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.add_faiss_index and .add_elasticsearch_index returns ImportError at Google Colab
|
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"I can fix this.\nIt's mainly because faiss-gpu requires python<=3.10 but the default python version in colab is 3.11. We just have to downgrade the CPython version down to 3.10 and it should work fine.\n",
"I think I just had no chance to meet with faiss-cpu.\nIt could be import problem? \n_has_faiss gets its value at the beginning of datasets/search.\nI tried to call object before import faiss, so _has_faiss took False. And never updated later. ",
"Yes you can't meet the requirements because faiss-cpu runs only on\r\npython3.10 and lower but the default version for colab is python3.11 which\r\nresults in pip not being able to find wheels for faiss-cpu with python3.11.\r\n\r\nOn Mon, 17 Mar, 2025, 3:56 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>\r\n>\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> you can't meet the requirements\n\nIt is not the case (or I didn't reach this point) because the same code in notebook\n```importlib.util.find_spec(\"faiss\")```\nfinds faiss. I've mention it.\nI think the problem is in the very moment when _has_faiss takes its value and never try again. \n(or it couldn't find the path that was easily found when started from my code)",
"When you run the first cell containing pip install faiss-cpu does it\r\ninstall it?\r\n\r\nOn Mon, 17 Mar, 2025, 8:01 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>\r\n>\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> When you run the first cell containing pip install faiss-cpu does it\n> install it?\n> […](#)\n\nYes. It was installed succesfully. \nMethods of datasets library that depends on _has_faiss constant didn't start to work."
] | 2025-03-16T00:51:49
| 2025-03-16T08:34:40
| null |
NONE
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### Describe the bug
At Google Colab
```!pip install faiss-cpu``` works
```import faiss``` no error
but
```embeddings_dataset.add_faiss_index(column='embeddings')```
returns
```
[/usr/local/lib/python3.11/dist-packages/datasets/search.py](https://localhost:8080/#) in init(self, device, string_factory, metric_type, custom_index)
247 self.faiss_index = custom_index
248 if not _has_faiss:
--> 249 raise ImportError(
250 "You must install Faiss to use FaissIndex. To do so you can run conda install -c pytorch faiss-cpu or conda install -c pytorch faiss-gpu. "
251 "A community supported package is also available on pypi: pip install faiss-cpu or pip install faiss-gpu. "
```
because
```_has_faiss = importlib.util.find_spec("faiss") is not None``` at the beginning of ```datasets/search.py``` returns ```False```
when
the same code at colab notebook returns
```ModuleSpec(name='faiss', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7b7851449f50>, origin='/usr/local/lib/python3.11/dist-packages/faiss/init.py', submodule_search_locations=['/usr/local/lib/python3.11/dist-packages/faiss'])```
But
```
import datasets
datasets.search._has_faiss
```
at ```colab notebook``` also returns ```False```
The same story with ```_has_elasticsearch```
### Steps to reproduce the bug
1. Follow https://huggingface.co/learn/nlp-course/chapter5/6?fw=pt at Google Colab
2. till ```embeddings_dataset.add_faiss_index(column='embeddings')```
3. ```embeddings_dataset.add_elasticsearch_index(column='embeddings')```
4. https://colab.research.google.com/drive/1h2cjuiClblqzbNQgrcoLYOC8zBqTLLcv#scrollTo=3ddzRp72auOF
### Expected behavior
I've only started Tutorial and don't know exactly. But something tells me that ```embeddings_dataset.add_faiss_index(column='embeddings')```
should work without ```Import Error```
### Environment info
Google Colab notebook with default config
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I_kwDODunzps6uKSnC
| 7,455
|
Problems with local dataset after upgrade from 3.3.2 to 3.4.0
|
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[
"Hi ! I just released 3.4.1 with a fix, let me know if it's working now !"
] | 2025-03-15T09:22:50
| 2025-03-15T09:23:55
| null |
NONE
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### Describe the bug
I was not able to open a local saved dataset anymore that was created using an older datasets version after the upgrade yesterday from datasets 3.3.2 to 3.4.0
The traceback is
```
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 67, in _generate_tables
batches = pa.ipc.open_stream(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 190, in open_stream
return RecordBatchStreamReader(source, options=options,
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 52, in __init__
self._open(source, options=options, memory_pool=memory_pool)
File "pyarrow/ipc.pxi", line 1006, in pyarrow.lib._RecordBatchStreamReader._open
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: Expected to read 538970747 metadata bytes, but only read 2126
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1855, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 69, in _generate_tables
reader = pa.ipc.open_file(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 234, in open_file
return RecordBatchFileReader(
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 110, in __init__
self._open(source, footer_offset=footer_offset,
File "pyarrow/ipc.pxi", line 1090, in pyarrow.lib._RecordBatchFileReader._open
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: Not an Arrow file
```
### Steps to reproduce the bug
Load a dataset from a local folder with
```
dataset = load_dataset(
args.train_data_dir,
cache_dir=args.cache_dir,
)
```
as it is done for example in the training script for SD3 controlnet.
This is the minimal script to test it:
```
from datasets import load_dataset
def main():
dataset = load_dataset(
"local_dataset",
)
print(dataset)
print("Sample data:", dataset["train"][0])
if __name__ == "__main__":
main()
````
### Expected behavior
Work in 3.4.0 like in 3.3.2
### Environment info
- `datasets` version: 3.4.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps6t0jdU
| 7,449
|
Cannot load data with different schemas from different parquet files
|
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[
"Hi ! `load_dataset` expects all the data_files to have the same schema.\n\nMaybe you can try enforcing certain `features` using:\n\n```python\nfeatures = Features({\"conversations\": {'content': Value('string'), 'role': Value('string',)}})\nds = load_dataset(..., features=features)\n```",
"Thanks! It works if I explicitly specify all nested fields of the data."
] | 2025-03-13T08:14:49
| 2025-03-13T11:19:06
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NONE
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### Describe the bug
Cannot load samples with optional fields from different files. The schema cannot be correctly derived.
### Steps to reproduce the bug
When I place two samples with an optional field `some_extra_field` within a single parquet file, it can be loaded via `load_dataset`.
```python
import pandas as pd
from datasets import load_dataset
data = [
{'conversations': {'role': 'user', 'content': 'hello'}},
{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}
]
df = pd.DataFrame(data)
df.to_parquet('data.parquet')
dataset = load_dataset('parquet', data_files='data.parquet', split='train')
print(dataset.features)
```
The schema can be derived. `some_extra_field` is set to None for the first row where it is absent.
```
{'conversations': {'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None), 'some_extra_field': Value(dtype='string', id=None)}}
```
However, when I separate the samples into different files, it cannot be loaded.
```python
import pandas as pd
from datasets import load_dataset
data1 = [{'conversations': {'role': 'user', 'content': 'hello'}}]
pd.DataFrame(data1).to_parquet('data1.parquet')
data2 = [{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}]
pd.DataFrame(data2).to_parquet('data2.parquet')
dataset = load_dataset('parquet', data_files=['data1.parquet', 'data2.parquet'], split='train')
print(dataset.features)
```
Traceback:
```
Traceback (most recent call last):
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single
for _, table in generator:
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
arrays = [
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
cast_array_to_feature(
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<content: string, role: string, some_extra_field: string>
to
{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}
```
### Expected behavior
Correctly load data with optional fields from different parquet files.
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.10.135.bsk.4-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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| 7,448
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`datasets.disable_caching` doesn't work
|
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When I use `Dataset.from_generator(my_gen)` to load my dataset, it simply skips my changes to the generator function.
I tried `datasets.disable_caching`, but it doesn't work!
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I_kwDODunzps6twu3g
| 7,447
|
Epochs shortened after resuming mid-epoch with Iterable dataset+StatefulDataloader(persistent_workers=True)
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[
"Thanks for reporting ! Maybe we should store the epoch in the state_dict, and then when the dataset is iterated on again after setting a new epoch it should restart from scratch instead of resuming ? wdyt ?",
"But why does this only happen when `persistent_workers=True`? I would expect it to work correctly even without storing the epoch number in the state_dict of the iterable dataset. ",
"I think persistent_workers=False simply ignores the dataset state_dict when it starts a new epoch, that's why the issue doesn't appear in that case",
"I opened https://github.com/huggingface/datasets/pull/7451 to fix the issue, let me know if it works for you",
"I just released `datasets` 3.4 that includes the fix :)\n\nPS: in your script you probably want to set the epoch like this, otherwise it's still set to 0 after the first epoch:\n\n```diff\n if state_dict is None:\n- ds.set_epoch(epoch)\n epoch += 1\n+ ds.set_epoch(epoch)\n```"
] | 2025-03-12T21:41:05
| 2025-03-14T17:26:59
| 2025-03-14T10:50:10
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### Describe the bug
When `torchdata.stateful_dataloader.StatefulDataloader(persistent_workers=True)` the epochs after resuming only iterate through the examples that were left in the epoch when the training was interrupted. For example, in the script below training is interrupted on step 124 (epoch 1) when 3 batches are left. Then after resuming, the rest of epochs (2 and 3) only iterate through these 3 batches.
### Steps to reproduce the bug
Run the following script with and with PERSISTENT_WORKERS=true.
```python
# !/usr/bin/env python3
# torch==2.5.1
# datasets==3.3.2
# torchdata>=0.9.0
import datasets
import pprint
from torchdata.stateful_dataloader import StatefulDataLoader
import os
PERSISTENT_WORKERS = (
os.environ.get("PERSISTENT_WORKERS", "False").lower() == "true"
)
# PERSISTENT_WORKERS = True # Incorrect resume
# ds = datasets.load_from_disk("dataset").to_iterable_dataset(num_shards=4)
def generator():
for i in range(128):
yield {"x": i}
ds = datasets.Dataset.from_generator(
generator, features=datasets.Features({"x": datasets.Value("int32")})
).to_iterable_dataset(num_shards=4)
dl = StatefulDataLoader(
ds, batch_size=2, num_workers=2, persistent_workers=PERSISTENT_WORKERS
)
global_step = 0
epoch = 0
ds_state_dict = None
state_dict = None
resumed = False
while True:
if epoch >= 3:
break
if state_dict is not None:
dl.load_state_dict(state_dict)
state_dict = None
ds_state_dict = None
resumed = True
print("resumed")
for i, batch in enumerate(dl):
print(f"epoch: {epoch}, global_step: {global_step}, batch: {batch}")
global_step += 1 # consume datapoint
# simulate error
if global_step == 124 and not resumed:
ds_state_dict = ds.state_dict()
state_dict = dl.state_dict()
print("checkpoint")
print("ds_state_dict")
pprint.pprint(ds_state_dict)
print("dl_state_dict")
pprint.pprint(state_dict)
break
if state_dict is None:
ds.set_epoch(epoch)
epoch += 1
```
The script checkpoints when there are three batches left in the second epoch. After resuming, only the last three batches are repeated in the rest of the epochs.
If it helps, following are the two state_dicts for the dataloader save at the same step with the two settings. The left one is for `PERSISTENT_WORKERS=False`
### Expected behavior
All the elements in the dataset should be iterated through in the epochs following the one where we resumed. The expected behavior can be seen by setting `PERSISTENT_WORKERS=False`.
### Environment info
torch==2.5.1
datasets==3.3.2
torchdata>=0.9.0
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pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
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### Describe the bug
A dict with its keys are all str but get following error
```python
test_data=[{'input_ids':[1,2,3],'labels':[[Counter({2:1})]]}]
dataset = datasets.Dataset.from_list(test_data)
```
```bash
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
datasets 3.3.2
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I_kwDODunzps6thWyN
| 7,444
|
Excessive warnings when resuming an IterableDataset+buffered shuffle+DDP.
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### Describe the bug
I have a large dataset that I shared into 1024 shards and save on the disk during pre-processing. During training, I load the dataset using load_from_disk() and convert it into an iterable dataset, shuffle it and split the shards to different DDP nodes using the recommended method.
However, when the training is resumed mid-epoch, I get thousands of identical warning messages:
```
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
```
### Steps to reproduce the bug
1. Run a multi-node training job using the following python script and interrupt the training after a few seconds to save a mid-epoch checkpoint.
```python
#!/usr/bin/env python
import os
import time
from typing import Dict, List
import torch
import lightning as pl
from torch.utils.data import DataLoader
from datasets import Dataset
from datasets.distributed import split_dataset_by_node
import datasets
from transformers import AutoTokenizer
from more_itertools import flatten, chunked
from torchdata.stateful_dataloader import StatefulDataLoader
from lightning.pytorch.callbacks.on_exception_checkpoint import (
OnExceptionCheckpoint,
)
datasets.logging.set_verbosity_debug()
def dummy_generator():
# Generate 60 examples: integers from $0$ to $59$
# 64 sequences of different lengths
dataset = [
list(range(3, 10)),
list(range(10, 15)),
list(range(15, 21)),
list(range(21, 27)),
list(range(27, 31)),
list(range(31, 36)),
list(range(36, 45)),
list(range(45, 50)),
]
for i in range(8):
for j, ids in enumerate(dataset):
yield {"token_ids": [idx + i * 50 for idx in ids]}
def group_texts(
examples: Dict[str, List[List[int]]],
block_size: int,
eos_token_id: int,
bos_token_id: int,
pad_token_id: int,
) -> Dict[str, List[List[int]]]:
real_block_size = block_size - 2 # make space for bos and eos
# colapse the sequences into a single list of tokens and then create blocks of real_block_size
input_ids = []
attention_mask = []
for block in chunked(flatten(examples["token_ids"]), real_block_size):
s = [bos_token_id] + list(block) + [eos_token_id]
ls = len(s)
attn = [True] * ls
s += [pad_token_id] * (block_size - ls)
attn += [False] * (block_size - ls)
input_ids.append(s)
attention_mask.append(attn)
return {"input_ids": input_ids, "attention_mask": attention_mask}
def collate_fn(batch):
return {
"input_ids": torch.tensor(
[item["input_ids"] for item in batch], dtype=torch.long
),
"attention_mask": torch.tensor(
[item["attention_mask"] for item in batch], dtype=torch.long
),
}
class DummyModule(pl.LightningModule):
def __init__(self):
super().__init__()
# A dummy linear layer (not used for actual computation)
self.layer = torch.nn.Linear(1, 1)
self.ds = None
self.prepare_data_per_node = False
def on_train_start(self):
# This hook is called once training begins on each process.
print(f"[Rank {self.global_rank}] Training started.", flush=True)
self.data_file = open(f"data_{self.global_rank}.txt", "w")
def on_train_end(self):
self.data_file.close()
def training_step(self, batch, batch_idx):
# Print batch information to verify data loading.
time.sleep(5)
# print("batch", batch, flush=True)
print(
f"\n[Rank {self.global_rank}] Training step, epoch {self.trainer.current_epoch}, batch {batch_idx}: {batch['input_ids']}",
flush=True,
)
self.data_file.write(
f"[Rank {self.global_rank}] Training step, epoch {self.trainer.current_epoch}, batch {batch_idx}: {batch['input_ids']}\n"
)
# Compute a dummy loss (here, simply a constant tensor)
loss = torch.tensor(0.0, requires_grad=True)
return loss
def on_train_epoch_start(self):
epoch = self.trainer.current_epoch
print(
f"[Rank {self.global_rank}] Training epoch {epoch} started.",
flush=True,
)
self.data_file.write(
f"[Rank {self.global_rank}] Training epoch {epoch} started.\n"
)
def configure_optimizers(self):
# Return a dummy optimizer.
return torch.optim.SGD(self.parameters(), lr=0.001)
class DM(pl.LightningDataModule):
def __init__(self):
super().__init__()
self.ds = None
self.prepare_data_per_node = False
def set_epoch(self, epoch: int):
self.ds.set_epoch(epoch)
def prepare_data(self):
# download the dataset
dataset = Dataset.from_generator(dummy_generator)
# save the dataset
dataset.save_to_disk("dataset", num_shards=4)
def setup(self, stage: str):
# load the dataset
ds = datasets.load_from_disk("dataset").to_iterable_dataset(
num_shards=4
)
ds = ds.map(
group_texts,
batched=True,
batch_size=5,
fn_kwargs={
"block_size": 5,
"eos_token_id": 1,
"bos_token_id": 0,
"pad_token_id": 2,
},
remove_columns=["token_ids"],
).shuffle(seed=42, buffer_size=8)
ds = split_dataset_by_node(
ds,
rank=self.trainer.global_rank,
world_size=self.trainer.world_size,
)
self.ds = ds
def train_dataloader(self):
print(
f"[Rank {self.trainer.global_rank}] Preparing train_dataloader...",
flush=True,
)
rank = self.trainer.global_rank
print(
f"[Rank {rank}] Global rank: {self.trainer.global_rank}",
flush=True,
)
world_size = self.trainer.world_size
print(f"[Rank {rank}] World size: {world_size}", flush=True)
return StatefulDataLoader(
self.ds,
batch_size=2,
num_workers=2,
collate_fn=collate_fn,
drop_last=True,
persistent_workers=True,
)
if __name__ == "__main__":
print("Starting Lightning training", flush=True)
# Optionally, print some SLURM environment info for debugging.
print(f"SLURM_NNODES: {os.environ.get('SLURM_NNODES', '1')}", flush=True)
# Determine the number of nodes from SLURM (defaulting to 1 if not set)
num_nodes = int(os.environ.get("SLURM_NNODES", "1"))
model = DummyModule()
dm = DM()
on_exception = OnExceptionCheckpoint(
dirpath="checkpoints",
filename="on_exception",
)
# Configure the Trainer to use distributed data parallel (DDP).
trainer = pl.Trainer(
accelerator="gpu" if torch.cuda.is_available() else "cpu",
devices=1,
strategy=(
"ddp" if num_nodes > 1 else "auto"
), # Use DDP strategy for multi-node training.
num_nodes=num_nodes,
max_epochs=2,
logger=False,
enable_checkpointing=True,
num_sanity_val_steps=0,
enable_progress_bar=False,
callbacks=[on_exception],
)
# resume (uncomment to resume)
# trainer.fit(model, datamodule=dm, ckpt_path="checkpoints/on_exception.ckpt")
# train
trainer.fit(model, datamodule=dm)
```
```bash
#!/bin/bash
#SBATCH --job-name=pl_ddp_test
#SBATCH --nodes=2 # Adjust number of nodes as needed
#SBATCH --ntasks-per-node=1 # One GPU (process) per node
#SBATCH --cpus-per-task=3 # At least as many dataloader workers as required
#SBATCH --gres=gpu:1 # Request one GPU per node
#SBATCH --time=00:10:00 # Job runtime (adjust as needed)
#SBATCH --partition=gpu-preempt # Partition or queue name
#SBATCH -o script.out
# Disable Python output buffering.
export PYTHONUNBUFFERED=1
echo "SLURM job starting on $(date)"
echo "Running on nodes: $SLURM_NODELIST"
echo "Current directory: $(pwd)"
ls -l
# Launch the script using srun so that each process starts the Lightning module.
srun script.py
```
2. Uncomment the "resume" line (second to last) and comment the original `trainer.fit` call (last line).
It will produce the following log.
```
[Rank 0] Preparing train_dataloader...
[Rank 0] Global rank: 0
[Rank 0] World size: 2
[Rank 1] Preparing train_dataloader...
[Rank 1] Global rank: 1
[Rank 1] World size: 2
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Assigning 2 shards (or data sources) of the dataset to each node.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#1, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#0 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#0 dataloader worker#0, ': Finished iterating over 1/1 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
[Rank 0] Training started.
[Rank 0] Training epoch 0 started.
[Rank 0] Training epoch 1 started.
Assigning 2 shards (or data sources) of the dataset to each node.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#1, ': Starting to iterate over 1/2 shards.
node#0 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#0 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#0 dataloader worker#0, ': Finished iterating over 1/1 shards.
`Trainer.fit` stopped: `max_epochs=2` reached.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#1 dataloader worker#0, ': Finished iterating over 1/1 shards.
[Rank 1] Training started.
[Rank 1] Training epoch 0 started.
[Rank 1] Training epoch 1 started.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
node#1 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#1 dataloader worker#0, ': Finished iterating over 1/1 shards.
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
```
I'm also attaching the relevant state_dict to make sure that the state is being checkpointed as expected.
```
{'_iterator_finished': True,
'_snapshot': {'_last_yielded_worker_id': 1,
'_main_snapshot': {'_IterableDataset_len_called': None,
'_base_seed': 3992758080362545099,
'_index_sampler_state': {'samples_yielded': 64},
'_num_workers': 2,
'_sampler_iter_state': None,
'_sampler_iter_yielded': 32,
'_shared_seed': None},
'_snapshot_step': 32,
'_worker_snapshots': {'worker_0': {'dataset_state': {'ex_iterable': {'shard_example_idx': 0,
'shard_idx': 1},
'num_examples_since_previous_state': 0,
'previous_state': {'shard_example_idx': 0,
'shard_idx': 1},
'previous_state_example_idx': 33},
'fetcher_state': {'dataset_iter_state': None,
'fetcher_ended': False},
'worker_id': 0},
'worker_1': {'dataset_state': {'ex_iterable': {'shard_example_idx': 0,
'shard_idx': 1},
'num_examples_since_previous_state': 0,
'previous_state': {'shard_example_idx': 0,
'shard_idx': 1},
'previous_state_example_idx': 33},
'fetcher_state': {'dataset_iter_state': None,
'fetcher_ended': False},
'worker_id': 1}}},
'_steps_since_snapshot': 0}
```
### Expected behavior
Since I'm following all the recommended steps, I don't expect to see any warning when resuming. Am I doing something wrong? Also, can someone explain why I'm seeing 20 identical messages in the log in this reproduction setting? I'm trying to understand why I see thousands of these messages with the actual dataset.
One more surprising thing I noticed in the logs is the change in a number of shards per worker. In the following messages, the denominator changes from 2 to 1.
```
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
...
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
```
### Environment info
python: 3.11.10
datasets: 3.3.2
lightning: 2.3.1
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I_kwDODunzps6tXX64
| 7,443
|
index error when num_shards > len(dataset)
|
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"Actually, looking at the code a bit more carefully, maybe an even better solution is to explicitly set `num_shards=len(self)` somewhere inside both `push_to_hub()` and `save_to_disk()` when these functions are invoked with `num_shards > len(dataset)`."
] | 2025-03-10T22:40:59
| 2025-03-10T23:43:08
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In `ds.push_to_hub()` and `ds.save_to_disk()`, `num_shards` must be smaller than or equal to the number of rows in the dataset, but currently this is not checked anywhere inside these functions. Attempting to invoke these functions with `num_shards > len(dataset)` should raise an informative `ValueError`.
I frequently work with datasets with a small number of rows where each row is pretty large, so I often encounter this issue, where the function runs until the shard index in `ds.shard(num_shards, indx)` goes out of bounds. Ideally, a `ValueError` should be raised before reaching this point (i.e. as soon as `ds.push_to_hub()` or `ds.save_to_disk()` is invoked with `num_shards > len(dataset)`).
It seems that adding something like:
```python
if len(self) < num_shards:
raise ValueError(f"num_shards ({num_shards}) must be smaller than or equal to the number of rows in the dataset ({len(self)}). Please either reduce num_shards or increase max_shard_size to make sure num_shards <= len(dataset).")
```
to the beginning of the definition of the `ds.shard()` function [here](https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/arrow_dataset.py#L4728) would deal with this issue for both `ds.push_to_hub()` and `ds.save_to_disk()`, but I'm not exactly sure if this is the best place to raise the `ValueError` (it seems that a more correct way to do it would be to write separate checks for `ds.push_to_hub()` and `ds.save_to_disk()`). I'd be happy to submit a PR if you think something along these lines would be acceptable.
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| 7,442
|
Flexible Loader
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"Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?",
"> Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?\n\nThat would be perfect if not at least a flexible loader."
] | 2025-03-09T16:55:03
| 2025-03-13T11:15:02
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### Feature request
Can we have a utility function that will use `load_from_disk` when given the local path and `load_dataset` if given an HF dataset?
It can be something as simple as this one:
```
def load_hf_dataset(path_or_name):
if os.path.exists(path_or_name):
return load_from_disk(path_or_name)
else:
return load_dataset(path_or_name)
```
### Motivation
This can be done inside the user codebase, too, but in my experience, it becomes repetitive code.
### Your contribution
I can open a pull request.
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I_kwDODunzps6tIj15
| 7,441
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`drop_last_batch` does not drop the last batch using IterableDataset + interleave_datasets + multi_worker
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"Hi @memray, I’d like to help fix the issue with `drop_last_batch` not working when `num_workers > 1`. I’ll investigate and propose a solution. Thanks!\n",
"Thank you very much for offering to help! I also noticed a problem related to a previous issue and left a comment [here](https://github.com/huggingface/datasets/issues/6565#issuecomment-2708169303) (the code checks the validity before certain columns removed). Can you take a look as well?"
] | 2025-03-08T10:28:44
| 2025-03-09T21:27:33
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### Describe the bug
See the script below
`drop_last_batch=True` is defined using map() for each dataset.
The last batch for each dataset is expected to be dropped, id 21-25.
The code behaves as expected when num_workers=0 or 1.
When using num_workers>1, 'a-11', 'b-11', 'a-12', 'b-12' are gone and instead 21 and 22 are sampled.
### Steps to reproduce the bug
```
from datasets import Dataset
from datasets import interleave_datasets
from torch.utils.data import DataLoader
def convert_to_str(batch, dataset_name):
batch['a'] = [f"{dataset_name}-{e}" for e in batch['a']]
return batch
def gen1():
for ii in range(1, 25):
yield {"a": ii}
def gen2():
for ii in range(1, 25):
yield {"a": ii}
# https://github.com/huggingface/datasets/issues/6565
if __name__ == '__main__':
dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=2)
dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=2)
dataset1 = dataset1.map(lambda x: convert_to_str(x, dataset_name="a"), batched=True, batch_size=10, drop_last_batch=True)
dataset2 = dataset2.map(lambda x: convert_to_str(x, dataset_name="b"), batched=True, batch_size=10, drop_last_batch=True)
interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted")
print(f"num_workers=0")
loader = DataLoader(interleaved, batch_size=5, num_workers=0)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=1")
loader = DataLoader(interleaved, batch_size=5, num_workers=1)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=2")
loader = DataLoader(interleaved, batch_size=5, num_workers=2)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=3")
loader = DataLoader(interleaved, batch_size=5, num_workers=3)
i = 0
for b in loader:
print(i, b['a'])
i += 1
```
output is:
```
num_workers=0
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=1
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=2
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=3
Too many dataloader workers: 3 (max is dataset.num_shards=2). Stopping 1 dataloader workers.
