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https://api.github.com/repos/huggingface/datasets/issues/6029

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https://github.com/huggingface/datasets/pull/6029

PR_kwDODunzps5VcbPW

[docs] Fix link

closed

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Fixes link to the builder classes :)

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https://github.com/huggingface/datasets/pull/6028

PR_kwDODunzps5Vb3LJ

Use new hffs

closed

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Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem. Switching to `HfFileSystem` will help implementing optimization in data files resolution ## Implementation details I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution. I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used. I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface. ## New features hf:// paths are now supported in data_files ## Breaking changes DataFilesList and DataFilesDict: - use `str` paths instead of `Union[Path, Url]` - require posix paths for windows paths close https://github.com/huggingface/datasets/issues/6017

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https://github.com/huggingface/datasets/pull/6027

PR_kwDODunzps5Va4g3

Delete `task_templates` in `IterableDataset` when they are no longer valid

closed

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Fix #6025

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PR_kwDODunzps5VanI8

Fix style with ruff 0.0.278

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I_kwDODunzps5rZha5

Using a dataset for a use other than it was intended for.

closed

NONE

### Describe the bug Hi, I want to use the rotten tomatoes dataset but for a task other than classification, but when I interleave the dataset, it throws ```'ValueError: Column label is not present in features.'```. It seems that the label_col must be there in the dataset for some reason? Here is the full stacktrace ``` File "/home/suryahari/Vornoi/tryage-handoff-other-datasets.py", line 276, in create_dataloaders dataset = interleave_datasets(dsfold, stopping_strategy="all_exhausted") File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py", line 134, in interleave_datasets return _interleave_iterable_datasets( File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1833, in _interleave_iterable_datasets info = DatasetInfo.from_merge([d.info for d in datasets]) File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in from_merge dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None] File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in <listcomp> dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None] File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 378, in copy return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File "<string>", line 20, in __init__ File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 208, in __post_init__ self.task_templates = [ File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 209, in <listcomp> template.align_with_features(self.features) for template in (self.task_templates) File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/tasks/text_classification.py", line 20, in align_with_features raise ValueError(f"Column {self.label_column} is not present in features.") ValueError: Column label is not present in features. ``` ### Steps to reproduce the bug Delete the column `labels` from the `rotten_tomatoes` dataset. Try to interleave it with other datasets. ### Expected behavior Should let me use the dataset with just the `text` field ### Environment info latest datasets library? I don't think this was an issue in earlier versions.

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https://github.com/huggingface/datasets/pull/6024

PR_kwDODunzps5VWbGe

Don't reference self in Spark._validate_cache_dir

closed

CONTRIBUTOR

Fix for https://github.com/huggingface/datasets/issues/5963

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https://github.com/huggingface/datasets/pull/6023

PR_kwDODunzps5VU7EG

Fix `ClassLabel` min max check for `None` values

closed

CONTRIBUTOR

Fix #6022

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I_kwDODunzps5rSzut

Batch map raises TypeError: '>=' not supported between instances of 'NoneType' and 'int'

closed

NONE

### Describe the bug When mapping some datasets with `batched=True`, datasets may raise an exeception: ```python Traceback (most recent call last): File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(*args, **kwds)) ^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3483, in _map_single writer.write_batch(batch) File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_writer.py", line 549, in write_batch array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 2063, in cast_array_to_feature return feature.cast_storage(array) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/features/features.py", line 1098, in cast_storage if min_max["max"] >= self.num_classes: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: '>=' not supported between instances of 'NoneType' and 'int' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/codingl2k1/Work/datasets/t1.py", line 33, in <module> ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 850, in map { File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 851, in <dictcomp> k: dataset.map( ^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 577, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 542, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3179, in map for rank, done, content in iflatmap_unordered( File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get raise self._value TypeError: '>=' not supported between instances of 'NoneType' and 'int' ``` ### Steps to reproduce the bug 1. Checkout the latest main of datasets. 2. Run the code: ```python from datasets import load_dataset def transforms(examples): # examples["pixel_values"] = [image.convert("RGB").resize((100, 100)) for image in examples["image"]] return examples ds = load_dataset("scene_parse_150") ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5) print(ds) ``` ### Expected behavior map without exception. ### Environment info Datasets: https://github.com/huggingface/datasets/commit/b8067c0262073891180869f700ebef5ac3dc5cce Python: 3.11.4 System: Macos

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https://github.com/huggingface/datasets/pull/6021

PR_kwDODunzps5VP11Q

[docs] Update return statement of index search

closed

MEMBER

Clarifies in the return statement of the docstring that the retrieval score is `IndexFlatL2` by default (see [PR](https://github.com/huggingface/transformers/issues/24739) and internal Slack [convo](https://huggingface.slack.com/archives/C01229B19EX/p1689105179711689)), and fixes the formatting because multiple return values are not supported.

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I_kwDODunzps5rRY5Y

Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs

open

NONE

### Describe the bug I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used. I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)]) def test_func(row, idx): if idx==58: return {'output': []} else: return {'output' : [{'test':1}, {'test':2}]} # this works fine test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4) # this fails test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32) >ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null"). ``` The error occurs during the check ```python _check_if_features_can_be_aligned([dset.features for dset in dsets]) ``` When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error. ### Expected behavior Expected behavior is the result would be the same regardless of the `num_proc` value used. ### Environment info Datasets version 2.11.0 Python 3.9.16

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PR_kwDODunzps5VPAlD

Improve logging

closed

CONTRIBUTOR

Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO (Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them) Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444

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PR_kwDODunzps5VOmKY

test1

closed

NONE

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I_kwDODunzps5rP0dM

Switch to huggingface_hub's HfFileSystem

closed

MEMBER

instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919

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PR_kwDODunzps5VNEvn

Dataset string representation enhancement

open

NONE

my attempt at #6010 not sure if this is the right way to go about it, I will wait for your feedback

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PR_kwDODunzps5VMhgB

Add metadata ui screenshot in docs

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I_kwDODunzps5rLpC4

Request to Share/Update Dataset Viewer Code

closed

NONE

Overview: The repository (huggingface/datasets-viewer) was recently archived and when I tried to run the code, there was the error message "AttributeError: module 'datasets.load' has no attribute 'prepare_module'". I could not resolve the issue myself due to lack of documentation of that attribute. Request: I kindly request the sharing of the code responsible for the dataset preview functionality or help with resolving the error. The dataset viewer on the Hugging Face website is incredibly useful since it is compatible with different types of inputs. It allows users to find datasets that meet their needs more efficiently. If needed, I am willing to contribute to the project by testing, documenting, and providing feedback on the dataset viewer code. Thank you for considering this request, and I look forward to your response.

