-0.013046) | 0.089301 / 0.014526 (0.074775) | 0.092964 / 0.176557 (-0.083592) | 0.131724 / 0.737135 (-0.605412) | 0.094792 / 0.296338 (-0.201546) |\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) | 0.305119 / 0.215209 (0.089910) | 2.932192 / 2.077655 (0.854537) | 1.610573 / 1.504120 (0.106453) | 1.487502 / 1.541195 (-0.053693) | 1.533300 / 1.468490 (0.064810) | 0.717223 / 4.584777 (-3.867554) | 0.964402 / 3.745712 (-2.781310) | 3.111398 / 5.269862 (-2.158464) | 1.957942 / 4.565676 (-2.607734) | 0.079160 / 0.424275 (-0.345116) | 0.005639 / 0.007607 (-0.001968) | 0.358971 / 0.226044 (0.132927) | 3.564401 / 2.268929 (1.295472) | 2.043079 / 55.444624 (-53.401546) | 1.742681 / 6.876477 (-5.133795) | 1.784758 / 2.142072 (-0.357314) | 0.798508 / 4.805227 (-4.006719) | 0.133905 / 6.500664 (-6.366759) | 0.043008 / 0.075469 (-0.032461) |\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) | 1.031715 / 1.841788 (-0.810073) | 13.374312 / 8.074308 (5.300004) | 10.789098 / 10.191392 (0.597706) | 0.133663 / 0.680424 (-0.546761) | 0.016692 / 0.534201 (-0.517509) | 0.304716 / 0.579283 (-0.274567) | 0.129074 / 0.434364 (-0.305290) | 0.346440 / 0.540337 (-0.193897) | 0.464593 / 1.386936 (-0.922343) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#880a52cea337032d39e90e6f0dcc55198a75a285 \"CML watermark\")\n"
] | 2024-08-26T05:09:35Z
| 2024-08-26T05:33:15Z
| 2024-08-26T05:27:09Z
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Fix wrong SHA in CI tests of HubDatasetModuleFactoryWithParquetExport.
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https://github.com/huggingface/datasets/pull/7124
| 7,124
|
Test get_dataset_config_info with non-existing/gated/private dataset
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7124). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005339 / 0.011353 (-0.006014) | 0.003640 / 0.011008 (-0.007368) | 0.064012 / 0.038508 (0.025504) | 0.030424 / 0.023109 (0.007314) | 0.239966 / 0.275898 (-0.035932) | 0.264361 / 0.323480 (-0.059119) | 0.004247 / 0.007986 (-0.003739) | 0.002847 / 0.004328 (-0.001481) | 0.049640 / 0.004250 (0.045390) | 0.044903 / 0.037052 (0.007851) | 0.250174 / 0.258489 (-0.008315) | 0.281423 / 0.293841 (-0.012418) | 0.029419 / 0.128546 (-0.099127) | 0.012221 / 0.075646 (-0.063426) | 0.205907 / 0.419271 (-0.213365) | 0.036654 / 0.043533 (-0.006878) | 0.245805 / 0.255139 (-0.009334) | 0.265029 / 0.283200 (-0.018170) | 0.018081 / 0.141683 (-0.123602) | 1.113831 / 1.452155 (-0.338324) | 1.156443 / 1.492716 (-0.336274) |\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) | 0.134389 / 0.018006 (0.116383) | 0.300637 / 0.000490 (0.300147) | 0.000240 / 0.000200 (0.000040) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019111 / 0.037411 (-0.018300) | 0.062585 / 0.014526 (0.048059) | 0.075909 / 0.176557 (-0.100647) | 0.121382 / 0.737135 (-0.615753) | 0.074980 / 0.296338 (-0.221359) |\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) | 0.285062 / 0.215209 (0.069853) | 2.850130 / 2.077655 (0.772476) | 1.519877 / 1.504120 (0.015757) | 1.388711 / 1.541195 (-0.152484) | 1.397284 / 1.468490 (-0.071206) | 0.723100 / 4.584777 (-3.861677) | 2.393184 / 3.745712 (-1.352529) | 2.908418 / 5.269862 (-2.361443) | 1.871024 / 4.565676 (-2.694653) | 0.078230 / 0.424275 (-0.346045) | 0.005158 / 0.007607 (-0.002449) | 0.345622 / 0.226044 (0.119577) | 3.357611 / 2.268929 (1.088683) | 1.844492 / 55.444624 (-53.600132) | 1.584237 / 6.876477 (-5.292240) | 1.577158 / 2.142072 (-0.564915) | 0.789702 / 4.805227 (-4.015525) | 0.132045 / 6.500664 (-6.368619) | 0.042304 / 0.075469 (-0.033165) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977166 / 1.841788 (-0.864622) | 11.306118 / 8.074308 (3.231810) | 9.490778 / 10.191392 (-0.700614) | 0.143536 / 0.680424 (-0.536888) | 0.015304 / 0.534201 (-0.518897) | 0.313892 / 0.579283 (-0.265391) | 0.267009 / 0.434364 (-0.167355) | 0.345560 / 0.540337 (-0.194778) | 0.435649 / 1.386936 (-0.951287) |\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) | 0.005700 / 0.011353 (-0.005653) | 0.003490 / 0.011008 (-0.007519) | 0.049990 / 0.038508 (0.011482) | 0.032070 / 0.023109 (0.008961) | 0.272622 / 0.275898 (-0.003276) | 0.298265 / 0.323480 (-0.025215) | 0.004379 / 0.007986 (-0.003606) | 0.002786 / 0.004328 (-0.001543) | 0.048271 / 0.004250 (0.044020) | 0.040102 / 0.037052 (0.003050) | 0.286433 / 0.258489 (0.027944) | 0.319306 / 0.293841 (0.025465) | 0.032872 / 0.128546 (-0.095675) | 0.011870 / 0.075646 (-0.063776) | 0.059886 / 0.419271 (-0.359385) | 0.034281 / 0.043533 (-0.009252) | 0.275588 / 0.255139 (0.020450) | 0.292951 / 0.283200 (0.009751) | 0.018095 / 0.141683 (-0.123588) | 1.130870 / 1.452155 (-0.321285) | 1.190761 / 1.492716 (-0.301955) |\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) | 0.093346 / 0.018006 (0.075340) | 0.307506 / 0.000490 (0.307016) | 0.000214 / 0.000200 (0.000014) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022873 / 0.037411 (-0.014538) | 0.077070 / 0.014526 (0.062544) | 0.089152 / 0.176557 (-0.087404) | 0.130186 / 0.737135 (-0.606949) | 0.090244 / 0.296338 (-0.206095) |\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) | 0.297950 / 0.215209 (0.082740) | 2.942360 / 2.077655 (0.864705) | 1.614324 / 1.504120 (0.110204) | 1.495795 / 1.541195 (-0.045400) | 1.506155 / 1.468490 (0.037665) | 0.730307 / 4.584777 (-3.854470) | 0.966312 / 3.745712 (-2.779400) | 2.928955 / 5.269862 (-2.340906) | 1.940049 / 4.565676 (-2.625627) | 0.079589 / 0.424275 (-0.344686) | 0.006004 / 0.007607 (-0.001604) | 0.356630 / 0.226044 (0.130585) | 3.516652 / 2.268929 (1.247724) | 1.963196 / 55.444624 (-53.481429) | 1.674489 / 6.876477 (-5.201988) | 1.677558 / 2.142072 (-0.464514) | 0.806447 / 4.805227 (-3.998780) | 0.133819 / 6.500664 (-6.366845) | 0.040762 / 0.075469 (-0.034707) |\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) | 1.038495 / 1.841788 (-0.803293) | 11.829186 / 8.074308 (3.754878) | 10.214158 / 10.191392 (0.022766) | 0.140590 / 0.680424 (-0.539834) | 0.014729 / 0.534201 (-0.519472) | 0.300557 / 0.579283 (-0.278726) | 0.122772 / 0.434364 (-0.311592) | 0.344618 / 0.540337 (-0.195720) | 0.460064 / 1.386936 (-0.926872) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be5cff059a2a5b89d7a97bc04739c4919ab8089f \"CML watermark\")\n"
] | 2024-08-26T04:53:59Z
| 2024-08-26T06:15:33Z
| 2024-08-26T06:09:42Z
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Test get_dataset_config_info with non-existing/gated/private dataset.