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
```
### Expected behavior
`'a-21', 'b-21', 'a-22', 'b-22'` should be dropped
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-1056-aws-x86_64-with-glibc2.31
- Python version: 3.10.16
- `huggingface_hub` version: 0.28.0
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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[
"I have since been training more models with identical architectures over the same dataset, and it is completely unstable. One has now failed at chunk9/1215, whilst others have gotten past that.\n```python\nFileNotFoundError: zstd://example_train_1215.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_1215.jsonl.zst\n```\nBelow is the full training log, where you can clearly see the intermittent dataset issues. Note again that this model only got to epoch 0.11, whereas I have other models training on the exact same dataset right now that have gotten way beyond that. This is quickly turning into a highly expensive bug which I didn't have issues with in the past half year of using the same setup.\n<details>\n<summary>Training log of failed run</summary>\n\n```python\n 1%| | 64/8192 [56:27<87:25:33, 38.72s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 5ef28452-e903-4bd8-946d-f0c77f558a2a)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_4799.jsonl.zst\n 1%| | 64/8192 [56:51<87:25:33, 38.72s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:40:14<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: ba6e4c51-f4a4-407e-9934-3772550b7ce9)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_2770.jsonl.zst\n 2%|▏ | 192/8192 [2:40:53<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:40:53<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: bdf2cfaa-7e0b-46a0-bec1-b1e573fa7998)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_4386.jsonl.zst\n 2%|▏ | 192/8192 [2:42:16<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:42:16<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 1dc5e455-8042-4c7b-9b97-5ded33dfea34)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_1763.jsonl.zst\n 2%|▏ | 192/8192 [2:42:30<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:42:30<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 9cf29917-8111-41fe-80aa-953df65c5803)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_5509.jsonl.zst\n 2%|▏ | 192/8192 [2:44:31<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:44:31<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 2515a0b0-3d81-409f-940c-e78ed5e2dbf8)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3093.jsonl.zst\n 2%|▏ | 192/8192 [2:45:13<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:45:13<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: a4c1e0c7-1c7a-4377-bc7e-6f076473072b)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3422.jsonl.zst\n 2%|▏ | 192/8192 [2:46:26<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:46:26<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: c7b0d366-db86-4d0c-a4e0-be251d26519e)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_2250.jsonl.zst\n 2%|▏ | 192/8192 [2:47:24<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:47:24<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: b0df5a1a-4836-46cf-8e45-58a7c1553309)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_6161.jsonl.zst\n 2%|▏ | 192/8192 [2:49:10<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:49:10<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: c1d97368-c0ae-45bb-ae10-5559b3ebc4e4)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_5782.jsonl.zst\n 2%|▏ | 192/8192 [2:49:30<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n{'eval_loss': 10.482319831848145, 'eval_runtime': 902.7516, 'eval_samples_per_second': 18.149, 'eval_steps_per_second': 0.142, 'epoch': 0, 'num_input_tokens_seen': 0}\n{'loss': 10.4895, 'grad_norm': 2.9147818088531494, 'learning_rate': 3.90625e-06, 'epoch': 0.0, 'num_input_tokens_seen': 1048576}\n{'loss': 10.4832, 'grad_norm': 2.8206892013549805, 'learning_rate': 7.8125e-06, 'epoch': 0.0, 'num_input_tokens_seen': 2097152}\n{'loss': 10.4851, 'grad_norm': 2.910552978515625, 'learning_rate': 1.171875e-05, 'epoch': 0.0, 'num_input_tokens_seen': 3145728}\n{'loss': 10.486, 'grad_norm': 2.8042073249816895, 'learning_rate': 1.5625e-05, 'epoch': 0.0, 'num_input_tokens_seen': 4194304}\n{'loss': 10.4719, 'grad_norm': 2.83260440826416, 'learning_rate': 1.953125e-05, 'epoch': 0.0, 'num_input_tokens_seen': 5242880}\n{'loss': 10.4482, 'grad_norm': 2.916527032852173, 'learning_rate': 2.34375e-05, 'epoch': 0.0, 'num_input_tokens_seen': 6291456}\n{'loss': 10.4113, 'grad_norm': 2.911870241165161, 'learning_rate': 2.734375e-05, 'epoch': 0.0, 'num_input_tokens_seen': 7340032}\n{'loss': 10.3863, 'grad_norm': 2.8873367309570312, 'learning_rate': 3.125e-05, 'epoch': 0.0, 'num_input_tokens_seen': 8388608}\n{'loss': 10.3557, 'grad_norm': 2.7183432579040527, 'learning_rate': 3.5156250000000004e-05, 'epoch': 0.0, 'num_input_tokens_seen': 9437184}\n{'loss': 10.2795, 'grad_norm': 2.6743927001953125, 'learning_rate': 3.90625e-05, 'epoch': 0.0, 'num_input_tokens_seen': 10485760}\n{'loss': 10.2148, 'grad_norm': 2.3173940181732178, 'learning_rate': 4.296875e-05, 'epoch': 0.0, 'num_input_tokens_seen': 11534336}\n{'loss': 10.1482, 'grad_norm': 2.09787917137146, 'learning_rate': 4.6875e-05, 'epoch': 0.0, 'num_input_tokens_seen': 12582912}\n{'loss': 10.1024, 'grad_norm': 1.889390468597412, 'learning_rate': 5.0781250000000004e-05, 'epoch': 0.0, 'num_input_tokens_seen': 13631488}\n{'loss': 10.0418, 'grad_norm': 1.8319090604782104, 'learning_rate': 5.46875e-05, 'epoch': 0.0, 'num_input_tokens_seen': 14680064}\n{'loss': 10.0081, 'grad_norm': 1.7302652597427368, 'learning_rate': 5.859375e-05, 'epoch': 0.0, 'num_input_tokens_seen': 15728640}\n{'loss': 9.9525, 'grad_norm': 1.767600417137146, 'learning_rate': 6.25e-05, 'epoch': 0.0, 'num_input_tokens_seen': 16777216}\n{'loss': 9.9326, 'grad_norm': 2.1608240604400635, 'learning_rate': 6.640625e-05, 'epoch': 0.0, 'num_input_tokens_seen': 17825792}\n{'loss': 9.8478, 'grad_norm': 1.7399269342422485, 'learning_rate': 7.031250000000001e-05, 'epoch': 0.0, 'num_input_tokens_seen': 18874368}\n{'loss': 9.8215, 'grad_norm': 1.6564425230026245, 'learning_rate': 7.421875e-05, 'epoch': 0.0, 'num_input_tokens_seen': 19922944}\n{'loss': 9.7732, 'grad_norm': 1.6452653408050537, 'learning_rate': 7.8125e-05, 'epoch': 0.0, 'num_input_tokens_seen': 20971520}\n{'loss': 9.6896, 'grad_norm': 1.7053238153457642, 'learning_rate': 8.203125e-05, 'epoch': 0.0, 'num_input_tokens_seen': 22020096}\n{'loss': 9.6356, 'grad_norm': 1.7050201892852783, 'learning_rate': 8.59375e-05, 'epoch': 0.0, 'num_input_tokens_seen': 23068672}\n{'loss': 9.5781, 'grad_norm': 1.7155998945236206, 'learning_rate': 8.984375e-05, 'epoch': 0.0, 'num_input_tokens_seen': 24117248}\n{'loss': 9.5355, 'grad_norm': 1.697864294052124, 'learning_rate': 9.375e-05, 'epoch': 0.0, 'num_input_tokens_seen': 25165824}\n{'loss': 9.4718, 'grad_norm': 1.7598071098327637, 'learning_rate': 9.765625e-05, 'epoch': 0.0, 'num_input_tokens_seen': 26214400}\n{'loss': 9.3972, 'grad_norm': 1.7407673597335815, 'learning_rate': 0.00010156250000000001, 'epoch': 0.0, 'num_input_tokens_seen': 27262976}\n{'loss': 9.3303, 'grad_norm': 1.7710134983062744, 'learning_rate': 0.00010546875, 'epoch': 0.0, 'num_input_tokens_seen': 28311552}\n{'loss': 9.2973, 'grad_norm': 1.716180682182312, 'learning_rate': 0.000109375, 'epoch': 0.0, 'num_input_tokens_seen': 29360128}\n{'loss': 9.2049, 'grad_norm': 1.7579947710037231, 'learning_rate': 0.00011328125, 'epoch': 0.0, 'num_input_tokens_seen': 30408704}\n{'loss': 9.1656, 'grad_norm': 1.6988558769226074, 'learning_rate': 0.0001171875, 'epoch': 0.0, 'num_input_tokens_seen': 31457280}\n{'loss': 9.0966, 'grad_norm': 1.7036350965499878, 'learning_rate': 0.00012109375, 'epoch': 0.0, 'num_input_tokens_seen': 32505856}\n{'loss': 9.0107, 'grad_norm': 1.752451777458191, 'learning_rate': 0.000125, 'epoch': 0.0, 'num_input_tokens_seen': 33554432}\n{'loss': 8.9788, 'grad_norm': 1.6769776344299316, 'learning_rate': 0.00012890625, 'epoch': 0.0, 'num_input_tokens_seen': 34603008}\n{'loss': 8.9155, 'grad_norm': 1.6497987508773804, 'learning_rate': 0.0001328125, 'epoch': 0.0, 'num_input_tokens_seen': 35651584}\n{'loss': 8.8008, 'grad_norm': 1.722798466682434, 'learning_rate': 0.00013671875, 'epoch': 0.0, 'num_input_tokens_seen': 36700160}\n{'loss': 8.7727, 'grad_norm': 1.6046854257583618, 'learning_rate': 0.00014062500000000002, 'epoch': 0.0, 'num_input_tokens_seen': 37748736}\n{'loss': 8.682, 'grad_norm': 1.6132164001464844, 'learning_rate': 0.00014453125, 'epoch': 0.0, 'num_input_tokens_seen': 38797312}\n{'loss': 8.6516, 'grad_norm': 1.558968424797058, 'learning_rate': 0.0001484375, 'epoch': 0.0, 'num_input_tokens_seen': 39845888}\n{'loss': 8.5935, 'grad_norm': 1.6083673238754272, 'learning_rate': 0.00015234375, 'epoch': 0.0, 'num_input_tokens_seen': 40894464}\n{'loss': 8.4852, 'grad_norm': 1.5469273328781128, 'learning_rate': 0.00015625, 'epoch': 0.0, 'num_input_tokens_seen': 41943040}\n{'loss': 8.4342, 'grad_norm': 1.46219801902771, 'learning_rate': 0.00016015625, 'epoch': 0.01, 'num_input_tokens_seen': 42991616}\n{'loss': 8.3213, 'grad_norm': 1.473191261291504, 'learning_rate': 0.0001640625, 'epoch': 0.01, 'num_input_tokens_seen': 44040192}\n{'loss': 8.3193, 'grad_norm': 1.4024137258529663, 'learning_rate': 0.00016796875000000001, 'epoch': 0.01, 'num_input_tokens_seen': 45088768}\n{'loss': 8.1853, 'grad_norm': 1.3591463565826416, 'learning_rate': 0.000171875, 'epoch': 0.01, 'num_input_tokens_seen': 46137344}\n{'loss': 8.1109, 'grad_norm': 1.3547109365463257, 'learning_rate': 0.00017578125, 'epoch': 0.01, 'num_input_tokens_seen': 47185920}\n{'loss': 8.0741, 'grad_norm': 1.268977403640747, 'learning_rate': 0.0001796875, 'epoch': 0.01, 'num_input_tokens_seen': 48234496}\n{'loss': 8.0032, 'grad_norm': 1.222671389579773, 'learning_rate': 0.00018359375, 'epoch': 0.01, 'num_input_tokens_seen': 49283072}\n{'loss': 7.9346, 'grad_norm': 1.154278039932251, 'learning_rate': 0.0001875, 'epoch': 0.01, 'num_input_tokens_seen': 50331648}\n{'loss': 7.8823, 'grad_norm': 1.1396397352218628, 'learning_rate': 0.00019140625, 'epoch': 0.01, 'num_input_tokens_seen': 51380224}\n{'loss': 7.8444, 'grad_norm': 1.0608373880386353, 'learning_rate': 0.0001953125, 'epoch': 0.01, 'num_input_tokens_seen': 52428800}\n{'loss': 7.7794, 'grad_norm': 1.0165436267852783, 'learning_rate': 0.00019921875000000001, 'epoch': 0.01, 'num_input_tokens_seen': 53477376}\n{'loss': 7.7567, 'grad_norm': 0.8742461204528809, 'learning_rate': 0.00020312500000000002, 'epoch': 0.01, 'num_input_tokens_seen': 54525952}\n{'loss': 7.6489, 'grad_norm': 0.8699902296066284, 'learning_rate': 0.00020703125, 'epoch': 0.01, 'num_input_tokens_seen': 55574528}\n{'loss': 7.6062, 'grad_norm': 0.809831440448761, 'learning_rate': 0.0002109375, 'epoch': 0.01, 'num_input_tokens_seen': 56623104}\n{'loss': 7.5511, 'grad_norm': 0.7423847317695618, 'learning_rate': 0.00021484375, 'epoch': 0.01, 'num_input_tokens_seen': 57671680}\n{'loss': 7.4435, 'grad_norm': 0.7614696025848389, 'learning_rate': 0.00021875, 'epoch': 0.01, 'num_input_tokens_seen': 58720256}\n{'loss': 7.564, 'grad_norm': 0.5147746801376343, 'learning_rate': 0.00022265625, 'epoch': 0.01, 'num_input_tokens_seen': 59768832}\n{'loss': 7.5278, 'grad_norm': 0.4705545902252197, 'learning_rate': 0.0002265625, 'epoch': 0.01, 'num_input_tokens_seen': 60817408}\n{'loss': 7.5479, 'grad_norm': 0.3745419979095459, 'learning_rate': 0.00023046875000000001, 'epoch': 0.01, 'num_input_tokens_seen': 61865984}\n{'loss': 7.4759, 'grad_norm': 0.3893500566482544, 'learning_rate': 0.000234375, 'epoch': 0.01, 'num_input_tokens_seen': 62914560}\n{'loss': 7.5032, 'grad_norm': 0.31959569454193115, 'learning_rate': 0.00023828125, 'epoch': 0.01, 'num_input_tokens_seen': 63963136}\n{'loss': 7.421, 'grad_norm': 0.3203206956386566, 'learning_rate': 0.0002421875, 'epoch': 0.01, 'num_input_tokens_seen': 65011712}\n{'loss': 7.4998, 'grad_norm': 0.2730390429496765, 'learning_rate': 0.00024609375, 'epoch': 0.01, 'num_input_tokens_seen': 66060288}\n{'loss': 7.4157, 'grad_norm': 0.34872403740882874, 'learning_rate': 0.00025, 'epoch': 0.01, 'num_input_tokens_seen': 67108864}\n[2025-03-10 16:17:04 WARNING] '(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 5ef28452-e903-4bd8-946d-f0c77f558a2a)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_4799.jsonl.zst\n[2025-03-10 16:17:04 WARNING] Retrying in 1s [Retry 1/5].\n{'eval_loss': 7.471163749694824, 'eval_runtime': 651.4801, 'eval_samples_per_second': 25.149, 'eval_steps_per_second': 0.196, 'epoch': 0.01, 'num_input_tokens_seen': 67108864}\n{'loss': 7.5083, 'grad_norm': 0.339502215385437, 'learning_rate': 0.00025390625, 'epoch': 0.01, 'num_input_tokens_seen': 68157440}\n{'loss': 7.7083, 'grad_norm': 0.6426190137863159, 'learning_rate': 0.0002578125, 'epoch': 0.01, 'num_input_tokens_seen': 69206016}\n{'loss': 7.446, 'grad_norm': 0.9138129353523254, 'learning_rate': 0.00026171875, 'epoch': 0.01, 'num_input_tokens_seen': 70254592}\n{'loss': 7.3747, 'grad_norm': 1.2179911136627197, 'learning_rate': 0.000265625, 'epoch': 0.01, 'num_input_tokens_seen': 71303168}\n{'loss': 7.367, 'grad_norm': 0.7108445167541504, 'learning_rate': 0.00026953125, 'epoch': 0.01, 'num_input_tokens_seen': 72351744}\n{'loss': 7.4751, 'grad_norm': 0.7580183744430542, 'learning_rate': 0.0002734375, 'epoch': 0.01, 'num_input_tokens_seen': 73400320}\n{'loss': 7.3405, 'grad_norm': 0.7545790076255798, 'learning_rate': 0.00027734375000000003, 'epoch': 0.01, 'num_input_tokens_seen': 74448896}\n{'loss': 7.4194, 'grad_norm': 0.4764443039894104, 'learning_rate': 0.00028125000000000003, 'epoch': 0.01, 'num_input_tokens_seen': 75497472}\n{'loss': 7.2826, 'grad_norm': 0.5942808985710144, 'learning_rate': 0.00028515625, 'epoch': 0.01, 'num_input_tokens_seen': 76546048}\n{'loss': 7.3945, 'grad_norm': 0.5272891521453857, 'learning_rate': 0.0002890625, 'epoch': 0.01, 'num_input_tokens_seen': 77594624}\n{'loss': 7.3492, 'grad_norm': 0.465085506439209, 'learning_rate': 0.00029296875, 'epoch': 0.01, 'num_input_tokens_seen': 78643200}\n{'loss': 7.3658, 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'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: ba6e4c51-f4a4-407e-9934-3772550b7ce9)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_2770.jsonl.zst\n[2025-03-10 18:01:06 WARNING] Retrying in 1s [Retry 1/5].\n[2025-03-10 18:02:30 WARNING] '(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: bdf2cfaa-7e0b-46a0-bec1-b1e573fa7998)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_4386.jsonl.zst\n[2025-03-10 18:02:30 WARNING] Retrying in 1s [Retry 1/5].\n[2025-03-10 18:02:44 WARNING] '(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. 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(read timeout=10)\"), '(Request ID: 2515a0b0-3d81-409f-940c-e78ed5e2dbf8)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3093.jsonl.zst\n[2025-03-10 18:05:26 WARNING] Retrying in 1s [Retry 1/5].\n[2025-03-10 18:06:39 WARNING] '(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: a4c1e0c7-1c7a-4377-bc7e-6f076473072b)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3422.jsonl.zst\n[2025-03-10 18:06:39 WARNING] Retrying in 1s [Retry 1/5].\n[2025-03-10 18:07:37 WARNING] '(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. 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timed out. (read timeout=10)\"), '(Request ID: 0faae356-e828-4cff-9a49-42b397431927)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_185.jsonl.zst\n 9%|▊ | 704/8192 [9:38:28<79:08:04, 38.05s/it]Retrying in 1s [Retry 1/5].\n 9%|▊ | 704/8192 [9:38:28<79:08:04, 38.05s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 9557423f-6937-4f70-b50f-05b0c01f5bf3)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_4035.jsonl.zst\n 9%|▊ | 704/8192 [9:44:58<79:08:04, 38.05s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:28:20<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 939d1d36-c607-4d3c-a0a0-8e447579340b)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_165.jsonl.zst\n 10%|█ | 832/8192 [11:30:25<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:30:25<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 0b99bfd1-07ae-46db-81fa-fc6ef0eabdbc)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_1529.jsonl.zst\n 10%|█ | 832/8192 [11:38:24<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:38:24<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: c208d1bb-5d13-45d2-9a01-1d5a2defa598)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_4562.jsonl.zst\n 10%|█ | 832/8192 [11:39:58<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:39:58<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 2bf98b5c-473b-4e00-aca2-b152efddb992)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_4414.jsonl.zst\n 10%|█ | 832/8192 [11:41:00<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 11%|█ | 896/8192 [12:24:54<77:09:28, 38.07s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 3b8321b9-2d88-4bfa-9eca-b201c444cba3)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_405.jsonl.zst\n 11%|█ | 896/8192 [12:25:55<77:09:28, 38.07s/it]Retrying in 1s [Retry 1/5].\n 11%|█ | 896/8192 [12:25:55<77:09:28, 38.07s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. 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'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 918552576}\n{'loss': 2.8372, 'grad_norm': 0.3432702422142029, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 919601152}\n{'loss': 2.5638, 'grad_norm': 0.3493041396141052, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 920649728}\n{'loss': 2.8759, 'grad_norm': 0.3401539623737335, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 921698304}\n{'loss': 3.0048, 'grad_norm': 0.4632040858268738, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 922746880}\n{'loss': 2.9394, 'grad_norm': 0.4968065023422241, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 923795456}\n{'loss': 2.8441, 'grad_norm': 0.5426673889160156, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 924844032}\n{'loss': 2.9975, 'grad_norm': 0.4630672037601471, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 925892608}\n{'loss': 2.9584, 'grad_norm': 0.38806748390197754, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 926941184}\n{'loss': 2.8904, 'grad_norm': 0.39797642827033997, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 927989760}\n{'loss': 2.5774, 'grad_norm': 0.4063512980937958, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 929038336}\n{'loss': 2.812, 'grad_norm': 0.3161136209964752, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 930086912}\n{'loss': 2.7483, 'grad_norm': 0.3628361225128174, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 931135488}\n{'loss': 2.7916, 'grad_norm': 0.37376269698143005, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 932184064}\n{'loss': 2.7985, 'grad_norm': 0.3399117887020111, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 933232640}\n{'loss': 2.7107, 'grad_norm': 0.3453179597854614, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 934281216}\n{'loss': 2.9254, 'grad_norm': 0.39461833238601685, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 935329792}\n{'loss': 2.8487, 'grad_norm': 0.3668413460254669, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 936378368}\n{'loss': 2.7928, 'grad_norm': 0.28304487466812134, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 937426944}\n{'loss': 2.8503, 'grad_norm': 0.35816267132759094, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 938475520}\n{'loss': 3.0328, 'grad_norm': 0.3540339469909668, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 939524096}\n[2025-03-11 03:46:08 WARNING] '(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 3b8321b9-2d88-4bfa-9eca-b201c444cba3)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_405.jsonl.zst\n[2025-03-11 03:46:08 WARNING] Retrying in 1s [Retry 1/5].\n[2025-03-11 03:53:27 WARNING] '(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: a98a238a-c0a4-4295-8502-316a89a7ae29)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_2524.jsonl.zst\n[2025-03-11 03:53:27 WARNING] Retrying in 1s [Retry 1/5].\n{'eval_loss': 2.7651162147521973, 'eval_runtime': 687.962, 'eval_samples_per_second': 23.815, 'eval_steps_per_second': 0.186, 'epoch': 0.11, 'num_input_tokens_seen': 939524096}\n{'loss': 2.9368, 'grad_norm': 0.34962671995162964, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 940572672}\n{'loss': 2.3627, 'grad_norm': 0.37516310811042786, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 941621248}\n{'loss': 2.8854, 'grad_norm': 0.3487492501735687, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 942669824}\n{'loss': 2.7892, 'grad_norm': 0.37180987000465393, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 943718400}\n{'loss': 2.8067, 'grad_norm': 0.3387952744960785, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 944766976}\n{'loss': 2.841, 'grad_norm': 0.32076528668403625, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 945815552}\n{'loss': 2.7965, 'grad_norm': 0.3348572552204132, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 946864128}\n{'loss': 2.6788, 'grad_norm': 0.3531329929828644, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 947912704}\n{'loss': 2.7276, 'grad_norm': 0.300353467464447, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 948961280}\n{'loss': 2.8189, 'grad_norm': 0.3258875012397766, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 950009856}\n{'loss': 2.8388, 'grad_norm': 0.3434987962245941, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 951058432}\n{'loss': 2.856, 'grad_norm': 0.33045029640197754, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 952107008}\n{'loss': 2.658, 'grad_norm': 0.34896957874298096, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 953155584}\n{'loss': 2.8484, 'grad_norm': 0.3819083273410797, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 954204160}\n{'loss': 2.8402, 'grad_norm': 0.39541998505592346, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 955252736}\n{'loss': 2.8281, 'grad_norm': 0.3843367397785187, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 956301312}\n{'loss': 2.8339, 'grad_norm': 0.4067714214324951, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 957349888}\n{'loss': 2.8693, 'grad_norm': 0.3071018159389496, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 958398464}\n{'loss': 2.6747, 'grad_norm': 0.3676702380180359, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 959447040}\n{'loss': 2.6961, 'grad_norm': 0.357799232006073, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 960495616}\n{'loss': 2.7944, 'grad_norm': 0.318391352891922, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 961544192}\n{'loss': 2.8084, 'grad_norm': 0.32000190019607544, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 962592768}\n{'loss': 2.8024, 'grad_norm': 0.3250137269496918, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 963641344}\n{'loss': 2.7951, 'grad_norm': 0.33021438121795654, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 964689920}\n{'loss': 2.8069, 'grad_norm': 0.3257495164871216, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 965738496}\n{'loss': 2.8148, 'grad_norm': 0.3608018159866333, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 966787072}\n[2025-03-11 04:13:12 WARNING] '(ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 36a7cc72-4605-416a-8742-59488d719150)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_5267.jsonl.zst\n[2025-03-11 04:13:12 WARNING] Retrying in 1s [Retry 1/5].\n{'loss': 2.8089, 'grad_norm': 0.3657573163509369, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 967835648}\n{'loss': 2.8243, 'grad_norm': 0.3791966736316681, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 968884224}\n{'loss': 2.6837, 'grad_norm': 0.4036826193332672, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 969932800}\n{'loss': 2.6694, 'grad_norm': 0.34643635153770447, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 970981376}\n{'loss': 2.8455, 'grad_norm': 0.35321497917175293, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 972029952}\n{'loss': 2.5156, 'grad_norm': 0.3488744795322418, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 973078528}\n{'loss': 2.7185, 'grad_norm': 0.33396172523498535, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 974127104}\n{'loss': 2.856, 'grad_norm': 0.36425134539604187, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 975175680}\n{'loss': 2.7639, 'grad_norm': 0.34361588954925537, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 976224256}\n{'loss': 2.7777, 'grad_norm': 0.45501893758773804, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 977272832}\n{'loss': 2.8692, 'grad_norm': 0.4391760230064392, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 978321408}\n{'loss': 2.7885, 'grad_norm': 0.385729044675827, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 979369984}\n{'loss': 2.8622, 'grad_norm': 0.4122815728187561, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 980418560}\n{'loss': 2.674, 'grad_norm': 0.3223947584629059, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 981467136}\n{'loss': 2.7148, 'grad_norm': 0.39820024371147156, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 982515712}\n{'loss': 2.6975, 'grad_norm': 0.38311144709587097, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 983564288}\n{'loss': 2.8515, 'grad_norm': 0.4324709177017212, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 984612864}\n{'loss': 2.5684, 'grad_norm': 0.3579341471195221, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 985661440}\n{'loss': 2.9478, 'grad_norm': 0.4081536531448364, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 986710016}\n{'loss': 2.7375, 'grad_norm': 0.4332145154476166, 'learning_rate': 0.001, 'epoch': 0.11, 'num_input_tokens_seen': 987758592}\n{'loss': 2.7773, 'grad_norm': 0.43510711193084717, 'learning_rate': 0.001, 'epoch': 0.12, 'num_input_tokens_seen': 988807168}\n...\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 1378, in _iter_from_urlpaths\n raise FileNotFoundError(urlpath)\nFileNotFoundError: zstd://example_train_1215.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_1215.jsonl.zst\n```\n\n</details>",
"Two more today:\n```python\nFileNotFoundError: zstd://example_holdout_5012.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_5012.jsonl.zst\n```\nand\n```python\nFileNotFoundError: zstd://example_holdout_3073.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk2/example_holdout_3073.jsonl.zst\n```\nboth of which exist on the hub ([here](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/validation/chunk4/example_holdout_5012.jsonl.zst) and [here](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/validation/chunk2/example_holdout_3073.jsonl.zst)).",
"I also observe the same thing when using streaming with DCLM dataset with 64 GPUs. I have tried ```export HF_DATASETS_STREAMING_PARALLELISM=1``` but doesn't help.",
"Another error today, this time a 504 gateway timeout `HfHubHTTPError`. I have no idea if this is related, but I suspect that it is considering the setup is identical. Notably though, the two errors I posted yesterday were for evaluation (hence the `holdout` in the URLs) whereas today there was no problem doing that first evaluation, but now the `train` split failed.\n```python\n...\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 2226, in __iter__\n for key, example in ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1499, in __iter__\n for x in self.ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1067, in __iter__\n yield from self._iter()\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1231, in _iter\n for key, transformed_example in iter_outputs():\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1207, in iter_outputs\n for i, key_example in inputs_iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1111, in iter_inputs\n for key, example in iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 371, in __iter__\n for key, pa_table in self.generate_tables_fn(**gen_kwags):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 114, in _generate_tables\n with open(file, \"rb\") as f:\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/streaming.py\", line 75, in wrapper\n return function(*args, download_config=download_config, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 948, in xopen\n file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 147, in open\n return self.__enter__()\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 105, in __enter__\n f = self.fs.open(self.path, mode=mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py\", line 1301, in open\n f = self._open(\n ^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/filesystems/compression.py\", line 85, in _open\n return self._open_with_fsspec().open()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 147, in open\n return self.__enter__()\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 105, in __enter__\n f = self.fs.open(self.path, mode=mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py\", line 1301, in open\n f = self._open(\n ^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 234, in _open\n return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 691, in __init__\n self.details = fs.info(self.resolved_path.unresolve(), expand_info=False)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 524, in info\n self.ls(parent_path, expand_info=False)\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 284, in ls\n out = self._ls_tree(path, refresh=refresh, revision=revision, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 375, in _ls_tree\n for path_info in tree:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_api.py\", line 3080, in list_repo_tree\n for path_info in paginate(path=tree_url, headers=headers, params={\"recursive\": recursive, \"expand\": expand}):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_pagination.py\", line 46, in paginate\n hf_raise_for_status(r)\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_http.py\", line 477, in hf_raise_for_status\n raise _format(HfHubHTTPError, str(e), response) from e\nhuggingface_hub.errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/cerebras/SlimPajama-627B/tree/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train%2Fchunk8?recursive=False&expand=False&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5amFIVnVhemd2WlhoaGJYQnNaVjkwY21GcGJsOHpOams0TG1wemIyNXNMbnB6ZENKOTozMDAw\n```"
] | 2025-03-07T19:14:18
| 2025-03-13T15:00:47
| null |
NONE
|
{
"completed": 0,
"percent_completed": 0,
"total": 0
}
| null |
### Describe the bug
In https://github.com/huggingface/datasets/issues/6843 it was noted that the streaming feature of `datasets` is highly susceptible to outages and doesn't back off for long (or even *at all*).
I was training a model while streaming SlimPajama and training crashed with a `FileNotFoundError`. I can only assume that this was due to a momentary outage considering the file in question, `train/chunk9/example_train_3889.jsonl.zst`, [exists like all other files in SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/train/chunk9/example_train_3889.jsonl.zst).
```python
...
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 2226, in __iter__
for key, example in ex_iterable:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1499, in __iter__
for x in self.ex_iterable:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1067, in __iter__
yield from self._iter()
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1231, in _iter
for key, transformed_example in iter_outputs():
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1207, in iter_outputs
for i, key_example in inputs_iterator:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1111, in iter_inputs
for key, example in iterator:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 371, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py", line 99, in _generate_tables
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/track.py", line 50, in __iter__
for x in self.generator(*self.args):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py", line 1378, in _iter_from_urlpaths
raise FileNotFoundError(urlpath)
FileNotFoundError: zstd://example_train_3889.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_3889.jsonl.zst
```
That final `raise` is at the bottom of the following snippet:
https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/utils/file_utils.py#L1354-L1379
So clearly, something choked up in `xisfile`.
