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https://api.github.com/repos/huggingface/datasets/issues/5475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5475/comments | https://api.github.com/repos/huggingface/datasets/issues/5475/events | https://github.com/huggingface/datasets/issues/5475 | 1,559,030,149 | I_kwDODunzps5c7OmF | 5,475 | Dataset scan time is much slower than using native arrow | {
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"Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:\r\n```diff\r\n- for _ in range(0, len(table), bsz):\r\n- _ = {k:table[k][_ : _ + bsz] for k in cols}\r\n+ for _ in range(0, len(table)... | 2023-01-27T01:32:25 | 2023-01-30T16:17:11 | 2023-01-30T16:17:11 | CONTRIBUTOR | null | null | null | ### Describe the bug
I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.
I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that explains this phenomenon?
### Steps to reproduce the bug
https://colab.research.google.com/drive/11EtHDaGAf1DKCpvYnAPJUW-LFfAcDzHY?usp=sharing
### Expected behavior
I expect scan times to be on par with using pyarrow directly.
### Environment info
standard colab environment | {
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https://api.github.com/repos/huggingface/datasets/issues/5474 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5474/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5474/comments | https://api.github.com/repos/huggingface/datasets/issues/5474/events | https://github.com/huggingface/datasets/issues/5474 | 1,558,827,155 | I_kwDODunzps5c6dCT | 5,474 | Column project operation on `datasets.Dataset` | {
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"Hi ! This would be a nice addition indeed :) This sounds like a duplicate of https://github.com/huggingface/datasets/issues/5468\r\n\r\n> Not sure. Some of my PRs are still open and some do not have any discussions.\r\n\r\nSorry to hear that, feel free to ping me on those PRs"
] | 2023-01-26T21:47:53 | 2023-02-13T09:59:37 | 2023-02-13T09:59:37 | CONTRIBUTOR | null | null | null | ### Feature request
There is no operation to select a subset of columns of original dataset. Expected API follows.
```python
a = Dataset.from_dict({
'int': [0, 1, 2]
'char': ['a', 'b', 'c'],
'none': [None] * 3,
})
b = a.project('int', 'char') # usually, .select()
print(a.column_names) # stdout: ['int', 'char', 'none']
print(b.column_names) # stdout: ['int', 'char']
```
Method project can easily accept not only column names (as a `str)` but univariant function applied to corresponding column as an example. Or keyword arguments can be used in order to rename columns in advance (see `pandas`, `pyspark`, `pyarrow`, and SQL)..
### Motivation
Projection is a typical operation in every data processing library. And it is a basic block of a well-known data manipulation language like SQL. Without this operation `datasets.Dataset` interface is not complete.
### Your contribution
Not sure. Some of my PRs are still open and some do not have any discussions. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-26T19:34:44 | 2023-01-26T19:47:34 | 2023-01-26T19:38:30 | MEMBER | null | false | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-26T19:29:42 | 2023-01-26T19:40:44 | 2023-01-26T19:33:00 | MEMBER | null | false | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I thought this issue was resolved in my parallel `to_tf_dataset` PR! I changed the default `num_test_batches` in `_get_output_signature` to 20 and used a test batch size of 1 to maximize variance to detect shorter samples. I think it... | 2023-01-26T18:09:40 | 2023-01-27T18:16:45 | 2023-01-27T18:08:36 | CONTRIBUTOR | null | false | {
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} | `to_tf_dataset` calls can be very costly because of the number of test batches drawn during `_get_output_signature`. The test batches are draw in order to estimate the shapes when creating the tensorflow dataset. This is necessary when the shapes can be irregular, but not in cases when the tensor shapes are the same across all samples. This PR adds an option to change the number of batches drawn, so the user can speed this conversion up.
Running the following, and modifying `num_test_batches`
```
import time
from datasets import load_dataset
from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()
dataset = load_dataset("beans")
dataset = dataset["train"].with_format("np")
start = time.time()
dataset = dataset.to_tf_dataset(
columns=["image"],
label_cols=["label"],
batch_size=8,
collate_fn=data_collator,
num_test_batches=NUM_TEST_BATCHES,
)
end = time.time()
print(end - start)
```
NUM_TEST_BATCHES=200: 0.8197s
NUM_TEST_BATCHES=50: 0.3070s
NUM_TEST_BATCHES=2: 0.1417s
NUM_TEST_BATCHES=1: 0.1352s | {
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https://api.github.com/repos/huggingface/datasets/issues/5470 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5470/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5470/comments | https://api.github.com/repos/huggingface/datasets/issues/5470/events | https://github.com/huggingface/datasets/pull/5470 | 1,558,542,611 | PR_kwDODunzps5InLw9 | 5,470 | Update dataset card creation | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"The CI failure is unrelated to your PR - feel free to merge :)",
"Haha thanks, you read my mind :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n##... | 2023-01-26T17:57:51 | 2023-01-27T16:27:00 | 2023-01-27T16:20:10 | MEMBER | null | false | {
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} | Encourages users to create a dataset card on the Hub directly with the new metadata ui + import dataset card template instead of telling users to manually create and upload one. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
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} | The docstrings say that it was supposed to be deprecated since version 2.4.0, can we remove it? | {
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https://api.github.com/repos/huggingface/datasets/issues/5468 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5468/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5468/comments | https://api.github.com/repos/huggingface/datasets/issues/5468/events | https://github.com/huggingface/datasets/issues/5468 | 1,558,066,625 | I_kwDODunzps5c3jXB | 5,468 | Allow opposite of remove_columns on Dataset and DatasetDict | {
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"node_id": "MDU6... | closed | false | null | [] | null | [
"Hi! I agree it would be nice to have a method like that. Instead of `keep_columns`, we can name it `select_columns` to be more aligned with PyArrow's naming convention (`pa.Table.select`).",
"Hi, I am a newbie to open source and would like to contribute. @mariosasko can I take up this issue ?",
"Hey, I also wa... | 2023-01-26T12:28:09 | 2023-02-13T09:59:38 | 2023-02-13T09:59:38 | NONE | null | null | null | ### Feature request
In this blog post https://huggingface.co/blog/audio-datasets, I noticed the following code:
```python
COLUMNS_TO_KEEP = ["text", "audio"]
all_columns = gigaspeech["train"].column_names
columns_to_remove = set(all_columns) - set(COLUMNS_TO_KEEP)
gigaspeech = gigaspeech.remove_columns(columns_to_remove)
```
This kind of thing happens a lot when you don't need to keep all columns from the dataset. It would be more convenient (and less error prone) if you could just write:
```python
gigaspeech = gigaspeech.keep_columns(["text", "audio"])
```
Internally, `keep_columns` could still call `remove_columns`, but it expresses more clearly what the user's intent is.
### Motivation
Less code to write for the user of the dataset.
### Your contribution
- | {
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https://api.github.com/repos/huggingface/datasets/issues/5467 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5467/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5467/comments | https://api.github.com/repos/huggingface/datasets/issues/5467/events | https://github.com/huggingface/datasets/pull/5467 | 1,557,898,273 | PR_kwDODunzps5IlAlk | 5,467 | Fix conda command in readme | {
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"ah didn't read well - it's all good",
"or maybe it isn't ? `-c huggingface -c conda-forge` installs from HF or from conda-forge ?",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | ... | 2023-01-26T10:03:01 | 2023-01-26T18:32:16 | 2023-01-26T18:29:37 | MEMBER | null | false | {
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} | The [conda forge channel](https://anaconda.org/conda-forge/datasets) is lagging behind (as of right now, only 2.7.1 is available), we should recommend using the [Hugging face channel](https://anaconda.org/HuggingFace/datasets) that we are maintaining
```
conda install -c huggingface datasets
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5466 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5466/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5466/comments | https://api.github.com/repos/huggingface/datasets/issues/5466/events | https://github.com/huggingface/datasets/pull/5466 | 1,557,584,845 | PR_kwDODunzps5Ij-z1 | 5,466 | remove pathlib.Path with URIs | {
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"Thanks !\r\n`os.path.join` will use a backslash `\\` on windows which will also fail. You can use this instead in `load_from_disk`:\r\n```python\r\nfrom .filesystems import is_remote_filesystem\r\n\r\nis_local = not is_remote_filesystem(fs)\r\npath_join = os.path.join if is_local else posixpath.join\r\n```",
"Th... | 2023-01-26T03:25:45 | 2023-01-26T17:08:57 | 2023-01-26T16:59:11 | CONTRIBUTOR | null | false | {
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} | Pathlib will convert "//" to "/" which causes retry errors when downloading from cloud storage | {
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https://api.github.com/repos/huggingface/datasets/issues/5465 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5465/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5465/comments | https://api.github.com/repos/huggingface/datasets/issues/5465/events | https://github.com/huggingface/datasets/issues/5465 | 1,557,510,618 | I_kwDODunzps5c1bna | 5,465 | audiofolder creates empty dataset even though the dataset passed in follows the correct structure | {
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The structure of my dataset folder called "my_dataset" is : data metadata.csv
The data folder consists of all mp3 files and metadata.csv consist of file locations like 'data/...mp3 and transcriptions. There's 400+ mp3 files and corresponding transcriptions for my dataset.
When I run the following:
ds = load_dataset("audiofolder", data_dir="my_dataset")
I get:
Using custom data configuration default-...
Downloading and preparing dataset audiofolder/default to /...
Downloading data files: 0%| | 0/2 [00:00<?, ?it/s]
Downloading data files: 0it [00:00, ?it/s]
Extracting data files: 0it [00:00, ?it/s]
Generating train split: 0 examples [00:00, ? examples/s]
Dataset audiofolder downloaded and prepared to /.... Subsequent calls will reuse this data.
0%| | 0/1 [00:00<?, ?it/s]
DatasetDict({
train: Dataset({
features: ['audio', 'transcription'],
num_rows: 1
})
})
### Steps to reproduce the bug
Create a dataset folder called 'my_dataset' with a subfolder called 'data' that has mp3 files. Also, create metadata.csv that has file locations like 'data/...mp3' and their corresponding transcription.
Run:
ds = load_dataset("audiofolder", data_dir="my_dataset")
### Expected behavior
It should generate a dataset with numerous rows.
### Environment info
Run on Jupyter notebook | {
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"Thanks for reporting, @sarahwie.\r\n\r\nPlease note this issue was already fixed in `datasets` 2.6.0 version:\r\n- #5040\r\n\r\nIf you update your `datasets` version, you will be able to load the dataset:\r\n```\r\npip install -U datasets\r\n```",
"Oops, missed that I needed to upgrade. Thanks!"
] | 2023-01-26T00:43:23 | 2023-01-27T05:44:31 | 2023-01-26T07:41:58 | NONE | null | null | null | ### Describe the bug
The checksum of the file has likely changed on the remote host.