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I_kwDODunzps5rDg7t

[FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage

open

CONTRIBUTOR

### Feature request Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns. ### Motivation This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented. ### Your contribution _

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I_kwDODunzps5rBk6I

[FR] Transform Chaining, Lazy Mapping

open

CONTRIBUTOR

### Feature request Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space. The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested. The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset. ### Motivation Lazy processing allows lower disk usage and faster experimentation. ### Your contribution _

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I_kwDODunzps5rAg04

Documentation: wiki_dpr Dataset has no metric_type for Faiss Index

closed

NONE

### Describe the bug After loading `wiki_dpr` using: ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None ``` the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`. ### Steps to reproduce the bug System: Python 3.9.16, Transformers 4.30.2, WSL After loading `wiki_dpr` using: ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None ``` the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`. ```py from transformers import DPRQuestionEncoder, DPRContextEncoder, DPRQuestionEncoderTokenizer, DPRContextEncoderTokenizer tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") def encode_question(query, tokenizer=tokenizer, encoder=encoder): inputs = tokenizer(query, return_tensors='pt') question_embedding = encoder(**inputs)[0].detach().numpy() return question_embedding def get_knn(query, k=5, tokenizer=tokenizer, encoder=encoder, verbose=False): enc_question = encode_question(query, tokenizer, encoder) topk_results = ds.get_nearest_examples(index_name='embeddings', query=enc_question, k=k) a = torch.tensor(enc_question[0]).reshape(768) b = torch.tensor(topk_results.examples['embeddings'][0]) print(a.shape, b.shape) print(torch.dot(a, b)) print((a-b).pow(2).sum()) return topk_results ``` The [FAISS documentation](https://github.com/facebookresearch/faiss/wiki/MetricType-and-distances) suggests the metric is usually L2 distance (without the square root) or the inner product. I compute both for the sample query: ```py query = """ it catapulted into popular culture along with a line of action figures and other toys by Bandai.[2] By 2001, the media franchise had generated over $6 billion in toy sales. Despite initial criticism that its action violence targeted child audiences, the franchise has been commercially successful.""" get_knn(query,k=5) ``` Here, I get dot product of 80.6020 and L2 distance of 77.6616 and ```py NearestExamplesResults(scores=array([76.20431 , 75.312416, 74.945404, 74.866394, 74.68506 ], dtype=float32), examples={'id': ['3081096', '2004811', '8908258', '9594124', '286575'], 'text': ['actors, resulting in the "Power Rangers" franchise which has continued since then into sequel TV series (with "Power Rangers Beast Morphers" set to premiere in 2019), comic books, video games, and three feature films, with a further cinematic universe planned. Following from the success of "Power Rangers", Saban acquired the rights to more of Toei\'s library, creating "VR Troopers" and "Big Bad Beetleborgs" from several Metal Hero Series shows and "Masked Rider" from Kamen Rider Series footage. DIC Entertainment joined this boom by acquiring the rights to "Gridman the Hyper Agent" and turning it into "Superhuman Samurai Syber-Squad". In 2002,', ``` Doing `k=1` indicates the higher the outputted number, the better the match, so the metric should not be L2 distance. However, my manually computed inner product (80.6) has a discrepancy with the reported (76.2). Perhaps, this has to do with me using the `compressed` embeddings? ### Expected behavior ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # METRIC_INNER_PRODUCT ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-4.18.0-477.13.1.el8_8.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1

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I_kwDODunzps5q68xI

Improve `Dataset`'s string representation

open

CONTRIBUTOR

Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows. We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.

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PR_kwDODunzps5U1mus

Fix cast for dictionaries with no keys

closed

CONTRIBUTOR

Fix #5677

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I_kwDODunzps5qrz0g

Dataset.from_generator consistently freezes at ~1000 rows

closed

NONE

### Describe the bug Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset. I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset. Let me know if you have ideas how to resolve it! ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np def gen(): for row in range(10000): yield {"i": np.random.rand(512, 512, 3)} Dataset.from_generator(gen) # -> 90% of the time gets stuck around 1000 rows ``` ### Expected behavior Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 12.0.1 - Pandas version: 1.5.1

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I_kwDODunzps5qreql

Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset

open

CONTRIBUTOR

### Describe the bug When load a large dataset with the following code ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ``` We encountered the error: "OverflowError: Python int too large to convert to C long" The error look something like: ``` OverflowError: Python int too large to convert to C long During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) <ipython-input-7-0ed8700e662d> in <module> ----> 1 dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', cache_dir='/sfs/MNBVC/.cache/') /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, -> 1751 use_auth_token=use_auth_token, 1752 ) 1753 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 703 if not downloaded_from_gcs: 704 self._download_and_prepare( --> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 791 try: 792 # Prepare split will record examples associated to the split --> 793 self._prepare_split(split_generator, **prepare_split_kwargs) 794 except OSError as e: 795 raise OSError( /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) 1219 writer.write(example, key) 1220 finally: -> 1221 num_examples, num_bytes = writer.finalize() 1222 1223 split_generator.split_info.num_examples = num_examples /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 536 # Re-intializing to empty list for next batch 537 self.hkey_record = [] --> 538 self.write_examples_on_file() 539 if self.pa_writer is None: 540 if self.schema: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 407 # Since current_examples contains (example, key) tuples 408 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 409 self.write_batch(batch_examples=batch_examples) 410 self.current_examples = [] 411 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 506 col_try_type = try_features[col] if try_features is not None and col in try_features else None 507 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 508 arrays.append(pa.array(typed_sequence)) 509 inferred_features[col] = typed_sequence.get_inferred_type() 510 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 180 else: 181 trying_cast_to_python_objects = True --> 182 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) 183 # use smaller integer precisions if possible 184 if self.trying_int_optimization: /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() OverflowError: Python int too large to convert to C long ``` However, that dataset can be loaded in a streaming manner: ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', streaming=True) for i in dataset: pass # it work well ``` Another issue is reported in our dataset hub: https://huggingface.co/datasets/liwu/MNBVC/discussions/2 ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ### Expected behavior the dataset can be safely loaded ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-3.10.0-1160.an7.x86_64-x86_64-with-centos-7.9 - Python version: 3.6.8 - PyArrow version: 6.0.1 - Pandas version: 1.1.5