### Steps to reproduce the bug
This happens when streaming a dataset and iterating over it. In my case, that iteration is done in Trainer's `inner_training_loop`, but this is not relevant to the iterator.
```python
File "/miniconda3/envs/draft/lib/python3.11/site-packages/accelerate/data_loader.py", line 835, in __iter__
next_batch, next_batch_info = self._fetch_batches(main_iterator)
```
### Expected behavior
This bug and the linked issue have one thing in common: *when streaming fails to retrieve an example, the entire program gives up and crashes*. As users, we cannot even protect ourselves from this: when we are iterating over a dataset, we can't make `datasets` skip over a bad example or wait a little longer to retry the iteration, because when a Python generator/iterator raises an error, it loses all its context.
In other words: if you have something that looks like `for b in a: for c in b: for d in c:`, errors in the innermost loop can only be caught by a `try ... except` in `c.__iter__()`. There should be such exception handling in `datasets` and it should have a **configurable exponential back-off**: first wait and retry after 1 minute, then 2 minutes, then 4 minutes, then 8 minutes, ... and after a given amount of retries, **skip the bad example**, and **only after** skipping a given amount of examples, give up and crash. This was requested in https://github.com/huggingface/datasets/issues/6843 too, since currently there is only linear backoff *and* it is clearly not applied to `xisfile`.
### Environment info
- `datasets` version: 3.3.2 *(the latest version)*
- Platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.11.7
- `huggingface_hub` version: 0.26.5
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2024.10.0
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`Dataset.map` ignores existing caches and remaps when ran with different `num_proc`
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[
"This feels related: https://github.com/huggingface/datasets/issues/3044",
"@lhoestq This comment specifically, I agree:\n\n* https://github.com/huggingface/datasets/issues/3044#issuecomment-1239877570\n\n> Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the cached datasets are saved with a template at the end of the filename (something like \"000001_of_000008\" for every process of num_proc). So if in the next time you run the script you set num_proc to a different number, the cache cannot be used.\n> \n> Is there any way to get around this? I am processing a huge dataset so I do the processing on one machine and then transfer the processed data to another in its cache dir but currently that's not possible due to num_proc mismatch.\n\n"
] | 2025-03-03T05:51:26
| 2025-03-04T05:55:08
| null |
CONTRIBUTOR
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### Describe the bug
If you `map` a dataset and save it to a specific `cache_file_name` with a specific `num_proc`, and then call map again with that same existing `cache_file_name` but a different `num_proc`, the dataset will be re-mapped.
### Steps to reproduce the bug
1. Download a dataset
```python
import datasets
dataset = datasets.load_dataset("ylecun/mnist")
```
```
Generating train split: 100%|██████████| 60000/60000 [00:00<00:00, 116429.85 examples/s]
Generating test split: 100%|██████████| 10000/10000 [00:00<00:00, 103310.27 examples/s]
```
2. `map` and cache it with a specific `num_proc`
```python
cache_file_name="./cache/train.map"
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=2)
```
```
Map (num_proc=2): 100%|██████████| 60000/60000 [00:01<00:00, 53764.03 examples/s]
```
3. `map` it with a different `num_proc` and the same `cache_file_name` as before
```python
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=3)
```
```
Map (num_proc=3): 100%|██████████| 60000/60000 [00:00<00:00, 65377.12 examples/s]
```
### Expected behavior
If I specify an existing `cache_file_name`, I don't expect using a different `num_proc` than the one that was used to generate it to cause the dataset to have be be re-mapped.
### Environment info
```console
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```
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Issues with large Datasets
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[
"what's the error message ?",
"This was the final error message that it was giving pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0",
"Here is the list of errors:\n\nTraceback (most recent call last):\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 160, in _generate_tables\n df = pandas_read_json(f)\n ^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 38, in pandas_read_json\n return pd.read_json(path_or_buf, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 815, in read_json\n return json_reader.read()\n ^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1025, in read\n obj = self._get_object_parser(self.data)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1051, in _get_object_parser\n obj = FrameParser(json, **kwargs).parse()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1187, in parse\n self._parse()\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1402, in _parse\n self.obj = DataFrame(\n ^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 778, in __init__\n mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 503, in dict_to_mgr\n return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 114, in arrays_to_mgr\n index = _extract_index(arrays)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 677, in _extract_index\n raise ValueError(\"All arrays must be of the same length\")\nValueError: All arrays must be of the same length\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1854, in _prepare_split_single\n for _, table in generator:\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 163, in _generate_tables\n raise e\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 137, in _generate_tables\n pa_table = paj.read_json(\n ^^^^^^^^^^^^^^\n File \"pyarrow/_json.pyx\", line 308, in pyarrow._json.read_json\n File \"pyarrow/error.pxi\", line 155, in pyarrow.lib.pyarrow_internal_check_status\n File \"pyarrow/error.pxi\", line 92, in pyarrow.lib.check_status\npyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to number in row 0\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"run_object_detection.py\", line 582, in <module>\n main()\n File \"run_object_detection.py\", line 407, in main\n dataset = load_dataset(\n ^^^^^^^^^^^^^\n File \"venv/lib/python3.12/site-packages/datasets/load.py\", line 2151, in load_dataset\n builder_instance.download_and_prepare(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 924, in download_and_prepare\n self._download_and_prepare(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1000, in _download_and_prepare\n self._prepare_split(split_generator, **prepare_split_kwargs)\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1741, in _prepare_split\n for job_id, done, content in self._prepare_split_single(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1897, in _prepare_split_single\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset",
"`datasets` is based on Arrow which expects all the lists inside the data to be of fixed type. Arrow can't load lists that contain a mix of integers and strings for example. In your case it looks like one of the lists contains numbers and JSON objects.\n\nI'd suggest you to reformat the data to end up with list of fixed types, otherwise you won't be able to load the data in `datasets`"
] | 2025-02-28T14:05:22
| 2025-03-04T15:02:26
| null |
NONE
|
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| null |
### Describe the bug
If the coco annotation file is too large the dataset will not be able to load it, not entirely sure were the issue is but I am guessing it is due to the code trying to load it all as one line into a dataframe. This was for object detections.
My current work around is the following code but would be nice to be able to do it without worrying about it also probably there is a better way of doing it:
`
dataset_dict = json.load(open("./local_data/annotations/train.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'], 'categories': dataset_dict['categories']}, ignore_index=True)
train=Dataset.from_pandas(df)
dataset_dict = json.load(open("./local_data/annotations/validation.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'],
'categories': dataset_dict['categories']}, ignore_index=True)
val = Dataset.from_pandas(df)
dataset_dict = json.load(open("./local_data/annotations/test.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'],
'categories': dataset_dict['categories']}, ignore_index=True)
test = Dataset.from_pandas(df)
dataset = DatasetDict({'train': train, 'validation': val, 'test': test})
`
### Steps to reproduce the bug
1) step up directory in and have the json files in coco format
-local_data
|-images
|---1.jpg
|---2.jpg
|---....
|---n.jpg
|-annotations
|---test.json
|---train.json
|---validation.json
2) try to load local_data into a dataset if the file is larger than about 300kb it will cause an error.
### Expected behavior
That it loads the jsons preferably in the same format as it has done with a smaller size.
### Environment info
- `datasets` version: 3.3.3.dev0
- Platform: Linux-6.11.0-17-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.29.0
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps6sEvFN
| 7,430
|
Error in code "Time to slice and dice" from course "NLP Course"
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[
"You should open an issue in the NLP course website / github page. I'm closing this issue if you don't mind",
"ok, i don't mind, i'll mark the error there"
] | 2025-02-28T11:36:10
| 2025-03-05T11:32:47
| 2025-03-03T17:52:15
|
NONE
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### Describe the bug
When we execute code
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "condition": "frequency"})
)
frequencies.head()
```
answer should be like this
condition | frequency
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
but he is different
frequency | count
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
this is not correct, correct code
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "count": "frequency"})
)
````
### Steps to reproduce the bug
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "condition": "frequency"})
)
frequencies.head()
```
### Expected behavior
condition | frequency
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
### Environment info
Google Colab
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I_kwDODunzps6sBVy7
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|
Error splitting the input into NAL units.
|
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"First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`",
"> First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`\n\nany recommendation for `multiprocess` and `dill`"
] | 2025-02-28T02:30:15
| 2025-03-04T01:40:28
| null |
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### Describe the bug
I am trying to finetune qwen2.5-vl on 16 * 80G GPUS, and I use `LLaMA-Factory` and set `preprocessing_num_workers=16`. However, I met the following error and the program seem to got crush. It seems that the error come from `datasets` library
The error logging is like following:
```text
Converting format of dataset (num_proc=16): 100%|█████████▉| 19265/19267 [11:44<00:00, 5.88 examples/s]
Converting format of dataset (num_proc=16): 100%|█████████▉| 19266/19267 [11:44<00:00, 5.02 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 19267/19267 [11:44<00:00, 5.44 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 19267/19267 [11:44<00:00, 27.34 examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [00:00<?, ? examples/s]
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
```
### Others
_No response_
### Steps to reproduce the bug
None
### Expected behavior
excpect to run successfully
### Environment info
```
transformers==4.49.0
datasets==3.2.0
accelerate==1.2.1
peft==0.12.0
trl==0.9.6
tokenizers==0.21.0
gradio>=4.38.0,<=5.18.0
pandas>=2.0.0
scipy
einops
sentencepiece
tiktoken
protobuf
uvicorn
pydantic
fastapi
sse-starlette
matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0
av
librosa
tyro<0.9.0
openlm-hub
qwen-vl-utils
```
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| 2,883,684,686
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I_kwDODunzps6r4YlO
| 7,425
|
load_dataset("livecodebench/code_generation_lite", version_tag="release_v2") TypeError: 'NoneType' object is not callable
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[
"> datasets\n\nHi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n\n",
"Hey guys,\nI tried to reproduce the issue and it works fine. I used google colab as enviroment.\n\n",
"> Hey guys, I tried to reproduce the issue and it works fine. I used google colab as enviroment.\n> \n> \n\nThanks for your kind reply! I wonder which Python version do you use? My Python version is 3.11.11 and datasets version is 3.3.2 but I still met this bug.\n\n<img width=\"1121\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/7c2c5007-ee55-4030-94b9-01fcdea0bf4a\" />",
"@zwxandy It's Python 3.11.11",
"@Serzhanov @zwxandy I have met the same problem, have this problem be solved?",
"> [@Serzhanov](https://github.com/Serzhanov) [@zwxandy](https://github.com/zwxandy) I have met the same problem, have this problem be solved?\n\nI try to downgrade datasets version to 2.20.0，and it works for me @Serzhanov @dshwei , hope this work for you too :)"
] | 2025-02-27T07:36:02
| 2025-03-10T07:49:45
| null |
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### Describe the bug
from datasets import load_dataset
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
or
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
both error:
Traceback (most recent call last):
File "", line 1, in
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 2131, in load_dataset
builder_instance = load_dataset_builder(
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 1888, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
TypeError: 'NoneType' object is not callable
### Steps to reproduce the bug
from datasets import get_dataset_config_names
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
OR
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
### Expected behavior
load datasets livecodebench/code_generation_lite
### Environment info
import datasets
version '3.3.2'
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Row indexing a dataset with numpy integers
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[
"Would be cool to be consistent when it comes to indexing with numpy objects, if we do accept numpy arrays we should indeed accept numpy integers. Your idea sounds reasonable, I'd also be in favor of adding a simple test as well"
] | 2025-02-25T18:44:45
| 2025-03-03T17:55:24
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### Feature request
Allow indexing datasets with a scalar numpy integer type.
### Motivation
Indexing a dataset with a scalar numpy.int* object raises a TypeError. This is due to the test in `datasets/formatting/formatting.py:key_to_query_type`
``` python
def key_to_query_type(key: Union[int, slice, range, str, Iterable]) -> str:
if isinstance(key, int):
return "row"
elif isinstance(key, str):
return "column"
elif isinstance(key, (slice, range, Iterable)):
return "batch"
_raise_bad_key_type(key)
```
In the row case, it checks if key is an int, which returns false when key is integer like but not a builtin python integer type. This is counterintuitive because a numpy array of np.int64s can be used for the batch case.
For example:
``` python
import numpy as np
import datasets
dataset = datasets.Dataset.from_dict({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
# Regular indexing
dataset[0]
dataset[:2]
# Indexing with numpy data types (expect same results)
idx = np.asarray([0, 1])
dataset[idx] # Succeeds when using an array of np.int64 values
dataset[idx[0]] # Fails with TypeError when using scalar np.int64
```
For the user, this can be solved by wrapping `idx[0]` in `int` but the test could also be changed in `key_to_query_type` to accept a less strict definition of int.
``` diff
+import numbers
+
def key_to_query_type(key: Union[int, slice, range, str, Iterable]) -> str:
+ if isinstance(key, numbers.Integral):
- if isinstance(key, int):
return "row"
elif isinstance(key, str):
return "column"
elif isinstance(key, (slice, range, Iterable)):
return "batch"
_raise_bad_key_type(key)
```
Looking at how others do it, pandas has an `is_integer` definition that it checks which uses `is_integer_object` defined in `pandas/_libs/utils.pxd`:
``` cython
cdef inline bint is_integer_object(object obj) noexcept:
"""
Cython equivalent of
`isinstance(val, (int, np.integer)) and not isinstance(val, (bool, np.timedelta64))`
Parameters
----------
val : object
Returns
-------
is_integer : bool
Notes
-----
This counts np.timedelta64 objects as integers.
"""
return (not PyBool_Check(obj) and isinstance(obj, (int, cnp.integer))
and not is_timedelta64_object(obj))
```
This would be less flexible as it explicitly checks for numpy integer, but worth noting that they had the need to ensure the key is not a bool.
### Your contribution
I can submit a pull request with the above changes after checking that indexing succeeds with the numpy integer type. Or if there is a different integer check that would be preferred I could add that.
If there is a reason not to want this behavior that is fine too.
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I_kwDODunzps6rkG0c
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|
DVC integration broken
|
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[
"Unfortunately `url` is a reserved argument in `fsspec.url_to_fs`, so ideally file system implementations like DVC should use another argument name to avoid this kind of errors"
] | 2025-02-25T13:14:31
| 2025-03-03T17:42:02
| null |
NONE
|
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### Describe the bug
The DVC integration seems to be broken.
Followed this guide: https://dvc.org/doc/user-guide/integrations/huggingface
### Steps to reproduce the bug
#### Script to reproduce
~~~python
from datasets import load_dataset
dataset = load_dataset(
"csv",
data_files="dvc://workshop/satellite-data/jan_train.csv",
storage_options={"url": "https://github.com/iterative/dataset-registry.git"},
)
print(dataset)
~~~
#### Error log
~~~
Traceback (most recent call last):
File "C:\tmp\test\load.py", line 3, in <module>
dataset = load_dataset(
^^^^^^^^^^^^^
File "C:\tmp\test\.venv\Lib\site-packages\datasets\load.py", line 2151, in load_dataset
builder_instance.download_and_prepare(
File "C:\tmp\test\.venv\Lib\site-packages\datasets\builder.py", line 808, in download_and_prepare
fs, output_dir = url_to_fs(output_dir, **(storage_options or {}))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: url_to_fs() got multiple values for argument 'url'
~~~
### Expected behavior
Integration would work and the indicated file is downloaded and opened.
### Environment info
#### Python version
~~~
python --version
Python 3.11.10
~~~
#### Venv (pip install datasets dvc):
~~~
Package Version
---------------------- -----------
aiohappyeyeballs 2.4.6
aiohttp 3.11.13
aiohttp-retry 2.9.1
aiosignal 1.3.2
amqp 5.3.1
annotated-types 0.7.0
antlr4-python3-runtime 4.9.3
appdirs 1.4.4
asyncssh 2.20.0
atpublic 5.1
attrs 25.1.0
billiard 4.2.1
celery 5.4.0
certifi 2025.1.31
cffi 1.17.1
charset-normalizer 3.4.1
click 8.1.8
click-didyoumean 0.3.1
click-plugins 1.1.1
click-repl 0.3.0
colorama 0.4.6
configobj 5.0.9
cryptography 44.0.1
datasets 3.3.2
dictdiffer 0.9.0
dill 0.3.8
diskcache 5.6.3
distro 1.9.0
dpath 2.2.0
dulwich 0.22.7
dvc 3.59.1
dvc-data 3.16.9
dvc-http 2.32.0
dvc-objects 5.1.0
dvc-render 1.0.2
dvc-studio-client 0.21.0
dvc-task 0.40.2
entrypoints 0.4
filelock 3.17.0
flatten-dict 0.4.2
flufl-lock 8.1.0
frozenlist 1.5.0
fsspec 2024.12.0
funcy 2.0
gitdb 4.0.12
gitpython 3.1.44
grandalf 0.8
gto 1.7.2
huggingface-hub 0.29.1
hydra-core 1.3.2
idna 3.10
iterative-telemetry 0.0.10
kombu 5.4.2
markdown-it-py 3.0.0
mdurl 0.1.2
multidict 6.1.0
multiprocess 0.70.16
networkx 3.4.2
numpy 2.2.3
omegaconf 2.3.0
orjson 3.10.15
packaging 24.2
pandas 2.2.3
pathspec 0.12.1
platformdirs 4.3.6
prompt-toolkit 3.0.50
propcache 0.3.0
psutil 7.0.0
pyarrow 19.0.1
pycparser 2.22
pydantic 2.10.6
pydantic-core 2.27.2
pydot 3.0.4
pygit2 1.17.0
pygments 2.19.1
pygtrie 2.5.0
pyparsing 3.2.1
python-dateutil 2.9.0.post0
pytz 2025.1
pywin32 308
pyyaml 6.0.2
requests 2.32.3
rich 13.9.4
ruamel-yaml 0.18.10
ruamel-yaml-clib 0.2.12
scmrepo 3.3.10
semver 3.0.4
setuptools 75.8.0
shellingham 1.5.4
shortuuid 1.0.13
shtab 1.7.1
six 1.17.0
smmap 5.0.2
sqltrie 0.11.2
tabulate 0.9.0
tomlkit 0.13.2
tqdm 4.67.1
typer 0.15.1
typing-extensions 4.12.2
tzdata 2025.1
urllib3 2.3.0
vine 5.1.0
voluptuous 0.15.2
wcwidth 0.2.13
xxhash 3.5.0
yarl 1.18.3
zc-lockfile 3.0.post1
~~~
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I_kwDODunzps6rcJRI
| 7,420
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better correspondence between cached and saved datasets created using from_generator
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[] | 2025-02-24T22:14:37
| 2025-02-26T03:10:22
| null |
CONTRIBUTOR
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### Feature request
At the moment `.from_generator` can only create a dataset that lives in the cache. The cached dataset cannot be loaded with `load_from_disk` because the cache folder is missing `state.json`. So the only way to convert this cached dataset to a regular is to use `save_to_disk` which needs to create a copy of the cached dataset. For large datasets this can end up wasting a lot of space. In my case the saving operation failed so I am stuck with a large cached dataset and no clear way to convert to a `Dataset` that I can use. The requested feature is to provide a way to be able to load a cached dataset using `.load_from_disk`. Alternatively `.from_generator` can create the dataset at a specified location so that it can be loaded from there with `.load_from_disk`.
### Motivation
I have the following workflow which has exposed some awkwardness about the Datasets saving/caching.
1. I created a cached dataset using `.from_generator` which was cached in a folder. This dataset is rather large (~600GB) with many shards.
2. I tried to save this dataset using `.save_to_disk` to another location so that I can use later as a `Dataset`. This essentially creates another copy (for a total of 1.2TB!) of what is already in the cache... In my case the saving operation keeps dying for some reason and I am stuck with a cached dataset and no copy.
3. Now I am trying to "save" the existing cached dataset but it is not clear how to access the cached files after `.from_generator` has finished e.g. from a different process. I should not be even looking at the cache but I really do not want to waste another 2hr to generate the set so that if fails agains (I already did this couple of times).
- I tried `.load_from_disk` but it does not work with cached files and complains that this is not a `Dataset` (!).
- I looked at `.from_file` which takes one file but the cached file has many (shards) so I am not sure how to make this work.
- I tried `.load_dataset` but this seems to either try to "download" a copy (of a file which is already in the local file system!) which I will then need to save or I need to use `streaming=False` to create an `IterableDataset `which then I need to convert (using the cache) to `Dataset` so that I can save it. With both options I will end up with 3 copies of the same dataset for a total of ~2TB! I am hoping here is another way to do this...
Maybe I am missing something here: I looked at docs and forums but no luck. I have a bunch of arrow files cached by `Dataset.from_generator` and no clean way to make them into a `Dataset` that I can use.
This all could be so much easer if `load_from_disk` can recognize the cached files and produce a `Dataset`: after the cache is created I would not have to "save" it again and I can just load it when I need. At the moment `load_from_disk` needs `state.json` which is lacking in the cache folder. So perhaps `.from_generator` could be made to "finalize" (e.g. create `state.json`) the dataset once it is done so that it can be loaded easily. Or provide `.from_generator` with a `save_to_dir` parameter in addition to `cache_dir` which can be used for the whole process including creating the `state.json` at the end.
As a proof of concept I just created `state.json` by hand and `load_from_disk` worked using the cache! So it seems to be the missing piece here.
### Your contribution
Time permitting I can look into `.from_generator` to see if adding `state.json` is feasible.
| null |
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I_kwDODunzps6rZrZ4
| 7,419
|
Import order crashes script execution
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[] | 2025-02-24T17:03:43
| 2025-02-24T17:03:43
| null |
NONE
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### Describe the bug
Hello,
I'm trying to convert an HF dataset into a TFRecord so I'm importing `tensorflow` and `datasets` to do so.
Depending in what order I'm importing those librairies, my code hangs forever and is unkillable (CTRL+C doesn't work, I need to kill my shell entirely).
Thank you for your help
🙏
### Steps to reproduce the bug
If you run the following script, this will hang forever :
```python
import tensorflow as tf
import datasets
dataset = datasets.load_dataset("imagenet-1k", split="validation", streaming=True)
print(next(iter(dataset)))
```
however running the following will work fine (I just changed the order of the imports) :
```python
import datasets
import tensorflow as tf
dataset = datasets.load_dataset("imagenet-1k", split="validation", streaming=True)
print(next(iter(dataset)))
```
### Expected behavior
I'm expecting the script to reach the end and my case print the content of the first item in the dataset
```
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=408x500 at 0x70C646A03110>, 'label': 91}
```
### Environment info
```
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.35
- Python version: 3.11.7
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```
I'm also using `tensorflow==2.18.0`.
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I_kwDODunzps6q_Okf
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pyarrow.lib.arrowinvalid: cannot mix list and non-list, non-null values with map function
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[
"@lhoestq ",
"Can you try passing text: None for the image object ? Pyarrow expects all the objects to have the exact same type, in particular the dicttionaries in \"content\" should all have the keys \"type\" and \"text\"",
"The following modification on system prompt works, but it is different from the usual way to use it.\n```\ndef make_conversation(example):\n prompt = []\n\n prompt.append({\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": system_prompt}]})\n prompt.append(\n {\n \"role\": \"user\", \n \"content\": [\n {\"type\": \"image\"},\n {\"type\": \"text\", \"text\": example[\"problem\"]},\n ]\n }\n )\n return {\"prompt\": prompt}\n```",
"Good to know ! But yes Arrow / Parquet have this typing limitation (which is great to ensure data integrity, but constraining at the same time). It's is really blocking you, feel free to ping the arrow team / community if they plan to have a Union type or a JSON type"
] | 2025-02-21T10:58:06
| 2025-02-25T15:26:46
| null |
NONE
|
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### Describe the bug
Encounter pyarrow.lib.arrowinvalid error with map function in some example when loading the dataset
### Steps to reproduce the bug
```
from datasets import load_dataset
from PIL import Image, PngImagePlugin
dataset = load_dataset("leonardPKU/GEOQA_R1V_Train_8K")
system_prompt="You are a helpful AI Assistant"
def make_conversation(example):
prompt = []
prompt.append({"role": "system", "content": system_prompt})
prompt.append(
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": example["problem"]},
]
}
)
return {"prompt": prompt}
def check_data_types(example):
for key, value in example.items():
if key == 'image':
if not isinstance(value, PngImagePlugin.PngImageFile):
print(value)
if key == "problem" or key == "solution":
if not isinstance(value, str):
print(value)
return example
dataset = dataset.map(check_data_types)
dataset = dataset.map(make_conversation)
```
### Expected behavior
Successfully process the dataset with map
### Environment info
datasets==3.3.1
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| 2,865,774,546
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I_kwDODunzps6q0D_S
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Shard Dataset at specific indices
|
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[
"Hi ! if it's an option I'd suggest to have one sequence per row instead.\n\nOtherwise you'd have to make your own save/load mechanism",
"Saving one sequence per row is very difficult and heavy and makes all the optimizations pointless. How would a custom save/load mechanism look like?",
"You can use `pyarrow` for example to save/load individual arrow or parquet files and control what they contain"
] | 2025-02-20T10:43:10
| 2025-02-24T11:06:45
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I have a dataset of sequences, where each example in the sequence is a separate row in the dataset (similar to LeRobotDataset). When running `Dataset.save_to_disk` how can I provide indices where it's possible to shard the dataset such that no episode spans more than 1 shard. Consequently, when I run `Dataset.load_from_disk`, how can I load just a subset of the shards to save memory and time on different ranks?
I guess an alternative to this would be, given a loaded `Dataset`, how can I run `Dataset.shard` such that sharding doesn't split any episode across shards?
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Documentation on multiple media files of the same type with WebDataset
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"Yes this is correct and it works with huggingface datasets as well ! Feel free to include an example here: https://github.com/huggingface/datasets/blob/main/docs/source/video_dataset.mdx"
] | 2025-02-18T16:13:20
| 2025-02-20T14:17:54
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The [current documentation](https://huggingface.co/docs/datasets/en/video_dataset) on a creating a video dataset includes only examples with one media file and one json. It would be useful to have examples where multiple files of the same type are included. For example, in a sign language dataset, you may have a base video and a video annotation of the extracted pose. According to the WebDataset documentation, this should be able to be done with period separated filenames. For example:
```e39871fd9fd74f55.base.mp4
e39871fd9fd74f55.pose.mp4
e39871fd9fd74f55.json
f18b91585c4d3f3e.base.mp4
f18b91585c4d3f3e.pose.mp4
f18b91585c4d3f3e.json
...
```
If you can confirm that this method of including multiple media files works with huggingface datasets and include an example in the documentation, I'd appreciate it.
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Index Error Invalid Ket is out of bounds for size 0 for code-search-net/code_search_net dataset
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NONE
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### Describe the bug
I am trying to do model pruning on sentence-transformers/all-mini-L6-v2 for the code-search-net/code_search_net dataset using INCTrainer class
However I am getting below error
```
raise IndexError(f"Invalid Key: {key is our of bounds for size {size}")
IndexError: Invalid key: 1840208 is out of bounds for size 0
```
### Steps to reproduce the bug
Model pruning on the above dataset using the below guide
https://huggingface.co/docs/optimum/en/intel/neural_compressor/optimization#pruning
### Expected behavior
The modsl should be successfully pruned
### Environment info
Torch version: 2.4.1
Python version: 3.8.10
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Adding Core Maintainer List to CONTRIBUTING.md
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"@lhoestq",
"there is no per-module maintainer and the list is me alone nowadays ^^'",
"@lhoestq \nOh... I feel for you. \nWhat are your criteria for choosing a core maintainer? \nIt seems like it's too much work for you to manage all this code by yourself.\n\nAlso, if you don't mind, can you check this PR for me?\n#7368 I'd like this to be added as soon as possible because I need it."
] | 2025-02-17T00:32:40
| 2025-02-19T01:28:38
| null |
CONTRIBUTOR
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### Feature request
I propose adding a core maintainer list to the `CONTRIBUTING.md` file.
### Motivation
The Transformers and Liger-Kernel projects maintain lists of core maintainers for each module.
However, the Datasets project doesn't have such a list.
### Your contribution
I have nothing to add here.
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I_kwDODunzps6qQMpO
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|
Lazy loading of environment variables
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[
"Many python packages out there, including `huggingface_hub`, do load the environment variables on import.\nYou should `load_dotenv()` before importing the libraries.\n\nFor example you can move all you imports inside your `main()` function"
] | 2025-02-16T22:31:41
| 2025-02-17T15:17:18
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### Describe the bug
Loading a `.env` file after an `import datasets` call does not correctly use the environment variables.