### Steps to reproduce the bug
`dataset = nlp.load_dataset("hendrycks_test", "anatomy")`
### Expected behavior
no error thrown
### Environment info
- `datasets` version: 2.2.1
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-25T17:24:01 | 2023-01-25T18:33:35 | 2023-01-25T18:26:15 | MEMBER | null | false | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-01-25T12:33:22 | 2023-01-26T09:37:00 | 2023-01-26T09:27:19 | MEMBER | null | false | {
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} | Allow to concatenate on axis 1 two tables made of misaligned blocks.
For example if the first table has 2 row blocks of 3 rows each, and the second table has 3 row blocks or 2 rows each.
To do that, I slice the row blocks to re-align the blocks.
Fix https://github.com/huggingface/datasets/issues/5413 | {
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"Ccing @dwofk (the author of `fast-depth`). \r\n\r\nThanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed. \r\n\r\nIf you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/ny... | 2023-01-24T19:15:46 | 2023-02-06T20:52:00 | null | CONTRIBUTOR | null | null | null | ### Describe the bug
I think there is a discrepancy between depth map of `nyu_depth_v2` dataset [here](https://huggingface.co/docs/datasets/main/en/depth_estimation) and actual depth map. Depth values somehow got **discretized/clipped** resulting in depth maps that are different from actual ones. Here is a side-by-side comparison,
I tried to find the origin of this issue but sadly as I mentioned in tensorflow/datasets/issues/4674, the download link from `fast-depth` doesn't work anymore hence couldn't verify if the error originated there or during porting data from there to HF.
Hi @sayakpaul, as you worked on huggingface/datasets/issues/5255, if you still have access to that data could you please share the data or perhaps checkout this issue?
### Steps to reproduce the bug
This [notebook](https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing#scrollTo=UEW7QSh0jf0i) from @sayakpaul could be used to generate depth maps and actual ground truths could be checked from this [dataset](https://www.kaggle.com/datasets/awsaf49/nyuv2-bts-dataset) from BTS repo.
> Note: BTS dataset has only 36K data compared to the train-test 50K. They sampled the data as adjacent frames look quite the same
### Expected behavior
Expected depth maps should be smooth rather than discrete/clipped.
### Environment info
- `datasets` version: 2.8.1.dev0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5460 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5460/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5460/comments | https://api.github.com/repos/huggingface/datasets/issues/5460/events | https://github.com/huggingface/datasets/pull/5460 | 1,555,387,532 | PR_kwDODunzps5Icn9C | 5,460 | Document that removing all the columns returns an empty document and the num_row is lost | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-24T17:33:38 | 2023-01-25T16:11:10 | 2023-01-25T16:04:03 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5459 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5459/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5459/comments | https://api.github.com/repos/huggingface/datasets/issues/5459/events | https://github.com/huggingface/datasets/pull/5459 | 1,555,367,504 | PR_kwDODunzps5Icjwe | 5,459 | Disable aiohttp requoting of redirection URL | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Comment by @lhoestq:\r\n> Do you think we need this in `datasets` if it's fixed on the moon landing side ? In the aiohttp doc they consider those symbols as \"non-safe\" ",
"The lib `requests` does not perform that requote on redir... | 2023-01-24T17:18:59 | 2023-02-01T08:45:33 | 2023-01-31T08:37:54 | MEMBER | null | false | {
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} | The library `aiohttp` performs a requoting of redirection URLs that unquotes the single quotation mark character: `%27` => `'`
This is a problem for our Hugging Face Hub, which requires exact URL from location header.
Specifically, in the query component of the URL (`https://netloc/path?query`), the value for `response-content-disposition` contains `%27`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
and after the requoting, the `%27` characters get unquoted to `'`:
```
response-content-disposition=attachment%3B+filename*%3DUTF-8''sample.jsonl.gz%3B+filename%3D%22sample.jsonl.gz%22%3B
```
This PR disables the `aiohttp` requoting of redirection URLs. | {
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https://api.github.com/repos/huggingface/datasets/issues/5458 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5458/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5458/comments | https://api.github.com/repos/huggingface/datasets/issues/5458/events | https://github.com/huggingface/datasets/issues/5458 | 1,555,054,737 | I_kwDODunzps5csECR | 5,458 | slice split while streaming | {
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"Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.\r\n\r\nE.g. \r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train[:3]\")`\r\n\r\nrewritten with `.skip`/`.take`:\r\n`load_dataset(\... | 2023-01-24T14:08:17 | 2023-01-24T15:11:47 | 2023-01-24T15:11:47 | NONE | null | null | null | ### Describe the bug
When using the `load_dataset` function with streaming set to True, slicing splits is apparently not supported.
Did I miss this in the documentation?
### Steps to reproduce the bug
`load_dataset("lhoestq/demo1",revision=None, streaming=True, split="train[:3]")`
causes ValueError: Bad split: train[:3]. Available splits: ['train', 'test'] in builder.py, line 1213, in as_streaming_dataset
### Expected behavior
The first 3 entries of the dataset as a stream
### Environment info
- `datasets` version: 2.8.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.10.9
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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https://api.github.com/repos/huggingface/datasets/issues/5457 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5457/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5457/comments | https://api.github.com/repos/huggingface/datasets/issues/5457/events | https://github.com/huggingface/datasets/issues/5457 | 1,554,171,264 | I_kwDODunzps5cosWA | 5,457 | prebuilt dataset relies on `downloads/extracted` | {
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"Hi! \r\n\r\nThis issue is due to our audio/image datasets not being self-contained. This allows us to save disk space (files are written only once) but also leads to the issues like this one. We plan to make all our datasets self-contained in Datasets 3.0.\r\n\r\nIn the meantime, you can run the following map to e... | 2023-01-24T02:09:32 | 2023-01-24T18:14:10 | null | MEMBER | null | null | null | ### Describe the bug
I pre-built the dataset:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
and it can be used just fine.
now I wipe out `downloads/extracted` and it no longer works.
```
rm -r ~/.cache/huggingface/datasets/downloads
```
That is I can still load it:
```
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
No config specified, defaulting to: general-pmd-synthetic-testing/100.unique
Found cached dataset general-pmd-synthetic-testing (/home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2)
```
but if I try to use it:
```
E stderr: Traceback (most recent call last):
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/main.py", line 116, in <module>
E stderr: train_loader, val_loader = get_dataloaders(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 170, in get_dataloaders
E stderr: train_loader = get_dataloader_from_config(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 443, in get_dataloader_from_config
E stderr: dataloader = get_dataloader(
E stderr: File "/mnt/nvme0/code/huggingface/m4-master-6/m4/training/dataset.py", line 264, in get_dataloader
E stderr: is_pmd = "meta" in hf_dataset[0] and "source" in hf_dataset[0]
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2601, in __getitem__
E stderr: return self._getitem(
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/arrow_dataset.py", line 2586, in _getitem
E stderr: formatted_output = format_table(
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 634, in format_table
E stderr: return formatter(pa_table, query_type=query_type)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 406, in __call__
E stderr: return self.format_row(pa_table)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 442, in format_row
E stderr: row = self.python_features_decoder.decode_row(row)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/formatting/formatting.py", line 225, in decode_row
E stderr: return self.features.decode_example(row) if self.features else row
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1846, in decode_example
E stderr: return {
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1847, in <dictcomp>
E stderr: column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1304, in decode_nested_example
E stderr: return decode_nested_example([schema.feature], obj)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1296, in decode_nested_example
E stderr: if decode_nested_example(sub_schema, first_elmt) != first_elmt:
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/features.py", line 1309, in decode_nested_example
E stderr: return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
E stderr: File "/mnt/nvme0/code/huggingface/datasets-master/src/datasets/features/image.py", line 144, in decode_example
E stderr: image = PIL.Image.open(path)
E stderr: File "/home/stas/anaconda3/envs/py38-pt113/lib/python3.8/site-packages/PIL/Image.py", line 3092, in open
E stderr: fp = builtins.open(filename, "rb")
E stderr: FileNotFoundError: [Errno 2] No such file or directory: '/mnt/nvme0/code/data/cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data/101/images_01.jpg'
```
Only if I wipe out the cached dir and rebuild then it starts working as `download/extracted` is back again with extracted files.
```
rm -r ~/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing
python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
```
I think there are 2 issues here:
1. why does it still rely on extracted files after `arrow` files were printed - did I do something incorrectly when creating this dataset?
2. why doesn't the dataset know that it has been gutted and loads just fine? If it has a dependency on `download/extracted` then `load_dataset` should check if it's there and fail or force rebuilding. I am sure this could be a very expensive operation, so probably really solving #1 will not require this check. and this second item is probably an overkill. Other than perhaps if it had an optional `check_consistency` flag to do that.
### Environment info
datasets@main | {
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https://api.github.com/repos/huggingface/datasets/issues/5456 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5456/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5456/comments | https://api.github.com/repos/huggingface/datasets/issues/5456/events | https://github.com/huggingface/datasets/pull/5456 | 1,553,905,148 | PR_kwDODunzps5IXq92 | 5,456 | feat: tqdm for `to_parquet` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-23T22:05:38 | 2023-01-24T11:26:47 | 2023-01-24T11:17:12 | CONTRIBUTOR | null | false | {
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} | As described in #5418
I noticed also that the `to_json` function supports multi-workers whereas `to_parquet`, is that not possible/not needed with Parquet or something that hasn't been implemented yet? | {
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https://api.github.com/repos/huggingface/datasets/issues/5455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5455/comments | https://api.github.com/repos/huggingface/datasets/issues/5455/events | https://github.com/huggingface/datasets/pull/5455 | 1,553,040,080 | PR_kwDODunzps5IUvAZ | 5,455 | Single TQDM bar in multi-proc map | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-23T12:49:40 | 2023-02-13T20:23:34 | 2023-02-13T20:16:38 | CONTRIBUTOR | null | false | {
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} | Use the "shard generator approach with periodic progress updates" (used in `save_to_disk` and multi-proc `load_dataset`) in `Dataset.map` to enable having a single TQDM progress bar in the multi-proc mode.
Closes https://github.com/huggingface/datasets/issues/771, closes https://github.com/huggingface/datasets/issues/3177
TODO:
- [x] cleaner refactor of the `_map_single` decorators now that they also have to wrap generator functions (decorate `map` instead of `map_single` with the `transmit_` decorators and predict the shards' fingerprint in `map`) | {
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https://api.github.com/repos/huggingface/datasets/issues/5454 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5454/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5454/comments | https://api.github.com/repos/huggingface/datasets/issues/5454/events | https://github.com/huggingface/datasets/issues/5454 | 1,552,890,419 | I_kwDODunzps5cjzoz | 5,454 | Save and resume the state of a DataLoader | {
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"Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.",
"Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra fe... | 2023-01-23T10:58:54 | 2023-01-24T01:45:48 | null | MEMBER | null | null | null | It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)
What I have in mind (but lmk if you have other ideas or comments):
For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker.
For iterable datasets, this requires to save the state of the dataset iterator, which includes:
- the current shard idx and row position in the current shard
- the epoch number
- the rng state
- the shuffle buffer
Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point.
cc @stas00 @sgugger | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-23T08:57:40 | 2023-01-24T01:34:20 | 2023-01-23T10:10:42 | MEMBER | null | false | {
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} | This PR fixes the extraction of insecure TAR files by changing the base path against which TAR members are compared:
- from: "."