Related to:
- #7109
See also:
- https://github.com/huggingface/dataset-viewer/pull/3037: https://github.com/huggingface/dataset-viewer/pull/3037/commits/bb1a7e00c53c242088597cab6572e4fd57797ecb
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https://github.com/huggingface/datasets/issues/7123
| 7,123
|
Make dataset viewer more flexible in displaying metadata alongside images
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[
"Note that you can already have one directory per subset just for the metadata, e.g.\r\n\r\n```\r\nconfigs:\r\n - config_name: subset0\r\n data_files:\r\n - subset0/metadata.csv\r\n - images/*.jpg\r\n - config_name: subset1\r\n data_files:\r\n - subset1/metadata.csv\r\n - images/*.jpg\r\n```\r\n\r\nEDIT: ah maybe it doesn't work because you'd have to provide relative paths from the metadata files to the images",
"Yes, that's part of the issue. Also, `metadata.csv` is a very ambiguous name and we generally try to avoid using the same name for different files within a dataset, as this can quickly lead to confusion.",
"I think supporting `**/*-metadata.csv` or `**/*_metadata.csv` makes sense to me. If it sounds good to you feel free to open a PR to update the patterns here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/d4422cc24a56dc7132ddc3fd6b285c5edbd60b8c/src/datasets/data_files.py#L104-L115"
] | 2024-08-23T22:56:01Z
| 2024-10-17T09:13:47Z
| null |
NONE
| null | null |
### Feature request
To display images with their associated metadata in the dataset viewer, a `metadata.csv` file is required. In the case of a dataset with multiple subsets, this would require the CSVs to be contained in the same folder as the images since they all need to be named `metadata.csv`. The request is that this be made more flexible for datasets with multiple subsets to avoid the need to put a `metadata.csv` into each image directory where they are not as easily accessed.
### Motivation
When creating datasets with multiple subsets I can't get the images to display alongside their associated metadata (it's usually one or the other that will show up). Since this requires a file specifically named `metadata.csv`, I then have to place that file within the image directory, which makes it much more difficult to access. Additionally, it still doesn't necessarily display the images alongside their metadata correctly (see, for instance, [this discussion](https://huggingface.co/datasets/imageomics/2018-NEON-beetles/discussions/8)).
It was suggested I bring this discussion to GitHub on another dataset struggling with a similar issue ([discussion](https://huggingface.co/datasets/imageomics/fish-vista/discussions/4)). In that case, it's a mix of data subsets, where some just reference the image URLs, while others actually have the images uploaded. The ones with images uploaded are not displaying images, but renaming that file to just `metadata.csv` would diminish the clarity of the construction of the dataset itself (and I'm not entirely convinced it would solve the issue).
### Your contribution
I can make a suggestion for one approach to address the issue:
For instance, even if it could just end in `_metadata.csv` or `-metadata.csv`, that would be very helpful to allow for more flexibility of dataset structure without impacting clarity. I would think that the functionality on the backend looking for `metadata.csv` could reasonably be adapted to look for such an ending on a filename (maybe also check that it has a `file_name` column?).
Presumably, requiring the `configs` in a setup like on [this dataset](https://huggingface.co/datasets/imageomics/rare-species/blob/main/README.md) could also help in figuring out how it should work?
```
configs:
- config_name: <image subset>
data_files:
- <image-metadata>.csv
- <path/to/images>/*.jpg
```
I'd also be happy to look at whatever solution is decided upon and contribute to the ideation.
Thanks for your time and consideration! The dataset viewer really is fabulous when it works :)
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| 7,122
|
[interleave_dataset] sample batches from a single source at a time
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[] | 2024-08-23T07:21:15Z
| 2024-08-23T07:21:15Z
| null |
NONE
| null | null |
### Feature request
interleave_dataset and [RandomlyCyclingMultiSourcesExamplesIterable](https://github.com/huggingface/datasets/blob/3813ce846e52824b38e53895810682f0a496a2e3/src/datasets/iterable_dataset.py#L816) enable us to sample data examples from different sources. But can we also sample batches in a similar manner (each batch only contains data from a single source)?
### Motivation
Some recent research [[1](https://blog.salesforceairesearch.com/sfr-embedded-mistral/), [2](https://arxiv.org/pdf/2310.07554)] shows that source homogenous batching can be helpful for contrastive learning. Can we add a function called `RandomlyCyclingMultiSourcesBatchesIterable` to support this functionality?
### Your contribution
I can contribute a PR. But I wonder what the best way is to test its correctness and robustness.