This is due the fact that environment variables are read at import time:
https://github.com/huggingface/datasets/blob/de062f0552a810c52077543c1169c38c1f0c53fc/src/datasets/config.py#L155C1-L155C80
### Steps to reproduce the bug
```bash
# make tmp dir
mkdir -p /tmp/debug-env
# make .env file
echo HF_HOME=/tmp/debug-env/data > /tmp/debug-env/.env
# first load dotenv, downloads to /tmp/debug-env/data
uv run --with datasets,python-dotenv python3 -c \
'import dotenv; dotenv.load_dotenv("/tmp/debug-env/.env"); import datasets; datasets.load_dataset("Anthropic/hh-rlhf")'
# first import datasets, downloads to `~/.cache/huggingface`
uv run --with datasets,python-dotenv python3 -c \
'import datasets; import dotenv; dotenv.load_dotenv("/tmp/debug-env/.env"); datasets.load_dataset("Anthropic/hh-rlhf")'
```
### Expected behavior
I expect that setting environment variables with something like this:
```python3
if __name__ == "__main__":
load_dotenv()
main()
```
works correctly.
### Environment info
"datasets>=3.3.0",
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Performance regression in `dataset.filter`
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[
"Thanks for reporting, I'll fix the regression today",
"I just released `datasets` 3.3.1 with a fix, let me know if it's good now :)",
"@lhoestq it fixed the issue.\n\nThis was (very) fast, thank you very much!"
] | 2025-02-16T22:19:14
| 2025-02-17T17:46:06
| 2025-02-17T14:28:48
|
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### Describe the bug
We're filtering dataset of ~1M (small-ish) records. At some point in the code we do `dataset.filter`, before (including 3.2.0) it was taking couple of seconds, and now it takes 4 hours.
We use 16 threads/workers, and stack trace at them look as follows:
```
Traceback (most recent call last):
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 314, in _bootstrap
self.run()
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/python/lib/python3.12/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 678, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3511, in _map_single
for i, batch in iter_outputs(shard_iterable):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3461, in iter_outputs
yield i, apply_function(example, i, offset=offset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3390, in apply_function
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 6416, in get_indices_from_mask_function
indices_array = indices_mapping.column(0).take(indices_array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 1079, in pyarrow.lib.ChunkedArray.take
File "/python/lib/python3.12/site-packages/pyarrow/compute.py", line 458, in take
def take(data, indices, *, boundscheck=True, memory_pool=None):
```
### Steps to reproduce the bug
1. Save dataset of 1M records in arrow
2. Filter it with 16 threads
3. Watch it take too long
### Expected behavior
Filtering done fast
### Environment info
datasets 3.3.0, python 3.12
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I_kwDODunzps6qDtPK
| 7,399
|
Synchronize parameters for various datasets
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[
"Hi ! the `desc` parameter is only available for Dataset / DatasetDict for the progress bar of `map()``\n\nSince IterableDataset only runs the map functions when you iterate over the dataset, there is no progress bar and `desc` is useless. We could still add the argument for parity but it wouldn't be used for anything",
"I think you should add it. It doesn't hurt. The reason I ran into it was because I re-wrote a pipeline to use either a stream or a fully loaded dataset. Of course I can simply remove it but it is nice to have on the memory loaded dataset. "
] | 2025-02-14T09:15:11
| 2025-02-19T11:50:29
| null |
NONE
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### Describe the bug
[IterableDatasetDict](https://huggingface.co/docs/datasets/v3.2.0/en/package_reference/main_classes#datasets.IterableDatasetDict.map) map function is missing the `desc` parameter. You can see the equivalent map function for [Dataset here](https://huggingface.co/docs/datasets/v3.2.0/en/package_reference/main_classes#datasets.Dataset.map).
There might be other parameters missing - I haven't checked.
### Steps to reproduce the bug
from datasets import Dataset, IterableDataset, IterableDatasetDict
ds = IterableDatasetDict({"train": Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=3),
"validate": Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=3)})
for d in ds["train"]:
print(d)
ds = ds.map(lambda x: {k: v+1 for k, v in x.items()}, desc="increment")
for d in ds["train"]:
print(d)
### Expected behavior
The description parameter should be available for all datasets (or none).
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.9.0
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I_kwDODunzps6qEGV9
| 7,400
|
504 Gateway Timeout when uploading large dataset to Hugging Face Hub
|
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"I transferred to the `datasets` repository. Is there any retry mechanism in `datasets` @lhoestq ?\n\nAnother solution @hotchpotch if you want to get your dataset pushed to the Hub in a robust way is to save it to a local folder first and then use `huggingface-cli upload-large-folder` (see https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder). It has better retry mechanism in case of failure.",
"There is no retry mechanism for `api.preupload_lfs_files` in `push_to_hub()` but we can definitely add one here\n\nhttps://github.com/huggingface/datasets/blob/de062f0552a810c52077543c1169c38c1f0c53fc/src/datasets/arrow_dataset.py#L5372",
"@Wauplin \n\nThank you! I believe that to use load_dataset() to read data from Hugging Face, we need to first save the markdown metadata and parquet files in our local filesystem, then upload them using upload-large-folder. If you know how to do this, could you please let me know?\n\n",
"@lhoestq \n\nI see, so adding a retry mechanism there would solve it. If I continue to have issues, I'll consider implementing that kind of solution."
] | 2025-02-14T02:18:35
| 2025-02-14T23:48:36
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### Description
I encountered consistent 504 Gateway Timeout errors while attempting to upload a large dataset (approximately 500GB) to the Hugging Face Hub. The upload fails during the process with a Gateway Timeout error.
I will continue trying to upload. While it might succeed in future attempts, I wanted to report this issue in the meantime.
### Reproduction
- I attempted the upload 3 times
- Each attempt resulted in the same 504 error during the upload process (not at the start, but in the middle of the upload)
- Using `dataset.push_to_hub()` method
### Environment Information
```
- huggingface_hub version: 0.28.0
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.39
- Python version: 3.11.10
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /home/hotchpotch/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: hotchpotch
- Configured git credential helpers: store
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.5.1
- Jinja2: 3.1.5
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: 10.4.0
- hf_transfer: N/A
- gradio: N/A
- tensorboard: N/A
- numpy: 1.26.4
- pydantic: 2.10.6
- aiohttp: 3.11.11
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /home/hotchpotch/.cache/huggingface/hub
- HF_ASSETS_CACHE: /home/hotchpotch/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/hotchpotch/.cache/huggingface/token
- HF_STORED_TOKENS_PATH: /home/hotchpotch/.cache/huggingface/stored_tokens
- 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
```
### Full Error Traceback
```python
Traceback (most recent call last):
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status
response.raise_for_status()
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese.git/info/lfs/objects/batch
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/create_edu_japanese_ds/upload_edu_japanese_ds.py", line 12, in <module>
ds.push_to_hub("hotchpotch/fineweb-2-edu-japanese", private=True)
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/datasets/dataset_dict.py", line 1665, in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 5301, in _push_parquet_shards_to_hub
api.preupload_lfs_files(
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/hf_api.py", line 4215, in preupload_lfs_files
_upload_lfs_files(
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/_commit_api.py", line 395, in _upload_lfs_files
batch_actions_chunk, batch_errors_chunk = post_lfs_batch_info(
^^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/lfs.py", line 168, in post_lfs_batch_info
hf_raise_for_status(resp)
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese.git/info/lfs/objects/batch
```
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I_kwDODunzps6ptGYT
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|
Using load_dataset with data_files and split arguments yields an error
|
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### Describe the bug
It seems the list of valid splits recorded by the package becomes incorrectly overwritten when using the `data_files` argument.
If I run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="all_examples", data_files="tasks/expert.jsonl")
```
then I get the error
```
ValueError: Unknown split "all_examples". Should be one of ['train'].
```
However, if I run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="train", name="Expert")
```
then I get
```
ValueError: Unknown split "train". Should be one of ['all_examples'].
```
### Steps to reproduce the bug
Run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="all_examples", data_files="tasks/expert.jsonl")
```
### Expected behavior
No error.
### Environment info
Python = 3.12
datasets = 3.2.0
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I_kwDODunzps6po_bD
| 7,392
|
push_to_hub payload too large error when using large ClassLabel feature
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"See also <https://discuss.huggingface.co/t/datasetdict-push-to-hub-failing-with-payload-to-large/140083/8>\n"
] | 2025-02-11T17:51:34
| 2025-02-11T18:01:31
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### Describe the bug
When using `datasets.DatasetDict.push_to_hub` an `HfHubHTTPError: 413 Client Error: Payload Too Large for url` is raised if the dataset contains a large `ClassLabel` feature. Even if the total size of the dataset is small.
### Steps to reproduce the bug
``` python
import random
import sys
import datasets
random.seed(42)
def random_str(sz):
return "".join(chr(random.randint(ord("a"), ord("z"))) for _ in range(sz))
data = datasets.DatasetDict(
{
str(i): datasets.Dataset.from_dict(
{
"label": [list(range(3)) for _ in range(10)],
"abstract": [random_str(10_000) for _ in range(10)],
},
)
for i in range(3)
}
)
features = data["1"].features.copy()
features["label"] = datasets.Sequence(
datasets.ClassLabel(names=[str(i) for i in range(50_000)])
)
data = data.map(lambda examples: {}, features=features)
feat_size = sys.getsizeof(data["1"].features["label"].feature.names)
print(f"Size of ClassLabel names: {feat_size}")
# Size of ClassLabel names: 444376
data.push_to_hub("dconnell/pubtator3_test")
```
Note that this succeeds if `ClassLabel` has fewer names or if `ClassLabel` is replaced with `Value("int64")`
### Expected behavior
Should push the dataset to hub.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-126-generic-x86_64-with-glibc2.35
- Python version: 3.12.8
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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pyarrow 尝试了若干个版本都不可以
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Re-add py.typed
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CONTRIBUTOR
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### Feature request
The motivation for removing py.typed no longer seems to apply. Would a solution like [this one](https://github.com/huggingface/huggingface_hub/pull/2752) work here?
### Motivation
MyPy support is broken. As more type checkers come out, such as RedKnot, these may also be broken. It would be good to be PEP 561 compliant as long as it's not too onerous.
### Your contribution
I can re-add py.typed, but I don't know how to make sur all of the `__all__` files are provided (although you may not need to with modern PyRight).
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Getting statistics about filtered examples
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"You can actually track a running sum in map() or filter() :)\n\n```python\nnum_filtered = 0\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered += 1\n return condition\n\nds = ds.filter(f)\nprint(num_filtered)\n```\n\nand if you want to use multiprocessing, make sure to use a variable that is shared across processes\n\n\n```python\nfrom multiprocess import Manager\n\nmanager = Manager()\nnum_filtered = manager.Value('i', 0)\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered.value += 1\n return condition\n\nds = ds.filter(f, num_proc=4)\nprint(num_filtered.value)\n```\n\nPS: `datasets` uses `multiprocess` instead of the `multiprocessing` package to support lambda functions in map() and filter()",
"Oh that's great to know!\n\nI guess this value would not be exactly synced with the batch in cases of pre-fetch and shuffle buffers and so on, but that's probably fine. Thanks!"
] | 2025-02-10T20:48:29
| 2025-02-11T20:44:15
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@lhoestq wondering if the team has thought about this and if there are any recommendations?
Currently when processing datasets some examples are bound to get filtered out, whether it's due to bad format, or length is too long, or any other custom filters that might be getting applied. Let's just focus on the filter by length for now, since that would be something that gets applied dynamically for each training run. Say we want to show a graph in W&B with the running total of the number of filtered examples so far.
What would be a good way to go about hooking this up? Because the map/filter operations happen before the DataLoader batches are created, at training time if we're just grabbing batches from the DataLoader then we won't know how many things have been filtered already. But there's not really a good way to include a 'num_filtered' key into the dataset itself either because dataset map/filter process examples independently and don't have a way to track a running sum.
The only approach I can kind of think of is having a 'is_filtered' key in the dataset, and then creating a custom batcher/collator that reads that and tracks the metric?
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OSError: [Errno 22] Invalid argument forbidden character
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"You can probably copy the dataset in your HF account and rename the files (without having to download them to your disk). Or alternatively feel free to open a Pull Request to this dataset with the renamed file",
"Thank you, that will help me work around this problem"
] | 2025-02-10T17:46:31
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### Describe the bug
I'm on Windows and i'm trying to load a datasets but i'm having title error because files in the repository are named with charactere like < >which can't be in a name file. Could it be possible to load this datasets but removing those charactere ?
### Steps to reproduce the bug
load_dataset("CATMuS/medieval") on Windows
### Expected behavior
Making the function to erase the forbidden character to allow loading the datasets who have those characters.
### Environment info
- `datasets` version: 3.2.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.12.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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Dynamic adjusting dataloader sampling weight
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[
"You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes",
"Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weight gets higher and higher, and if one data sample's prediction is already correct, then we rarely sample it",
"it's not possible to use `interleave_datasets()` and modify the probabilities while iterating on the dataset at the moment, so you'd have to implement your own torch `Sampler` or your own`IterableDataset` to implement this logic"
] | 2025-02-10T03:18:47
| 2025-03-07T14:06:54
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Hi,
Thanks for your wonderful work! I'm wondering is there a way to dynamically adjust the sampling weight of each data in the dataset during training? Looking forward to your reply, thanks again.
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Add bookfolder Dataset Builder for Digital Book Formats
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"On second thought, probably not a good idea."
] | 2025-02-08T14:27:55
| 2025-02-08T14:30:10
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### Feature request
This feature proposes adding a new dataset builder called bookfolder to the datasets library. This builder would allow users to easily load datasets consisting of various digital book formats, including: AZW, AZW3, CB7, CBR, CBT, CBZ, EPUB, MOBI, and PDF.
### Motivation
Currently, loading datasets of these digital book files requires manual effort. This would also lower the barrier to entry for working with these formats, enabling more diverse and interesting datasets to be used within the Hugging Face ecosystem.
### Your contribution
This feature is rather simple as it will be based on the folder-based builder, similar to imagefolder. I'm willing to contribute to this feature by submitting a PR
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Iterating over values of a column in the IterableDataset
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"I'd be in favor of that ! I saw many people implementing their own iterables that wrap a dataset just to iterate on a single column, that would make things more practical.\n\nKinda related: https://github.com/huggingface/datasets/issues/5847",
"(For anyone's information, I'm going on vacation for the next 3 weeks, so the work is postponed. If anyone can implement this feature within the next 4 weeks, go ahead :) )"
] | 2025-01-28T13:17:36
| 2025-02-18T17:15:51
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NONE
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### Feature request
I would like to be able to iterate (and re-iterate if needed) over a column of an `IterableDataset` instance. The following example shows the supposed API:
```
def gen():
yield {"text": "Good", "label": 0}
yield {"text": "Bad", "label": 1}
ds = IterableDataset.from_generator(gen)
texts = ds["text"]
for v in texts:
print(v) # Prints "Good" and "Bad"
for v in texts:
print(v) # Prints "Good" and "Bad" again
```
### Motivation
In the real world problems, huge NNs like Transformer are not always the best option, so there is a need to conduct experiments with different methods. While 🤗Datasets is perfectly adapted to 🤗Transformers, it may be inconvenient when being used with other libraries. The ability to retrieve a particular column is the case (e.g., gensim's FastText [requires](https://radimrehurek.com/gensim/models/fasttext.html#gensim.models.fasttext.FastText.train) only lists of strings, not dictionaries).
While there are ways to achieve the desired functionality, they are not good ([forum](https://discuss.huggingface.co/t/how-to-iterate-over-values-of-a-column-in-the-iterabledataset/135649)). It would be great if there was a built-in solution.
### Your contribution
Theoretically, I can submit a PR, but I have very little knowledge of the internal structure of 🤗Datasets, so some help may be needed.
Moreover, I can only work on weekends, since I have a full-time job. However, the feature does not seem to be popular, so there is no need to implement it as fast as possible.
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I_kwDODunzps6nEbxM
| 7,378
|
Allow pushing config version to hub
|
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"Hi ! This sounds reasonable to me, feel free to open a PR :)"
] | 2025-01-21T22:35:07
| 2025-01-30T13:56:56
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### Feature request
Currently, when datasets are created, they can be versioned by passing the `version` argument to `load_dataset(...)`. For example creating `outcomes.csv` on the command line
```
echo "id,value\n1,0\n2,0\n3,1\n4,1\n" > outcomes.csv
```
and creating it
```
import datasets
dataset = datasets.load_dataset(
"csv",
data_files ="outcomes.csv",
keep_in_memory = True,
version = '1.0.0')
```
The version info is stored in the `info` and can be accessed e.g. by `next(iter(dataset.values())).info.version`
This dataset can be uploaded to the hub with `dataset.push_to_hub(repo_id = "maomlab/example_dataset")`. This will create a dataset on the hub with the following in the `README.md`, but it doesn't upload the version information:
```
---
dataset_info:
features:
- name: id
dtype: int64
- name: value
dtype: int64
splits:
- name: train
num_bytes: 64
num_examples: 4
download_size: 1332
dataset_size: 64
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
```
However, when I download from the hub, the version information is missing:
```
dataset_from_hub_no_version = datasets.load_dataset("maomlab/example_dataset")
next(iter(dataset.values())).info.version
```
I can add the version information manually to the hub, by appending it to the end of config section:
```
...
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
version: 1.0.0
---
```
And then when I download it, the version information is correct.
### Motivation
### Why adding version information for each config makes sense
1. The version information is already recorded in the dataset config info data structure and is able to parse it correctly, so it makes sense to sync it with `push_to_hub`.
2. Keeping the version info in at the config level is different from version info at the branch level. As the former relates to the version of the specific dataset the config refers to rather than the version of the dataset curation itself.
## A explanation for the current behavior:
In [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
), the `_INCLUDED_INFO_IN_YAML` variable doesn't include `"version"`.
If my reading of the code is right, adding `"version"` to `_INCLUDED_INFO_IN_YAML`, would allow the version information to be uploaded to the hub.
### Your contribution
Request: add `"version"` to `_INCLUDE_INFO_IN_YAML` in [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
)
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| 7,377
|
Support for sparse arrays with the Arrow Sparse Tensor format?
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"Hi ! Unfortunately the Sparse Tensor structure in Arrow is not part of the Arrow format (yes it's confusing...), so it's not possible to use it in `datasets`. It's a separate structure that doesn't correspond to any type or extension type in Arrow.\n\nThe Arrow community recently added an extension type for fixed shape tensors at https://arrow.apache.org/docs/format/CanonicalExtensions.html#fixed-shape-tensor, it should be possible to contribute an extension type for sparse tensors as well."
] | 2025-01-21T20:14:35
| 2025-01-30T14:06:45
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### Feature request
AI in biology is becoming a big thing. One thing that would be a huge benefit to the field that Huggingface Datasets doesn't currently have is native support for **sparse arrays**.
Arrow has support for sparse tensors.
https://arrow.apache.org/docs/format/Other.html#sparse-tensor
It would be a big deal if Hugging Face Datasets supported sparse tensors as a feature type, natively.
### Motivation
This is important for example in the field of transcriptomics (modeling and understanding gene expression), because a large fraction of the genes are not expressed (zero). More generally, in science, sparse arrays are very common, so adding support for them would be very benefitial, it would make just using Hugging Face Dataset objects a lot more straightforward and clean.
### Your contribution
We can discuss this further once the team comments of what they think about the feature, and if there were previous attempts at making it work, and understanding their evaluation of how hard it would be. My intuition is that it should be fairly straightforward, as the Arrow backend already supports it.
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vllm批量推理报错
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### Describe the bug
### Steps to reproduce the bug
### Expected behavior
### Environment info
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|
Excessive RAM Usage After Dataset Concatenation concatenate_datasets
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### Describe the bug
When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.
https://github.com/huggingface/datasets/issues/2276
### Steps to reproduce the bug
```
rom datasets import DatasetDict, concatenate_datasets
dataset = DatasetDict.load_from_disk("data")
...
...
combined_dataset = concatenate_datasets(
[dataset[split] for split in dataset]
)
#start SentenceTransformer training
```
### Expected behavior
I would not expect RAM utilization to increase after concatenation. Removing the concatenation step resolves the issue
### Environment info
sentence-transformers==3.1.1
datasets==3.2.0
python3.10
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|
Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets
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### Description
I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue:
#### Code 1: Using `load_dataset`
```python
from datasets import Dataset, load_dataset
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_dataset("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 1350 samples.
- `test` has 150 samples.
#### Code 2: Using `load_from_disk`
```python
from datasets import Dataset, load_from_disk
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_from_disk("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 450 samples.
- `test` has 50 samples.
### Expected Behavior
I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly:
- `load_dataset` seems to merge all shards, resulting in a combined dataset.
- `load_from_disk` only loads the last saved dataset, ignoring previous shards.
### Questions
1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation?
2. If this is not intentional, could this be considered a bug?
3. What is the recommended way to handle cases where multiple datasets are saved to the same directory?
Thank you for your time and effort in maintaining this great library! I look forward to your feedback.
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I_kwDODunzps6mVGmB
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|
500 Server error with pushing a dataset
|
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"EDIT: seems to be all good now. I'll add a comment if the error happens again within the next 48 hours. If it doesn't, I'll just close the topic."
] | 2025-01-15T18:23:02
| 2025-01-15T20:06:05
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### Describe the bug
Suddenly, I started getting this error message saying it was an internal error.
`Error creating/pushing dataset: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928)
Internal Error - We're working hard to fix this as soon as possible!
Traceback (most recent call last):
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status
response.raise_for_status()
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/uufs/chpc.utah.edu/common/home/u1295595/grasp_dataset_converter/src/grasp_dataset_converter/main.py", line 142, in main
subset_train.push_to_hub(dataset_name, split='train')
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5624, in push_to_hub
commit_info = api.create_commit(
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 1518, in _inner
return fn(self, *args, **kwargs)
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 4087, in create_commit
hf_raise_for_status(commit_resp, endpoint_name="commit")
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928)
Internal Error - We're working hard to fix this as soon as possible!`
### Steps to reproduce the bug
I am pushing a Dataset in a loop via push_to_hub API
### Expected behavior
It worked fine until it stopped working suddenly.
Expected behavior: It should start working again
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-4.18.0-477.15.1.el8_8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.0
- `huggingface_hub` version: 0.27.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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|
Importing dataset gives unhelpful error message when filenames in metadata.csv are not found in the directory
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[
"I'd prefer even more verbose errors; like `\"file123.mp3\" is referenced in metadata.csv, but not found in the data directory '/path/to/audiofolder' ! (and 100+ more missing files)` Or something along those lines."
] | 2025-01-14T13:53:21
| 2025-01-14T15:05:51
| null |
NONE
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### Describe the bug
While importing an audiofolder dataset, where the names of the audiofiles don't correspond to the filenames in the metadata.csv, we get an unclear error message that is not helpful for the debugging, i.e.
```
ValueError: Instruction "train" corresponds to no data!
```
### Steps to reproduce the bug
Assume an audiofolder with audiofiles, filename1.mp3, filename2.mp3 etc and a file metadata.csv which contains the columns file_name and sentence. The file_names are formatted like filename1.mp3, filename2.mp3 etc.
Load the audio
```
from datasets import load_dataset
load_dataset("audiofolder", data_dir='/path/to/audiofolder')
```
When the file_names in the csv are not in sync with the filenames in the audiofolder, then we get an Error message:
```
File /opt/conda/lib/python3.12/site-packages/datasets/arrow_reader.py:251, in BaseReader.read(self, name, instructions, split_infos, in_memory)
249 if not files:
250 msg = f'Instruction "{instructions}" corresponds to no data!'
--> 251 raise ValueError(msg)
252 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction "train" corresponds to no data!
```
load_dataset has a default value for the argument split = 'train'.
### Expected behavior
It would be better to get an error report something like:
```
The metadata.csv file has different filenames than the files in the datadirectory.
```
It would have saved me 4 hours of debugging.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-427.40.1.el9_4.x86_64-x86_64-with-glibc2.39
- Python version: 3.12.8
- `huggingface_hub` version: 0.27.0
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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I_kwDODunzps6lyqvO
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Dataset.from_dict() can't handle large dict
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[] | 2025-01-11T02:05:21
| 2025-01-11T02:05:21
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NONE
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### Describe the bug
I have 26,000,000 3-tuples. When I use Dataset.from_dict() to load, neither. py nor Jupiter notebook can run successfully. This is my code:
```
# len(example_data) is 26,000,000, 'diff' is a text
diff1_list = [example_data[i].texts[0] for i in range(len(example_data))]
diff2_list = [example_data[i].texts[1] for i in range(len(example_data))]
label_list = [example_data[i].label for i in range(len(example_data))]
embedding_dataset = Dataset.from_dict({
"diff1": diff1_list,
"diff2": diff2_list,
"label": label_list
})
```
### Steps to reproduce the bug
1. Initialize a large 3-tuple, e.g. 26,000,000
2. Use Dataset.from_dict() to load
### Expected behavior
Dataset.from_dict() run successfully
### Environment info
sentence-transformers 3.3.1
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A parameter is specified but not used in datasets.arrow_dataset.Dataset.from_pandas()
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[] | 2025-01-10T13:39:33
| 2025-01-10T13:39:33
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NONE
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### Describe the bug
I am interested in creating train, test and eval splits from a pandas Dataframe, therefore I was looking at the possibilities I can follow. I noticed the split parameter and was hopeful to use it in order to generate the 3 at once, however, while trying to understand the code, i noticed that it has no added value (correct me if I am wrong or misunderstood the code).
from_pandas function code :
```python
if info is not None and features is not None and info.features != features:
raise ValueError(
f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
)
features = features if features is not None else info.features if info is not None else None
if info is None:
info = DatasetInfo()
info.features = features
table = InMemoryTable.from_pandas(
df=df,
preserve_index=preserve_index,
)
if features is not None:
# more expensive cast than InMemoryTable.from_pandas(..., schema=features.arrow_schema)
# needed to support the str to Audio conversion for instance
table = table.cast(features.arrow_schema)
return cls(table, info=info, split=split)
```
### Steps to reproduce the bug
```python
from datasets import Dataset
# Filling the split parameter with whatever causes no harm at all
data = Dataset.from_pandas(self.raw_data, split='egiojegoierjgoiejgrefiergiuorenvuirgurthgi')
```
### Expected behavior
Would be great if there is no split parameter (if it isn't working), or to add a concrete example of how it can be used.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-127-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.27.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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API endpoints for gated dataset access requests
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[
"Looks like a [similar feature request](https://github.com/huggingface/huggingface_hub/issues/1198) was made to the HF Hub team. Is handling this at the Hub level more appropriate?\r\n\r\n(As an aside, I've gotten the [HTTP-based solution](https://github.com/huggingface/huggingface_hub/issues/1198#issuecomment-1905774983) proposed in that forum to work for simple cases.)",
"yes it's more for https://github.com/huggingface/huggingface_hub cc @hanouticelina ",
"yes i think @Wauplin's comment on that thread is still what we recommend"
] | 2025-01-09T06:21:20
| 2025-01-09T11:17:40
| 2025-01-09T11:17:20
|
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### Feature request
I would like a programatic way of requesting access to gated datasets. The current solution to gain access forces me to visit a website and physically click an "agreement" button (as per the [documentation](https://huggingface.co/docs/hub/en/datasets-gated#access-gated-datasets-as-a-user)).
An ideal approach would be HF API download methods that negotiate access on my behalf based on information from my CLI login and/or token. I realise that may be naive given the various types of access semantics available to dataset authors (automatic versus manual approval, for example) and complexities it might add to existing methods, but something along those lines would be nice.
Perhaps using the `*_access_request` methods available to dataset authors can be a precedent; see [`reject_access_request`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.reject_access_request) for example.
### Motivation
When trying to download files from a gated dataset, I'm met with a `GatedRepoError` and instructed to visit the repository's website to gain access:
```
Cannot access gated repo for url https://huggingface.co/datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details/resolve/main/meta-llama__Meta-Llama-3.1-70B-Instruct/samples_leaderboard_math_precalculus_hard_2024-07-19T18-47-29.522341.jsonl.
Access to dataset open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details is restricted and you are not in the authorized list. Visit https://huggingface.co/datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details to ask for access.
```
This makes task automation extremely difficult. For example, I'm interested in studying sample-level responses of models on the LLM leaderboard -- how they answered particular questions on a given evaluation framework. As I come across more and more participants that gate their data, it's becoming unwieldy to continue my work (there over 2,000 participants, so in the worst case that's the number of website visits I'd need to manually undertake).
One approach is use Selenium to react to the `GatedRepoError`, but that seems like overkill; and a potential violation HF terms of service (?).
As mentioned in the previous section, there seems to be an [API for gated dataset owners](https://huggingface.co/docs/hub/en/datasets-gated#via-the-api) to managed access requests, and thus some appetite for allowing automated management of gating. This feature request is to extend that to dataset users.
### Your contribution
Whether I can help depends on a few things; one being the complexity of the underlying gated access design. If this feature request is accepted I am open to being involved in discussions and testing, and even development under the right time-outcome tradeoff.
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I_kwDODunzps6lWUEc
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ImportError: To support decoding images, please install 'Pillow'.