- to: `output_path`
This PR also adds tests for extracting insecure TAR files.
Related to:
- #5441
- #5452
@stas00 please note this PR addresses just one of the issues you pointed out: the use of the cwd by the extractor. The other issues (actionable error messages, raise instead of log error) should be addressed in other PRs. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-23T07:53:38 | 2023-01-23T09:40:55 | 2023-01-23T08:31:17 | MEMBER | null | false | {
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} | The log messages do not match their if-condition. This PR swaps them.
Found while investigating:
- #5441
CC: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5451 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5451/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5451/comments | https://api.github.com/repos/huggingface/datasets/issues/5451/events | https://github.com/huggingface/datasets/issues/5451 | 1,552,336,300 | I_kwDODunzps5chsWs | 5,451 | ImageFolder BadZipFile: Bad offset for central directory | {
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"Hi ! Could you share the full stack trace ? Which dataset did you try to load ?\r\n\r\nit may be related to https://github.com/huggingface/datasets/pull/5640",
"The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.",
"For others that find ... | 2023-01-22T23:50:12 | 2023-05-23T10:35:48 | 2023-02-10T16:31:36 | NONE | null | null | null | ### Describe the bug
I'm getting the following exception:
```
lib/python3.10/zipfile.py:1353 in _RealGetContents โ
โ โ
โ 1350 โ โ # self.start_dir: Position of start of central directory โ
โ 1351 โ โ self.start_dir = offset_cd + concat โ
โ 1352 โ โ if self.start_dir < 0: โ
โ โฑ 1353 โ โ โ raise BadZipFile("Bad offset for central directory") โ
โ 1354 โ โ fp.seek(self.start_dir, 0) โ
โ 1355 โ โ data = fp.read(size_cd) โ
โ 1356 โ โ fp = io.BytesIO(data) โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
BadZipFile: Bad offset for central directory
Extracting data files: 35%|โโโโโโโโโโโโโโโโโโ | 38572/110812 [00:10<00:20, 3576.26it/s]
```
### Steps to reproduce the bug
```
load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
),
```
### Expected behavior
loads the dataset
### Environment info
datasets==2.8.0
Python 3.10.8
Linux 129-146-3-202 5.15.0-52-generic #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux | {
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https://api.github.com/repos/huggingface/datasets/issues/5450 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5450/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5450/comments | https://api.github.com/repos/huggingface/datasets/issues/5450/events | https://github.com/huggingface/datasets/issues/5450 | 1,551,109,365 | I_kwDODunzps5cdAz1 | 5,450 | to_tf_dataset with a TF collator causes bizarrely persistent slowdown | {
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"wtf",
"Couldn't find what's causing this, this will need more investigation",
"A possible hint: The function it seems to be spending a lot of time in (when iterating over the original dataset) is `_get_mp` in the PIL JPEG decoder: \r\n
Briefly, there are several datasets that, when you iterate over them with `to_tf_dataset` **and** a data collator that returns `tf` tensors, become very slow. We haven't been able to figure this one out - it can be intermittent, and we have no idea what could possibly cause it. The weirdest thing is that **the slowdown affects other attempts to access the underlying dataset**. If you try to iterate over the `tf.data.Dataset`, then interrupt execution, and then try to iterate over the original dataset, the original dataset is now also very slow! This is true even if the dataset format is not set to `tf` - the iteration is slow even though it's not calling TF at all!
There is a simple workaround for this - we can simply get our data collators to return `np` tensors. When we do this, the bug is never triggered and everything is fine. In general, `np` is preferred for this kind of preprocessing work anyway, when the preprocessing is not going to be compiled into a pure `tf.data` pipeline! However, the issue is fascinating, and the TF team were wondering if anyone in datasets (cc @lhoestq @mariosasko) might have an idea of what could cause this.
### Steps to reproduce the bug
Run the attached Colab.
### Expected behavior
The slowdown should go away, or at least not persist after we stop iterating over the `tf.data.Dataset`
### Environment info
The issue occurs on multiple versions of Python and TF, both on local machines and on Colab.
All testing was done using the latest versions of `transformers` and `datasets` from `main` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-20T12:53:17 | 2023-01-20T13:32:50 | 2023-01-20T13:26:03 | MEMBER | null | false | {
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} | Support fsspec 2023.1.0 in CI.
In the 2023.1.0 fsspec release, they replaced the type of `fsspec.registry`:
- from `ReadOnlyRegistry`, with an attribute called `target`
- to `MappingProxyType`, without that attribute
Consequently, we need to change our `mock_fsspec` fixtures, that were using the `target` attribute.
Fix #5448. | {
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- #5445
we should revert the temporary pin on fsspec introduced by:
- #5447 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
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} | Temporarily pin fsspec < 2023.1.0
Fix #5445. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@Wauplin I was testing it in a dedicated branch without opening a PR: https://github.com/huggingface/datasets/commits/test-hfh-0.12.0rc0",
"Oops, sorry @albertvillanova. I thought for next time I'll start the CIs before pinging eve... | 2023-01-20T10:05:19 | 2023-01-20T10:43:22 | 2023-01-20T10:13:48 | CONTRIBUTOR | null | false | {
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} | DO NOT MERGE.
Only to test the CI.
cc @lhoestq @albertvillanova | {
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```
...
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - AttributeError: 'mappingproxy' object has no attribute 'target'
===== 2076 passed, 19 skipped, 15 warnings, 47 errors in 115.54s (0:01:55) =====
``` | {
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"Looks like a duplicate of https://github.com/huggingface/datasets/issues/1948. \r\n\r\nI also think these should be logged as INFO messages, but let's see what @lhoestq thinks.",
"It can be considered unexpected to see a `map` function return instantaneously. The warning is here to explain this case by mentionin... | 2023-01-20T01:19:18 | 2023-04-17T15:39:54 | null | NONE | null | null | null | ### Describe the bug
Code in `datasets` is using `logger.warning` when it should be using `logger.info`.
Some of these are probably a matter of opinion, but I think anything starting with `logger.warning(f"Loading chached` clearly falls into the info category.
Definitions from the Python docs for reference:
* INFO: Confirmation that things are working as expected.
* WARNING: An indication that something unexpected happened, or indicative of some problem in the near future (e.g. โdisk space lowโ). The software is still working as expected.
In theory, a user should be able to resolve things such that there are no warnings.
### Steps to reproduce the bug
Load any dataset that's already cached.
### Expected behavior
No output when log level is at the default WARNING level.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 9.0.0
- Pandas version: 1.5.2 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-20T01:09:14 | 2023-01-20T15:44:45 | 2023-01-20T15:37:30 | MEMBER | null | false | {
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} | Based on feedback from discussion #5423, this PR updates the sharing tutorial with a mention of writing your own dataset loading script to support more advanced dataset creation options like multiple configs.
I'll open a separate PR to update the *Create a Dataset card* with the new Hub metadata UI update ๐ | {
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"Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://githu... | 2023-01-19T23:12:08 | 2023-02-24T16:17:51 | 2023-02-24T16:17:51 | NONE | null | null | null | ### Feature request
First of all , I would like to thank all community who are developed DataSet storage and make it free available
How to integrate our Onedrive account or any other possible storage clouds (like google drive,...) with the **HF** datasets section.
For example, if I have **50GB** on my **Onedrive** account and I want to move between drive and Hugging face repo or vis versa
### Motivation
make the dataset section more flexible with other possible storage
like the integration between Google Collab and Google drive the storage
### Your contribution
Can be done using Hugging face CLI | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-01-19T02:17:21 | 2023-01-20T16:49:22 | null | MEMBER | null | false | {
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} | ok, every so often, I have been getting a strange failure on dataset install:
```
$ python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
No config specified, defaulting to: general-pmd-synthetic-testing/100.unique
Downloading and preparing dataset general-pmd-synthetic-testing/100.unique (download: 3.21 KiB, generated: 16.01 MiB, post-processed: Unknown size, total: 16.02 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2...
Extraction of data is blocked (illegal path)
Extraction of data/1 is blocked (illegal path)
Extraction of data/1/text.null is blocked (illegal path)
[...]
```
I had no idea what to do with that - what in the world does **illegal path** mean?
I started looking at the code in `TarExtractor` and added a debug print of `base` so that told me that there was a problem with the current directory - which was a clone of one of the hf repos.
This particular dataset extracts into a directory `data` and the current dir I was running the tests from already had `data` in it which was a symbolic link to another partition and somehow all that `badpath` code was blowing up there.
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L113-L114
I tried hard to come up with a repro, but no matter what I tried it only fails in that particular clone directory that has a `data` symlink and not anywhere else.
In any case, in this PR I'm proposing to at least give a user a hint of what seems to be an issue.
I'm not at all happy with the info I got with this proposed change, but at least it gave me a hint that `TarExtractor` tries to extract into the current directory without any respect to pre-existing files. Say what?
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L110
why won't it use the `datasets` designated directory for that? There would never be a problem if it were to do that.
I had to look at all those `resolved`, `badpath` calls and see what it did and why it failed, since it was far from obvious. It appeared like it resolved a symlink and compared it to the original path which of course wasn't matching.
So perhaps you have a better solution than what I proposed in this PR. I think that code line I quoted is the one that should be fixed instead.
But if you can't think of a better solution let's merge this at least so that the user will have a clue that the current dir is somehow involved.
p.s. I double checked that if I remove the pre-existing `data` symlink in the current dir I'm running the dataset install command from, the problem goes away too.
Thanks.
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"@polinaeterna any tentative date on when the Common Voice 12.0 dataset will be added ?"
] | 2023-01-18T13:07:05 | 2023-01-25T18:38:53 | null | NONE | null | null | null | ### Feature request
Please add the common voice 12_0 datasets. Apart from English, a significant amount of audio-data has been added to the other minor-language datasets.