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I_kwDODunzps5qn8Ue

NotADirectoryError when loading gigawords

closed

NONE

### Describe the bug got `NotADirectoryError` whtn loading gigawords dataset ### Steps to reproduce the bug When running ``` import datasets datasets.load_dataset('gigaword') ``` Got the following exception: ```bash Traceback (most recent call last): [0/1862] File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1629, in _prepare_split_single for key, record in generator: File "/home/x/.cache/huggingface/modules/datasets_modules/datasets/gigaword/ea83a8b819190acac5f2dae011fad51dccf269a0604ec5dd24795b 64efb424b6/gigaword.py", line 115, in _generate_examples with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s: File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/streaming.py", line 71, in wrapper return function(*args, use_auth_token=use_auth_token, **kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/download/streaming_download_manager.py", line 493, in xope n return open(main_hop, mode, *args, **kwargs) NotADirectoryError: [Errno 20] Not a directory: '/home/x/.cache/huggingface/datasets/downloads/6da52431bb5124d90cf51a0187d2dbee9046e 89780c4be7599794a4f559048ec/org_data/train.src.txt' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "gigaword.py", line 38, in <module> main() File "gigaword.py", line 35, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/home/x/MICL/preprocess/fewshot_gym_dataset.py", line 199, in generate_k_shot_data dataset = self.load_dataset() File "gigaword.py", line 29, in load_dataset return datasets.load_dataset('gigaword') File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/load.py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1670, in _download_and_prepare super()._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1508, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1665, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Download and process the dataset successfully ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.0.0-1032-azure-x86_64-with-glibc2.10 - Python version: 3.8.0 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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PR_kwDODunzps5UoJ91

Drop Python 3.7 support

closed

CONTRIBUTOR

`hfh` and `transformers` have dropped Python 3.7 support, so we should do the same :). (Based on the stats, it seems less than 10% of the users use `datasets` with Python 3.7)

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PR_kwDODunzps5UjN2h

Misc improvements

closed

CONTRIBUTOR

Contains the following improvements: * fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section * updates `Makefile` to also run the style checks on `utils` and `setup.py` * deletes a test for GH-hosted datasets (no longer supported) * deletes `convert_dataset.sh` (outdated) * aligns `utils/release.py` with `transformers` (the current version is outdated)

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I_kwDODunzps5qfKb-

interleave_datasets & DataCollatorForLanguageModeling having a conflict ?

open

NONE

### Describe the bug Hi everyone :) I have two local & custom datasets (1 "sentence" per line) which I split along the 95/5 lines for pre-training a Bert model. I use a modified version of `run_mlm.py` in order to be able to make use of `interleave_dataset`: - `tokenize()` runs fine - `group_text()` runs fine Everytime, on step 19, I get ```pytb File "env/lib/python3.9/site-packages/transformers/data/data_collator.py", line 779, in torch_mask_tokens inputs[indices_random] = random_words[indices_random] RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source. ``` I tried: - training without interleave on dataset 1, it runs - training without interleave on dataset 2, it runs - training without `.to_iterable_dataset()`, it hangs then crash - training without group_text() and padding to max_length seemed to fix the issue, but who knows if this was just because it was an issue that would come much later in terms of steps. I might have coded something wrong, but I don't get what ### Steps to reproduce the bug I have this function: ```py def build_dataset(path: str, percent: str): dataset = load_dataset( "text", data_files={"train": [path]}, split=f"train[{percent}]" ) dataset = dataset.map( lambda examples: tokenize(examples["text"]), batched=True, num_proc=num_proc, ) dataset = dataset.map( group_texts, batched=True, num_proc=num_proc, desc=f"Grouping texts in chunks of {tokenizer.max_seq_length}", remove_columns=["text"] ) print(len(dataset)) return dataset.to_iterable_dataset() ``` I hardcoded group_text: ```py def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict. # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. total_length = (total_length // 512) * 512 # Split by chunks of max_len. result = { k: [t[i: i + 512] for i in range(0, total_length, 512)] for k, t in concatenated_examples.items() } # result = {k: [el for el in elements if el] for k, elements in result.items()} return result ``` And then I build datasets using the following code: ```py train1 = build_dataset("d1.txt", ":95%") train2 = build_dataset("d2.txt", ":95%") dev1 = build_dataset("d1.txt", "95%:") dev2 = build_dataset("d2.txt", "95%:") ``` and finally I run ```py train_dataset = interleave_datasets( [train1, train2], probabilities=[0.8, 0.2], seed=42 ) eval_dataset = interleave_datasets( [dev1, dev2], probabilities=[0.8, 0.2], seed=42 ) ``` Then I run the training part which remains mostly untouched: > CUDA_VISIBLE_DEVICES=1 python custom_dataset.py --model_type bert --per_device_train_batch_size 32 --do_train --output_dir /var/mlm/training-bert/model --max_seq_length 512 --save_steps 10000 --save_total_limit 3 --auto_find_batch_size --logging_dir ./logs-bert --learning_rate 0.0001 --do_train --num_train_epochs 25 --warmup_steps 10000 --max_step 45000 --fp16 ### Expected behavior The model should then train normally, but fails every time at the same step (19). printing the variables at `inputs[indices_random] = random_words[indices_random]` shows a magnificient empty tensor (, 32) [if I remember well] ### Environment info transformers[torch] 4.30.2 Ubuntu A100 0 CUDA 12 Driver Version: 525.116.04