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[
"what's your `pip show Pillow` output",
"same issue.. my pip show Pillow output as below:\n\n```\nName: pillow\nVersion: 11.1.0\nSummary: Python Imaging Library (Fork)\nHome-page: https://python-pillow.github.io/\nAuthor: \nAuthor-email: \"Jeffrey A. Clark\" <aclark@aclark.net>\nLicense: MIT-CMU\nLocation: [/opt/homebrew/lib/python3.10/site-packages](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/lib/python3.10/site-packages)\nRequires: \nRequired-by:\n```",
"I encountered the same problem on Ubuntu system, my pip show Pillow output as below:\n\n```\nName: pillow\nVersion: 10.4.0\nSummary: Python Imaging Library (Fork)\nHome-page: https://python-pillow.org/\nAuthor: \nAuthor-email: \"Jeffrey A. Clark\" <[aclark@aclark.net](mailto:aclark@aclark.net)>\nLicense: HPND\nLocation: /home/shunying/.local/lib/python3.8/site-packages\nRequires: \nRequired-by: \n```\n\nWell, solved this by specifying the pip version to my conda virtual environment :)"
] | 2025-01-08T02:22:57
| 2025-02-07T07:30:33
| null |
NONE
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### Describe the bug
Following this tutorial locally using a macboko and VSCode: https://huggingface.co/docs/diffusers/en/tutorials/basic_training
This line of code: for i, image in enumerate(dataset[:4]["image"]):
throws: ImportError: To support decoding images, please install 'Pillow'.
Pillow is installed.
### Steps to reproduce the bug
Run the tutorial
### Expected behavior
Images should be rendered
### Environment info
MacBook, VSCode
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HuggingFace CLI dataset download raises error
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[
"I got the same error and was able to resolve it by upgrading from 2.15.0 to 3.2.0.",
"> I got the same error and was able to resolve it by upgrading from 2.15.0 to 3.2.0.\r\n\r\nWhat is needed is upgrading `huggingface-hub==0.27.1`. `datasets` does not appear to have anything to do with the error. The upgrade is a workaround, if the workaround works for your use case. Otherwise, this issue breaks all existing Python clients not using some minimum version of `huggingface-hub`. ",
"Correct, this has to do with `huggingface_hub`, not `datasets`. Some old versions of `huggingface_hub` are unfortunately not robust to recent changes on HF. Updating `huggingface_hub` fixes the issue :)\r\n\r\nClosing this issue since it's not directly related to `datasets`"
] | 2025-01-07T21:03:30
| 2025-01-08T15:00:37
| 2025-01-08T14:35:52
|
NONE
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### Describe the bug
Trying to download Hugging Face datasets using Hugging Face CLI raises error. This error only started after December 27th, 2024. For example:
```
huggingface-cli download --repo-type dataset gboleda/wikicorpus
Traceback (most recent call last):
File "/home/ubuntu/test_venv/bin/huggingface-cli", line 8, in <module>
sys.exit(main())
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/huggingface_cli.py", line 51, in main
service.run()
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 146, in run
print(self._download()) # Print path to downloaded files
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/commands/download.py", line 180, in _download
return snapshot_download(
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/_snapshot_download.py", line 164, in snapshot_download
repo_info = api.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision, token=token)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2491, in repo_info
return method(
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2366, in dataset_info
return DatasetInfo(**data)
File "/home/ubuntu/test_venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 799, in __init__
self.tags = kwargs.pop("tags")
KeyError: 'tags'
```
### Steps to reproduce the bug
```
1. huggingface-cli download --repo-type dataset gboleda/wikicorpus
```
### Expected behavior
There should be no error.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.8.0-1015-aws-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.5
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.3.1
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I_kwDODunzps6lNZHu
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error when loading dataset in Hugging Face: NoneType error is not callable
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[
"Hi ! I couldn't reproduce on my side, can you try deleting your cache at `~/.cache/huggingface/modules/datasets_modules/datasets/InstaDeepAI--nucleotide_transformer_downstream_tasks_revised` and try again ? For some reason `datasets` wasn't able to find the DatasetBuilder class in the python script of this dataset",
"I've met the same problem when importing [LongBench-v1](https://github.com/THUDM/LongBench/blob/main/LongBench/README.md). the debugger reports `dataset_module.builder_configs_parameters.builder_configs` as `None` so that no `builder_cls` gets created:\n\n<img width=\"711\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/b62bdea7-442b-47dc-b892-87f4d235e324\" />\n\ndoes this mean that I need to downgrade `datasets`?",
"I tried downgrading `datasets` to v2.20.0 and it works fine now...\n\nI think there might be some compatibility issues during code updates between `v2.20.0` and `v3.0.0` 🤔 \n\nalso I suggest @nanu23333 to see if downgrading works.",
"Found the same problem. When I tried to downgrade the datasets to version below v3.0.0, another problem was raised: `UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 1: invalid start byte`",
"\nwhen I use the pip install datasets==3.3, I come across the error。Then I \n```\npip uninstall datasets\npip install datasets==2.21.0\n```\nIt is OK now"
] | 2025-01-07T02:11:36
| 2025-02-24T13:32:52
| null |
NONE
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### Describe the bug
I met an error when running a notebook provide by Hugging Face, and met the error.
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[2], line 5
3 # Load the enhancers dataset from the InstaDeep Hugging Face ressources
4 dataset_name = "enhancers_types"
----> 5 train_dataset_enhancers = load_dataset(
6 "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
7 dataset_name,
8 split="train",
9 streaming= False,
10 )
11 test_dataset_enhancers = load_dataset(
12 "InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
13 dataset_name,
14 split="test",
15 streaming= False,
16 )
File /public/home/hhl/miniconda3/envs/transformer/lib/python3.9/site-packages/datasets/load.py:2129, 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)
2124 verification_mode = VerificationMode(
2125 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2126 )
2128 # Create a dataset builder
-> 2129 builder_instance = load_dataset_builder(
2130 path=path,
2131 name=name,
2132 data_dir=data_dir,
2133 data_files=data_files,
2134 cache_dir=cache_dir,
2135 features=features,
2136 download_config=download_config,
2137 download_mode=download_mode,
2138 revision=revision,
2139 token=token,
2140 storage_options=storage_options,
2141 trust_remote_code=trust_remote_code,
2142 _require_default_config_name=name is None,
2143 **config_kwargs,
2144 )
2146 # Return iterable dataset in case of streaming
2147 if streaming:
File /public/home/hhl/miniconda3/envs/transformer/lib/python3.9/site-packages/datasets/load.py:1886, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
1884 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name)
1885 # Instantiate the dataset builder
-> 1886 builder_instance: DatasetBuilder = builder_cls(
1887 cache_dir=cache_dir,
1888 dataset_name=dataset_name,
1889 config_name=config_name,
1890 data_dir=data_dir,
1891 data_files=data_files,
1892 hash=dataset_module.hash,
1893 info=info,
1894 features=features,
1895 token=token,
1896 storage_options=storage_options,
1897 **builder_kwargs,
1898 **config_kwargs,
1899 )
1900 builder_instance._use_legacy_cache_dir_if_possible(dataset_module)
1902 return builder_instance
TypeError: 'NoneType' object is not callable
```
I have checked my internet, it worked well. And the dataset name was just copied from the Hugging Face.
Totally no idea what is wrong!
### Steps to reproduce the bug
To reproduce the bug you may run
```
from datasets import load_dataset, Dataset
# Load the enhancers dataset from the InstaDeep Hugging Face ressources
dataset_name = "enhancers_types"
train_dataset_enhancers = load_dataset(
"InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
dataset_name,
split="train",
streaming= False,
)
test_dataset_enhancers = load_dataset(
"InstaDeepAI/nucleotide_transformer_downstream_tasks_revised",
dataset_name,
split="test",
streaming= False,
)
```
### Expected behavior
1. what may be the reasons of the error
2. how can I fine which reason lead to the error
3. how can I save the problem
### Environment info
```
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-117-generic-x86_64-with-glibc2.31
- Python version: 3.9.21
- `huggingface_hub` version: 0.27.0
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
```
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| 2,771,137,842
|
I_kwDODunzps6lLDUy
| 7,359
|
There are multiple 'mteb/arguana' configurations in the cache: default, corpus, queries with HF_HUB_OFFLINE=1
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[
"Related to https://github.com/embeddings-benchmark/mteb/issues/1714"
] | 2025-01-06T17:42:49
| 2025-01-06T17:43:31
| null |
NONE
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| null |
### Describe the bug
Hey folks,
I am trying to run this code -
```python
from datasets import load_dataset, get_dataset_config_names
ds = load_dataset("mteb/arguana")
```
with HF_HUB_OFFLINE=1
But I get the following error -
```python
Using the latest cached version of the dataset since mteb/arguana couldn't be found on the Hugging Face Hub (offline mode is enabled).
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 1
----> 1 ds = load_dataset("mteb/arguana")
File ~/env/lib/python3.10/site-packages/datasets/load.py:2129, 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)
2124 verification_mode = VerificationMode(
2125 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2126 )
2128 # Create a dataset builder
-> 2129 builder_instance = load_dataset_builder(
2130 path=path,
2131 name=name,
2132 data_dir=data_dir,
2133 data_files=data_files,
2134 cache_dir=cache_dir,
2135 features=features,
2136 download_config=download_config,
2137 download_mode=download_mode,
2138 revision=revision,
2139 token=token,
2140 storage_options=storage_options,
2141 trust_remote_code=trust_remote_code,
2142 _require_default_config_name=name is None,
2143 **config_kwargs,
2144 )
2146 # Return iterable dataset in case of streaming
2147 if streaming:
File ~/env/lib/python3.10/site-packages/datasets/load.py:1886, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
1884 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name)
1885 # Instantiate the dataset builder
-> 1886 builder_instance: DatasetBuilder = builder_cls(
1887 cache_dir=cache_dir,
1888 dataset_name=dataset_name,
1889 config_name=config_name,
1890 data_dir=data_dir,
1891 data_files=data_files,
1892 hash=dataset_module.hash,
1893 info=info,
1894 features=features,
1895 token=token,
1896 storage_options=storage_options,
1897 **builder_kwargs,
1898 **config_kwargs,
1899 )
1900 builder_instance._use_legacy_cache_dir_if_possible(dataset_module)
1902 return builder_instance
File ~/env/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py:124, in Cache.__init__(self, cache_dir, dataset_name, config_name, version, hash, base_path, info, features, token, repo_id, data_files, data_dir, storage_options, writer_batch_size, **config_kwargs)
122 config_kwargs["data_dir"] = data_dir
123 if hash == "auto" and version == "auto":
--> 124 config_name, version, hash = _find_hash_in_cache(
125 dataset_name=repo_id or dataset_name,
126 config_name=config_name,
127 cache_dir=cache_dir,
128 config_kwargs=config_kwargs,
129 custom_features=features,
130 )
131 elif hash == "auto" or version == "auto":
132 raise NotImplementedError("Pass both hash='auto' and version='auto' instead")
File ~/env/lib/python3.10/site-packages/datasets/packaged_modules/cache/cache.py:84, in _find_hash_in_cache(dataset_name, config_name, cache_dir, config_kwargs, custom_features)
72 other_configs = [
73 Path(_cached_directory_path).parts[-3]
74 for _cached_directory_path in glob.glob(os.path.join(cached_datasets_directory_path_root, "*", version, hash))
(...)
81 )
82 ]
83 if not config_id and len(other_configs) > 1:
---> 84 raise ValueError(
85 f"There are multiple '{dataset_name}' configurations in the cache: {', '.join(other_configs)}"
86 f"\nPlease specify which configuration to reload from the cache, e.g."
87 f"\n\tload_dataset('{dataset_name}', '{other_configs[0]}')"
88 )
89 config_name = cached_directory_path.parts[-3]
90 warning_msg = (
91 f"Found the latest cached dataset configuration '{config_name}' at {cached_directory_path} "
92 f"(last modified on {time.ctime(_get_modification_time(cached_directory_path))})."
93 )
ValueError: There are multiple 'mteb/arguana' configurations in the cache: queries, corpus, default
Please specify which configuration to reload from the cache, e.g.
load_dataset('mteb/arguana', 'queries')
```
It works when I run the same code with HF_HUB_OFFLINE=0, but after the data is downloaded, I turn off the HF hub cache with HF_HUB_OFFLINE=1, and then this error appears.
Are there some files I am missing with hub disabled?
### Steps to reproduce the bug
from datasets import load_dataset, get_dataset_config_names
ds = load_dataset("mteb/arguana")
with HF_HUB_OFFLINE=1
(after already running it with HF_HUB_OFFLINE=0 and populating the datasets cache)
### Expected behavior
Dataset loaded successfully as it does with HF_HUB_OFFLINE=1
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.15.148.2-2.cm2-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.27.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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I_kwDODunzps6lIc4_
| 7,357
|
Python process aborded with GIL issue when using image dataset
|
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[
"The issue seems to come from `pyarrow`, I opened an issue on their side at https://github.com/apache/arrow/issues/45214"
] | 2025-01-06T11:29:30
| 2025-03-08T15:59:36
| null |
NONE
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### Describe the bug
The issue is visible only with the latest `datasets==3.2.0`.
When using image dataset the Python process gets aborted right before the exit with the following error:
```
Fatal Python error: PyGILState_Release: thread state 0x7fa1f409ade0 must be current when releasing
Python runtime state: finalizing (tstate=0x0000000000ad2958)
Thread 0x00007fa33d157740 (most recent call first):
<no Python frame>
Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._boun
ded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, pyarrow.lib, pandas._libs.tslibs.ccalendar, pandas._libs.ts
libs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.t
slibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._l
ibs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pan
das._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join,
pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, charset_normalizer.md, requests.pa
ckages.charset_normalizer.md, requests.packages.chardet.md, yaml._yaml, markupsafe._speedups, PIL._imaging, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards
, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, sentencepiece._sentencepiece, sklearn.__check_build._check_build, psutil._psut
il_linux, psutil._psutil_posix, scipy._lib._ccallback_c, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.l
inalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_up
date, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack,
scipy.sparse.linalg._propack._zpropack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flo
w, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial
._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.spatial.transform._rotation, scipy.optimize._group_columns, s
cipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, sc
ipy.optimize._zeros, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.l
inalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integr
ate._lsoda, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._r
gi_cython, scipy.special.cython_special, scipy.stats._stats, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy._lib._uarray._uarray, scipy.stats._ansari_swilk_statis
tics, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._mvn, scipy.stats._rcont.rcont, scipy.stats._unuran.unuran_wrapper, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, sklearn.utils._isf
inite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.p
reprocessing._target_encoder_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._bas
e, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distanc
es_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, s
klearn.metrics._pairwise_fast, PIL._imagingft, google._upb._message, h5py._errors, h5py.defs, h5py._objects, h5py.h5, h5py.utils, h5py.h5t, h5py.h5s, h5py.h5ac, h5py.h5p, h5py.h5r, h5py._proxy, h5py._conv,
h5py.h5z, h5py.h5a, h5py.h5d, h5py.h5ds, h5py.h5g, h5py.h5i, h5py.h5o, h5py.h5f, h5py.h5fd, h5py.h5pl, h5py.h5l, h5py._selector, _cffi_backend, pyarrow._parquet, pyarrow._fs, pyarrow._azurefs, pyarrow._hdfs
, pyarrow._gcsfs, pyarrow._s3fs, multidict._multidict, propcache._helpers_c, yarl._quoting_c, aiohttp._helpers, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket, frozenlist._frozenlist, xxhash
._xxhash, pyarrow._json, pyarrow._acero, pyarrow._csv, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, regex._regex, scipy
.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, PIL._imagingmath, PIL._webp (total: 236)
Aborted (core dumped)
```an
### Steps to reproduce the bug
Install `datasets==3.2.0`
Run the following script:
```python
import datasets
DATASET_NAME = "phiyodr/InpaintCOCO"
NUM_SAMPLES = 10
def preprocess_fn(example):
return {
"prompts": example["inpaint_caption"],
"images": example["coco_image"],
"masks": example["mask"],
}
default_dataset = datasets.load_dataset(
DATASET_NAME, split="test", streaming=True
).filter(lambda example: example["inpaint_caption"] != "").take(NUM_SAMPLES)
test_data = default_dataset.map(
lambda x: preprocess_fn(x), remove_columns=default_dataset.column_names
)
for data in test_data:
print(data["prompts"])
``
### Expected behavior
The script should not hang or crash.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-50-generic-x86_64-with-glibc2.31
- Python version: 3.11.0
- `huggingface_hub` version: 0.25.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.2.0
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I_kwDODunzps6lHEv_
| 7,356
|
How about adding a feature to pass the key when performing map on DatasetDict?
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[
"@lhoestq \r\nIf it's okay with you, can I work on this?",
"Hi ! Can you give an example of what it would look like to use this new feature ?\r\n\r\nNote that currently you can already do\r\n\r\n```python\r\nds[\"train\"] = ds[\"train\"].map(process_train)\r\nds[\"test\"] = ds[\"test\"].map(process_test)\r\n```",
"@lhoestq \nThanks for the response! \nLet me clarify what I'm looking for with an example:\n\nCurrently, we need to write separate processing functions or call .map() separately:\n```python\n# Current approach\ndef process_train(example):\n # Training-specific processing\n return example\n\ndef process_valid(example):\n # Validation-specific processing\n return example\n\nds[\"train\"] = ds[\"train\"].map(process_train)\nds[\"valid\"] = ds[\"valid\"].map(process_valid)\n```\n\nWhat I'm proposing is to have a single processing function that knows which split it's processing:\n\n```python\n# Proposed feature\ndef process(example, split_key):\n if split_key == \"train\":\n # Training-specific processing\n elif split_key == \"valid\":\n # Validation-specific processing\n return example\n\n# Using with_key=True to pass the split information\nds = ds.map(process, with_key=True)\n```\n\nThis becomes particularly useful when:\n1. The processing logic is heavily shared between splits but needs minor adjustments\n2. You want to maintain the processing logic in one place for better maintainability\n3. The processing function is complex and you want to avoid duplicating code\n\nSo I wanted to request this feature to achieve this kind of functionality. \nI've created a draft PR implementing this: https://github.com/huggingface/datasets/pull/7240/files\n",
"I see ! I think it makes sense, and it's more readable than doing something like this:\r\n```python\r\nfrom functools import partial\r\nds = DatasetDict({key: ds[key].map(partial(process, split_key=key)) for key in ds})\r\n```\r\n\r\nPS: you named the argument `with_key` in your example, but it might be even clearer with it's named `with_split` maybe no ?",
"@lhoestq I agree. \nIt seems better to use `with_split`.\nSo can I open a PR with this change?",
"Sure !"
] | 2025-01-06T08:13:52
| 2025-01-13T14:30:48
| null |
CONTRIBUTOR
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### Feature request
Add a feature to pass the key of the DatasetDict when performing map
### Motivation
I often preprocess using map on DatasetDict.
Sometimes, I need to preprocess train and valid data differently depending on the task.
So, I thought it would be nice to pass the key (like train, valid) when performing map on DatasetDict.
What do you think?
### Your contribution
I can submit a pull request to add the feature to pass the key of the DatasetDict when performing map.
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Not available datasets[audio] on python 3.13
|
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[
"It looks like an issue with `numba` which can't be installed on 3.13 ? `numba` is a dependency of `librosa`, used to decode audio files"
] | 2025-01-04T18:37:08
| 2025-01-10T10:46:00
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### Describe the bug
This is the error I got, it seems numba package does not support python 3.13
PS C:\Users\sergi\Documents> pip install datasets[audio]
Defaulting to user installation because normal site-packages is not writeable
Collecting datasets[audio]
Using cached datasets-3.2.0-py3-none-any.whl.metadata (20 kB)
... (OTHER PACKAGES)
Collecting numba>=0.51.0 (from librosa->datasets[audio])
Downloading numba-0.60.0.tar.gz (2.7 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.7/2.7 MB 44.1 MB/s eta 0:00:00
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [24 lines of output]
Traceback (most recent call last):
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
main()
~~~~^^
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.13_3.13.496.0_x64__qbz5n2kfra8p0\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 334, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=[])
~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\sergi\AppData\Local\Temp\pip-build-env-yauns_qh\overlay\Lib\site-packages\setuptools\build_meta.py", line 304, in _get_build_requires
self.run_setup()
~~~~~~~~~~~~~~^^
RuntimeError: Cannot install on Python version 3.13.1; only versions >=3.9,<3.13 are supported.
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
### Steps to reproduce the bug
1. install python >=3.13
2. !pip install datasets[audio]
### Expected behavior
I needed datasets[audio] in the python 3.13
### Environment info
python 3.13.1
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| 7,354
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A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
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[
"recreated .venv and run this: pip install diffusers[training]==0.11.1"
] | 2025-01-04T18:30:17
| 2025-01-08T02:20:58
| 2025-01-08T02:20:58
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### Describe the bug
Following this tutorial: https://huggingface.co/docs/diffusers/en/tutorials/basic_training and running it locally using VSCode on my MacBook. The first line in the tutorial fails: from datasets import load_dataset
dataset = load_dataset('huggan/smithsonian_butterflies_subset', split="train"). with this error:
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2. and ImportError: numpy.core.multiarray failed to import.
Does from datasets import load_dataset really use NumPy 1.x?
### Steps to reproduce the bug
Open VSCode. create a new venv. Create a new ipynb file. Import pip install diffusers[training] try to run this line of code: from datasets import load_dataset
### Expected behavior
data is loaded
### Environment info
ran this: datasets-cli env
and got A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
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.add_faiss_index and .add_elasticsearch_index returns ImportError at Google Colab
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"I can fix this.\nIt's mainly because faiss-gpu requires python<=3.10 but the default python version in colab is 3.11. We just have to downgrade the CPython version down to 3.10 and it should work fine.\n",
"I think I just had no chance to meet with faiss-cpu.\nIt could be import problem? \n_has_faiss gets its value at the beginning of datasets/search.\nI tried to call object before import faiss, so _has_faiss took False. And never updated later. ",
"Yes you can't meet the requirements because faiss-cpu runs only on\r\npython3.10 and lower but the default version for colab is python3.11 which\r\nresults in pip not being able to find wheels for faiss-cpu with python3.11.\r\n\r\nOn Mon, 17 Mar, 2025, 3:56 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>\r\n>\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> you can't meet the requirements\n\nIt is not the case (or I didn't reach this point) because the same code in notebook\n```importlib.util.find_spec(\"faiss\")```\nfinds faiss. I've mention it.\nI think the problem is in the very moment when _has_faiss takes its value and never try again. \n(or it couldn't find the path that was easily found when started from my code)",
"When you run the first cell containing pip install faiss-cpu does it\r\ninstall it?\r\n\r\nOn Mon, 17 Mar, 2025, 8:01 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>\r\n>\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> When you run the first cell containing pip install faiss-cpu does it\n> install it?\n> […](#)\n\nYes. It was installed succesfully. \nMethods of datasets library that depends on _has_faiss constant didn't start to work."
] | 2025-03-16T00:51:49
| 2025-03-16T08:34:40
| null |
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### Describe the bug
At Google Colab
```!pip install faiss-cpu``` works
```import faiss``` no error
but
```embeddings_dataset.add_faiss_index(column='embeddings')```
returns
```
[/usr/local/lib/python3.11/dist-packages/datasets/search.py](https://localhost:8080/#) in init(self, device, string_factory, metric_type, custom_index)
247 self.faiss_index = custom_index
248 if not _has_faiss:
--> 249 raise ImportError(
250 "You must install Faiss to use FaissIndex. To do so you can run conda install -c pytorch faiss-cpu or conda install -c pytorch faiss-gpu. "
251 "A community supported package is also available on pypi: pip install faiss-cpu or pip install faiss-gpu. "
```
because
```_has_faiss = importlib.util.find_spec("faiss") is not None``` at the beginning of ```datasets/search.py``` returns ```False```
when
the same code at colab notebook returns
```ModuleSpec(name='faiss', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7b7851449f50>, origin='/usr/local/lib/python3.11/dist-packages/faiss/init.py', submodule_search_locations=['/usr/local/lib/python3.11/dist-packages/faiss'])```
But
```
import datasets
datasets.search._has_faiss
```
at ```colab notebook``` also returns ```False```
The same story with ```_has_elasticsearch```
### Steps to reproduce the bug
1. Follow https://huggingface.co/learn/nlp-course/chapter5/6?fw=pt at Google Colab
2. till ```embeddings_dataset.add_faiss_index(column='embeddings')```
3. ```embeddings_dataset.add_elasticsearch_index(column='embeddings')```
4. https://colab.research.google.com/drive/1h2cjuiClblqzbNQgrcoLYOC8zBqTLLcv#scrollTo=3ddzRp72auOF
### Expected behavior
I've only started Tutorial and don't know exactly. But something tells me that ```embeddings_dataset.add_faiss_index(column='embeddings')```
should work without ```Import Error```
### Environment info
Google Colab notebook with default config
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Problems with local dataset after upgrade from 3.3.2 to 3.4.0
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[
"Hi ! I just released 3.4.1 with a fix, let me know if it's working now !"
] | 2025-03-15T09:22:50
| 2025-03-15T09:23:55
| null |
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### Describe the bug
I was not able to open a local saved dataset anymore that was created using an older datasets version after the upgrade yesterday from datasets 3.3.2 to 3.4.0
The traceback is
```
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 67, in _generate_tables
batches = pa.ipc.open_stream(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 190, in open_stream
return RecordBatchStreamReader(source, options=options,
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 52, in __init__
self._open(source, options=options, memory_pool=memory_pool)
File "pyarrow/ipc.pxi", line 1006, in pyarrow.lib._RecordBatchStreamReader._open
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: Expected to read 538970747 metadata bytes, but only read 2126
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1855, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/arrow/arrow.py", line 69, in _generate_tables
reader = pa.ipc.open_file(f)
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 234, in open_file
return RecordBatchFileReader(
File "/usr/local/lib/python3.10/dist-packages/pyarrow/ipc.py", line 110, in __init__
self._open(source, footer_offset=footer_offset,
File "pyarrow/ipc.pxi", line 1090, in pyarrow.lib._RecordBatchFileReader._open
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: Not an Arrow file
```
### Steps to reproduce the bug
Load a dataset from a local folder with
```
dataset = load_dataset(
args.train_data_dir,
cache_dir=args.cache_dir,
)
```
as it is done for example in the training script for SD3 controlnet.
This is the minimal script to test it:
```
from datasets import load_dataset
def main():
dataset = load_dataset(
"local_dataset",
)
print(dataset)
print("Sample data:", dataset["train"][0])
if __name__ == "__main__":
main()
````
### Expected behavior
Work in 3.4.0 like in 3.3.2
### Environment info
- `datasets` version: 3.4.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.29.3
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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| 2,916,235,092
|
I_kwDODunzps6t0jdU
| 7,449
|
Cannot load data with different schemas from different parquet files
|
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[
"Hi ! `load_dataset` expects all the data_files to have the same schema.\n\nMaybe you can try enforcing certain `features` using:\n\n```python\nfeatures = Features({\"conversations\": {'content': Value('string'), 'role': Value('string',)}})\nds = load_dataset(..., features=features)\n```",
"Thanks! It works if I explicitly specify all nested fields of the data."
] | 2025-03-13T08:14:49
| 2025-03-13T11:19:06
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### Describe the bug
Cannot load samples with optional fields from different files. The schema cannot be correctly derived.
### Steps to reproduce the bug
When I place two samples with an optional field `some_extra_field` within a single parquet file, it can be loaded via `load_dataset`.
```python
import pandas as pd
from datasets import load_dataset
data = [
{'conversations': {'role': 'user', 'content': 'hello'}},
{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}
]
df = pd.DataFrame(data)
df.to_parquet('data.parquet')
dataset = load_dataset('parquet', data_files='data.parquet', split='train')
print(dataset.features)
```
The schema can be derived. `some_extra_field` is set to None for the first row where it is absent.
```
{'conversations': {'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None), 'some_extra_field': Value(dtype='string', id=None)}}
```
However, when I separate the samples into different files, it cannot be loaded.
```python
import pandas as pd
from datasets import load_dataset
data1 = [{'conversations': {'role': 'user', 'content': 'hello'}}]
pd.DataFrame(data1).to_parquet('data1.parquet')
data2 = [{'conversations': {'role': 'user', 'content': 'hi', 'some_extra_field': 'some_value'}}]
pd.DataFrame(data2).to_parquet('data2.parquet')
dataset = load_dataset('parquet', data_files=['data1.parquet', 'data2.parquet'], split='train')
print(dataset.features)
```
Traceback:
```
Traceback (most recent call last):
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/builder.py", line 1854, in _prepare_split_single
for _, table in generator:
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
arrays = [
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
cast_array_to_feature(
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/tiger/.local/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<content: string, role: string, some_extra_field: string>
to
{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}
```
### Expected behavior
Correctly load data with optional fields from different parquet files.
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.10.135.bsk.4-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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| 2,916,025,762
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I_kwDODunzps6tzwWi
| 7,448
|
`datasets.disable_caching` doesn't work
|
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When I use `Dataset.from_generator(my_gen)` to load my dataset, it simply skips my changes to the generator function.