### Motivation
The dataset link:
https://commonvoice.mozilla.org/en/datasets
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"_The documentation is not available anymore as the PR was closed or merged._",
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"Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\r\n\r\n",
"> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode... | 2023-01-17T18:22:27 | 2023-01-18T20:20:15 | 2023-01-18T20:20:15 | NONE | null | null | null | I try to create dataset which contains about 9000 png images 64x64 in size, and they are all 4-channel (RGBA). When trying to use load_dataset() then a dataset is created from only 2 images. What exactly interferes I can not understand. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-17T15:59:50 | 2023-01-18T09:05:49 | 2023-01-18T06:29:06 | CONTRIBUTOR | null | false | {
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} | Closes #5433, reverts #5432, and also:
* Uses [ghcr.io container images](https://cml.dev/doc/self-hosted-runners/#docker-images) for extra speed
* Updates `actions/checkout` to `v3` (note that `v2` is [deprecated](https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/))
* Follows the new naming convention for environment variables introduced with [iterative/cml#1272](https://github.com/iterative/cml/pull/1272) | {
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https://api.github.com/repos/huggingface/datasets/issues/5435 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5435/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5435/comments | https://api.github.com/repos/huggingface/datasets/issues/5435/events | https://github.com/huggingface/datasets/issues/5435 | 1,536,099,300 | I_kwDODunzps5bjwPk | 5,435 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage | {
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"Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)",
"Thanks for reporting, @HaoyuYang59.\r\n\r\nPlease note that these are different \"dataset\" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Datase... | 2023-01-17T10:04:16 | 2023-01-19T09:56:03 | 2023-01-19T09:56:03 | NONE | null | null | null | ### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer. Therefore it is advised to shuffle the dataset before splitting using take or skip. See more details in the [Shuffling the dataset: shuffle](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#iterable-dataset-shuffling) section.`
>> \# You can also create splits from a shuffled dataset
>> train_dataset = shuffled_dataset.skip(1000)
>> eval_dataset = shuffled_dataset.take(1000)
Where the shuffled dataset comes from:
`shuffled_dataset = dataset.shuffle(buffer_size=10_000, seed=42)`
At least in Tensorflow 2.9/2.10/2.11, [docs](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) states the `reshuffle_each_iteration` argument is `True` by default. This means the dataset would be shuffled after each epoch, and as a result **the validation data would leak into training test**.
### Steps to reproduce the bug
N/A
### Expected behavior
The `reshuffle_each_iteration` argument should be set to `False`.
### Environment info
Tensorflow 2.9/2.10/2.11 | {
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https://api.github.com/repos/huggingface/datasets/issues/5434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5434/comments | https://api.github.com/repos/huggingface/datasets/issues/5434/events | https://github.com/huggingface/datasets/issues/5434 | 1,536,090,042 | I_kwDODunzps5bjt-6 | 5,434 | sample_dataset module not found | {
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"Hi! Can you describe what the actual error is?",
"working on the setfit example script\r\n\r\n from setfit import SetFitModel, SetFitTrainer, sample_dataset\r\n\r\nImportError: cannot import name 'sample_dataset' from 'setfit' (C:\\Python\\Python38\\lib\\site-packages\\setfit\\__init__.py)\r\n\r\n apart from t... | 2023-01-17T09:57:54 | 2023-01-19T13:52:12 | 2023-01-19T07:55:11 | NONE | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5433 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5433/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5433/comments | https://api.github.com/repos/huggingface/datasets/issues/5433/events | https://github.com/huggingface/datasets/issues/5433 | 1,536,017,901 | I_kwDODunzps5bjcXt | 5,433 | Support latest Docker image in CI benchmarks | {
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"Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).\r\n\r\n[^1]: More or less, see https://github.com/yargs/yargs/issues/873.",
"Opened htt... | 2023-01-17T09:06:08 | 2023-01-18T06:29:08 | 2023-01-18T06:29:08 | MEMBER | null | null | null | Once we find out the root cause of:
- #5431
we should revert the temporary pin on the Docker image version introduced by:
- #5432 | {
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https://api.github.com/repos/huggingface/datasets/issues/5432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5432/comments | https://api.github.com/repos/huggingface/datasets/issues/5432/events | https://github.com/huggingface/datasets/pull/5432 | 1,535,893,019 | PR_kwDODunzps5HhEA8 | 5,432 | Fix CI benchmarks by temporarily pinning Docker image version | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-17T07:15:31 | 2023-01-17T08:58:22 | 2023-01-17T08:51:17 | MEMBER | null | false | {
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} | This PR fixes CI benchmarks, by temporarily pinning Docker image version, instead of "latest" tag.
It also updates deprecated `cml-send-comment` command and using `cml comment create` instead.
Fix #5431. | {
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"followers_... | null | [] | 2023-01-17T06:49:57 | 2023-01-18T06:33:24 | 2023-01-17T08:51:18 | MEMBER | null | null | null | Our CI benchmarks are broken, raising `Unknown arguments` error: https://github.com/huggingface/datasets/actions/runs/3932397079/jobs/6724905161
```
Unknown arguments: runnerPath, path
```
Stack trace:
```
100%|โโโโโโโโโโ| 500/500 [00:01<00:00, 338.98ba/s]
Updating lock file 'dvc.lock'
To track the changes with git, run:
git add dvc.lock
To enable auto staging, run:
dvc config core.autostage true
Use `dvc push` to send your updates to remote storage.
cml send-comment <markdown file>
Global Options:
--log Logging verbosity
[string] [choices: "error", "warn", "info", "debug"] [default: "info"]
--driver Git provider where the repository is hosted
[string] [choices: "github", "gitlab", "bitbucket"] [default: infer from the
environment]
--repo Repository URL or slug
[string] [default: infer from the environment]
--driver-token, --token CI driver personal/project access token (PAT)
[string] [default: infer from the environment]
--help Show help [boolean]
Options:
--target Comment type (`commit`, `pr`, `commit/f00bar`,
`pr/42`, `issue/1337`),default is automatic (`pr`
but fallback to `commit`). [string]
--watch Watch for changes and automatically update the
comment [boolean]
--publish Upload any local images found in the Markdown
report [boolean] [default: true]
--publish-url Self-hosted image server URL
[string] [default: "https://asset.cml.dev/"]
--publish-native, --native Uses driver's native capabilities to upload assets
instead of CML's storage; not available on GitHub
[boolean]
--watermark-title Hidden comment marker (used for targeting in
subsequent `cml comment update`); "{workflow}" &
"{run}" are auto-replaced [string] [default: ""]
Unknown arguments: runnerPath, path
Error: Process completed with exit code 1.
```
Issue reported to iterative/cml:
- iterative/cml#1319 | {
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"Some of the shard files now have 0 number of rows.\r\n\r\nWe have opened an issue in the Apache Beam repo:\r\n- https://github.com/apache/beam/issues/25041"
] | 2023-01-17T06:42:12 | 2023-01-17T16:12:18 | null | MEMBER | null | null | null | Once we find out the root cause of:
- #5426
we should revert the temporary pin on apache-beam introduced by:
- #5429 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2023-01-16T16:20:09 | 2023-01-16T16:51:42 | 2023-01-16T16:49:03 | MEMBER | null | false | {
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} | Temporarily pin apache-beam < 2.44.0
Fix #5426. | {
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https://api.github.com/repos/huggingface/datasets/issues/5428 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5428/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5428/comments | https://api.github.com/repos/huggingface/datasets/issues/5428/events | https://github.com/huggingface/datasets/issues/5428 | 1,535,166,139 | I_kwDODunzps5bgMa7 | 5,428 | Load/Save FAISS index using fsspec | {
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"Hi! Sure, feel free to submit a PR. Maybe if we want to be consistent with the existing API, it would be cleaner to directly add support for `fsspec` paths in `Dataset.load_faiss_index`/`Dataset.save_faiss_index` in the same manner as it was done in `Dataset.load_from_disk`/`Dataset.save_to_disk`.",
"That's a gr... | 2023-01-16T16:08:12 | 2023-03-27T15:18:22 | 2023-03-27T15:18:22 | CONTRIBUTOR | null | null | null | ### Feature request
From what I understand `faiss` already support this [link](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning#generic-io-support)
I would like to use a stream as input to `Dataset.load_faiss_index` and `Dataset.save_faiss_index`.
### Motivation
In my case, I'm saving faiss index in cloud storage and use `fsspec` to load them. It would be ideal if I could send the stream directly instead of copying the file locally (or mounting the bucket) and then load the index.
### Your contribution
I can submit the PR | {
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https://api.github.com/repos/huggingface/datasets/issues/5427 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5427/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5427/comments | https://api.github.com/repos/huggingface/datasets/issues/5427/events | https://github.com/huggingface/datasets/issues/5427 | 1,535,162,889 | I_kwDODunzps5bgLoJ | 5,427 | Unable to download dataset id_clickbait | {
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"Thanks for reporting, @ilos-vigil.\r\n\r\nWe have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co/datasets/id_clickbait/discussions/1 "
] | 2023-01-16T16:05:36 | 2023-01-18T09:51:28 | 2023-01-18T09:25:19 | NONE | null | null | null | ### Describe the bug
I tried to download dataset `id_clickbait`, but receive this error message.
```
FileNotFoundError: Couldn't find file at https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip
```
When i open the link using browser, i got this XML data.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>NoSuchBucket</Code><Message>The specified bucket does not exist</Message><BucketName>md-datasets-cache-zipfiles-prod</BucketName><RequestId>NVRM6VEEQD69SD00</RequestId><HostId>W/SPDxLGvlCGi0OD6d7mSDvfOAUqLAfvs9nTX50BkJrjMny+X9Jnqp/Li2lG9eTUuT4MUkAA2jjTfCrCiUmu7A==</HostId></Error>
```
### Steps to reproduce the bug
Code snippet:
```
from datasets import load_dataset
load_dataset('id_clickbait', 'annotated')
load_dataset('id_clickbait', 'raw')
```
Link to Kaggle notebook: https://www.kaggle.com/code/ilosvigil/bug-check-on-id-clickbait-dataset
### Expected behavior
Successfully download and load `id_newspaper` dataset.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5426 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5426/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5426/comments | https://api.github.com/repos/huggingface/datasets/issues/5426/events | https://github.com/huggingface/datasets/issues/5426 | 1,535,158,555 | I_kwDODunzps5bgKkb | 5,426 | CI tests are broken: SchemaInferenceError | {
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```
FAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare_sharded - datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
```
Stack trace:
```
______________ BeamBuilderTest.test_download_and_prepare_sharded _______________
[gw1] linux -- Python 3.7.15 /opt/hostedtoolcache/Python/3.7.15/x64/bin/python
self = <tests.test_beam.BeamBuilderTest testMethod=test_download_and_prepare_sharded>
@require_beam
def test_download_and_prepare_sharded(self):
import apache_beam as beam
original_write_parquet = beam.io.parquetio.WriteToParquet
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
with patch("apache_beam.io.parquetio.WriteToParquet") as write_parquet_mock:
write_parquet_mock.side_effect = partial(original_write_parquet, num_shards=2)
> builder.download_and_prepare()
tests/test_beam.py:97:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:864: in download_and_prepare
**download_and_prepare_kwargs,
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/builder.py:1976: in _download_and_prepare
num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter))
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:694: in finalize
shard_num_bytes, _ = parquet_to_arrow(source, destination)
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:740: in parquet_to_arrow
num_bytes, num_examples = writer.finalize()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <datasets.arrow_writer.ArrowWriter object at 0x7f6dcbb3e810>
close_stream = True
def finalize(self, close_stream=True):
self.write_rows_on_file()
# In case current_examples < writer_batch_size, but user uses finalize()
if self._check_duplicates:
self.check_duplicate_keys()
# Re-intializing to empty list for next batch
self.hkey_record = []
self.write_examples_on_file()
# If schema is known, infer features even if no examples were written
if self.pa_writer is None and self.schema:
self._build_writer(self.schema)
if self.pa_writer is not None:
self.pa_writer.close()
self.pa_writer = None
if close_stream:
self.stream.close()
else:
if close_stream:
self.stream.close()
> raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/arrow_writer.py:593: SchemaInferenceError
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5425 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5425/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5425/comments | https://api.github.com/repos/huggingface/datasets/issues/5425/events | https://github.com/huggingface/datasets/issues/5425 | 1,534,581,850 | I_kwDODunzps5bd9xa | 5,425 | Sort on multiple keys with datasets.Dataset.sort() | {
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"node_id": "MDU6... | closed | false | null | [] | null | [
"Hi! \r\n\r\n`Dataset.sort` calls `df.sort_values` internally, and `df.sort_values` brings all the \"sort\" columns in memory, so sorting on multiple keys could be very expensive. This makes me think that maybe we can replace `df.sort_values` with `pyarrow.compute.sort_indices` - the latter can also sort on multipl... | 2023-01-16T09:22:26 | 2023-02-24T16:15:11 | 2023-02-24T16:15:11 | NONE | null | null | null | ### Feature request
From discussion on forum: https://discuss.huggingface.co/t/datasets-dataset-sort-does-not-preserve-ordering/29065/1
`sort()` does not preserve ordering, and it does not support sorting on multiple columns, nor a key function.