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PR_kwDODunzps5UhP-Z

Add KLUE-MRC metrics

closed

NONE

## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension) Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue). KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC. Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC. - [x] All tests passed - [x] Added a metric card (referred the metric card of SQuAD 2.0) - [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed ### References - [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf) - [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue) - #2416

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PR_kwDODunzps5UVMMh

Align `column_names` type check with type hint in `sort`

closed

CONTRIBUTOR

Fix #5998

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PR_kwDODunzps5UU_FB

Pin `joblib` to avoid `joblibspark` test failures

closed

CONTRIBUTOR

`joblibspark` doesn't support the latest `joblib` release. See https://github.com/huggingface/datasets/actions/runs/5401870932/jobs/9812337078 for the errors

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I_kwDODunzps5qNOV5

Getting a 409 error while loading xglue dataset

closed

NONE

### Describe the bug Unable to load xglue dataset ### Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("xglue", "ntg") ``` > ConnectionError: Couldn't reach https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz (error 409) ### Expected behavior Expected the dataset to load ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.107+-x86_64-with-glibc2.31 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5qNC_a

The current implementation has a potential bug in the sort method

closed

NONE

### Describe the bug In the sort method，here's a piece of code ```python # column_names: Union[str, Sequence_[str]] # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): column_names = [column_names] ``` I get an error when I pass in a tuple based on the column_names type annotation, it will raise an errror.As in the example below, while the type annotation implies that a tuple can be passed. ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` Of course, after I modified the tuple into a list, everything worked fine Change the code to the following so there will be no problem ```python # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): if isinstance(column_names, str): column_names = [column_names] else: column_names = list(column_names) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` ### Expected behavior Passing tuple into column_names should be equivalent to passing list ### Environment info - `datasets` version: 2.13.0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2

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I_kwDODunzps5qMMvi

extend the map function so it can wrap around long text that does not fit in the context window

open

NONE

### Feature request I understand `dataset` provides a [`map`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2849) function. This function in turn takes in a callable that is used to tokenize the text on which a model is trained. Frequently this text will not fit within a models's context window. In this case it would be useful to wrap around the text into multiple rows with each row fitting the model's context window. I tried to do it using this code as example which in turn I have borrowed from [here](https://stackoverflow.com/a/76343993/147530): ``` data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) ``` but running the code gives me this error: ``` File "/llm/fine-tune.py", line 117, in <module> data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3480, in _map_single writer.write_batch(batch) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_writer.py", line 556, in write_batch pa_table = pa.Table.from_arrays(arrays, schema=schema) File "pyarrow/table.pxi", line 3798, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 2962, in pyarrow.lib.Table.validate File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 394 but got length 447 ``` The lambda function I have provided is correctly chopping up long text so it wraps around (and because of this 394 samples become 447 after wrap around) but the dataset `map` function does not like it. ### Motivation please see above ### Your contribution I'm afraid I don't have much knowledge to help

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PR_kwDODunzps5UKP0i

Deprecate `use_auth_token` in favor of `token`

closed

CONTRIBUTOR

... to be consistent with `transformers` and `huggingface_hub`.

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PR_kwDODunzps5UCvYJ

Support returning dataframe in map transform

closed

CONTRIBUTOR

Allow returning Pandas DataFrames in `map` transforms. (Plus, raise an error in the non-batched mode if a returned PyArrow table/Pandas DataFrame has more than one row)

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PR_kwDODunzps5UB1cA

Fix select_columns columns order

closed

MEMBER

Fix the order of the columns in dataset.features when the order changes with `dataset.select_columns()`. I also fixed the same issue for `dataset.flatten()` Close https://github.com/huggingface/datasets/issues/5993

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I_kwDODunzps5p5W3j

ValueError: Table schema does not match schema used to create file

closed

NONE

### Describe the bug Saving a dataset as parquet fails with a `ValueError: Table schema does not match schema used to create file` if the dataset was obtained out of a `.select_columns()` call with columns selected out of order. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_parquet("demo.parquet") ``` ```shell >>> ValueError: Table schema does not match schema used to create file: table: x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 vs. file: x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ``` --- I think this is because after the `.select_columns()` call with out of order columns, the output dataset features' schema ends up being out of sync with the schema of the arrow table backing it. ```python ds.features.arrow_schema >>> x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ds.data.schema >>> x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 ``` So when we call `.to_parquet()`, the call behind the scenes to `datasets.io.parquet.ParquetDatasetWriter(...).write()` which initialises the backend `pyarrow.parquet.ParquetWriter` with `schema = self.dataset.features.arrow_schema` triggers `pyarrow` on write when [it checks](https://github.com/apache/arrow/blob/11b140a734a516e436adaddaeb35d23f30dcce44/python/pyarrow/parquet/core.py#L1086-L1090) that the `ParquetWriter` schema matches the schema of the table being written 🙌 https://github.com/huggingface/datasets/blob/6ed837325cb539a5deb99129e5ad181d0269e050/src/datasets/io/parquet.py#L139-L141 ### Expected behavior The dataset gets successfully saved as parquet. *In the same way as it does if saving it as csv: ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_csv("demo.csv") ``` ### Environment info `python==3.11` `datasets==2.13.1`

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PR_kwDODunzps5UAk3C

speedup

closed

CONTRIBUTOR

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I_kwDODunzps5pxA7G

`map` with any joblib backend

open

MEMBER

We recently enabled the (experimental) parallel backend switch for data download and extraction but not for `map` yet. Right now we're using our `iflatmap_unordered` implementation for multiprocessing that uses a shared Queue to gather progress updates from the subprocesses and show a progress bar in the main process. If a Queue implementation that would work on any joblib backend by leveraging the filesystem that is shared among workers, we can have `iflatmap_unordered` for joblib and therefore a `map` with any joblib backend with a progress bar ! Note that the Queue doesn't need to be that optimized though since we can choose a small frequency for progress updates (like 1 update per second).