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| 7,121
|
Fix typed examples iterable state dict
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7121). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005273 / 0.011353 (-0.006079) | 0.003789 / 0.011008 (-0.007219) | 0.062811 / 0.038508 (0.024303) | 0.031055 / 0.023109 (0.007946) | 0.238663 / 0.275898 (-0.037235) | 0.269706 / 0.323480 (-0.053774) | 0.004105 / 0.007986 (-0.003881) | 0.002781 / 0.004328 (-0.001547) | 0.048800 / 0.004250 (0.044549) | 0.045759 / 0.037052 (0.008707) | 0.260467 / 0.258489 (0.001978) | 0.288800 / 0.293841 (-0.005041) | 0.029341 / 0.128546 (-0.099205) | 0.012413 / 0.075646 (-0.063233) | 0.203493 / 0.419271 (-0.215778) | 0.037270 / 0.043533 (-0.006263) | 0.246130 / 0.255139 (-0.009009) | 0.269046 / 0.283200 (-0.014154) | 0.017788 / 0.141683 (-0.123895) | 1.175537 / 1.452155 (-0.276617) | 1.197909 / 1.492716 (-0.294808) |\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) | 0.098258 / 0.018006 (0.080251) | 0.305283 / 0.000490 (0.304794) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019066 / 0.037411 (-0.018345) | 0.062723 / 0.014526 (0.048197) | 0.075827 / 0.176557 (-0.100730) | 0.121371 / 0.737135 (-0.615764) | 0.075167 / 0.296338 (-0.221171) |\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) | 0.296650 / 0.215209 (0.081441) | 2.910593 / 2.077655 (0.832939) | 1.510798 / 1.504120 (0.006678) | 1.375461 / 1.541195 (-0.165733) | 1.386423 / 1.468490 (-0.082067) | 0.743818 / 4.584777 (-3.840959) | 2.437848 / 3.745712 (-1.307864) | 2.943661 / 5.269862 (-2.326201) | 1.888977 / 4.565676 (-2.676699) | 0.080126 / 0.424275 (-0.344149) | 0.005168 / 0.007607 (-0.002439) | 0.348699 / 0.226044 (0.122654) | 3.477686 / 2.268929 (1.208758) | 1.901282 / 55.444624 (-53.543343) | 1.574847 / 6.876477 (-5.301629) | 1.594359 / 2.142072 (-0.547714) | 0.793415 / 4.805227 (-4.011812) | 0.133982 / 6.500664 (-6.366682) | 0.042435 / 0.075469 (-0.033034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963057 / 1.841788 (-0.878731) | 11.597217 / 8.074308 (3.522909) | 9.285172 / 10.191392 (-0.906220) | 0.130510 / 0.680424 (-0.549914) | 0.013964 / 0.534201 (-0.520237) | 0.299334 / 0.579283 (-0.279949) | 0.267775 / 0.434364 (-0.166589) | 0.336922 / 0.540337 (-0.203416) | 0.430493 / 1.386936 (-0.956443) |\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) | 0.005701 / 0.011353 (-0.005652) | 0.003941 / 0.011008 (-0.007067) | 0.050204 / 0.038508 (0.011696) | 0.032275 / 0.023109 (0.009166) | 0.271076 / 0.275898 (-0.004822) | 0.295565 / 0.323480 (-0.027914) | 0.004393 / 0.007986 (-0.003592) | 0.002881 / 0.004328 (-0.001447) | 0.048032 / 0.004250 (0.043782) | 0.040430 / 0.037052 (0.003378) | 0.281631 / 0.258489 (0.023142) | 0.317964 / 0.293841 (0.024124) | 0.032318 / 0.128546 (-0.096228) | 0.012348 / 0.075646 (-0.063298) | 0.060336 / 0.419271 (-0.358936) | 0.034148 / 0.043533 (-0.009385) | 0.273803 / 0.255139 (0.018664) | 0.292068 / 0.283200 (0.008868) | 0.018693 / 0.141683 (-0.122990) | 1.155704 / 1.452155 (-0.296451) | 1.192245 / 1.492716 (-0.300472) |\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) | 0.097588 / 0.018006 (0.079582) | 0.311760 / 0.000490 (0.311270) | 0.000232 / 0.000200 (0.000032) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022825 / 0.037411 (-0.014586) | 0.077698 / 0.014526 (0.063172) | 0.088567 / 0.176557 (-0.087989) | 0.129689 / 0.737135 (-0.607446) | 0.090626 / 0.296338 (-0.205712) |\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) | 0.299791 / 0.215209 (0.084582) | 2.978558 / 2.077655 (0.900903) | 1.594095 / 1.504120 (0.089975) | 1.468476 / 1.541195 (-0.072719) | 1.482880 / 1.468490 (0.014390) | 0.717553 / 4.584777 (-3.867224) | 0.977501 / 3.745712 (-2.768211) | 2.954289 / 5.269862 (-2.315572) | 1.895473 / 4.565676 (-2.670203) | 0.078452 / 0.424275 (-0.345824) | 0.005508 / 0.007607 (-0.002099) | 0.350882 / 0.226044 (0.124837) | 3.480878 / 2.268929 (1.211949) | 1.965240 / 55.444624 (-53.479385) | 1.672448 / 6.876477 (-5.204029) | 1.674319 / 2.142072 (-0.467753) | 0.789049 / 4.805227 (-4.016178) | 0.132715 / 6.500664 (-6.367949) | 0.041081 / 0.075469 (-0.034388) |\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) | 1.022953 / 1.841788 (-0.818834) | 12.123349 / 8.074308 (4.049041) | 10.336115 / 10.191392 (0.144723) | 0.142233 / 0.680424 (-0.538191) | 0.015416 / 0.534201 (-0.518785) | 0.303088 / 0.579283 (-0.276195) | 0.124942 / 0.434364 (-0.309422) | 0.338454 / 0.540337 (-0.201883) | 0.460039 / 1.386936 (-0.926897) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3813ce846e52824b38e53895810682f0a496a2e3 \"CML watermark\")\n"
] | 2024-08-22T14:45:03Z
| 2024-08-22T14:54:56Z
| 2024-08-22T14:49:06Z
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fix https://github.com/huggingface/datasets/issues/7085 as noted by @VeryLazyBoy and reported by @AjayP13
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https://github.com/huggingface/datasets/pull/7120
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don't mention the script if trust_remote_code=False
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7120). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Note that in this case, we could even expect this kind of message:\r\n\r\n```\r\nDataFilesNotFoundError: Unable to find 'hf://datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes@12b0313ba4c3189ee5a24cb76200959e9bf7492e/data.csv'\r\n```\r\n\r\nWe generally return `DataFilesNotFoundError` for this case (data files passed as an argument), not sure why it does not occur with this dataset.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005484 / 0.011353 (-0.005869) | 0.003932 / 0.011008 (-0.007077) | 0.063177 / 0.038508 (0.024669) | 0.031311 / 0.023109 (0.008202) | 0.254881 / 0.275898 (-0.021017) | 0.273818 / 0.323480 (-0.049662) | 0.003312 / 0.007986 (-0.004674) | 0.003251 / 0.004328 (-0.001078) | 0.049307 / 0.004250 (0.045057) | 0.046189 / 0.037052 (0.009137) | 0.268182 / 0.258489 (0.009693) | 0.303659 / 0.293841 (0.009818) | 0.029312 / 0.128546 (-0.099234) | 0.013649 / 0.075646 (-0.061997) | 0.204240 / 0.419271 (-0.215032) | 0.036607 / 0.043533 (-0.006926) | 0.252232 / 0.255139 (-0.002907) | 0.271960 / 0.283200 (-0.011239) | 0.018043 / 0.141683 (-0.123640) | 1.148601 / 1.452155 (-0.303553) | 1.212313 / 1.492716 (-0.280403) |\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) | 0.096354 / 0.018006 (0.078348) | 0.302575 / 0.000490 (0.302085) | 0.000246 / 0.000200 (0.000046) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019023 / 0.037411 (-0.018389) | 0.064821 / 0.014526 (0.050295) | 0.077046 / 0.176557 (-0.099510) | 0.122896 / 0.737135 (-0.614239) | 0.078300 / 0.296338 (-0.218038) |\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) | 0.283681 / 0.215209 (0.068472) | 2.801473 / 2.077655 (0.723818) | 1.505611 / 1.504120 (0.001491) | 1.385832 / 1.541195 (-0.155363) | 1.430284 / 1.468490 (-0.038206) | 0.752041 / 4.584777 (-3.832736) | 2.406138 / 3.745712 (-1.339574) | 2.941370 / 5.269862 (-2.328492) | 1.887681 / 4.565676 (-2.677996) | 0.078693 / 0.424275 (-0.345582) | 0.005266 / 0.007607 (-0.002341) | 0.336484 / 0.226044 (0.110440) | 3.372262 / 2.268929 (1.103334) | 1.861541 / 55.444624 (-53.583084) | 1.572782 / 6.876477 (-5.303694) | 1.592387 / 2.142072 (-0.549685) | 