The suggested solution:
> ... having something similar to pandas and be able to specify multiple columns for sorting. Weโre already using pandas under the hood to do the sorting in datasets.
The suggested workaround:
> convert your dataset to pandas and use `df.sort_values()`
### Motivation
Preserved ordering when sorting is very handy when one needs to sort on multiple columns, A and B, so that e.g. whenever A is equal for two or more rows, B is kept sorted.
Having a parameter to do this in ๐คdatasets would be cleaner than going through pandas and back, and it wouldn't add much complexity to the library.
Alternatives:
- the possibility to specify multiple keys to sort by with decreasing priority (suggested solution),
- the ability to provide a key function for sorting, so that one can manually specify the sorting criteria.
### Your contribution
I'll be happy to contribute by submitting a PR. Will get documented on `CONTRIBUTING.MD`.
Would love to get thoughts on this, if anyone has anything to add. | {
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https://api.github.com/repos/huggingface/datasets/issues/5424 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5424/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5424/comments | https://api.github.com/repos/huggingface/datasets/issues/5424/events | https://github.com/huggingface/datasets/issues/5424 | 1,534,394,756 | I_kwDODunzps5bdQGE | 5,424 | When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset? | {
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"Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:\r\n```python\r\ninstructions = [\r\n ReadInstruction(split_name=\"train\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"dev\", from_=0, to=10, unit='%', rounding='closest'),\r\n R... | 2023-01-16T06:54:28 | 2023-02-24T16:19:00 | 2023-02-24T16:19:00 | NONE | null | null | null | ### Describe the bug
I am loading datasets from custom `tsv` files stored locally and applying split instructions for each split. Although the ReadInstruction is being applied correctly and I was expecting it to be `DatasetDict` but instead it is a list of `Dataset`.
### Steps to reproduce the bug
Steps to reproduce the behaviour:
1. Import
`from datasets import load_dataset, ReadInstruction`
2. Instruction to load the dataset
```
instructions = [
ReadInstruction(split_name="train", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="dev", from_=0, to=10, unit='%', rounding='closest'),
ReadInstruction(split_name="test", from_=0, to=5, unit='%', rounding='closest')
]
```
3. Load
`dataset = load_dataset('csv', data_dir="data/", data_files={"train":"train.tsv", "dev":"dev.tsv", "test":"test.tsv"}, delimiter="\t", split=instructions)`
### Expected behavior
**Current behaviour**
:
**Expected behaviour**
### Environment info
``datasets==2.8.0
``
`Python==3.8.5
`
`Platform - Ubuntu 20.04.4 LTS` | {
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https://api.github.com/repos/huggingface/datasets/issues/5422 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5422/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5422/comments | https://api.github.com/repos/huggingface/datasets/issues/5422/events | https://github.com/huggingface/datasets/issues/5422 | 1,533,385,239 | I_kwDODunzps5bZZoX | 5,422 | Datasets load error for saved github issues | {
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"I can confirm that the error exists!\r\nI'm trying to read 3 parquet files locally:\r\n```python\r\nfrom datasets import load_dataset, Features, Value, ClassLabel\r\n\r\nreview_dataset = load_dataset(\r\n \"parquet\",\r\n data_files={\r\n \"train\": os.path.join(sentiment_analysis_data_path, \"train.p... | 2023-01-14T17:29:38 | 2023-05-05T19:25:08 | null | NONE | null | null | null | ### Describe the bug
Loading a previously downloaded & saved dataset as described in the HuggingFace course:
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
Gives this error:
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
A work-around I found was to use streaming.
### Steps to reproduce the bug
Reproduce by executing the code provided:
https://huggingface.co/course/chapter5/5?fw=pt
From the heading:
'letโs create a function that can download all the issues from a GitHub repository'
### Expected behavior
No error
### Environment info
Datasets version 2.8.0. Note that version 2.6.1 gives the same error (related to null timestamp).
**[EDIT]**
This is the complete error trace confirming the issue is related to the timestamp (`Couldn't cast array of type timestamp[s] to null`)
```
Using custom data configuration default-950028611d2860c8
Downloading and preparing dataset json/default to [...]/.cache/huggingface/datasets/json/default-950028611d2860c8/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%|โโโโโโโโโโ| 1/1 [00:00<?, ?it/s]
Extracting data files: 100%|โโโโโโโโโโ| 1/1 [00:00<00:00, 500.63it/s]
Generating train split: 2619 examples [00:00, 7155.72 examples/s]Traceback (most recent call last):
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\arrow_writer.py", line 567, in write_table
pa_table = table_cast(pa_table, self._schema)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2282, in table_cast
return cast_table_to_schema(table, schema)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
return func(array, *args, **kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2101, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
return func(array, *args, **kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1990, in array_cast
raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type timestamp[s] to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\pydevconsole.py", line 364, in runcode
coro = func()
File "<input>", line 1, in <module>
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "[...]\PycharmProjects\TransformersTesting\dataset_issues.py", line 20, in <module>
issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\load.py", line 1757, in load_dataset
builder_instance.download_and_prepare(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 860, in download_and_prepare
self._download_and_prepare(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 953, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1706, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1849, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
Generating train split: 2619 examples [00:19, 7155.72 examples/s]
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5421 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5421/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5421/comments | https://api.github.com/repos/huggingface/datasets/issues/5421/events | https://github.com/huggingface/datasets/issues/5421 | 1,532,278,307 | I_kwDODunzps5bVLYj | 5,421 | Support case-insensitive Hub dataset name in load_dataset | {
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"description": "New feature or request"
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"Closing as case-insensitivity should be only for URL redirection on the Hub. In the APIs, we will only support the canonical name (https://github.com/huggingface/moon-landing/pull/2399#issuecomment-1382085611)"
] | 2023-01-13T13:07:07 | 2023-01-13T20:12:32 | 2023-01-13T20:12:32 | CONTRIBUTOR | null | null | null | ### Feature request
The dataset name on the Hub is case-insensitive (see https://github.com/huggingface/moon-landing/pull/2399, internal issue), i.e., https://huggingface.co/datasets/GLUE redirects to https://huggingface.co/datasets/glue.
Ideally, we could load the glue dataset using the following:
```
from datasets import load_dataset
load_dataset('GLUE', 'cola')
```
It breaks because the loading script `GLUE.py` does not exist (`glue.py` should be selected instead).
Minor additional comment: in other cases without a loading script, we can load the dataset, but the automatically generated config name depends on the casing:
- `load_dataset('severo/danish-wit')` generates the config name `severo--danish-wit-e6fda5b070deb133`, while
- `load_dataset('severo/danish-WIT')` generates the config name `severo--danish-WIT-e6fda5b070deb133`
### Motivation
To follow the same UX on the Hub and in the datasets library.
### Your contribution
... | {
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https://api.github.com/repos/huggingface/datasets/issues/5420 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5420/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5420/comments | https://api.github.com/repos/huggingface/datasets/issues/5420/events | https://github.com/huggingface/datasets/pull/5420 | 1,532,265,742 | PR_kwDODunzps5HVAhL | 5,420 | ci: ๐ก remove two obsolete issue templates | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-13T12:58:43 | 2023-01-13T13:36:00 | 2023-01-13T13:29:01 | CONTRIBUTOR | null | false | {
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"merged_at": "2023-01-13T13:29:01"
} | add-dataset is not needed anymore since the "canonical" datasets are on the Hub. And dataset-viewer is managed within the datasets-server project.
See https://github.com/huggingface/datasets/issues/new/choose
<img width="1245" alt="Capture dโeฬcran 2023-01-13 aฬ 13 59 58" src="https://user-images.githubusercontent.com/1676121/212325813-2d4c30e2-343e-4aa2-8cce-b2b77f45628e.png">
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https://api.github.com/repos/huggingface/datasets/issues/5419 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5419/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5419/comments | https://api.github.com/repos/huggingface/datasets/issues/5419/events | https://github.com/huggingface/datasets/issues/5419 | 1,531,999,850 | I_kwDODunzps5bUHZq | 5,419 | label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator | {
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"Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_inde... | 2023-01-13T09:40:07 | 2023-01-19T15:46:51 | null | NONE | null | null | null | ### Describe the bug
When preparing a dataset for a task using `datasets.TextClassification`, the output feature is named `labels`. When preparing the trainer using the `transformers.DataCollator` the default column name is `label` if binary or `label_ids` if multi-class problem.
It is required to rename the column accordingly to the expected name : `label` or `label_ids`
### Steps to reproduce the bug
```python
from datasets import TextClassification, AutoTokenized, DataCollatorWithPadding
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0'))
print(ds_prepared)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
ds_tokenized = ds_prepared.map(lambda x: tokenizer(x['text'], truncation=True), batched=True)
print(ds_tokenized)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
tf_data = model.prepare_tf_dataset(ds_tokenized, shuffle=True, batch_size=16, collate_fn=data_collator)
print(tf_data)
```
### Expected behavior
Without renaming the the column, the target column is not in the final tf_data since it is not in the column name expected by the data_collator.
To correct this, we have to rename the column:
```python
ds_prepared = my_dataset.prepare_for_task(TextClassification(text_column='TEXT', label_column='MY_LABEL_COLUMN_1_OR_0')).rename_column('labels', 'label')
```
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
- `transformers` version: 4.26.0.dev0
- Platform: Linux-5.15.79.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.11.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in> | {
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https://api.github.com/repos/huggingface/datasets/issues/5418 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5418/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5418/comments | https://api.github.com/repos/huggingface/datasets/issues/5418/events | https://github.com/huggingface/datasets/issues/5418 | 1,530,111,184 | I_kwDODunzps5bM6TQ | 5,418 | Add ProgressBar for `to_parquet` | {
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"Thanks for your proposal, @zanussbaum. Yes, I agree that would definitely be a nice feature to have!",
"@albertvillanova Iโm happy to make a quick PR for the feature! let me know ",
"That would be awesome ! You can comment `#self-assign` to assign you to this issue and open a PR :) Will be happy to review",
... | 2023-01-12T05:06:20 | 2023-01-24T18:18:24 | 2023-01-24T18:18:24 | CONTRIBUTOR | null | null | null | ### Feature request
Add a progress bar for `Dataset.to_parquet`, similar to how `to_json` works.