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I_kwDODunzps5pvyNL

Set a rule on the config and split names

open

CONTRIBUTOR

> should we actually allow characters like spaces? maybe it's better to add validation for whitespace symbols and directly in datasets and raise https://github.com/huggingface/datasets-server/issues/853

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I_kwDODunzps5pscRk

ConnectionError: Couldn't reach dataset_infos.json

closed

NONE

### Describe the bug I'm trying to load codeparrot/codeparrot-clean-train, but get the following error: ConnectionError: Couldn't reach https://huggingface.co/datasets/codeparrot/codeparrot-clean-train/resolve/main/dataset_infos.json (ConnectionError(ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer')))) ### Steps to reproduce the bug train_data = load_dataset('codeparrot/codeparrot-clean-train', split='train') ### Expected behavior download the dataset ### Environment info centos7

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I_kwDODunzps5prpBl

Why max_shard_size is not supported in load_dataset and passed to download_and_prepare

closed

CONTRIBUTOR

### Describe the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 What I can to is break the `load_dataset` and use `load_datset_builder` + `download_and_prepare` instead. ### Steps to reproduce the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 ### Expected behavior Users can define the max shard size. ### Environment info datasets==2.13.1

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PR_kwDODunzps5TygOZ

Make IterableDataset.from_spark more efficient

closed

CONTRIBUTOR

Moved the code from using collect() to using toLocalIterator, which allows for prefetching partitions that will be selected next, thus allowing for better performance when iterating.

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I_kwDODunzps5pmEo-

Cannot reuse tokenizer object for dataset map

closed

NONE

### Describe the bug Related to https://github.com/huggingface/transformers/issues/24441. Not sure if this is a tokenizer issue or caching issue, so filing in both. Passing the tokenizer to the dataset map function causes the tokenizer to be fingerprinted weirdly. After calling the tokenizer with arguments like padding and truncation the tokenizer object changes interanally, even though the hash remains the same. But dumps is able to detect that internal change which causes the tokenizer object's fingerprint to change. ### Steps to reproduce the bug ```python from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets t = AutoTokenizer.from_pretrained('bert-base-uncased') t.save_pretrained("tok1") th1 = hash(dumps(t)) text = "This is an example text" ttext = t(text, max_length=512, padding="max_length", truncation=True) t.save_pretrained("tok2") th2 = hash(dumps(t)) assert th1 == th2 # Assertion Error ``` But if you use just the hash of the object without dumps, the hashes don't change ```python from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets t = AutoTokenizer.from_pretrained('bert-base-uncased') th1 = hash(t) # Just hash no dumps text = "This is an example text" ttext = t(text, max_length=512, padding="max_length", truncation=True) th2 = hash(t) # Just hash no dumps assert th1 == th2 # This is OK ``` This causes situations such as the following 1. Create a text file like this `yes "This is an example text" | head -n 10000 > lines.txt` ```python from transformers import AutoTokenizer import datasets class TokenizeMapper(object): """Mapper for tokenizer. This is needed because the caching mechanism of HuggingFace does not work on lambdas. Each time a new lambda will be created by a new process which will lead to a different hash. This way we can have a universal mapper object in init and reuse it with the same hash for each process. """ def __init__(self, tokenizer): """Initialize the tokenizer.""" self.tokenizer = tokenizer def __call__(self, examples, **kwargs): """Run the mapper.""" texts = examples["text"] tt = self.tokenizer(texts, max_length=256, padding="max_length", truncation=True) batch_outputs = { "input_ids": tt.input_ids, "attention_mask": tt.attention_mask, } return batch_outputs t = AutoTokenizer.from_pretrained('bert-base-uncased') mapper = TokenizeMapper(t) ds = datasets.load_dataset("text", data_files="lines.txt") mds1 = ds.map( mapper, batched=False, remove_columns=["text"], ).with_format("torch") mds2 = ds.map( mapper, batched=False, remove_columns=["text"], ).with_format("torch") ``` The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps. ### Expected behavior We should be able to initialize a tokenizer. And reusing it should let us reuse the same map computation for the same dataset. The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-6.1.31_1-x86_64-with-glibc2.36 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2

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I_kwDODunzps5pmAkC

AutoSharding IterableDataset's when num_workers > 1

open

NONE

### Feature request Minimal Example ``` import torch from datasets import IterableDataset d = IterableDataset.from_file(<file_name>) dl = torch.utils.data.dataloader.DataLoader(d,num_workers=3) for sample in dl: print(sample) ``` Warning: Too many dataloader workers: 2 (max is dataset.n_shards=1). Stopping 1 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=1. To enable more parallelism, please split the dataset in more files than 1. Expected Behavior: Dataset is sharded each cpu uses subset (contiguously - so you can do checkpoint loading/saving) ### Motivation I have a lot of unused cpu's and would like to be able to shard iterable datasets with pytorch's dataloader when num_workers > 1. This is for a very large single file. I am aware that we can use the `split_dataset_by_node` to ensure that each node (for distributed) gets different shards, but we should extend it so that this also continues for multiple workers. ### Your contribution If someone points me to what needs to change, I can create a PR.

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PR_kwDODunzps5TtDdy

replaced PathLike as a variable for save_to_disk for dataset_path wit…

closed

NONE

…h str like that of load_from_disk

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I_kwDODunzps5phSRw

404 on Datasets Documentation Page

closed

NONE

### Describe the bug Getting a 404 from the Hugging Face Datasets docs page: https://huggingface.co/docs/datasets/index ### Steps to reproduce the bug 1. Go to URL https://huggingface.co/docs/datasets/index 2. Notice 404 not found ### Expected behavior URL should either show docs or redirect to new location ### Environment info hugginface.co