I tried `datasets.disable_caching`, but it doesn't work!
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| 2,915,233,248
|
I_kwDODunzps6twu3g
| 7,447
|
Epochs shortened after resuming mid-epoch with Iterable dataset+StatefulDataloader(persistent_workers=True)
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[
"Thanks for reporting ! Maybe we should store the epoch in the state_dict, and then when the dataset is iterated on again after setting a new epoch it should restart from scratch instead of resuming ? wdyt ?",
"But why does this only happen when `persistent_workers=True`? I would expect it to work correctly even without storing the epoch number in the state_dict of the iterable dataset. ",
"I think persistent_workers=False simply ignores the dataset state_dict when it starts a new epoch, that's why the issue doesn't appear in that case",
"I opened https://github.com/huggingface/datasets/pull/7451 to fix the issue, let me know if it works for you",
"I just released `datasets` 3.4 that includes the fix :)\n\nPS: in your script you probably want to set the epoch like this, otherwise it's still set to 0 after the first epoch:\n\n```diff\n if state_dict is None:\n- ds.set_epoch(epoch)\n epoch += 1\n+ ds.set_epoch(epoch)\n```"
] | 2025-03-12T21:41:05
| 2025-03-14T17:26:59
| 2025-03-14T10:50:10
|
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### Describe the bug
When `torchdata.stateful_dataloader.StatefulDataloader(persistent_workers=True)` the epochs after resuming only iterate through the examples that were left in the epoch when the training was interrupted. For example, in the script below training is interrupted on step 124 (epoch 1) when 3 batches are left. Then after resuming, the rest of epochs (2 and 3) only iterate through these 3 batches.
### Steps to reproduce the bug
Run the following script with and with PERSISTENT_WORKERS=true.
```python
# !/usr/bin/env python3
# torch==2.5.1
# datasets==3.3.2
# torchdata>=0.9.0
import datasets
import pprint
from torchdata.stateful_dataloader import StatefulDataLoader
import os
PERSISTENT_WORKERS = (
os.environ.get("PERSISTENT_WORKERS", "False").lower() == "true"
)
# PERSISTENT_WORKERS = True # Incorrect resume
# ds = datasets.load_from_disk("dataset").to_iterable_dataset(num_shards=4)
def generator():
for i in range(128):
yield {"x": i}
ds = datasets.Dataset.from_generator(
generator, features=datasets.Features({"x": datasets.Value("int32")})
).to_iterable_dataset(num_shards=4)
dl = StatefulDataLoader(
ds, batch_size=2, num_workers=2, persistent_workers=PERSISTENT_WORKERS
)
global_step = 0
epoch = 0
ds_state_dict = None
state_dict = None
resumed = False
while True:
if epoch >= 3:
break
if state_dict is not None:
dl.load_state_dict(state_dict)
state_dict = None
ds_state_dict = None
resumed = True
print("resumed")
for i, batch in enumerate(dl):
print(f"epoch: {epoch}, global_step: {global_step}, batch: {batch}")
global_step += 1 # consume datapoint
# simulate error
if global_step == 124 and not resumed:
ds_state_dict = ds.state_dict()
state_dict = dl.state_dict()
print("checkpoint")
print("ds_state_dict")
pprint.pprint(ds_state_dict)
print("dl_state_dict")
pprint.pprint(state_dict)
break
if state_dict is None:
ds.set_epoch(epoch)
epoch += 1
```
The script checkpoints when there are three batches left in the second epoch. After resuming, only the last three batches are repeated in the rest of the epochs.
If it helps, following are the two state_dicts for the dataloader save at the same step with the two settings. The left one is for `PERSISTENT_WORKERS=False`
### Expected behavior
All the elements in the dataset should be iterated through in the epochs following the one where we resumed. The expected behavior can be seen by setting `PERSISTENT_WORKERS=False`.
### Environment info
torch==2.5.1
datasets==3.3.2
torchdata>=0.9.0
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I_kwDODunzps6toZ-4
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pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
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### Describe the bug
A dict with its keys are all str but get following error
```python
test_data=[{'input_ids':[1,2,3],'labels':[[Counter({2:1})]]}]
dataset = datasets.Dataset.from_list(test_data)
```
```bash
pyarrow.lib.ArrowTypeError: Expected dict key of type str or bytes, got 'int'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
datasets 3.3.2
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I_kwDODunzps6thWyN
| 7,444
|
Excessive warnings when resuming an IterableDataset+buffered shuffle+DDP.
|
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### Describe the bug
I have a large dataset that I shared into 1024 shards and save on the disk during pre-processing. During training, I load the dataset using load_from_disk() and convert it into an iterable dataset, shuffle it and split the shards to different DDP nodes using the recommended method.
However, when the training is resumed mid-epoch, I get thousands of identical warning messages:
```
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
```
### Steps to reproduce the bug
1. Run a multi-node training job using the following python script and interrupt the training after a few seconds to save a mid-epoch checkpoint.
```python
#!/usr/bin/env python
import os
import time
from typing import Dict, List
import torch
import lightning as pl
from torch.utils.data import DataLoader
from datasets import Dataset
from datasets.distributed import split_dataset_by_node
import datasets
from transformers import AutoTokenizer
from more_itertools import flatten, chunked
from torchdata.stateful_dataloader import StatefulDataLoader
from lightning.pytorch.callbacks.on_exception_checkpoint import (
OnExceptionCheckpoint,
)
datasets.logging.set_verbosity_debug()
def dummy_generator():
# Generate 60 examples: integers from $0$ to $59$
# 64 sequences of different lengths
dataset = [
list(range(3, 10)),
list(range(10, 15)),
list(range(15, 21)),
list(range(21, 27)),
list(range(27, 31)),
list(range(31, 36)),
list(range(36, 45)),
list(range(45, 50)),
]
for i in range(8):
for j, ids in enumerate(dataset):
yield {"token_ids": [idx + i * 50 for idx in ids]}
def group_texts(
examples: Dict[str, List[List[int]]],
block_size: int,
eos_token_id: int,
bos_token_id: int,
pad_token_id: int,
) -> Dict[str, List[List[int]]]:
real_block_size = block_size - 2 # make space for bos and eos
# colapse the sequences into a single list of tokens and then create blocks of real_block_size
input_ids = []
attention_mask = []
for block in chunked(flatten(examples["token_ids"]), real_block_size):
s = [bos_token_id] + list(block) + [eos_token_id]
ls = len(s)
attn = [True] * ls
s += [pad_token_id] * (block_size - ls)
attn += [False] * (block_size - ls)
input_ids.append(s)
attention_mask.append(attn)
return {"input_ids": input_ids, "attention_mask": attention_mask}
def collate_fn(batch):
return {
"input_ids": torch.tensor(
[item["input_ids"] for item in batch], dtype=torch.long
),
"attention_mask": torch.tensor(
[item["attention_mask"] for item in batch], dtype=torch.long
),
}
class DummyModule(pl.LightningModule):
def __init__(self):
super().__init__()
# A dummy linear layer (not used for actual computation)
self.layer = torch.nn.Linear(1, 1)
self.ds = None
self.prepare_data_per_node = False
def on_train_start(self):
# This hook is called once training begins on each process.
print(f"[Rank {self.global_rank}] Training started.", flush=True)
self.data_file = open(f"data_{self.global_rank}.txt", "w")
def on_train_end(self):
self.data_file.close()
def training_step(self, batch, batch_idx):
# Print batch information to verify data loading.
time.sleep(5)
# print("batch", batch, flush=True)
print(
f"\n[Rank {self.global_rank}] Training step, epoch {self.trainer.current_epoch}, batch {batch_idx}: {batch['input_ids']}",
flush=True,
)
self.data_file.write(
f"[Rank {self.global_rank}] Training step, epoch {self.trainer.current_epoch}, batch {batch_idx}: {batch['input_ids']}\n"
)
# Compute a dummy loss (here, simply a constant tensor)
loss = torch.tensor(0.0, requires_grad=True)
return loss
def on_train_epoch_start(self):
epoch = self.trainer.current_epoch
print(
f"[Rank {self.global_rank}] Training epoch {epoch} started.",
flush=True,
)
self.data_file.write(
f"[Rank {self.global_rank}] Training epoch {epoch} started.\n"
)
def configure_optimizers(self):
# Return a dummy optimizer.
return torch.optim.SGD(self.parameters(), lr=0.001)
class DM(pl.LightningDataModule):
def __init__(self):
super().__init__()
self.ds = None
self.prepare_data_per_node = False
def set_epoch(self, epoch: int):
self.ds.set_epoch(epoch)
def prepare_data(self):
# download the dataset
dataset = Dataset.from_generator(dummy_generator)
# save the dataset
dataset.save_to_disk("dataset", num_shards=4)
def setup(self, stage: str):
# load the dataset
ds = datasets.load_from_disk("dataset").to_iterable_dataset(
num_shards=4
)
ds = ds.map(
group_texts,
batched=True,
batch_size=5,
fn_kwargs={
"block_size": 5,
"eos_token_id": 1,
"bos_token_id": 0,
"pad_token_id": 2,
},
remove_columns=["token_ids"],
).shuffle(seed=42, buffer_size=8)
ds = split_dataset_by_node(
ds,
rank=self.trainer.global_rank,
world_size=self.trainer.world_size,
)
self.ds = ds
def train_dataloader(self):
print(
f"[Rank {self.trainer.global_rank}] Preparing train_dataloader...",
flush=True,
)
rank = self.trainer.global_rank
print(
f"[Rank {rank}] Global rank: {self.trainer.global_rank}",
flush=True,
)
world_size = self.trainer.world_size
print(f"[Rank {rank}] World size: {world_size}", flush=True)
return StatefulDataLoader(
self.ds,
batch_size=2,
num_workers=2,
collate_fn=collate_fn,
drop_last=True,
persistent_workers=True,
)
if __name__ == "__main__":
print("Starting Lightning training", flush=True)
# Optionally, print some SLURM environment info for debugging.
print(f"SLURM_NNODES: {os.environ.get('SLURM_NNODES', '1')}", flush=True)
# Determine the number of nodes from SLURM (defaulting to 1 if not set)
num_nodes = int(os.environ.get("SLURM_NNODES", "1"))
model = DummyModule()
dm = DM()
on_exception = OnExceptionCheckpoint(
dirpath="checkpoints",
filename="on_exception",
)
# Configure the Trainer to use distributed data parallel (DDP).
trainer = pl.Trainer(
accelerator="gpu" if torch.cuda.is_available() else "cpu",
devices=1,
strategy=(
"ddp" if num_nodes > 1 else "auto"
), # Use DDP strategy for multi-node training.
num_nodes=num_nodes,
max_epochs=2,
logger=False,
enable_checkpointing=True,
num_sanity_val_steps=0,
enable_progress_bar=False,
callbacks=[on_exception],
)
# resume (uncomment to resume)
# trainer.fit(model, datamodule=dm, ckpt_path="checkpoints/on_exception.ckpt")
# train
trainer.fit(model, datamodule=dm)
```
```bash
#!/bin/bash
#SBATCH --job-name=pl_ddp_test
#SBATCH --nodes=2 # Adjust number of nodes as needed
#SBATCH --ntasks-per-node=1 # One GPU (process) per node
#SBATCH --cpus-per-task=3 # At least as many dataloader workers as required
#SBATCH --gres=gpu:1 # Request one GPU per node
#SBATCH --time=00:10:00 # Job runtime (adjust as needed)
#SBATCH --partition=gpu-preempt # Partition or queue name
#SBATCH -o script.out
# Disable Python output buffering.
export PYTHONUNBUFFERED=1
echo "SLURM job starting on $(date)"
echo "Running on nodes: $SLURM_NODELIST"
echo "Current directory: $(pwd)"
ls -l
# Launch the script using srun so that each process starts the Lightning module.
srun script.py
```
2. Uncomment the "resume" line (second to last) and comment the original `trainer.fit` call (last line).
It will produce the following log.
```
[Rank 0] Preparing train_dataloader...
[Rank 0] Global rank: 0
[Rank 0] World size: 2
[Rank 1] Preparing train_dataloader...
[Rank 1] Global rank: 1
[Rank 1] World size: 2
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Assigning 2 shards (or data sources) of the dataset to each node.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#1, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#0 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#0 dataloader worker#0, ': Finished iterating over 1/1 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
[Rank 0] Training started.
[Rank 0] Training epoch 0 started.
[Rank 0] Training epoch 1 started.
Assigning 2 shards (or data sources) of the dataset to each node.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#0 dataloader worker#1, ': Starting to iterate over 1/2 shards.
node#0 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#0 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#0 dataloader worker#0, ': Finished iterating over 1/1 shards.
`Trainer.fit` stopped: `max_epochs=2` reached.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
node#1 dataloader worker#0, ': Finished iterating over 1/1 shards.
[Rank 1] Training started.
[Rank 1] Training epoch 0 started.
[Rank 1] Training epoch 1 started.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
node#1 dataloader worker#0, ': Starting to iterate over 1/2 shards.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Loading a state dict of a shuffle buffer of a dataset without the buffer content.The shuffle buffer will be refilled before starting to yield new examples.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
Set __getitem__(key) output type to arrow for no columns (when key is int or slice) and don't output other (un-formatted) columns.
node#1 dataloader worker#0, ': Finished iterating over 1/1 shards.
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
```
I'm also attaching the relevant state_dict to make sure that the state is being checkpointed as expected.
```
{'_iterator_finished': True,
'_snapshot': {'_last_yielded_worker_id': 1,
'_main_snapshot': {'_IterableDataset_len_called': None,
'_base_seed': 3992758080362545099,
'_index_sampler_state': {'samples_yielded': 64},
'_num_workers': 2,
'_sampler_iter_state': None,
'_sampler_iter_yielded': 32,
'_shared_seed': None},
'_snapshot_step': 32,
'_worker_snapshots': {'worker_0': {'dataset_state': {'ex_iterable': {'shard_example_idx': 0,
'shard_idx': 1},
'num_examples_since_previous_state': 0,
'previous_state': {'shard_example_idx': 0,
'shard_idx': 1},
'previous_state_example_idx': 33},
'fetcher_state': {'dataset_iter_state': None,
'fetcher_ended': False},
'worker_id': 0},
'worker_1': {'dataset_state': {'ex_iterable': {'shard_example_idx': 0,
'shard_idx': 1},
'num_examples_since_previous_state': 0,
'previous_state': {'shard_example_idx': 0,
'shard_idx': 1},
'previous_state_example_idx': 33},
'fetcher_state': {'dataset_iter_state': None,
'fetcher_ended': False},
'worker_id': 1}}},
'_steps_since_snapshot': 0}
```
### Expected behavior
Since I'm following all the recommended steps, I don't expect to see any warning when resuming. Am I doing something wrong? Also, can someone explain why I'm seeing 20 identical messages in the log in this reproduction setting? I'm trying to understand why I see thousands of these messages with the actual dataset.
One more surprising thing I noticed in the logs is the change in a number of shards per worker. In the following messages, the denominator changes from 2 to 1.
```
node#1 dataloader worker#1, ': Starting to iterate over 1/2 shards.
...
node#1 dataloader worker#1, ': Finished iterating over 1/1 shards.
```
### Environment info
python: 3.11.10
datasets: 3.3.2
lightning: 2.3.1
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I_kwDODunzps6tXX64
| 7,443
|
index error when num_shards > len(dataset)
|
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[
"Actually, looking at the code a bit more carefully, maybe an even better solution is to explicitly set `num_shards=len(self)` somewhere inside both `push_to_hub()` and `save_to_disk()` when these functions are invoked with `num_shards > len(dataset)`."
] | 2025-03-10T22:40:59
| 2025-03-10T23:43:08
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In `ds.push_to_hub()` and `ds.save_to_disk()`, `num_shards` must be smaller than or equal to the number of rows in the dataset, but currently this is not checked anywhere inside these functions. Attempting to invoke these functions with `num_shards > len(dataset)` should raise an informative `ValueError`.
I frequently work with datasets with a small number of rows where each row is pretty large, so I often encounter this issue, where the function runs until the shard index in `ds.shard(num_shards, indx)` goes out of bounds. Ideally, a `ValueError` should be raised before reaching this point (i.e. as soon as `ds.push_to_hub()` or `ds.save_to_disk()` is invoked with `num_shards > len(dataset)`).
It seems that adding something like:
```python
if len(self) < num_shards:
raise ValueError(f"num_shards ({num_shards}) must be smaller than or equal to the number of rows in the dataset ({len(self)}). Please either reduce num_shards or increase max_shard_size to make sure num_shards <= len(dataset).")
```
to the beginning of the definition of the `ds.shard()` function [here](https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/arrow_dataset.py#L4728) would deal with this issue for both `ds.push_to_hub()` and `ds.save_to_disk()`, but I'm not exactly sure if this is the best place to raise the `ValueError` (it seems that a more correct way to do it would be to write separate checks for `ds.push_to_hub()` and `ds.save_to_disk()`). I'd be happy to submit a PR if you think something along these lines would be acceptable.
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| 2,905,543,017
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I_kwDODunzps6tLxFp
| 7,442
|
Flexible Loader
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"Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?",
"> Ideally `save_to_disk` should save in a format compatible with load_dataset, wdyt ?\n\nThat would be perfect if not at least a flexible loader."
] | 2025-03-09T16:55:03
| 2025-03-13T11:15:02
| null |
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### Feature request
Can we have a utility function that will use `load_from_disk` when given the local path and `load_dataset` if given an HF dataset?
It can be something as simple as this one:
```
def load_hf_dataset(path_or_name):
if os.path.exists(path_or_name):
return load_from_disk(path_or_name)
else:
return load_dataset(path_or_name)
```
### Motivation
This can be done inside the user codebase, too, but in my experience, it becomes repetitive code.
### Your contribution
I can open a pull request.
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I_kwDODunzps6tIj15
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`drop_last_batch` does not drop the last batch using IterableDataset + interleave_datasets + multi_worker
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[
"Hi @memray, I’d like to help fix the issue with `drop_last_batch` not working when `num_workers > 1`. I’ll investigate and propose a solution. Thanks!\n",
"Thank you very much for offering to help! I also noticed a problem related to a previous issue and left a comment [here](https://github.com/huggingface/datasets/issues/6565#issuecomment-2708169303) (the code checks the validity before certain columns removed). Can you take a look as well?"
] | 2025-03-08T10:28:44
| 2025-03-09T21:27:33
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### Describe the bug
See the script below
`drop_last_batch=True` is defined using map() for each dataset.
The last batch for each dataset is expected to be dropped, id 21-25.
The code behaves as expected when num_workers=0 or 1.
When using num_workers>1, 'a-11', 'b-11', 'a-12', 'b-12' are gone and instead 21 and 22 are sampled.
### Steps to reproduce the bug
```
from datasets import Dataset
from datasets import interleave_datasets
from torch.utils.data import DataLoader
def convert_to_str(batch, dataset_name):
batch['a'] = [f"{dataset_name}-{e}" for e in batch['a']]
return batch
def gen1():
for ii in range(1, 25):
yield {"a": ii}
def gen2():
for ii in range(1, 25):
yield {"a": ii}
# https://github.com/huggingface/datasets/issues/6565
if __name__ == '__main__':
dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=2)
dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=2)
dataset1 = dataset1.map(lambda x: convert_to_str(x, dataset_name="a"), batched=True, batch_size=10, drop_last_batch=True)
dataset2 = dataset2.map(lambda x: convert_to_str(x, dataset_name="b"), batched=True, batch_size=10, drop_last_batch=True)
interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted")
print(f"num_workers=0")
loader = DataLoader(interleaved, batch_size=5, num_workers=0)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=1")
loader = DataLoader(interleaved, batch_size=5, num_workers=1)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=2")
loader = DataLoader(interleaved, batch_size=5, num_workers=2)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=3")
loader = DataLoader(interleaved, batch_size=5, num_workers=3)
i = 0
for b in loader:
print(i, b['a'])
i += 1
```
output is:
```
num_workers=0
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=1
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=2
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=3
Too many dataloader workers: 3 (max is dataset.num_shards=2). Stopping 1 dataloader workers.
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
```
### Expected behavior
`'a-21', 'b-21', 'a-22', 'b-22'` should be dropped
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-1056-aws-x86_64-with-glibc2.31
- Python version: 3.10.16
- `huggingface_hub` version: 0.28.0
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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IterableDataset raises FileNotFoundError instead of retrying
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[
"I have since been training more models with identical architectures over the same dataset, and it is completely unstable. One has now failed at chunk9/1215, whilst others have gotten past that.\n```python\nFileNotFoundError: zstd://example_train_1215.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_1215.jsonl.zst\n```\nBelow is the full training log, where you can clearly see the intermittent dataset issues. Note again that this model only got to epoch 0.11, whereas I have other models training on the exact same dataset right now that have gotten way beyond that. This is quickly turning into a highly expensive bug which I didn't have issues with in the past half year of using the same setup.\n<details>\n<summary>Training log of failed run</summary>\n\n```python\n 1%| | 64/8192 [56:27<87:25:33, 38.72s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 5ef28452-e903-4bd8-946d-f0c77f558a2a)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_4799.jsonl.zst\n 1%| | 64/8192 [56:51<87:25:33, 38.72s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:40:14<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: ba6e4c51-f4a4-407e-9934-3772550b7ce9)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_2770.jsonl.zst\n 2%|▏ | 192/8192 [2:40:53<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:40:53<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: bdf2cfaa-7e0b-46a0-bec1-b1e573fa7998)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_4386.jsonl.zst\n 2%|▏ | 192/8192 [2:42:16<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:42:16<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 1dc5e455-8042-4c7b-9b97-5ded33dfea34)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_1763.jsonl.zst\n 2%|▏ | 192/8192 [2:42:30<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:42:30<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 9cf29917-8111-41fe-80aa-953df65c5803)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_5509.jsonl.zst\n 2%|▏ | 192/8192 [2:44:31<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:44:31<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 2515a0b0-3d81-409f-940c-e78ed5e2dbf8)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3093.jsonl.zst\n 2%|▏ | 192/8192 [2:45:13<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:45:13<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: a4c1e0c7-1c7a-4377-bc7e-6f076473072b)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_3422.jsonl.zst\n 2%|▏ | 192/8192 [2:46:26<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:46:26<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: c7b0d366-db86-4d0c-a4e0-be251d26519e)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_2250.jsonl.zst\n 2%|▏ | 192/8192 [2:47:24<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:47:24<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: b0df5a1a-4836-46cf-8e45-58a7c1553309)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_6161.jsonl.zst\n 2%|▏ | 192/8192 [2:49:10<85:29:44, 38.47s/it]Retrying in 1s [Retry 1/5].\n 2%|▏ | 192/8192 [2:49:10<85:29:44, 38.47s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. 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timed out. (read timeout=10)\"), '(Request ID: 0faae356-e828-4cff-9a49-42b397431927)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_185.jsonl.zst\n 9%|▊ | 704/8192 [9:38:28<79:08:04, 38.05s/it]Retrying in 1s [Retry 1/5].\n 9%|▊ | 704/8192 [9:38:28<79:08:04, 38.05s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 9557423f-6937-4f70-b50f-05b0c01f5bf3)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_4035.jsonl.zst\n 9%|▊ | 704/8192 [9:44:58<79:08:04, 38.05s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:28:20<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 939d1d36-c607-4d3c-a0a0-8e447579340b)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_165.jsonl.zst\n 10%|█ | 832/8192 [11:30:25<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:30:25<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 0b99bfd1-07ae-46db-81fa-fc6ef0eabdbc)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_1529.jsonl.zst\n 10%|█ | 832/8192 [11:38:24<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:38:24<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: c208d1bb-5d13-45d2-9a01-1d5a2defa598)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_4562.jsonl.zst\n 10%|█ | 832/8192 [11:39:58<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 10%|█ | 832/8192 [11:39:58<80:32:25, 39.39s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 2bf98b5c-473b-4e00-aca2-b152efddb992)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk3/example_holdout_4414.jsonl.zst\n 10%|█ | 832/8192 [11:41:00<80:32:25, 39.39s/it]Retrying in 1s [Retry 1/5].\n 11%|█ | 896/8192 [12:24:54<77:09:28, 38.07s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 3b8321b9-2d88-4bfa-9eca-b201c444cba3)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk5/example_holdout_405.jsonl.zst\n 11%|█ | 896/8192 [12:25:55<77:09:28, 38.07s/it]Retrying in 1s [Retry 1/5].\n 11%|█ | 896/8192 [12:25:55<77:09:28, 38.07s/it]'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: a98a238a-c0a4-4295-8502-316a89a7ae29)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk1/example_holdout_2524.jsonl.zst\n 11%|█ | 896/8192 [12:33:14<77:09:28, 38.07s/it]Retrying in 1s [Retry 1/5].\n 11%|█▏ | 922/8192 [12:52:49<76:09:46, 37.71s/it]'(ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 36a7cc72-4605-416a-8742-59488d719150)')' thrown while requesting GET https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_5267.jsonl.zst\n 11%|█▏ | 922/8192 [12:52:59<76:09:46, 37.71s/it]Retrying in 1s [Retry 1/5].\n 12%|█▏ | 943/8192 [13:06:07<76:15:57, 37.88s/it]\n{'loss': 3.7796, 'grad_norm': 0.4774172008037567, 'learning_rate': 0.001, 'epoch': 0.06, 'num_input_tokens_seen': 484442112}\n{'loss': 3.7779, 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\"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 1378, in _iter_from_urlpaths\n raise FileNotFoundError(urlpath)\nFileNotFoundError: zstd://example_train_1215.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_1215.jsonl.zst\n```\n\n</details>",
"Two more today:\n```python\nFileNotFoundError: zstd://example_holdout_5012.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk4/example_holdout_5012.jsonl.zst\n```\nand\n```python\nFileNotFoundError: zstd://example_holdout_3073.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/validation/chunk2/example_holdout_3073.jsonl.zst\n```\nboth of which exist on the hub ([here](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/validation/chunk4/example_holdout_5012.jsonl.zst) and [here](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/validation/chunk2/example_holdout_3073.jsonl.zst)).",
"I also observe the same thing when using streaming with DCLM dataset with 64 GPUs. I have tried ```export HF_DATASETS_STREAMING_PARALLELISM=1``` but doesn't help.",
"Another error today, this time a 504 gateway timeout `HfHubHTTPError`. I have no idea if this is related, but I suspect that it is considering the setup is identical. Notably though, the two errors I posted yesterday were for evaluation (hence the `holdout` in the URLs) whereas today there was no problem doing that first evaluation, but now the `train` split failed.\n```python\n...\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 2226, in __iter__\n for key, example in ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1499, in __iter__\n for x in self.ex_iterable:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1067, in __iter__\n yield from self._iter()\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1231, in _iter\n for key, transformed_example in iter_outputs():\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1207, in iter_outputs\n for i, key_example in inputs_iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 1111, in iter_inputs\n for key, example in iterator:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py\", line 371, in __iter__\n for key, pa_table in self.generate_tables_fn(**gen_kwags):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py\", line 114, in _generate_tables\n with open(file, \"rb\") as f:\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/streaming.py\", line 75, in wrapper\n return function(*args, download_config=download_config, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py\", line 948, in xopen\n file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 147, in open\n return self.__enter__()\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 105, in __enter__\n f = self.fs.open(self.path, mode=mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py\", line 1301, in open\n f = self._open(\n ^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/filesystems/compression.py\", line 85, in _open\n return self._open_with_fsspec().open()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 147, in open\n return self.__enter__()\n ^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/core.py\", line 105, in __enter__\n f = self.fs.open(self.path, mode=mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/fsspec/spec.py\", line 1301, in open\n f = self._open(\n ^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 234, in _open\n return HfFileSystemFile(self, path, mode=mode, revision=revision, block_size=block_size, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 691, in __init__\n self.details = fs.info(self.resolved_path.unresolve(), expand_info=False)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 524, in info\n self.ls(parent_path, expand_info=False)\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 284, in ls\n out = self._ls_tree(path, refresh=refresh, revision=revision, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_file_system.py\", line 375, in _ls_tree\n for path_info in tree:\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/hf_api.py\", line 3080, in list_repo_tree\n for path_info in paginate(path=tree_url, headers=headers, params={\"recursive\": recursive, \"expand\": expand}):\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_pagination.py\", line 46, in paginate\n hf_raise_for_status(r)\n File \"/miniconda3/envs/draft/lib/python3.11/site-packages/huggingface_hub/utils/_http.py\", line 477, in hf_raise_for_status\n raise _format(HfHubHTTPError, str(e), response) from e\nhuggingface_hub.errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/cerebras/SlimPajama-627B/tree/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train%2Fchunk8?recursive=False&expand=False&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5amFIVnVhemd2WlhoaGJYQnNaVjkwY21GcGJsOHpOams0TG1wemIyNXNMbnB6ZENKOTozMDAw\n```"
] | 2025-03-07T19:14:18
| 2025-03-13T15:00:47
| null |
NONE
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### Describe the bug
In https://github.com/huggingface/datasets/issues/6843 it was noted that the streaming feature of `datasets` is highly susceptible to outages and doesn't back off for long (or even *at all*).