### Motivation
It's a bit frustrating to not know how long a dataset will take to write to file and if it's stuck or not without a progress bar
### Your contribution
Sure I can help if needed | {
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https://api.github.com/repos/huggingface/datasets/issues/5416 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5416/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5416/comments | https://api.github.com/repos/huggingface/datasets/issues/5416/events | https://github.com/huggingface/datasets/pull/5416 | 1,526,988,113 | PR_kwDODunzps5HDLmR | 5,416 | Fix RuntimeError: Sharding is ambiguous for this dataset | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"By the way, do we know how many datasets are impacted by this issue?\r\n\r\nMaybe we should do a patch release with this fix.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated be... | 2023-01-10T08:43:19 | 2023-01-18T17:12:17 | 2023-01-18T14:09:02 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5416",
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} | This PR fixes the RuntimeError: Sharding is ambiguous for this dataset.
The error for ambiguous sharding will be raised only if num_proc > 1.
Fix #5415, fix #5414.
Fix https://huggingface.co/datasets/ami/discussions/3. | {
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https://api.github.com/repos/huggingface/datasets/issues/5415 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5415/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5415/comments | https://api.github.com/repos/huggingface/datasets/issues/5415/events | https://github.com/huggingface/datasets/issues/5415 | 1,526,904,861 | I_kwDODunzps5bArgd | 5,415 | RuntimeError: Sharding is ambiguous for this dataset | {
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"followers_... | null | [] | 2023-01-10T07:36:11 | 2023-01-18T14:09:04 | 2023-01-18T14:09:03 | MEMBER | null | null | null | ### Describe the bug
When loading some datasets, a RuntimeError is raised.
For example, for "ami" dataset: https://huggingface.co/datasets/ami/discussions/3
```
.../huggingface/datasets/src/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)
1415 fpath = path_join(self._output_dir, fname)
1416
-> 1417 num_input_shards = _number_of_shards_in_gen_kwargs(split_generator.gen_kwargs)
1418 if num_input_shards <= 1 and num_proc is not None:
1419 logger.warning(
.../huggingface/datasets/src/datasets/utils/sharding.py in _number_of_shards_in_gen_kwargs(gen_kwargs)
10 lists_lengths = {key: len(value) for key, value in gen_kwargs.items() if isinstance(value, list)}
11 if len(set(lists_lengths.values())) > 1:
---> 12 raise RuntimeError(
13 (
14 "Sharding is ambiguous for this dataset: "
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key samples_paths has length 6
- key ids has length 7
- key verification_ids has length 6
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.
```
This behavior was introduced when implementing multiprocessing by PR:
- #5107
### Steps to reproduce the bug
```python
ds = load_dataset("ami", "microphone-single", split="train", revision="2d7620bb7c3f1aab9f329615c3bdb598069d907a")
```
### Expected behavior
No error raised.
### Environment info
Since datasets 2.7.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5414/comments | https://api.github.com/repos/huggingface/datasets/issues/5414/events | https://github.com/huggingface/datasets/issues/5414 | 1,525,733,818 | I_kwDODunzps5a8Nm6 | 5,414 | Sharding error with Multilingual LibriSpeech | {
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"Thanks for reporting, @Nithin-Holla.\r\n\r\nThis is a known issue for multiple datasets and we are investigating it:\r\n- See e.g.: https://huggingface.co/datasets/ami/discussions/3",
"Main issue:\r\n- #5415",
"@albertvillanova Thanks! As a workaround for now, can I use the dataset in streaming mode?",
"Yes,... | 2023-01-09T14:45:31 | 2023-01-18T14:09:04 | 2023-01-18T14:09:04 | NONE | null | null | null | ### Describe the bug
Loading the German Multilingual LibriSpeech dataset results in a RuntimeError regarding sharding with the following stacktrace:
```
Downloading and preparing dataset multilingual_librispeech/german to /home/nithin/datadrive/cache/huggingface/datasets/facebook___multilingual_librispeech/german/2.1.0/1904af50f57a5c370c9364cc337699cfe496d4e9edcae6648a96be23086362d0...
Downloading data files: 100%
3/3 [00:00<00:00, 107.23it/s]
Downloading data files: 100%
1/1 [00:00<00:00, 35.08it/s]
Downloading data files: 100%
6/6 [00:00<00:00, 303.36it/s]
Downloading data files: 100%
3/3 [00:00<00:00, 130.37it/s]
Downloading data files: 100%
1049/1049 [00:00<00:00, 4491.40it/s]
Downloading data files: 100%
37/37 [00:00<00:00, 1096.78it/s]
Downloading data files: 100%
40/40 [00:00<00:00, 1003.93it/s]
Extracting data files: 100%
3/3 [00:11<00:00, 2.62s/it]
Generating train split:
469942/0 [34:13<00:00, 273.21 examples/s]
Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-14-74fa6d092bdc> in <module>
----> 1 mls = load_dataset(MLS_DATASET,
2 LANGUAGE,
3 cache_dir="~/datadrive/cache/huggingface/datasets",
4 ignore_verifications=True)
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)
1755
1756 # Download and prepare data
-> 1757 builder_instance.download_and_prepare(
1758 download_config=download_config,
1759 download_mode=download_mode,
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
858 if num_proc is not None:
859 prepare_split_kwargs["num_proc"] = num_proc
--> 860 self._download_and_prepare(
861 dl_manager=dl_manager,
862 verify_infos=verify_infos,
/anaconda/envs/py38_default/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs)
1609
1610 def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs):
...
RuntimeError: Sharding is ambiguous for this dataset: we found several data sources lists of different lengths, and we don't know over which list we should parallelize:
- key audio_archives has length 1049
- key local_extracted_archive has length 1049
- key limited_ids_paths has length 1
To fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.
```
### Steps to reproduce the bug
Here is the code to reproduce it:
```python
from datasets import load_dataset
MLS_DATASET = "facebook/multilingual_librispeech"
LANGUAGE = "german"
mls = load_dataset(MLS_DATASET,
LANGUAGE,
cache_dir="~/datadrive/cache/huggingface/datasets",
ignore_verifications=True)
```
### Expected behavior
The expected behaviour is that the dataset is successfully loaded.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-1094-azure-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 10.0.1
- Pandas version: 1.2.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/5413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5413/comments | https://api.github.com/repos/huggingface/datasets/issues/5413/events | https://github.com/huggingface/datasets/issues/5413 | 1,524,591,837 | I_kwDODunzps5a32zd | 5,413 | concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers | {
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"Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\... | 2023-01-08T17:01:52 | 2023-01-26T09:27:21 | 2023-01-26T09:27:21 | NONE | null | null | null | ### Describe the bug
When using `concatenate_datasets([dataset1, dataset2], axis = 1)` to concatenate two datasets with shards > 1, it fails:
```
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/combine.py", line 182, in concatenate_datasets
return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5499, in _concatenate_map_style_datasets
table = concat_tables([dset._data for dset in dsets], axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1778, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1483, in from_tables
blocks = _extend_blocks(blocks, table_blocks, axis=axis)
File "/home/xzg/anaconda3/envs/tri-transfer/lib/python3.9/site-packages/datasets/table.py", line 1477, in _extend_blocks
result[i].extend(row_blocks)
IndexError: list index out of range
```
### Steps to reproduce the bug
dataset = concatenate_datasets([dataset1, dataset2], axis = 1)
### Expected behavior
The datasets are correctly concatenated.
### Environment info
datasets==2.8.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5412/comments | https://api.github.com/repos/huggingface/datasets/issues/5412/events | https://github.com/huggingface/datasets/issues/5412 | 1,524,250,269 | I_kwDODunzps5a2jad | 5,412 | load_dataset() cannot find dataset_info.json with multiple training runs in parallel | {
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"Hi ! It fails because the dataset is already being prepared by your first run. I'd encourage you to prepare your dataset before using it for multiple trainings.\r\n\r\nYou can also specify another cache directory by passing `cache_dir=` to `load_dataset()`.",
"Thank you! What do you mean by prepare it beforehand... | 2023-01-08T00:44:32 | 2023-01-19T20:28:43 | 2023-01-19T20:28:43 | NONE | null | null | null | ### Describe the bug
I have a custom local dataset in JSON form. I am trying to do multiple training runs in parallel. The first training run runs with no issue. However, when I start another run on another GPU, the following code throws this error.
If there is a workaround to ignore the cache I think that would solve my problem too.
I am using datasets version 2.8.0.
### Steps to reproduce the bug
1. Start training run of GPU 0 loading dataset from
```
load_dataset(
"json",
data_files=tr_dataset_path,
split=f"train",
download_mode="force_redownload",
)
```
2. While GPU 0 is training, start an identical run on GPU 1. GPU 1 will produce the following error:
```
Traceback (most recent call last):
File "/local-scratch1/data/mt/code/qq/train.py", line 198, in <module>
main()
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1055, in main
rv = self.invoke(ctx)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/username/.local/lib/python3.8/site-packages/click/core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "/local-scratch1/data/mt/code/qq/train.py", line 113, in main
load_dataset(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1734, in load_dataset
builder_instance = load_dataset_builder(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/load.py", line 1518, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/builder.py", line 366, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/datasets/info.py", line 313, in from_directory
with fs.open(path_join(dataset_info_dir, config.DATASET_INFO_FILENAME), "r", encoding="utf-8") as f:
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1094, in open
self.open(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/spec.py", line 1106, in open
f = self._open(
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 175, in _open
return LocalFileOpener(path, mode, fs=self, **kwargs)
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 273, in __init__
self._open()
File "/home/username/miniconda3/envs/qq3/lib/python3.8/site-packages/fsspec/implementations/local.py", line 278, in _open
self.f = open(self.path, mode=self.mode)
FileNotFoundError: [Errno 2] No such file or directory: '/home/username/.cache/huggingface/datasets/json/default-43d06a4aedb25e6d/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/dataset_info.json'
```
### Expected behavior
Expected behavior: 2nd GPU training run should run the same as 1st GPU training run.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.8.0
- Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.10
- Python version: 3.8.15
- PyArrow version: 9.0.0
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5411 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5411/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5411/comments | https://api.github.com/repos/huggingface/datasets/issues/5411/events | https://github.com/huggingface/datasets/pull/5411 | 1,523,297,786 | PR_kwDODunzps5G23-T | 5,411 | Update docs of S3 filesystem with async aiobotocore | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-06T23:19:17 | 2023-01-18T11:18:59 | 2023-01-18T11:12:04 | CONTRIBUTOR | null | false | {
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} | [s3fs has migrated to all async calls](https://github.com/fsspec/s3fs/commit/0de2c6fb3d87c08ea694de96dca0d0834034f8bf).