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I_kwDODunzps5phMnH

Only two cores are getting used in sagemaker with pytorch 3.10 kernel

closed

NONE

### Describe the bug When using the newer pytorch 3.10 kernel, only 2 cores are being used by huggingface filter and map functions. The Pytorch 3.9 kernel would use as many cores as specified in the num_proc field. We have solved this in our own code by placing the following snippet in the code that is called inside subprocesses: ```os.sched_setaffinity(0, {i for i in range(1000)})``` The problem, as near as we can tell, us that once upon a time, cpu affinity was set using a bitmask ("0xfffff" and the like), and affinity recently changed to a list of processors rather than to using the mask. As such, only processors 1 and 17 are shown to be working in htop.  When running functions via `map`, the above resetting of affinity works to spread across the cores. When using `filter`, however, only two cores are active. ### Steps to reproduce the bug Repro steps: 1. Create an aws sagemaker instance 2. use the pytorch 3_10 kernel 3. Load a dataset 4. run a filter operation 5. watch as only 2 cores are used when num_proc > 2 6. run a map operation 7. watch as only 2 cores are used when num_proc > 2 8. run a map operation with processor affinity reset inside the function called via map 9. Watch as all cores run ### Expected behavior All specified cores are used via the num_proc argument. ### Environment info AWS sagemaker with the following init script run in the terminal after instance creation: conda init bash bash conda activate pytorch_p310 pip install Wand PyPDF pytesseract datasets seqeval pdfplumber transformers pymupdf sentencepiece timm donut-python accelerate optimum xgboost python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' sudo yum -y install htop sudo yum -y update sudo yum -y install wget libstdc++ autoconf automake libtool autoconf-archive pkg-config gcc gcc-c++ make libjpeg-devel libpng-devel libtiff-devel zlib-devel

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I_kwDODunzps5pg_Zl

Viewing dataset card returns “502 Bad Gateway”

closed

NONE

The url is: https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams I am able to successfully view the “Files and versions” tab: [Confirm-Labs/pile_ngrams_trigrams at main](https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams/tree/main) Any help would be appreciated! Thanks! I hope this is the right place to report an issue like this.

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PR_kwDODunzps5TrxS_

set dev version

closed

MEMBER

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PR_kwDODunzps5Tru2_

Release: 2.13.1

closed

MEMBER

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PR_kwDODunzps5TmAFp

Avoid stuck map operation when subprocesses crashes

closed

CONTRIBUTOR

I've been using Dataset.map() with `num_proc=os.cpu_count()` to leverage multicore processing for my datasets, but from time to time I get stuck processes waiting forever. Apparently, when one of the subprocesses is abruptly killed (OOM killer, segfault, SIGKILL, etc), the main process keeps waiting for the async task sent to that child process to finish. It seems to be easy to reproduce the issue with the following script: ``` import os from datasets import Dataset, Features, Value def do_stuck(item): os.kill(os.getpid(), 9) data = { "col1": list(range(5)), "col2": list(range(5)), } ds = Dataset.from_dict( data, features=Features({ "col1": Value("int64"), "col2": Value("int64"), }), ) print(ds.map(do_stuck, num_proc=4)) ``` This is an old behavior in Python, which apparently was fixed a few years ago in `concurrent.futures.ProcessPoolExecutor` ([ref](https://bugs.python.org/issue9205)), but not in `multiprocessing.pool.Pool` / `multiprocess.pool.Pool`, which is used by `Dataset.map` ([ref](https://bugs.python.org/issue22393)). This PR is an idea to try to detect when a child process gets killed, and raises a `RuntimeError` warning the dataset.map() caller. EDIT: Related proposal for future improvement: https://github.com/huggingface/datasets/discussions/5977

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I_kwDODunzps5pZa3v

Streaming Dataset behind Proxy - FileNotFoundError

closed

NONE

### Describe the bug When trying to stream a dataset i get the following error after a few minutes of waiting. ``` FileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json If the repo is private or gated, make sure to log in with `huggingface-cli login`. ``` I have already set the proxy environment variables. Downloading a Dataset without streaming works as expected. Still i suspect that this is connected to being behind a proxy. Is there a way to set the proxy for streaming datasets? Possibly a keyword argument that gets passed to ffspec? ### Steps to reproduce the bug This is the code i use. ``` import os os.environ['http_proxy'] = "http://example.com:xxxx" os.environ['https_proxy'] = "http://example.com:xxxx" from datasets import load_dataset ds = load_dataset("facebook/voxpopuli", name="de", streaming=True) ``` ### Expected behavior I would expect the streaming functionality to use the set proxy settings. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.2

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PR_kwDODunzps5TkXCb

Deprecate `errors` param in favor of `encoding_errors` in text builder

closed

CONTRIBUTOR

For consistency with the JSON builder and Pandas

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PR_kwDODunzps5TkE7K

Filter unsupported extensions

closed

MEMBER

I used a regex to filter the data files based on their extension for packaged builders. I tried and a regex is 10x faster that using `in` to check if the extension is in the list of supported extensions. Supersedes https://github.com/huggingface/datasets/pull/5850 Close https://github.com/huggingface/datasets/issues/5849 I also did a small change to favor the parquet module in case of a draw in the extension counter.

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I_kwDODunzps5pUxlD

Docs: make "repository structure" easier to find

open

CONTRIBUTOR

The page https://huggingface.co/docs/datasets/repository_structure explains how to create a simple repository structure without a dataset script. It's the simplest way to create a dataset and should be easier to find, particularly on the docs' first pages.

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I_kwDODunzps5pQy30

description disappearing from Info when Uploading a Dataset Created with `from_dict`