I was training a model while streaming SlimPajama and training crashed with a `FileNotFoundError`. I can only assume that this was due to a momentary outage considering the file in question, `train/chunk9/example_train_3889.jsonl.zst`, [exists like all other files in SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B/blob/main/train/chunk9/example_train_3889.jsonl.zst).
```python
...
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 2226, in __iter__
for key, example in ex_iterable:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1499, in __iter__
for x in self.ex_iterable:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1067, in __iter__
yield from self._iter()
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1231, in _iter
for key, transformed_example in iter_outputs():
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1207, in iter_outputs
for i, key_example in inputs_iterator:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1111, in iter_inputs
for key, example in iterator:
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 371, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/packaged_modules/json/json.py", line 99, in _generate_tables
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/track.py", line 50, in __iter__
for x in self.generator(*self.args):
File "/miniconda3/envs/draft/lib/python3.11/site-packages/datasets/utils/file_utils.py", line 1378, in _iter_from_urlpaths
raise FileNotFoundError(urlpath)
FileNotFoundError: zstd://example_train_3889.jsonl::hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk9/example_train_3889.jsonl.zst
```
That final `raise` is at the bottom of the following snippet:
https://github.com/huggingface/datasets/blob/f693f4e93aabafa878470c80fd42ddb10ec550d6/src/datasets/utils/file_utils.py#L1354-L1379
So clearly, something choked up in `xisfile`.
### Steps to reproduce the bug
This happens when streaming a dataset and iterating over it. In my case, that iteration is done in Trainer's `inner_training_loop`, but this is not relevant to the iterator.
```python
File "/miniconda3/envs/draft/lib/python3.11/site-packages/accelerate/data_loader.py", line 835, in __iter__
next_batch, next_batch_info = self._fetch_batches(main_iterator)
```
### Expected behavior
This bug and the linked issue have one thing in common: *when streaming fails to retrieve an example, the entire program gives up and crashes*. As users, we cannot even protect ourselves from this: when we are iterating over a dataset, we can't make `datasets` skip over a bad example or wait a little longer to retry the iteration, because when a Python generator/iterator raises an error, it loses all its context.
In other words: if you have something that looks like `for b in a: for c in b: for d in c:`, errors in the innermost loop can only be caught by a `try ... except` in `c.__iter__()`. There should be such exception handling in `datasets` and it should have a **configurable exponential back-off**: first wait and retry after 1 minute, then 2 minutes, then 4 minutes, then 8 minutes, ... and after a given amount of retries, **skip the bad example**, and **only after** skipping a given amount of examples, give up and crash. This was requested in https://github.com/huggingface/datasets/issues/6843 too, since currently there is only linear backoff *and* it is clearly not applied to `xisfile`.
### Environment info
- `datasets` version: 3.3.2 *(the latest version)*
- Platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28
- Python version: 3.11.7
- `huggingface_hub` version: 0.26.5
- PyArrow version: 15.0.0
- Pandas version: 2.2.0
- `fsspec` version: 2024.10.0
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| 2,890,240,400
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I_kwDODunzps6sRZGQ
| 7,433
|
`Dataset.map` ignores existing caches and remaps when ran with different `num_proc`
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[
"This feels related: https://github.com/huggingface/datasets/issues/3044",
"@lhoestq This comment specifically, I agree:\n\n* https://github.com/huggingface/datasets/issues/3044#issuecomment-1239877570\n\n> Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the cached datasets are saved with a template at the end of the filename (something like \"000001_of_000008\" for every process of num_proc). So if in the next time you run the script you set num_proc to a different number, the cache cannot be used.\n> \n> Is there any way to get around this? I am processing a huge dataset so I do the processing on one machine and then transfer the processed data to another in its cache dir but currently that's not possible due to num_proc mismatch.\n\n"
] | 2025-03-03T05:51:26
| 2025-03-04T05:55:08
| null |
CONTRIBUTOR
|
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### Describe the bug
If you `map` a dataset and save it to a specific `cache_file_name` with a specific `num_proc`, and then call map again with that same existing `cache_file_name` but a different `num_proc`, the dataset will be re-mapped.
### Steps to reproduce the bug
1. Download a dataset
```python
import datasets
dataset = datasets.load_dataset("ylecun/mnist")
```
```
Generating train split: 100%|██████████| 60000/60000 [00:00<00:00, 116429.85 examples/s]
Generating test split: 100%|██████████| 10000/10000 [00:00<00:00, 103310.27 examples/s]
```
2. `map` and cache it with a specific `num_proc`
```python
cache_file_name="./cache/train.map"
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=2)
```
```
Map (num_proc=2): 100%|██████████| 60000/60000 [00:01<00:00, 53764.03 examples/s]
```
3. `map` it with a different `num_proc` and the same `cache_file_name` as before
```python
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=3)
```
```
Map (num_proc=3): 100%|██████████| 60000/60000 [00:00<00:00, 65377.12 examples/s]
```
### Expected behavior
If I specify an existing `cache_file_name`, I don't expect using a different `num_proc` than the one that was used to generate it to cause the dataset to have be be re-mapped.
### Environment info
```console
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```
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| 2,887,244,074
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I_kwDODunzps6sF9kq
| 7,431
|
Issues with large Datasets
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[
"what's the error message ?",
"This was the final error message that it was giving pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0",
"Here is the list of errors:\n\nTraceback (most recent call last):\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 160, in _generate_tables\n df = pandas_read_json(f)\n ^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 38, in pandas_read_json\n return pd.read_json(path_or_buf, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 815, in read_json\n return json_reader.read()\n ^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1025, in read\n obj = self._get_object_parser(self.data)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1051, in _get_object_parser\n obj = FrameParser(json, **kwargs).parse()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1187, in parse\n self._parse()\n File \".venv/lib/python3.12/site-packages/pandas/io/json/_json.py\", line 1402, in _parse\n self.obj = DataFrame(\n ^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 778, in __init__\n mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 503, in dict_to_mgr\n return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 114, in arrays_to_mgr\n index = _extract_index(arrays)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \".venv/lib/python3.12/site-packages/pandas/core/internals/construction.py\", line 677, in _extract_index\n raise ValueError(\"All arrays must be of the same length\")\nValueError: All arrays must be of the same length\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1854, in _prepare_split_single\n for _, table in generator:\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 163, in _generate_tables\n raise e\n File \".venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py\", line 137, in _generate_tables\n pa_table = paj.read_json(\n ^^^^^^^^^^^^^^\n File \"pyarrow/_json.pyx\", line 308, in pyarrow._json.read_json\n File \"pyarrow/error.pxi\", line 155, in pyarrow.lib.pyarrow_internal_check_status\n File \"pyarrow/error.pxi\", line 92, in pyarrow.lib.check_status\npyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to number in row 0\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"run_object_detection.py\", line 582, in <module>\n main()\n File \"run_object_detection.py\", line 407, in main\n dataset = load_dataset(\n ^^^^^^^^^^^^^\n File \"venv/lib/python3.12/site-packages/datasets/load.py\", line 2151, in load_dataset\n builder_instance.download_and_prepare(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 924, in download_and_prepare\n self._download_and_prepare(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1000, in _download_and_prepare\n self._prepare_split(split_generator, **prepare_split_kwargs)\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1741, in _prepare_split\n for job_id, done, content in self._prepare_split_single(\n File \".venv/lib/python3.12/site-packages/datasets/builder.py\", line 1897, in _prepare_split_single\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset",
"`datasets` is based on Arrow which expects all the lists inside the data to be of fixed type. Arrow can't load lists that contain a mix of integers and strings for example. In your case it looks like one of the lists contains numbers and JSON objects.\n\nI'd suggest you to reformat the data to end up with list of fixed types, otherwise you won't be able to load the data in `datasets`"
] | 2025-02-28T14:05:22
| 2025-03-04T15:02:26
| null |
NONE
|
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| null |
### Describe the bug
If the coco annotation file is too large the dataset will not be able to load it, not entirely sure were the issue is but I am guessing it is due to the code trying to load it all as one line into a dataframe. This was for object detections.
My current work around is the following code but would be nice to be able to do it without worrying about it also probably there is a better way of doing it:
`
dataset_dict = json.load(open("./local_data/annotations/train.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'], 'categories': dataset_dict['categories']}, ignore_index=True)
train=Dataset.from_pandas(df)
dataset_dict = json.load(open("./local_data/annotations/validation.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'],
'categories': dataset_dict['categories']}, ignore_index=True)
val = Dataset.from_pandas(df)
dataset_dict = json.load(open("./local_data/annotations/test.json"))
df = pd.DataFrame(columns=['images', 'annotations', 'categories'])
df = df._append({'images': dataset_dict['images'], 'annotations': dataset_dict['annotations'],
'categories': dataset_dict['categories']}, ignore_index=True)
test = Dataset.from_pandas(df)
dataset = DatasetDict({'train': train, 'validation': val, 'test': test})
`
### Steps to reproduce the bug
1) step up directory in and have the json files in coco format
-local_data
|-images
|---1.jpg
|---2.jpg
|---....
|---n.jpg
|-annotations
|---test.json
|---train.json
|---validation.json
2) try to load local_data into a dataset if the file is larger than about 300kb it will cause an error.
### Expected behavior
That it loads the jsons preferably in the same format as it has done with a smaller size.
### Environment info
- `datasets` version: 3.3.3.dev0
- Platform: Linux-6.11.0-17-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.29.0
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
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I_kwDODunzps6sEvFN
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Error in code "Time to slice and dice" from course "NLP Course"
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[
"You should open an issue in the NLP course website / github page. I'm closing this issue if you don't mind",
"ok, i don't mind, i'll mark the error there"
] | 2025-02-28T11:36:10
| 2025-03-05T11:32:47
| 2025-03-03T17:52:15
|
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### Describe the bug
When we execute code
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "condition": "frequency"})
)
frequencies.head()
```
answer should be like this
condition | frequency
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
but he is different
frequency | count
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
this is not correct, correct code
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "count": "frequency"})
)
````
### Steps to reproduce the bug
```
frequencies = (
train_df["condition"]
.value_counts()
.to_frame()
.reset_index()
.rename(columns={"index": "condition", "condition": "frequency"})
)
frequencies.head()
```
### Expected behavior
condition | frequency
birth control | 27655
depression | 8023
acne | 5209
anxiety | 4991
pain | 4744
### Environment info
Google Colab
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Error splitting the input into NAL units.
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[
"First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`",
"> First time I see this error :/ maybe it's an issue with your version of `multiprocess` and `dill` ? Make sure they are compatible with `datasets`\n\nany recommendation for `multiprocess` and `dill`"
] | 2025-02-28T02:30:15
| 2025-03-04T01:40:28
| null |
NONE
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| null |
### Describe the bug
I am trying to finetune qwen2.5-vl on 16 * 80G GPUS, and I use `LLaMA-Factory` and set `preprocessing_num_workers=16`. However, I met the following error and the program seem to got crush. It seems that the error come from `datasets` library
The error logging is like following:
```text
Converting format of dataset (num_proc=16): 100%|█████████▉| 19265/19267 [11:44<00:00, 5.88 examples/s]
Converting format of dataset (num_proc=16): 100%|█████████▉| 19266/19267 [11:44<00:00, 5.02 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 19267/19267 [11:44<00:00, 5.44 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 19267/19267 [11:44<00:00, 27.34 examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [00:00<?, ? examples/s]
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
Invalid NAL unit size (45405 > 35540).
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (7131 > 3225).
missing picture in access unit with size 54860
Invalid NAL unit size (48042 > 33645).
missing picture in access unit with size 3229
missing picture in access unit with size 33649
Invalid NAL unit size (86720 > 54856).
Invalid NAL unit size (48042 > 33645).
Error splitting the input into NAL units.
missing picture in access unit with size 35544
Invalid NAL unit size (45405 > 35540).
Error splitting the input into NAL units.
Error splitting the input into NAL units.
Invalid NAL unit size (8187 > 7069).
missing picture in access unit with size 7073
Invalid NAL unit size (8187 > 7069).
Error splitting the input into NAL units.
Invalid NAL unit size (7131 > 3225).
Error splitting the input into NAL units.
Invalid NAL unit size (14013 > 5998).
missing picture in access unit with size 6002
Invalid NAL unit size (14013 > 5998).
Error splitting the input into NAL units.
Invalid NAL unit size (17173 > 7231).
missing picture in access unit with size 7235
Invalid NAL unit size (17173 > 7231).
Error splitting the input into NAL units.
Invalid NAL unit size (16964 > 6055).
missing picture in access unit with size 6059
Invalid NAL unit size (16964 > 6055).
Exception in thread Thread-9 (accepter)Error splitting the input into NAL units.
:
Traceback (most recent call last):
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
Running tokenizer on dataset (num_proc=16): 0%| | 0/19267 [13:22<?, ? examples/s] self.run()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 953, in run
Invalid NAL unit size (7032 > 2927).
missing picture in access unit with size 2931
self._target(*self._args, **self._kwargs)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/multiprocess/managers.py", line 194, in accepter
Invalid NAL unit size (7032 > 2927).
Error splitting the input into NAL units.
t.start()
File "/opt/conda/envs/python3.10.13/lib/python3.10/threading.py", line 935, in start
Invalid NAL unit size (28973 > 6121).
missing picture in access unit with size 6125
_start_new_thread(self._bootstrap, ())Invalid NAL unit size (28973 > 6121).
RuntimeError: can't start new threadError splitting the input into NAL units.
Invalid NAL unit size (4411 > 296).
missing picture in access unit with size 300
Invalid NAL unit size (4411 > 296).
Error splitting the input into NAL units.
Invalid NAL unit size (14414 > 1471).
missing picture in access unit with size 1475
Invalid NAL unit size (14414 > 1471).
Error splitting the input into NAL units.
Invalid NAL unit size (5283 > 1792).
missing picture in access unit with size 1796
Invalid NAL unit size (5283 > 1792).
Error splitting the input into NAL units.
Invalid NAL unit size (79147 > 10042).
missing picture in access unit with size 10046
Invalid NAL unit size (79147 > 10042).
Error splitting the input into NAL units.
```
### Others
_No response_
### Steps to reproduce the bug
None
### Expected behavior
excpect to run successfully
### Environment info
```
transformers==4.49.0
datasets==3.2.0
accelerate==1.2.1
peft==0.12.0
trl==0.9.6
tokenizers==0.21.0
gradio>=4.38.0,<=5.18.0
pandas>=2.0.0
scipy
einops
sentencepiece
tiktoken
protobuf
uvicorn
pydantic
fastapi
sse-starlette
matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0
av
librosa
tyro<0.9.0
openlm-hub
qwen-vl-utils
```
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load_dataset("livecodebench/code_generation_lite", version_tag="release_v2") TypeError: 'NoneType' object is not callable
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[
"> datasets\n\nHi, have you solved this bug? Today I also met the same problem about `livecodebench/code_generation_lite` when evaluating the `Open-R1` repo. I am looking forward to your reply!\n\n",
"Hey guys,\nI tried to reproduce the issue and it works fine. I used google colab as enviroment.\n\n",
"> Hey guys, I tried to reproduce the issue and it works fine. I used google colab as enviroment.\n> \n> \n\nThanks for your kind reply! I wonder which Python version do you use? My Python version is 3.11.11 and datasets version is 3.3.2 but I still met this bug.\n\n<img width=\"1121\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/7c2c5007-ee55-4030-94b9-01fcdea0bf4a\" />",
"@zwxandy It's Python 3.11.11",
"@Serzhanov @zwxandy I have met the same problem, have this problem be solved?",
"> [@Serzhanov](https://github.com/Serzhanov) [@zwxandy](https://github.com/zwxandy) I have met the same problem, have this problem be solved?\n\nI try to downgrade datasets version to 2.20.0，and it works for me @Serzhanov @dshwei , hope this work for you too :)"
] | 2025-02-27T07:36:02
| 2025-03-10T07:49:45
| null |
NONE
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### Describe the bug
from datasets import load_dataset
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
or
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
both error:
Traceback (most recent call last):
File "", line 1, in
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 2131, in load_dataset
builder_instance = load_dataset_builder(
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 1888, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
TypeError: 'NoneType' object is not callable
### Steps to reproduce the bug
from datasets import get_dataset_config_names
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
OR
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
### Expected behavior
load datasets livecodebench/code_generation_lite
### Environment info
import datasets
version '3.3.2'
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I_kwDODunzps6rnjHx
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|
Row indexing a dataset with numpy integers
|
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"Would be cool to be consistent when it comes to indexing with numpy objects, if we do accept numpy arrays we should indeed accept numpy integers. Your idea sounds reasonable, I'd also be in favor of adding a simple test as well"
] | 2025-02-25T18:44:45
| 2025-03-03T17:55:24
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NONE
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### Feature request
Allow indexing datasets with a scalar numpy integer type.
### Motivation
Indexing a dataset with a scalar numpy.int* object raises a TypeError. This is due to the test in `datasets/formatting/formatting.py:key_to_query_type`
``` python
def key_to_query_type(key: Union[int, slice, range, str, Iterable]) -> str:
if isinstance(key, int):
return "row"
elif isinstance(key, str):
return "column"
elif isinstance(key, (slice, range, Iterable)):
return "batch"
_raise_bad_key_type(key)
```
In the row case, it checks if key is an int, which returns false when key is integer like but not a builtin python integer type. This is counterintuitive because a numpy array of np.int64s can be used for the batch case.
For example:
``` python
import numpy as np
import datasets
dataset = datasets.Dataset.from_dict({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
# Regular indexing
dataset[0]
dataset[:2]
# Indexing with numpy data types (expect same results)
idx = np.asarray([0, 1])
dataset[idx] # Succeeds when using an array of np.int64 values
dataset[idx[0]] # Fails with TypeError when using scalar np.int64
```
For the user, this can be solved by wrapping `idx[0]` in `int` but the test could also be changed in `key_to_query_type` to accept a less strict definition of int.
``` diff
+import numbers
+
def key_to_query_type(key: Union[int, slice, range, str, Iterable]) -> str:
+ if isinstance(key, numbers.Integral):
- if isinstance(key, int):
return "row"
elif isinstance(key, str):
return "column"
elif isinstance(key, (slice, range, Iterable)):
return "batch"
_raise_bad_key_type(key)
```
Looking at how others do it, pandas has an `is_integer` definition that it checks which uses `is_integer_object` defined in `pandas/_libs/utils.pxd`:
``` cython
cdef inline bint is_integer_object(object obj) noexcept:
"""
Cython equivalent of
`isinstance(val, (int, np.integer)) and not isinstance(val, (bool, np.timedelta64))`
Parameters
----------
val : object
Returns
-------
is_integer : bool
Notes
-----
This counts np.timedelta64 objects as integers.
"""
return (not PyBool_Check(obj) and isinstance(obj, (int, cnp.integer))
and not is_timedelta64_object(obj))
```
This would be less flexible as it explicitly checks for numpy integer, but worth noting that they had the need to ensure the key is not a bool.
### Your contribution
I can submit a pull request with the above changes after checking that indexing succeeds with the numpy integer type. Or if there is a different integer check that would be preferred I could add that.
If there is a reason not to want this behavior that is fine too.
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I_kwDODunzps6rkG0c
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|
DVC integration broken
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[
"Unfortunately `url` is a reserved argument in `fsspec.url_to_fs`, so ideally file system implementations like DVC should use another argument name to avoid this kind of errors"
] | 2025-02-25T13:14:31
| 2025-03-03T17:42:02
| null |
NONE
|
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| null |
### Describe the bug
The DVC integration seems to be broken.
Followed this guide: https://dvc.org/doc/user-guide/integrations/huggingface
### Steps to reproduce the bug
#### Script to reproduce
~~~python
from datasets import load_dataset
dataset = load_dataset(
"csv",
data_files="dvc://workshop/satellite-data/jan_train.csv",
storage_options={"url": "https://github.com/iterative/dataset-registry.git"},
)
print(dataset)
~~~
#### Error log
~~~
Traceback (most recent call last):
File "C:\tmp\test\load.py", line 3, in <module>
dataset = load_dataset(
^^^^^^^^^^^^^
File "C:\tmp\test\.venv\Lib\site-packages\datasets\load.py", line 2151, in load_dataset
builder_instance.download_and_prepare(
File "C:\tmp\test\.venv\Lib\site-packages\datasets\builder.py", line 808, in download_and_prepare
fs, output_dir = url_to_fs(output_dir, **(storage_options or {}))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: url_to_fs() got multiple values for argument 'url'
~~~
### Expected behavior
Integration would work and the indicated file is downloaded and opened.
### Environment info
#### Python version
~~~
python --version
Python 3.11.10
~~~
#### Venv (pip install datasets dvc):
~~~
Package Version
---------------------- -----------
aiohappyeyeballs 2.4.6
aiohttp 3.11.13
aiohttp-retry 2.9.1
aiosignal 1.3.2
amqp 5.3.1
annotated-types 0.7.0
antlr4-python3-runtime 4.9.3
appdirs 1.4.4
asyncssh 2.20.0
atpublic 5.1
attrs 25.1.0
billiard 4.2.1
celery 5.4.0
certifi 2025.1.31
cffi 1.17.1
charset-normalizer 3.4.1
click 8.1.8
click-didyoumean 0.3.1
click-plugins 1.1.1
click-repl 0.3.0
colorama 0.4.6
configobj 5.0.9
cryptography 44.0.1
datasets 3.3.2
dictdiffer 0.9.0
dill 0.3.8
diskcache 5.6.3
distro 1.9.0
dpath 2.2.0
dulwich 0.22.7
dvc 3.59.1
dvc-data 3.16.9
dvc-http 2.32.0
dvc-objects 5.1.0
dvc-render 1.0.2
dvc-studio-client 0.21.0
dvc-task 0.40.2
entrypoints 0.4
filelock 3.17.0
flatten-dict 0.4.2
flufl-lock 8.1.0
frozenlist 1.5.0
fsspec 2024.12.0
funcy 2.0
gitdb 4.0.12
gitpython 3.1.44
grandalf 0.8
gto 1.7.2
huggingface-hub 0.29.1
hydra-core 1.3.2
idna 3.10
iterative-telemetry 0.0.10
kombu 5.4.2
markdown-it-py 3.0.0
mdurl 0.1.2
multidict 6.1.0
multiprocess 0.70.16
networkx 3.4.2
numpy 2.2.3
omegaconf 2.3.0
orjson 3.10.15
packaging 24.2
pandas 2.2.3
pathspec 0.12.1
platformdirs 4.3.6
prompt-toolkit 3.0.50
propcache 0.3.0
psutil 7.0.0
pyarrow 19.0.1
pycparser 2.22
pydantic 2.10.6
pydantic-core 2.27.2
pydot 3.0.4
pygit2 1.17.0
pygments 2.19.1
pygtrie 2.5.0
pyparsing 3.2.1
python-dateutil 2.9.0.post0
pytz 2025.1
pywin32 308
pyyaml 6.0.2
requests 2.32.3
rich 13.9.4
ruamel-yaml 0.18.10
ruamel-yaml-clib 0.2.12
scmrepo 3.3.10
semver 3.0.4
setuptools 75.8.0
shellingham 1.5.4
shortuuid 1.0.13
shtab 1.7.1
six 1.17.0
smmap 5.0.2
sqltrie 0.11.2
tabulate 0.9.0
tomlkit 0.13.2
tqdm 4.67.1
typer 0.15.1
typing-extensions 4.12.2
tzdata 2025.1
urllib3 2.3.0
vine 5.1.0
voluptuous 0.15.2
wcwidth 0.2.13
xxhash 3.5.0
yarl 1.18.3
zc-lockfile 3.0.post1
~~~
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I_kwDODunzps6rcJRI
| 7,420
|
better correspondence between cached and saved datasets created using from_generator
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[] | 2025-02-24T22:14:37
| 2025-02-26T03:10:22
| null |
CONTRIBUTOR
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### Feature request
At the moment `.from_generator` can only create a dataset that lives in the cache. The cached dataset cannot be loaded with `load_from_disk` because the cache folder is missing `state.json`. So the only way to convert this cached dataset to a regular is to use `save_to_disk` which needs to create a copy of the cached dataset. For large datasets this can end up wasting a lot of space. In my case the saving operation failed so I am stuck with a large cached dataset and no clear way to convert to a `Dataset` that I can use. The requested feature is to provide a way to be able to load a cached dataset using `.load_from_disk`. Alternatively `.from_generator` can create the dataset at a specified location so that it can be loaded from there with `.load_from_disk`.
### Motivation
I have the following workflow which has exposed some awkwardness about the Datasets saving/caching.
1. I created a cached dataset using `.from_generator` which was cached in a folder. This dataset is rather large (~600GB) with many shards.
2. I tried to save this dataset using `.save_to_disk` to another location so that I can use later as a `Dataset`. This essentially creates another copy (for a total of 1.2TB!) of what is already in the cache... In my case the saving operation keeps dying for some reason and I am stuck with a cached dataset and no copy.
3. Now I am trying to "save" the existing cached dataset but it is not clear how to access the cached files after `.from_generator` has finished e.g. from a different process. I should not be even looking at the cache but I really do not want to waste another 2hr to generate the set so that if fails agains (I already did this couple of times).
- I tried `.load_from_disk` but it does not work with cached files and complains that this is not a `Dataset` (!).
- I looked at `.from_file` which takes one file but the cached file has many (shards) so I am not sure how to make this work.
- I tried `.load_dataset` but this seems to either try to "download" a copy (of a file which is already in the local file system!) which I will then need to save or I need to use `streaming=False` to create an `IterableDataset `which then I need to convert (using the cache) to `Dataset` so that I can save it. With both options I will end up with 3 copies of the same dataset for a total of ~2TB! I am hoping here is another way to do this...
Maybe I am missing something here: I looked at docs and forums but no luck. I have a bunch of arrow files cached by `Dataset.from_generator` and no clean way to make them into a `Dataset` that I can use.
This all could be so much easer if `load_from_disk` can recognize the cached files and produce a `Dataset`: after the cache is created I would not have to "save" it again and I can just load it when I need. At the moment `load_from_disk` needs `state.json` which is lacking in the cache folder. So perhaps `.from_generator` could be made to "finalize" (e.g. create `state.json`) the dataset once it is done so that it can be loaded easily. Or provide `.from_generator` with a `save_to_dir` parameter in addition to `cache_dir` which can be used for the whole process including creating the `state.json` at the end.
As a proof of concept I just created `state.json` by hand and `load_from_disk` worked using the cache! So it seems to be the missing piece here.
### Your contribution
Time permitting I can look into `.from_generator` to see if adding `state.json` is feasible.
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I_kwDODunzps6rZrZ4
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Import order crashes script execution
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[] | 2025-02-24T17:03:43
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NONE
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### Describe the bug
Hello,
I'm trying to convert an HF dataset into a TFRecord so I'm importing `tensorflow` and `datasets` to do so.
Depending in what order I'm importing those librairies, my code hangs forever and is unkillable (CTRL+C doesn't work, I need to kill my shell entirely).
Thank you for your help
🙏
### Steps to reproduce the bug
If you run the following script, this will hang forever :
```python
import tensorflow as tf
import datasets
dataset = datasets.load_dataset("imagenet-1k", split="validation", streaming=True)
print(next(iter(dataset)))
```
however running the following will work fine (I just changed the order of the imports) :
```python
import datasets
import tensorflow as tf
dataset = datasets.load_dataset("imagenet-1k", split="validation", streaming=True)
print(next(iter(dataset)))
```
### Expected behavior
I'm expecting the script to reach the end and my case print the content of the first item in the dataset
```
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=408x500 at 0x70C646A03110>, 'label': 91}
```
### Environment info
```
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-6.8.0-1017-aws-x86_64-with-glibc2.35
- Python version: 3.11.7
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```
I'm also using `tensorflow==2.18.0`.