0.796557 / 4.805227 (-4.008670) | 0.134923 / 6.500664 (-6.365741) | 0.043007 / 0.075469 (-0.032462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982690 / 1.841788 (-0.859097) | 11.700213 / 8.074308 (3.625905) | 9.122642 / 10.191392 (-1.068750) | 0.141430 / 0.680424 (-0.538994) | 0.014971 / 0.534201 (-0.519230) | 0.300938 / 0.579283 (-0.278345) | 0.268315 / 0.434364 (-0.166049) | 0.339891 / 0.540337 (-0.200447) | 0.428302 / 1.386936 (-0.958634) |\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) | 0.005732 / 0.011353 (-0.005621) | 0.003905 / 0.011008 (-0.007103) | 0.049900 / 0.038508 (0.011392) | 0.032255 / 0.023109 (0.009145) | 0.267929 / 0.275898 (-0.007969) | 0.295595 / 0.323480 (-0.027885) | 0.004437 / 0.007986 (-0.003549) | 0.003008 / 0.004328 (-0.001321) | 0.048357 / 0.004250 (0.044107) | 0.040118 / 0.037052 (0.003066) | 0.282859 / 0.258489 (0.024370) | 0.319243 / 0.293841 (0.025402) | 0.032793 / 0.128546 (-0.095754) | 0.012091 / 0.075646 (-0.063555) | 0.060082 / 0.419271 (-0.359189) | 0.034426 / 0.043533 (-0.009107) | 0.273668 / 0.255139 (0.018529) | 0.292110 / 0.283200 (0.008910) | 0.019002 / 0.141683 (-0.122680) | 1.165850 / 1.452155 (-0.286304) | 1.209195 / 1.492716 (-0.283521) |\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) | 0.099267 / 0.018006 (0.081261) | 0.316746 / 0.000490 (0.316256) | 0.000267 / 0.000200 (0.000067) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023117 / 0.037411 (-0.014294) | 0.076691 / 0.014526 (0.062165) | 0.092190 / 0.176557 (-0.084367) | 0.130620 / 0.737135 (-0.606515) | 0.091068 / 0.296338 (-0.205271) |\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) | 0.296419 / 0.215209 (0.081210) | 2.933964 / 2.077655 (0.856309) | 1.595015 / 1.504120 (0.090895) | 1.467610 / 1.541195 (-0.073585) | 1.487386 / 1.468490 (0.018896) | 0.730927 / 4.584777 (-3.853850) | 0.971276 / 3.745712 (-2.774436) | 2.969735 / 5.269862 (-2.300127) | 1.916126 / 4.565676 (-2.649550) | 0.078863 / 0.424275 (-0.345412) | 0.005506 / 0.007607 (-0.002101) | 0.345191 / 0.226044 (0.119147) | 3.407481 / 2.268929 (1.138553) | 1.955966 / 55.444624 (-53.488659) | 1.677365 / 6.876477 (-5.199112) | 1.716052 / 2.142072 (-0.426020) | 0.797208 / 4.805227 (-4.008020) | 0.132853 / 6.500664 (-6.367811) | 0.041691 / 0.075469 (-0.033778) |\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) | 1.042331 / 1.841788 (-0.799456) | 12.186080 / 8.074308 (4.111772) | 10.288961 / 10.191392 (0.097569) | 0.141897 / 0.680424 (-0.538526) | 0.015321 / 0.534201 (-0.518880) | 0.308302 / 0.579283 (-0.270981) | 0.123292 / 0.434364 (-0.311072) | 0.348515 / 0.540337 (-0.191823) | 0.473045 / 1.386936 (-0.913891) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cedffa52879ebc5e4df43f0bcf8660ee7229f0dc \"CML watermark\")\n"
] | 2024-08-22T12:32:32Z
| 2024-08-22T14:39:52Z
| 2024-08-22T14:33:52Z
|
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See https://huggingface.co/datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes for example. The error is:
```
FileNotFoundError: Couldn't find a dataset script at /src/services/worker/Omega02gdfdd/bioclip-demo-zero-shot-mistakes/bioclip-demo-zero-shot-mistakes.py or any data file in the same directory. Couldn't find 'Omega02gdfdd/bioclip-demo-zero-shot-mistakes' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/Omega02gdfdd/bioclip-demo-zero-shot-mistakes@12b0313ba4c3189ee5a24cb76200959e9bf7492e/data.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
```
The issue there is that a `configs` parameter is set in the README, while the mentioned data file (`data.csv`) does not exist.
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