open

NONE

### Describe the bug When uploading a dataset created locally using `from_dict` with a specified `description` field. It appears before upload, but is missing after upload and re-download. ### Steps to reproduce the bug I think the most relevant pattern in the code might be the following lines: ``` description_json_str = json.dumps( { "dataset_id": dataset.spec.dataset_id, "env_name": dataset.spec.env_spec.id, "action_space": serialize_space(dataset.spec.action_space), "observation_space": serialize_space(dataset.spec.observation_space), } ) hugging_face_dataset = Dataset.from_dict( episodes_dict, info=DatasetInfo(description=description_json_str) ) ``` Which comes from this function https://github.com/balisujohn/minarai/blob/8e023727f0a8488c4451651d9f7a79b981412c40/minari/integrations/hugging_face.py#L39 To replicate, clone this branch of my Minari fork https://github.com/balisujohn/minarai/tree/dev-huggingface then run ``` python3.8 -m venv env source env/bin/activate python3 -m pip install -e . python3 -m pip install pytest ``` The change the hugging face repo path in the test called `test_hugging_face_push_and_pull_dataset` in `tests/integrations/test_hugging_face.py` to one you have permissions to write to. Then run: ``` pytest tests/integrations/test_hugging_face.py::test_hugging_face_push_and_pull_dataset ``` ### Expected behavior DATASET INFO BEFORE UPLOADING DatasetInfo(description='{"dataset_id": "dummy-combo-test-v0", "env_name": "DummyComboEnv-v0", "action_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}]}", "observation_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"component_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [-1.0], \\"high\\": [1.0]}, \\"component_2\\": {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"subcomponent_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, \\"subcomponent_2\\": {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}, {\\"type\\": \\"Discrete\\", \\"dtype\\": \\"int64\\", \\"start\\": 0, \\"n\\": 10}]}}}}}]}]}"}', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None) ... DATASET INFO AFTER UPLOADING AND DOWNLOADING DatasetInfo(description='', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits={'train': SplitInfo(name='train', num_bytes=4846, num_examples=60, shard_lengths=None, dataset_name='parquet')}, download_checksums={'https://huggingface.co/datasets/balisujohn/minari_test/resolve/8217b614ff9ba5edc1a30c7df430e92a46f65363/data/train-00000-of-00001-7c5900b93b35745e.parquet': {'num_bytes': 9052, 'checksum': None}}, download_size=9052, post_processing_size=None, dataset_size=4846, size_in_bytes=13898) ... ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2

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PR_kwDODunzps5Tcgq4

Add `encoding` and `errors` params to JSON loader

closed

CONTRIBUTOR

"Requested" in https://discuss.huggingface.co/t/utf-16-for-datasets/43828/3. `pd.read_json` also has these parameters, so it makes sense to be consistent.

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I_kwDODunzps5pN53R

Common Voice datasets still need `use_auth_token=True`

closed

MEMBER

### Describe the bug We don't need to pass `use_auth_token=True` anymore to download gated datasets or models, so the following should work if correctly logged in. ```py from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation") ``` However it throws an error - probably because something weird is hardcoded into the dataset loading script. ### Steps to reproduce the bug 1.) ``` huggingface-cli login ``` 2.) Make sure that you have accepted the license here: https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1 3.) Run: ```py from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation") ``` 4.) You'll get: ``` File ~/hf/lib/python3.10/site-packages/datasets/builder.py:963, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 961 split_dict = SplitDict(dataset_name=self.name) 962 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 963 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 965 # Checksums verification 966 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_1/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/common_voice_6_1.py:150, in CommonVoice._split_generators(self, dl_manager) 148 hf_auth_token = dl_manager.download_config.use_auth_token 149 if hf_auth_token is None: --> 150 raise ConnectionError( 151 "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset" 152 ) 154 bundle_url_template = STATS["bundleURLTemplate"] 155 bundle_version = bundle_url_template.split("/")[0] ConnectionError: Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset ``` ### Expected behavior One should not have to pass `use_auth_token=True`. Also see discussion here: https://github.com/huggingface/blog/pull/1243#discussion_r1235131150 ### Environment info ``` - `datasets` version: 2.13.0 - Platform: Linux-6.2.0-76060200-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.16.0.dev0 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 ```

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I_kwDODunzps5pI2H4

Config name / split name lost after map with multiproc

open

CONTRIBUTOR

### Describe the bug Performing a `.map` method on a dataset loses it's config name / split name only if run with multiproc ### Steps to reproduce the bug ```python from datasets import Audio, load_dataset from transformers import AutoFeatureExtractor import numpy as np # load dummy dataset libri = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") # make train / test splits libri = libri["validation"].train_test_split(seed=42, shuffle=True, test_size=0.1) # example feature extractor model_id = "ntu-spml/distilhubert" feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True, return_attention_mask=True) sampling_rate = feature_extractor.sampling_rate libri = libri.cast_column("audio", Audio(sampling_rate=sampling_rate)) max_duration = 30.0 def preprocess_function(examples): audio_arrays = [x["array"] for x in examples["audio"]] inputs = feature_extractor( audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=int(feature_extractor.sampling_rate * max_duration), truncation=True, return_attention_mask=True, ) return inputs # single proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=1 ) print(10 * "=" ,"Single processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) # multi proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=2 ) print(10 * "=" ,"Multi processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) ``` **Print Output:** ``` ========== Single processing ========== Config name before: clean Split name before: validation Config name after: clean Split name after: validation ========== Multi processing ========== Config name before: clean Split name before: validation Config name after: None Split name after: None ``` => we can see that the config/split names are lost in the multiprocessing setting ### Expected behavior Should retain both config / split names in the multiproc setting ### Environment info - `datasets` version: 2.13.1.dev0 - Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2

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PR_kwDODunzps5TXBLP

Fix JSON generation in benchmarks CI

closed

CONTRIBUTOR

Related to changes made in https://github.com/iterative/dvc/pull/9475

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I_kwDODunzps5pHyQc

"Couldn't cast array of type" in complex datasets

closed

NONE

### Describe the bug When doing a map of a dataset with complex types, sometimes `datasets` is unable to interpret the valid schema of a returned datasets.map() function. This often comes from conflicting types, like when both empty lists and filled lists are competing for the same field value. This is prone to happen in batch mapping, when the mapper returns a sequence of null/empty values and other batches are non-null. A workaround is to manually cast the new batch to a pyarrow table (like implemented in this [workaround](https://github.com/piercefreeman/lassen/pull/3)) but it feels like this ideally should be solved at the core library level. Note that the reproduction case only throws this error if the first datapoint has the empty list. If it is processed later, datasets already detects its representation as list-type and therefore allows the empty list to be provided. ### Steps to reproduce the bug A trivial reproduction case: ```python from typing import Iterator, Any import pandas as pd from datasets import Dataset def batch_to_examples(batch: dict[str, list[Any]]) -> Iterator[dict[str, Any]]: for i in range(next(iter(lengths))): yield {feature: values[i] for feature, values in batch.items()} def examples_to_batch(examples) -> dict[str, list[Any]]: batch = {} for example in examples: for feature, value in example.items(): if feature not in batch: batch[feature] = [] batch[feature].append(value) return batch def batch_process(examples, explicit_schema: bool): new_examples = [] for example in batch_to_examples(examples): new_examples.append(dict(texts=example["raw_text"].split())) return examples_to_batch(new_examples) df = pd.DataFrame( [ {"raw_text": ""}, {"raw_text": "This is a test"}, {"raw_text": "This is another test"}, ] ) dataset = Dataset.from_pandas(df) # datasets won't be able to typehint a dataset that starts with an empty example. with pytest.raises(TypeError, match="Couldn't cast array of type"): dataset = dataset.map( batch_process, batched=True, batch_size=1, num_proc=1, remove_columns=dataset.column_names, ) ``` This results in crashes like: ```bash File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 2109, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1998, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type string to null ``` ### Expected behavior The code should successfully map and create a new dataset without error. ### Environment info Mac OSX, Linux