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pyarrow.lib.arrowinvalid: cannot mix list and non-list, non-null values with map function
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[
"@lhoestq ",
"Can you try passing text: None for the image object ? Pyarrow expects all the objects to have the exact same type, in particular the dicttionaries in \"content\" should all have the keys \"type\" and \"text\"",
"The following modification on system prompt works, but it is different from the usual way to use it.\n```\ndef make_conversation(example):\n prompt = []\n\n prompt.append({\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": system_prompt}]})\n prompt.append(\n {\n \"role\": \"user\", \n \"content\": [\n {\"type\": \"image\"},\n {\"type\": \"text\", \"text\": example[\"problem\"]},\n ]\n }\n )\n return {\"prompt\": prompt}\n```",
"Good to know ! But yes Arrow / Parquet have this typing limitation (which is great to ensure data integrity, but constraining at the same time). It's is really blocking you, feel free to ping the arrow team / community if they plan to have a Union type or a JSON type"
] | 2025-02-21T10:58:06
| 2025-02-25T15:26:46
| null |
NONE
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### Describe the bug
Encounter pyarrow.lib.arrowinvalid error with map function in some example when loading the dataset
### Steps to reproduce the bug
```
from datasets import load_dataset
from PIL import Image, PngImagePlugin
dataset = load_dataset("leonardPKU/GEOQA_R1V_Train_8K")
system_prompt="You are a helpful AI Assistant"
def make_conversation(example):
prompt = []
prompt.append({"role": "system", "content": system_prompt})
prompt.append(
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": example["problem"]},
]
}
)
return {"prompt": prompt}
def check_data_types(example):
for key, value in example.items():
if key == 'image':
if not isinstance(value, PngImagePlugin.PngImageFile):
print(value)
if key == "problem" or key == "solution":
if not isinstance(value, str):
print(value)
return example
dataset = dataset.map(check_data_types)
dataset = dataset.map(make_conversation)
```
### Expected behavior
Successfully process the dataset with map
### Environment info
datasets==3.3.1
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| 2,865,774,546
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I_kwDODunzps6q0D_S
| 7,415
|
Shard Dataset at specific indices
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[
"Hi ! if it's an option I'd suggest to have one sequence per row instead.\n\nOtherwise you'd have to make your own save/load mechanism",
"Saving one sequence per row is very difficult and heavy and makes all the optimizations pointless. How would a custom save/load mechanism look like?",
"You can use `pyarrow` for example to save/load individual arrow or parquet files and control what they contain"
] | 2025-02-20T10:43:10
| 2025-02-24T11:06:45
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I have a dataset of sequences, where each example in the sequence is a separate row in the dataset (similar to LeRobotDataset). When running `Dataset.save_to_disk` how can I provide indices where it's possible to shard the dataset such that no episode spans more than 1 shard. Consequently, when I run `Dataset.load_from_disk`, how can I load just a subset of the shards to save memory and time on different ranks?
I guess an alternative to this would be, given a loaded `Dataset`, how can I run `Dataset.shard` such that sharding doesn't split any episode across shards?
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I_kwDODunzps6qhph-
| 7,413
|
Documentation on multiple media files of the same type with WebDataset
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[
"Yes this is correct and it works with huggingface datasets as well ! Feel free to include an example here: https://github.com/huggingface/datasets/blob/main/docs/source/video_dataset.mdx"
] | 2025-02-18T16:13:20
| 2025-02-20T14:17:54
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The [current documentation](https://huggingface.co/docs/datasets/en/video_dataset) on a creating a video dataset includes only examples with one media file and one json. It would be useful to have examples where multiple files of the same type are included. For example, in a sign language dataset, you may have a base video and a video annotation of the extracted pose. According to the WebDataset documentation, this should be able to be done with period separated filenames. For example:
```e39871fd9fd74f55.base.mp4
e39871fd9fd74f55.pose.mp4
e39871fd9fd74f55.json
f18b91585c4d3f3e.base.mp4
f18b91585c4d3f3e.pose.mp4
f18b91585c4d3f3e.json
...
```
If you can confirm that this method of including multiple media files works with huggingface datasets and include an example in the documentation, I'd appreciate it.
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I_kwDODunzps6qb37u
| 7,412
|
Index Error Invalid Ket is out of bounds for size 0 for code-search-net/code_search_net dataset
|
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[] | 2025-02-18T05:58:33
| 2025-02-18T06:42:07
| null |
NONE
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| null |
### Describe the bug
I am trying to do model pruning on sentence-transformers/all-mini-L6-v2 for the code-search-net/code_search_net dataset using INCTrainer class
However I am getting below error
```
raise IndexError(f"Invalid Key: {key is our of bounds for size {size}")
IndexError: Invalid key: 1840208 is out of bounds for size 0
```
### Steps to reproduce the bug
Model pruning on the above dataset using the below guide
https://huggingface.co/docs/optimum/en/intel/neural_compressor/optimization#pruning
### Expected behavior
The modsl should be successfully pruned
### Environment info
Torch version: 2.4.1
Python version: 3.8.10
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| 2,856,441,206
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I_kwDODunzps6qQdV2
| 7,406
|
Adding Core Maintainer List to CONTRIBUTING.md
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[
"@lhoestq",
"there is no per-module maintainer and the list is me alone nowadays ^^'",
"@lhoestq \nOh... I feel for you. \nWhat are your criteria for choosing a core maintainer? \nIt seems like it's too much work for you to manage all this code by yourself.\n\nAlso, if you don't mind, can you check this PR for me?\n#7368 I'd like this to be added as soon as possible because I need it."
] | 2025-02-17T00:32:40
| 2025-02-19T01:28:38
| null |
CONTRIBUTOR
|
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### Feature request
I propose adding a core maintainer list to the `CONTRIBUTING.md` file.
### Motivation
The Transformers and Liger-Kernel projects maintain lists of core maintainers for each module.
However, the Datasets project doesn't have such a list.
### Your contribution
I have nothing to add here.
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| 2,856,372,814
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I_kwDODunzps6qQMpO
| 7,405
|
Lazy loading of environment variables
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[
"Many python packages out there, including `huggingface_hub`, do load the environment variables on import.\nYou should `load_dotenv()` before importing the libraries.\n\nFor example you can move all you imports inside your `main()` function"
] | 2025-02-16T22:31:41
| 2025-02-17T15:17:18
| null |
NONE
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### Describe the bug
Loading a `.env` file after an `import datasets` call does not correctly use the environment variables.
This is due the fact that environment variables are read at import time:
https://github.com/huggingface/datasets/blob/de062f0552a810c52077543c1169c38c1f0c53fc/src/datasets/config.py#L155C1-L155C80
### Steps to reproduce the bug
```bash
# make tmp dir
mkdir -p /tmp/debug-env
# make .env file
echo HF_HOME=/tmp/debug-env/data > /tmp/debug-env/.env
# first load dotenv, downloads to /tmp/debug-env/data
uv run --with datasets,python-dotenv python3 -c \
'import dotenv; dotenv.load_dotenv("/tmp/debug-env/.env"); import datasets; datasets.load_dataset("Anthropic/hh-rlhf")'
# first import datasets, downloads to `~/.cache/huggingface`
uv run --with datasets,python-dotenv python3 -c \
'import datasets; import dotenv; dotenv.load_dotenv("/tmp/debug-env/.env"); datasets.load_dataset("Anthropic/hh-rlhf")'
```
### Expected behavior
I expect that setting environment variables with something like this:
```python3
if __name__ == "__main__":
load_dotenv()
main()
```
works correctly.
### Environment info
"datasets>=3.3.0",
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I_kwDODunzps6qQLB_
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|
Performance regression in `dataset.filter`
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[
"Thanks for reporting, I'll fix the regression today",
"I just released `datasets` 3.3.1 with a fix, let me know if it's good now :)",
"@lhoestq it fixed the issue.\n\nThis was (very) fast, thank you very much!"
] | 2025-02-16T22:19:14
| 2025-02-17T17:46:06
| 2025-02-17T14:28:48
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### Describe the bug
We're filtering dataset of ~1M (small-ish) records. At some point in the code we do `dataset.filter`, before (including 3.2.0) it was taking couple of seconds, and now it takes 4 hours.
We use 16 threads/workers, and stack trace at them look as follows:
```
Traceback (most recent call last):
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 314, in _bootstrap
self.run()
File "/python/lib/python3.12/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/python/lib/python3.12/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 678, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3511, in _map_single
for i, batch in iter_outputs(shard_iterable):
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3461, in iter_outputs
yield i, apply_function(example, i, offset=offset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 3390, in apply_function
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/python/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 6416, in get_indices_from_mask_function
indices_array = indices_mapping.column(0).take(indices_array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 1079, in pyarrow.lib.ChunkedArray.take
File "/python/lib/python3.12/site-packages/pyarrow/compute.py", line 458, in take
def take(data, indices, *, boundscheck=True, memory_pool=None):
```
### Steps to reproduce the bug
1. Save dataset of 1M records in arrow
2. Filter it with 16 threads
3. Watch it take too long
### Expected behavior
Filtering done fast
### Environment info
datasets 3.3.0, python 3.12
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Synchronize parameters for various datasets
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[
"Hi ! the `desc` parameter is only available for Dataset / DatasetDict for the progress bar of `map()``\n\nSince IterableDataset only runs the map functions when you iterate over the dataset, there is no progress bar and `desc` is useless. We could still add the argument for parity but it wouldn't be used for anything",
"I think you should add it. It doesn't hurt. The reason I ran into it was because I re-wrote a pipeline to use either a stream or a fully loaded dataset. Of course I can simply remove it but it is nice to have on the memory loaded dataset. "
] | 2025-02-14T09:15:11
| 2025-02-19T11:50:29
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### Describe the bug
[IterableDatasetDict](https://huggingface.co/docs/datasets/v3.2.0/en/package_reference/main_classes#datasets.IterableDatasetDict.map) map function is missing the `desc` parameter. You can see the equivalent map function for [Dataset here](https://huggingface.co/docs/datasets/v3.2.0/en/package_reference/main_classes#datasets.Dataset.map).
There might be other parameters missing - I haven't checked.
### Steps to reproduce the bug
from datasets import Dataset, IterableDataset, IterableDatasetDict
ds = IterableDatasetDict({"train": Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=3),
"validate": Dataset.from_dict({"a": range(6)}).to_iterable_dataset(num_shards=3)})
for d in ds["train"]:
print(d)
ds = ds.map(lambda x: {k: v+1 for k, v in x.items()}, desc="increment")
for d in ds["train"]:
print(d)
### Expected behavior
The description parameter should be available for all datasets (or none).
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.1.85+-x86_64-with-glibc2.35
- Python version: 3.11.11
- `huggingface_hub` version: 0.28.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.9.0
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I_kwDODunzps6qEGV9
| 7,400
|
504 Gateway Timeout when uploading large dataset to Hugging Face Hub
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"I transferred to the `datasets` repository. Is there any retry mechanism in `datasets` @lhoestq ?\n\nAnother solution @hotchpotch if you want to get your dataset pushed to the Hub in a robust way is to save it to a local folder first and then use `huggingface-cli upload-large-folder` (see https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder). It has better retry mechanism in case of failure.",
"There is no retry mechanism for `api.preupload_lfs_files` in `push_to_hub()` but we can definitely add one here\n\nhttps://github.com/huggingface/datasets/blob/de062f0552a810c52077543c1169c38c1f0c53fc/src/datasets/arrow_dataset.py#L5372",
"@Wauplin \n\nThank you! I believe that to use load_dataset() to read data from Hugging Face, we need to first save the markdown metadata and parquet files in our local filesystem, then upload them using upload-large-folder. If you know how to do this, could you please let me know?\n\n",
"@lhoestq \n\nI see, so adding a retry mechanism there would solve it. If I continue to have issues, I'll consider implementing that kind of solution."
] | 2025-02-14T02:18:35
| 2025-02-14T23:48:36
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### Description
I encountered consistent 504 Gateway Timeout errors while attempting to upload a large dataset (approximately 500GB) to the Hugging Face Hub. The upload fails during the process with a Gateway Timeout error.
I will continue trying to upload. While it might succeed in future attempts, I wanted to report this issue in the meantime.
### Reproduction
- I attempted the upload 3 times
- Each attempt resulted in the same 504 error during the upload process (not at the start, but in the middle of the upload)
- Using `dataset.push_to_hub()` method
### Environment Information
```
- huggingface_hub version: 0.28.0
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.39
- Python version: 3.11.10
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /home/hotchpotch/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: hotchpotch
- Configured git credential helpers: store
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.5.1
- Jinja2: 3.1.5
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: 10.4.0
- hf_transfer: N/A
- gradio: N/A
- tensorboard: N/A
- numpy: 1.26.4
- pydantic: 2.10.6
- aiohttp: 3.11.11
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /home/hotchpotch/.cache/huggingface/hub
- HF_ASSETS_CACHE: /home/hotchpotch/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/hotchpotch/.cache/huggingface/token
- HF_STORED_TOKENS_PATH: /home/hotchpotch/.cache/huggingface/stored_tokens
- 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
```
### Full Error Traceback
```python
Traceback (most recent call last):
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status
response.raise_for_status()
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese.git/info/lfs/objects/batch
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/create_edu_japanese_ds/upload_edu_japanese_ds.py", line 12, in <module>
ds.push_to_hub("hotchpotch/fineweb-2-edu-japanese", private=True)
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/datasets/dataset_dict.py", line 1665, in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 5301, in _push_parquet_shards_to_hub
api.preupload_lfs_files(
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/hf_api.py", line 4215, in preupload_lfs_files
_upload_lfs_files(
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/_commit_api.py", line 395, in _upload_lfs_files
batch_actions_chunk, batch_errors_chunk = post_lfs_batch_info(
^^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/lfs.py", line 168, in post_lfs_batch_info
hf_raise_for_status(resp)
File "/home/hotchpotch/src/github.com/hotchpotch/fineweb-2-edu-classifier-japanese/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese.git/info/lfs/objects/batch
```
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Using load_dataset with data_files and split arguments yields an error
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### Describe the bug
It seems the list of valid splits recorded by the package becomes incorrectly overwritten when using the `data_files` argument.
If I run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="all_examples", data_files="tasks/expert.jsonl")
```
then I get the error
```
ValueError: Unknown split "all_examples". Should be one of ['train'].
```
However, if I run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="train", name="Expert")
```
then I get
```
ValueError: Unknown split "train". Should be one of ['all_examples'].
```
### Steps to reproduce the bug
Run
```python
from datasets import load_dataset
load_dataset("allenai/super", split="all_examples", data_files="tasks/expert.jsonl")
```
### Expected behavior
No error.
### Environment info
Python = 3.12
datasets = 3.2.0
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I_kwDODunzps6po_bD
| 7,392
|
push_to_hub payload too large error when using large ClassLabel feature
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[
"See also <https://discuss.huggingface.co/t/datasetdict-push-to-hub-failing-with-payload-to-large/140083/8>\n"
] | 2025-02-11T17:51:34
| 2025-02-11T18:01:31
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### Describe the bug
When using `datasets.DatasetDict.push_to_hub` an `HfHubHTTPError: 413 Client Error: Payload Too Large for url` is raised if the dataset contains a large `ClassLabel` feature. Even if the total size of the dataset is small.
### Steps to reproduce the bug
``` python
import random
import sys
import datasets
random.seed(42)
def random_str(sz):
return "".join(chr(random.randint(ord("a"), ord("z"))) for _ in range(sz))
data = datasets.DatasetDict(
{
str(i): datasets.Dataset.from_dict(
{
"label": [list(range(3)) for _ in range(10)],
"abstract": [random_str(10_000) for _ in range(10)],
},
)
for i in range(3)
}
)
features = data["1"].features.copy()
features["label"] = datasets.Sequence(
datasets.ClassLabel(names=[str(i) for i in range(50_000)])
)
data = data.map(lambda examples: {}, features=features)
feat_size = sys.getsizeof(data["1"].features["label"].feature.names)
print(f"Size of ClassLabel names: {feat_size}")
# Size of ClassLabel names: 444376
data.push_to_hub("dconnell/pubtator3_test")
```
Note that this succeeds if `ClassLabel` has fewer names or if `ClassLabel` is replaced with `Value("int64")`
### Expected behavior
Should push the dataset to hub.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-126-generic-x86_64-with-glibc2.35
- Python version: 3.12.8
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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pyarrow 尝试了若干个版本都不可以
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|
Re-add py.typed
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[] | 2025-02-10T22:12:52
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CONTRIBUTOR
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### Feature request
The motivation for removing py.typed no longer seems to apply. Would a solution like [this one](https://github.com/huggingface/huggingface_hub/pull/2752) work here?
### Motivation
MyPy support is broken. As more type checkers come out, such as RedKnot, these may also be broken. It would be good to be PEP 561 compliant as long as it's not too onerous.
### Your contribution
I can re-add py.typed, but I don't know how to make sur all of the `__all__` files are provided (although you may not need to with modern PyRight).
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| 7,389
|
Getting statistics about filtered examples
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[
"You can actually track a running sum in map() or filter() :)\n\n```python\nnum_filtered = 0\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered += 1\n return condition\n\nds = ds.filter(f)\nprint(num_filtered)\n```\n\nand if you want to use multiprocessing, make sure to use a variable that is shared across processes\n\n\n```python\nfrom multiprocess import Manager\n\nmanager = Manager()\nnum_filtered = manager.Value('i', 0)\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered.value += 1\n return condition\n\nds = ds.filter(f, num_proc=4)\nprint(num_filtered.value)\n```\n\nPS: `datasets` uses `multiprocess` instead of the `multiprocessing` package to support lambda functions in map() and filter()",
"Oh that's great to know!\n\nI guess this value would not be exactly synced with the batch in cases of pre-fetch and shuffle buffers and so on, but that's probably fine. Thanks!"
] | 2025-02-10T20:48:29
| 2025-02-11T20:44:15
| 2025-02-11T20:44:13
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@lhoestq wondering if the team has thought about this and if there are any recommendations?
Currently when processing datasets some examples are bound to get filtered out, whether it's due to bad format, or length is too long, or any other custom filters that might be getting applied. Let's just focus on the filter by length for now, since that would be something that gets applied dynamically for each training run. Say we want to show a graph in W&B with the running total of the number of filtered examples so far.
What would be a good way to go about hooking this up? Because the map/filter operations happen before the DataLoader batches are created, at training time if we're just grabbing batches from the DataLoader then we won't know how many things have been filtered already. But there's not really a good way to include a 'num_filtered' key into the dataset itself either because dataset map/filter process examples independently and don't have a way to track a running sum.
The only approach I can kind of think of is having a 'is_filtered' key in the dataset, and then creating a custom batcher/collator that reads that and tracks the metric?
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OSError: [Errno 22] Invalid argument forbidden character
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[
"You can probably copy the dataset in your HF account and rename the files (without having to download them to your disk). Or alternatively feel free to open a Pull Request to this dataset with the renamed file",
"Thank you, that will help me work around this problem"
] | 2025-02-10T17:46:31
| 2025-02-11T13:42:32
| 2025-02-11T13:42:30
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### Describe the bug
I'm on Windows and i'm trying to load a datasets but i'm having title error because files in the repository are named with charactere like < >which can't be in a name file. Could it be possible to load this datasets but removing those charactere ?
### Steps to reproduce the bug
load_dataset("CATMuS/medieval") on Windows
### Expected behavior
Making the function to erase the forbidden character to allow loading the datasets who have those characters.
### Environment info
- `datasets` version: 3.2.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.12.2
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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Dynamic adjusting dataloader sampling weight
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[
"You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes",
"Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weight gets higher and higher, and if one data sample's prediction is already correct, then we rarely sample it",
"it's not possible to use `interleave_datasets()` and modify the probabilities while iterating on the dataset at the moment, so you'd have to implement your own torch `Sampler` or your own`IterableDataset` to implement this logic"
] | 2025-02-10T03:18:47
| 2025-03-07T14:06:54
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Hi,
Thanks for your wonderful work! I'm wondering is there a way to dynamically adjust the sampling weight of each data in the dataset during training? Looking forward to your reply, thanks again.
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| 7,386
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Add bookfolder Dataset Builder for Digital Book Formats
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"On second thought, probably not a good idea."
] | 2025-02-08T14:27:55
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| 2025-02-08T14:30:09
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### Feature request
This feature proposes adding a new dataset builder called bookfolder to the datasets library. This builder would allow users to easily load datasets consisting of various digital book formats, including: AZW, AZW3, CB7, CBR, CBT, CBZ, EPUB, MOBI, and PDF.
### Motivation
Currently, loading datasets of these digital book files requires manual effort. This would also lower the barrier to entry for working with these formats, enabling more diverse and interesting datasets to be used within the Hugging Face ecosystem.
### Your contribution
This feature is rather simple as it will be based on the folder-based builder, similar to imagefolder. I'm willing to contribute to this feature by submitting a PR
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Iterating over values of a column in the IterableDataset
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"I'd be in favor of that ! I saw many people implementing their own iterables that wrap a dataset just to iterate on a single column, that would make things more practical.\n\nKinda related: https://github.com/huggingface/datasets/issues/5847",
"(For anyone's information, I'm going on vacation for the next 3 weeks, so the work is postponed. If anyone can implement this feature within the next 4 weeks, go ahead :) )"
] | 2025-01-28T13:17:36
| 2025-02-18T17:15:51
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NONE
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### Feature request
I would like to be able to iterate (and re-iterate if needed) over a column of an `IterableDataset` instance. The following example shows the supposed API:
```
def gen():
yield {"text": "Good", "label": 0}
yield {"text": "Bad", "label": 1}
ds = IterableDataset.from_generator(gen)
texts = ds["text"]
for v in texts:
print(v) # Prints "Good" and "Bad"
for v in texts:
print(v) # Prints "Good" and "Bad" again
```
### Motivation
In the real world problems, huge NNs like Transformer are not always the best option, so there is a need to conduct experiments with different methods. While 🤗Datasets is perfectly adapted to 🤗Transformers, it may be inconvenient when being used with other libraries. The ability to retrieve a particular column is the case (e.g., gensim's FastText [requires](https://radimrehurek.com/gensim/models/fasttext.html#gensim.models.fasttext.FastText.train) only lists of strings, not dictionaries).
While there are ways to achieve the desired functionality, they are not good ([forum](https://discuss.huggingface.co/t/how-to-iterate-over-values-of-a-column-in-the-iterabledataset/135649)). It would be great if there was a built-in solution.
### Your contribution
Theoretically, I can submit a PR, but I have very little knowledge of the internal structure of 🤗Datasets, so some help may be needed.
Moreover, I can only work on weekends, since I have a full-time job. However, the feature does not seem to be popular, so there is no need to implement it as fast as possible.
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| 7,378
|
Allow pushing config version to hub
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[
"Hi ! This sounds reasonable to me, feel free to open a PR :)"
] | 2025-01-21T22:35:07
| 2025-01-30T13:56:56
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NONE
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### Feature request
Currently, when datasets are created, they can be versioned by passing the `version` argument to `load_dataset(...)`. For example creating `outcomes.csv` on the command line
```
echo "id,value\n1,0\n2,0\n3,1\n4,1\n" > outcomes.csv
```
and creating it
```
import datasets
dataset = datasets.load_dataset(
"csv",
data_files ="outcomes.csv",
keep_in_memory = True,
version = '1.0.0')
```
The version info is stored in the `info` and can be accessed e.g. by `next(iter(dataset.values())).info.version`
This dataset can be uploaded to the hub with `dataset.push_to_hub(repo_id = "maomlab/example_dataset")`. This will create a dataset on the hub with the following in the `README.md`, but it doesn't upload the version information:
```
---
dataset_info:
features:
- name: id
dtype: int64
- name: value
dtype: int64
splits:
- name: train
num_bytes: 64
num_examples: 4
download_size: 1332
dataset_size: 64
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
```
However, when I download from the hub, the version information is missing:
```
dataset_from_hub_no_version = datasets.load_dataset("maomlab/example_dataset")
next(iter(dataset.values())).info.version
```
I can add the version information manually to the hub, by appending it to the end of config section:
```
...
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
version: 1.0.0
---
```
And then when I download it, the version information is correct.
### Motivation
### Why adding version information for each config makes sense
1. The version information is already recorded in the dataset config info data structure and is able to parse it correctly, so it makes sense to sync it with `push_to_hub`.
2. Keeping the version info in at the config level is different from version info at the branch level. As the former relates to the version of the specific dataset the config refers to rather than the version of the dataset curation itself.
## A explanation for the current behavior:
In [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
), the `_INCLUDED_INFO_IN_YAML` variable doesn't include `"version"`.
If my reading of the code is right, adding `"version"` to `_INCLUDED_INFO_IN_YAML`, would allow the version information to be uploaded to the hub.
### Your contribution
Request: add `"version"` to `_INCLUDE_INFO_IN_YAML` in [datasets/src/datasets/info.py:159](https://github.com/huggingface/datasets/blob/fb91fd3c9ea91a818681a777faf8d0c46f14c680/src/datasets/info.py#L159C1-L160C1
)
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|
Support for sparse arrays with the Arrow Sparse Tensor format?
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"Hi ! Unfortunately the Sparse Tensor structure in Arrow is not part of the Arrow format (yes it's confusing...), so it's not possible to use it in `datasets`. It's a separate structure that doesn't correspond to any type or extension type in Arrow.\n\nThe Arrow community recently added an extension type for fixed shape tensors at https://arrow.apache.org/docs/format/CanonicalExtensions.html#fixed-shape-tensor, it should be possible to contribute an extension type for sparse tensors as well."
] | 2025-01-21T20:14:35
| 2025-01-30T14:06:45
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### Feature request
AI in biology is becoming a big thing. One thing that would be a huge benefit to the field that Huggingface Datasets doesn't currently have is native support for **sparse arrays**.
Arrow has support for sparse tensors.
https://arrow.apache.org/docs/format/Other.html#sparse-tensor
It would be a big deal if Hugging Face Datasets supported sparse tensors as a feature type, natively.
### Motivation
This is important for example in the field of transcriptomics (modeling and understanding gene expression), because a large fraction of the genes are not expressed (zero). More generally, in science, sparse arrays are very common, so adding support for them would be very benefitial, it would make just using Hugging Face Dataset objects a lot more straightforward and clean.
### Your contribution
We can discuss this further once the team comments of what they think about the feature, and if there were previous attempts at making it work, and understanding their evaluation of how hard it would be. My intuition is that it should be fairly straightforward, as the Arrow backend already supports it.
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vllm批量推理报错
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| 2025-01-30T14:02:40
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NONE
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### Describe the bug
### Steps to reproduce the bug
### Expected behavior
### Environment info
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Excessive RAM Usage After Dataset Concatenation concatenate_datasets
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"\n\n\n\nAdding a img from memray\nhttps://gist.github.com/sam-hey/00c958f13fb0f7b54d17197fe353002f"
] | 2025-01-16T16:33:10
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### Describe the bug
When loading a dataset from disk, concatenating it, and starting the training process, the RAM usage progressively increases until the kernel terminates the process due to excessive memory consumption.
https://github.com/huggingface/datasets/issues/2276
### Steps to reproduce the bug
```
rom datasets import DatasetDict, concatenate_datasets
dataset = DatasetDict.load_from_disk("data")
...
...
combined_dataset = concatenate_datasets(
[dataset[split] for split in dataset]
)
#start SentenceTransformer training
```
### Expected behavior
I would not expect RAM utilization to increase after concatenation. Removing the concatenation step resolves the issue
### Environment info
sentence-transformers==3.1.1
datasets==3.2.0
python3.10
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I_kwDODunzps6mZuRI
| 7,372
|
Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets
|
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### Description
I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue:
#### Code 1: Using `load_dataset`
```python
from datasets import Dataset, load_dataset
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_dataset("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 1350 samples.
- `test` has 150 samples.
#### Code 2: Using `load_from_disk`
```python
from datasets import Dataset, load_from_disk
# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)
# Load the DatasetDict
loaded_datasetdict = load_from_disk("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 450 samples.
- `test` has 50 samples.
### Expected Behavior
I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly:
- `load_dataset` seems to merge all shards, resulting in a combined dataset.
- `load_from_disk` only loads the last saved dataset, ignoring previous shards.
### Questions
1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation?
2. If this is not intentional, could this be considered a bug?
3. What is the recommended way to handle cases where multiple datasets are saved to the same directory?
Thank you for your time and effort in maintaining this great library! I look forward to your feedback.
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I_kwDODunzps6mVGmB
| 7,371
|
500 Server error with pushing a dataset
|
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[
"EDIT: seems to be all good now. I'll add a comment if the error happens again within the next 48 hours. If it doesn't, I'll just close the topic."
] | 2025-01-15T18:23:02
| 2025-01-15T20:06:05
| null |
NONE
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### Describe the bug
Suddenly, I started getting this error message saying it was an internal error.
`Error creating/pushing dataset: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928)
Internal Error - We're working hard to fix this as soon as possible!
Traceback (most recent call last):
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 406, in hf_raise_for_status
response.raise_for_status()
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/uufs/chpc.utah.edu/common/home/u1295595/grasp_dataset_converter/src/grasp_dataset_converter/main.py", line 142, in main
subset_train.push_to_hub(dataset_name, split='train')
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5624, in push_to_hub
commit_info = api.create_commit(
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 1518, in _inner
return fn(self, *args, **kwargs)
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 4087, in create_commit
hf_raise_for_status(commit_resp, endpoint_name="commit")
File "/uufs/chpc.utah.edu/common/home/hermans-group1/martin/software/pkg/miniforge3/envs/myenv2/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 477, in hf_raise_for_status
raise _format(HfHubHTTPError, str(e), response) from e
huggingface_hub.errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/ll4ma-lab/grasp-dataset/commit/main (Request ID: Root=1-6787f0b7-66d5bd45413e481c4c2fb22d;670d04ff-65f5-4741-a353-2eacc47a3928)
Internal Error - We're working hard to fix this as soon as possible!`
### Steps to reproduce the bug
I am pushing a Dataset in a loop via push_to_hub API
### Expected behavior
It worked fine until it stopped working suddenly.
Expected behavior: It should start working again
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-4.18.0-477.15.1.el8_8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.0
- `huggingface_hub` version: 0.27.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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