Updating documentation to use `AioSession` while using s3fs for download manager as well as working with datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/5410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5410/comments | https://api.github.com/repos/huggingface/datasets/issues/5410/events | https://github.com/huggingface/datasets/pull/5410 | 1,521,168,032 | PR_kwDODunzps5GvnJH | 5,410 | Map-style Dataset to IterableDataset | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-01-05T18:12:17 | 2023-02-01T18:11:45 | 2023-02-01T16:36:01 | MEMBER | null | false | {
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} | Added `ds.to_iterable()` to get an iterable dataset from a map-style arrow dataset.
It also has a `num_shards` argument to split the dataset before converting to an iterable dataset. Sharding is important to enable efficient shuffling and parallel loading of iterable datasets.
TODO:
- [x] tests
- [x] docs
Fix https://github.com/huggingface/datasets/issues/5265 | {
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https://api.github.com/repos/huggingface/datasets/issues/5409 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5409/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5409/comments | https://api.github.com/repos/huggingface/datasets/issues/5409/events | https://github.com/huggingface/datasets/pull/5409 | 1,520,374,219 | PR_kwDODunzps5Gs3nL | 5,409 | Fix deprecation warning when use_auth_token passed to download_and_prepare | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-05T09:10:58 | 2023-01-06T11:06:16 | 2023-01-06T10:59:13 | MEMBER | null | false | {
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} | The `DatasetBuilder.download_and_prepare` argument `use_auth_token` was deprecated in:
- #5302
However, `use_auth_token` is still passed to `download_and_prepare` in our built-in `io` readers (csv, json, parquet,...).
This PR fixes it, so that no deprecation warning is raised.
Fix #5407. | {
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https://api.github.com/repos/huggingface/datasets/issues/5408 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5408/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5408/comments | https://api.github.com/repos/huggingface/datasets/issues/5408/events | https://github.com/huggingface/datasets/issues/5408 | 1,519,890,752 | I_kwDODunzps5al7FA | 5,408 | dataset map function could not be hash properly | {
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"Hi ! On macos I tried with\r\n- py 3.9.11\r\n- datasets 2.8.0\r\n- transformers 4.25.1\r\n- dill 0.3.4\r\n\r\nand I was able to hash `prepare_dataset` correctly:\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nHasher.hash(prepare_dataset)\r\n```\r\n\r\nWhat version of transformers do you have ? Can you ... | 2023-01-05T01:59:59 | 2023-01-06T13:22:19 | 2023-01-06T13:22:18 | NONE | null | null | null | ### Describe the bug
I follow the [blog post](https://huggingface.co/blog/fine-tune-whisper#building-a-demo) to finetune a Cantonese transcribe model.
When using map function to prepare dataset, following warning pop out:
`common_voice = common_voice.map(prepare_dataset,
remove_columns=common_voice.column_names["train"], num_proc=1)`
> Parameter 'function'=<function prepare_dataset at 0x000001D1D9D79A60> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
I read https://github.com/huggingface/datasets/issues/4521 and https://github.com/huggingface/datasets/issues/3178 but cannot solve the issue.
### Steps to reproduce the bug
```python
from datasets import load_dataset, DatasetDict
common_voice = DatasetDict()
common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK",
split="train+validation")
common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "zh-HK",
split="test")
common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])
from transformers import WhisperFeatureExtractor, WhisperTokenizer, WhisperProcessor
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")
processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="chinese", task="transcribe")
from datasets import Audio
common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))
def prepare_dataset(batch):
# load and resample audio data from 48 to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = feature_extractor(audio["array"],
sampling_rate=audio["sampling_rate"]).input_features[0]
# encode target text to label ids
batch["labels"] = tokenizer(batch["sentence"]).input_ids
return batch
common_voice = common_voice.map(prepare_dataset,
remove_columns=common_voice.column_names["train"], num_proc=1)
```
### Expected behavior
Should be no warning shown.
### Environment info
- `datasets` version: 2.7.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5
- dill version: 0.3.4
- multiprocess version: 0.70.12.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5407 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5407/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5407/comments | https://api.github.com/repos/huggingface/datasets/issues/5407/events | https://github.com/huggingface/datasets/issues/5407 | 1,519,797,345 | I_kwDODunzps5alkRh | 5,407 | Datasets.from_sql() generates deprecation warning | {
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"Thanks for reporting @msummerfield. We are fixing it."
] | 2023-01-05T00:43:17 | 2023-01-06T10:59:14 | 2023-01-06T10:59:14 | NONE | null | null | null | ### Describe the bug
Calling `Datasets.from_sql()` generates a warning:
`.../site-packages/datasets/builder.py:712: FutureWarning: 'use_auth_token' was deprecated in version 2.7.1 and will be removed in 3.0.0. Pass 'use_auth_token' to the initializer/'load_dataset_builder' instead.`
### Steps to reproduce the bug
Any valid call to `Datasets.from_sql()` will produce the deprecation warning.
### Expected behavior
No warning.
The fix should be simply to remove the parameter `use_auth_token` from the call to `builder.download_and_prepare()` at line 43 of `io/sql.py` (it is set to `None` anyway, and is not needed).
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-4.15.0-169-generic-x86_64-with-glibc2.27
- Python version: 3.9.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
| {
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https://api.github.com/repos/huggingface/datasets/issues/5406 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5406/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5406/comments | https://api.github.com/repos/huggingface/datasets/issues/5406/events | https://github.com/huggingface/datasets/issues/5406 | 1,519,140,544 | I_kwDODunzps5ajD7A | 5,406 | [2.6.1][2.7.0] Upgrade `datasets` to fix `TypeError: can only concatenate str (not "int") to str` | {
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"I still get this error on 2.9.0\r\n<img width=\"1925\" alt=\"image\" src=\"https://user-images.githubusercontent.com/7208470/215597359-2f253c76-c472-4612-8099-d3a74d16eb29.png\">\r\n",
"Hi ! I just tested locally and or colab and it works fine for 2.9 on `sst2`.\r\n\r\nAlso the code that is shown in your stack t... | 2023-01-04T15:10:04 | 2023-06-21T18:45:38 | null | MEMBER | null | null | null | `datasets` 2.6.1 and 2.7.0 started to stop supporting datasets like IMDB, ConLL or MNIST datasets.
When loading a dataset using 2.6.1 or 2.7.0, you may this error when loading certain datasets:
```python
TypeError: can only concatenate str (not "int") to str
```
This is because we started to update the metadata of those datasets to a format that is not supported in 2.6.1 and 2.7.0
This change is required or those datasets won't be supported by the Hugging Face Hub.
Therefore if you encounter this error or if you're using `datasets` 2.6.1 or 2.7.0, we encourage you to update to a newer version.
For example, versions 2.6.2 and 2.7.1 patch this issue.
```python
pip install -U datasets
```
All the datasets affected are the ones with a ClassLabel feature type and YAML "dataset_info" metadata. More info [here](https://github.com/huggingface/datasets/issues/5275).
We apologize for the inconvenience. | {
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https://api.github.com/repos/huggingface/datasets/issues/5405 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5405/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5405/comments | https://api.github.com/repos/huggingface/datasets/issues/5405/events | https://github.com/huggingface/datasets/issues/5405 | 1,517,879,386 | I_kwDODunzps5aeQBa | 5,405 | size_in_bytes the same for all splits | {
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"Hi @Breakend,\r\n\r\nIndeed, the attribute `size_in_bytes` refers to the size of the entire dataset configuration, for all splits (size of downloaded files + Arrow files), not the specific split.\r\nThis is also the case for `download_size` (downloaded files) and `dataset_size` (Arrow files).\r\n\r\nThe size of th... | 2023-01-03T20:25:48 | 2023-01-04T09:22:59 | null | NONE | null | null | null | ### Describe the bug
Hi, it looks like whenever you pull a dataset and get size_in_bytes, it returns the same size for all splits (and that size is the combined size of all splits). It seems like this shouldn't be the intended behavior since it is misleading. Here's an example:
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
Found cached dataset glue (/Users/breakend/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 3/3 [00:00<00:00, 1097.70it/s]
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
>>>
```
### Steps to reproduce the bug
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
```
### Expected behavior
The expected behavior is that it should return the separate sizes for all splits.
### Environment info
- `datasets` version: 2.7.1
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5404 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5404/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5404/comments | https://api.github.com/repos/huggingface/datasets/issues/5404/events | https://github.com/huggingface/datasets/issues/5404 | 1,517,566,331 | I_kwDODunzps5adDl7 | 5,404 | Better integration of BIG-bench | {
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"Hi, I made my version : https://huggingface.co/datasets/tasksource/bigbench"
] | 2023-01-03T15:37:57 | 2023-02-09T20:30:26 | null | MEMBER | null | null | null | ### Feature request
Ideally, it would be nice to have a maintained PyPI package for `bigbench`.
### Motivation
We'd like to allow anyone to access, explore and use any task.
### Your contribution
@lhoestq has opened an issue in their repo:
- https://github.com/google/BIG-bench/issues/906 | {
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https://api.github.com/repos/huggingface/datasets/issues/5403 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5403/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5403/comments | https://api.github.com/repos/huggingface/datasets/issues/5403/events | https://github.com/huggingface/datasets/pull/5403 | 1,517,466,492 | PR_kwDODunzps5Gi3d9 | 5,403 | Replace one letter import in docs | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Thanks for the docs fix for consistency.\r\n> \r\n> Again for consistency, it would be nice to make the same fix across all the docs, e.g.\r\n> \r\n> https://github.com/huggingface/datasets/blob/310cdddd1c43f9658de172b85b6509d07d5e... | 2023-01-03T14:26:32 | 2023-01-03T15:06:18 | 2023-01-03T14:59:01 | CONTRIBUTOR | null | false | {
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"merged_at": "2023-01-03T14:59:01"
} | This PR updates a code example for consistency across the docs based on [feedback from this comment](https://github.com/huggingface/transformers/pull/20925/files/9fda31634d203a47d3212e4e8d43d3267faf9808#r1058769500):
"In terms of style we usually stay away from one-letter imports like this (even if the community uses them) as they are not always known by beginners and one letter is very undescriptive. Here it wouldn't change anything to use albumentations instead of A."