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https://github.com/huggingface/datasets/pull/5964

PR_kwDODunzps5TVweZ

Always return list in `list_datasets`

closed

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Fix #5925 Plus, deprecate `list_datasets`/`inspect_dataset` in favor of `huggingface_hub.list_datasets`/"git clone workflow" (downloads data files)

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I_kwDODunzps5pEc25

Got an error _pickle.PicklingError use Dataset.from_spark.

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python 3.9.2 Got an error _pickle.PicklingError use Dataset.from_spark. Did the dataset import load data from spark dataframe using multi-node Spark cluster df = spark.read.parquet(args.input_data).repartition(50) ds = Dataset.from_spark(df, keep_in_memory=True, cache_dir="/pnc-data/data/nuplan/t5_spark/cache_data") ds.save_to_disk(args.output_data) Error : _pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforma tion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063. 23/06/16 21:17:20 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.) _Originally posted by @yanzia12138 in https://github.com/huggingface/datasets/issues/5701#issuecomment-1594674306_ W Traceback (most recent call last): File "/home/work/main.py", line 100, in <module> run(args) File "/home/work/main.py", line 80, in run ds = Dataset.from_spark(df1, keep_in_memory=True, File "/home/work/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1281, in from_spark return SparkDatasetReader( File "/home/work/.local/lib/python3.9/site-packages/datasets/io/spark.py", line 53, in read self.builder.download_and_prepare( File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 254, in _prepare_split self._validate_cache_dir() File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 122, in _validate_cache_dir self._spark.sparkContext.parallelize(range(1), 1).mapPartitions(create_cache_and_write_probe).collect() File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 950, in collect sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2951, in _jrdd wrapped_func = _wrap_function(self.ctx, self.func, self._prev_jrdd_deserializer, File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2830, in _wrap_function pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command) File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2816, in _prepare_for_python_RDD pickled_command = ser.dumps(command) File "/home/work/.local/lib/python3.9/site-packages/pyspark/serializers.py", line 447, in dumps raise pickle.PicklingError(msg) _pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. S parkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063. 23/06/19 13:51:21 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)

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I_kwDODunzps5o_7p6

Issue with train_test_split maintaining the same underlying PyArrow Table

open

NONE

### Describe the bug I've been using the train_test_split method in the datasets module to split my HuggingFace Dataset into separate training, validation, and testing subsets. However, I've noticed an issue where the split datasets appear to maintain the same underlying PyArrow Table. ### Steps to reproduce the bug 1. Load any dataset ```dataset = load_dataset("lhoestq/demo1")``` 2. Try the next code: ```python from datasets import Dataset, DatasetDict train_size = 0.6 split_train = dataset["train"].train_test_split( train_size=train_size, ) separate_dataset_dict = DatasetDict({ "train": split_train["train"], "test": split_train["test"], }) ``` 3. The next code ```print(separate_dataset_dict)``` when printing the dataset it gives the indication that they have 3 and 2 rows respectively. 4. But the next code: ```python print(len(separate_dataset_dict["train"].data['id'])) print(len(separate_dataset_dict["test"].data['id'])) ``` Indicates that both tables still have 5 rows. ### Expected behavior However, I've noticed that train_test_split["train"].data, test_val_split["train"].data, and test_val_split["test"].data are identical, suggesting that they all point to the same underlying PyArrow Table. This means that the split datasets are not independent, as I expected. I believe this is a bug in the train_test_split implementation, as I would expect this function to return datasets with separate underlying PyArrow Tables. Could you please help me understand if this is expected behavior, or if there's a workaround to create truly independent split datasets? I would appreciate any assistance with this issue. Thank you. ### Environment info I tried in Colab: - `datasets` version: 2.13.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 and my PC: - `datasets` version: 2.13.0 - Platform: Linux-5.15.107+-x86_64-with-glibc2.31 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5o0Pa3

IterableDataset: split by node and map may preprocess samples that will be skipped anyway

open

NONE

There are two ways an iterable dataset can be split by node: 1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU 2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others. In case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU. This doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end. Could you open a new issue so that we can discuss about this and find a solution ? _Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/5360#issuecomment-1592729051_

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I_kwDODunzps5ov8ID

read metric glue.py from local file

closed

NONE

### Describe the bug Currently, The server is off-line. I am using the glue metric from the local file downloaded from the hub. I download / cached datasets using `load_dataset('glue','sst2', cache_dir='/xxx')` to cache them and then in the off-line mode, I use `load_dataset('xxx/glue.py','sst2', cache_dir='/xxx')`. I can successfully reuse cached datasets. My problem is about the load_metric. When I run `load_dataset('xxx/glue_metric.py','sst2',cache_dir='/xxx')` , it returns ` File "xx/lib64/python3.9/site-packages/datasets/utils/deprecation_utils.py", line 46, in wrapper return deprecated_function(*args, **kwargs) File "xx//lib64/python3.9/site-packages/datasets/load.py", line 1392, in load_metric metric = metric_cls( TypeError: 'NoneType' object is not callable` Thanks in advance for help! ### Steps to reproduce the bug N/A ### Expected behavior N/A ### Environment info `datasets == 2.12.0`

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PR_kwDODunzps5TA3__

set dev version

closed

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PR_kwDODunzps5TA1EB

Release: 2.13.0

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PR_kwDODunzps5S_1o2

Fix ArrowExamplesIterable.shard_data_sources

closed

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