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https://api.github.com/repos/huggingface/datasets/issues/5402 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5402/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5402/comments | https://api.github.com/repos/huggingface/datasets/issues/5402/events | https://github.com/huggingface/datasets/issues/5402 | 1,517,409,429 | I_kwDODunzps5acdSV | 5,402 | Missing state.json when creating a cloud dataset using a dataset_builder | {
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"`load_from_disk` must be used on datasets saved using `save_to_disk`: they correspond to fully serialized datasets including their state.\r\n\r\nOn the other hand, `download_and_prepare` just downloads the raw data and convert them to arrow (or parquet if you want). We are working on allowing you to reload a datas... | 2023-01-03T13:39:59 | 2023-01-04T17:23:57 | null | NONE | null | null | null | ### Describe the bug
Using `load_dataset_builder` to create a builder, run `download_and_prepare` do upload it to S3. However when trying to load it, there are missing `state.json` files. Complete example:
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_datase, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
builder = load_dataset_builder("imdb")
builder.download_and_prepare(output_dir, storage_options=storage_options)
load_from_disk(output_dir, fs=fs) # ERROR
# [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json'
```
As a comparison, if you use the non lazy `load_dataset`, it works and the S3 folder has different structure + state.json files. Example:
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_dataset, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
dataset = load_dataset("imdb",)
dataset.save_to_disk(output_dir, fs=fs)
load_from_disk(output_dir, fs=fs) # WORKS
```
You still want the 1st option for the laziness and the parquet conversion. Thanks!
### Steps to reproduce the bug
```python
from aiobotocore.session import AioSession as Session
from datasets import load_from_disk, load_datase, load_dataset_builder
import s3fs
storage_options = {"session": Session()}
fs = s3fs.S3FileSystem(**storage_options)
output_dir = "s3://bucket/imdb"
builder = load_dataset_builder("imdb")
builder.download_and_prepare(output_dir, storage_options=storage_options)
load_from_disk(output_dir, fs=fs) # ERROR
# [Errno 2] No such file or directory: '/tmp/tmpy22yys8o/bucket/imdb/state.json'
```
BTW, you need the AioSession as s3fs is now based on aiobotocore, see https://github.com/fsspec/s3fs/issues/385.
### Expected behavior
Expected to be able to load the dataset from S3.
### Environment info
```
s3fs 2022.11.0
s3transfer 0.6.0
datasets 2.8.0
aiobotocore 2.4.2
boto3 1.24.59
botocore 1.27.59
```
python 3.7.15. | {
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https://api.github.com/repos/huggingface/datasets/issues/5401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5401/comments | https://api.github.com/repos/huggingface/datasets/issues/5401/events | https://github.com/huggingface/datasets/pull/5401 | 1,517,160,935 | PR_kwDODunzps5Gh1XQ | 5,401 | Support Dataset conversion from/to Spark | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5401). All of your documentation changes will be reflected on that endpoint.",
"Cool thanks !\r\n\r\nSpark DataFrame are usually quite big, and I believe here `from_spark` would load everything in the driver node's RAM, which i... | 2023-01-03T09:57:40 | 2023-01-05T14:21:33 | null | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5400 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5400/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5400/comments | https://api.github.com/repos/huggingface/datasets/issues/5400/events | https://github.com/huggingface/datasets/pull/5400 | 1,517,032,972 | PR_kwDODunzps5GhaGI | 5,400 | Support streaming datasets with os.path.exists and Path.exists | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-01-03T07:42:37 | 2023-01-06T10:42:44 | 2023-01-06T10:35:44 | MEMBER | null | false | {
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} | Support streaming datasets with `os.path.exists` and `pathlib.Path.exists`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5399 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5399/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5399/comments | https://api.github.com/repos/huggingface/datasets/issues/5399/events | https://github.com/huggingface/datasets/issues/5399 | 1,515,548,427 | I_kwDODunzps5aVW8L | 5,399 | Got disconnected from remote data host. Retrying in 5sec [2/20] | {
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} | [] | closed | false | null | [] | null | [] | 2023-01-01T13:00:11 | 2023-01-02T07:21:52 | 2023-01-02T07:21:52 | NONE | null | null | null | ### Describe the bug
While trying to upload my image dataset of a CSV file type to huggingface by running the below code. The dataset consists of a little over 100k of image-caption pairs
### Steps to reproduce the bug
```
df = pd.read_csv('x.csv', encoding='utf-8-sig')
features = Features({
'link': Image(decode=True),
'caption': Value(dtype='string'),
})
#make sure u r logged in to HF
ds = Dataset.from_pandas(df, features=features)
ds.features
ds.push_to_hub("x/x")
```
I got the below error and It always stops at the same progress
```
100%|โโโโโโโโโโ| 4/4 [23:53<00:00, 358.48s/ba]
100%|โโโโโโโโโโ| 4/4 [24:37<00:00, 369.47s/ba]%|โ | 1/22 [00:06<02:09, 6.16s/it]
100%|โโโโโโโโโโ| 4/4 [25:00<00:00, 375.15s/ba]%|โ | 2/22 [25:54<2:36:15, 468.80s/it]
100%|โโโโโโโโโโ| 4/4 [24:53<00:00, 373.29s/ba]%|โโ | 3/22 [51:01<4:07:07, 780.39s/it]
100%|โโโโโโโโโโ| 4/4 [24:01<00:00, 360.34s/ba]%|โโ | 4/22 [1:17:00<5:04:07, 1013.74s/it]
100%|โโโโโโโโโโ| 4/4 [23:59<00:00, 359.91s/ba]%|โโโ | 5/22 [1:41:07<5:24:06, 1143.90s/it]
100%|โโโโโโโโโโ| 4/4 [24:16<00:00, 364.06s/ba]%|โโโ | 6/22 [2:05:14<5:29:15, 1234.74s/it]
100%|โโโโโโโโโโ| 4/4 [25:24<00:00, 381.10s/ba]%|โโโโ | 7/22 [2:29:38<5:25:52, 1303.52s/it]
100%|โโโโโโโโโโ| 4/4 [25:24<00:00, 381.24s/ba]%|โโโโ | 8/22 [2:56:02<5:23:46, 1387.58s/it]
100%|โโโโโโโโโโ| 4/4 [25:08<00:00, 377.23s/ba]%|โโโโ | 9/22 [3:22:24<5:13:17, 1445.97s/it]
100%|โโโโโโโโโโ| 4/4 [24:11<00:00, 362.87s/ba]%|โโโโโ | 10/22 [3:48:24<4:56:02, 1480.19s/it]
100%|โโโโโโโโโโ| 4/4 [24:44<00:00, 371.11s/ba]%|โโโโโ | 11/22 [4:12:42<4:30:10, 1473.66s/it]
100%|โโโโโโโโโโ| 4/4 [24:35<00:00, 368.81s/ba]%|โโโโโโ | 12/22 [4:37:34<4:06:29, 1478.98s/it]
100%|โโโโโโโโโโ| 4/4 [24:02<00:00, 360.67s/ba]%|โโโโโโ | 13/22 [5:03:24<3:45:04, 1500.45s/it]
100%|โโโโโโโโโโ| 4/4 [24:07<00:00, 361.78s/ba]%|โโโโโโโ | 14/22 [5:27:33<3:17:59, 1484.97s/it]
100%|โโโโโโโโโโ| 4/4 [23:39<00:00, 354.85s/ba]%|โโโโโโโ | 15/22 [5:51:48<2:52:10, 1475.82s/it]
Pushing dataset shards to the dataset hub: 73%|โโโโโโโโ | 16/22 [6:16:58<2:28:37, 1486.31s/it]Got disconnected from remote data host. Retrying in 5sec [1/20]
Got disconnected from remote data host. Retrying in 5sec [2/20]
Got disconnected from remote data host. Retrying in 5sec [3/20]
Got disconnected from remote data host. Retrying in 5sec [4/20]
Got disconnected from remote data host. Retrying in 5sec [5/20]
Got disconnected from remote data host. Retrying in 5sec [6/20]
Got disconnected from remote data host. Retrying in 5sec [7/20]
Got disconnected from remote data host. Retrying in 5sec [8/20]
Got disconnected from remote data host. Retrying in 5sec [9/20]
...
Got disconnected from remote data host. Retrying in 5sec [19/20]
Got disconnected from remote data host. Retrying in 5sec [20/20]
75%|โโโโโโโโ | 3/4 [24:47<08:15, 495.86s/ba]
Pushing dataset shards to the dataset hub: 73%|โโโโโโโโ | 16/22 [6:41:46<2:30:39, 1506.65s/it]
Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-1-dbf8530779e9> in <module>
16 ds.features
```
### Expected behavior
I was trying to upload an image dataset and expected it to be fully uploaded
### Environment info
- `datasets` version: 2.8.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 10.0.1
- Pandas version: 1.3.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5398/comments | https://api.github.com/repos/huggingface/datasets/issues/5398/events | https://github.com/huggingface/datasets/issues/5398 | 1,514,425,231 | I_kwDODunzps5aREuP | 5,398 | Unpin pydantic | {
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} | [] | closed | false | null | [] | null | [] | 2022-12-30T10:37:31 | 2022-12-30T10:43:41 | 2022-12-30T10:43:41 | MEMBER | null | null | null | Once `pydantic` fixes their issue in their 1.10.3 version, unpin it.
See issue:
- #5394
See temporary fix:
- #5395 | {
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https://api.github.com/repos/huggingface/datasets/issues/5397 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5397/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5397/comments | https://api.github.com/repos/huggingface/datasets/issues/5397/events | https://github.com/huggingface/datasets/pull/5397 | 1,514,412,246 | PR_kwDODunzps5GYirs | 5,397 | Unpin pydantic test dependency | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2022-12-30T10:22:09 | 2022-12-30T10:53:11 | 2022-12-30T10:43:40 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5397",
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} | Once pydantic-1.10.3 has been yanked, we can unpin it: https://pypi.org/project/pydantic/1.10.3/
See reply by pydantic team https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367819807
```
v1.10.3 has been yanked.
```
in response to spacy request: https://github.com/pydantic/pydantic/issues/4885#issuecomment-1367810049
```
On behalf of spacy-related packages: would it be possible for you to temporarily yank v1.10.3?
To address this and be compatible with v1.10.4, we'd have to release new versions of a whole series of packages and nearly everyone (including me) is currently on vacation. Even if v1.10.4 is released with a fix, pip would still back off to v1.10.3 for spacy, etc. because of its current pins for typing_extensions. If it could instead back off to v1.10.2, we'd have a bit more breathing room to make the updates on our end.
```
Close #5398.
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https://api.github.com/repos/huggingface/datasets/issues/5396 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5396/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5396/comments | https://api.github.com/repos/huggingface/datasets/issues/5396/events | https://github.com/huggingface/datasets/pull/5396 | 1,514,002,934 | PR_kwDODunzps5GXMhp | 5,396 | Fix checksum verification | {
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"Hi ! If I'm not mistaken both `expected_checksums[url]` and `recorded_checksums[url]` are dictionaries with keys \"checksum\" and \"num_bytes\". So we need to check whether `expected_checksums[url] != recorded_checksums[url]` (or simply `expected_checksums[url][\"checksum\"] != recorded_checksums[url][\"checksum\"... | 2022-12-29T19:45:17 | 2023-02-13T11:11:22 | 2023-02-13T11:11:22 | CONTRIBUTOR | null | false | {
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} | Expected checksum was verified against checksum dict (not checksum). | {
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https://api.github.com/repos/huggingface/datasets/issues/5395 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5395/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5395/comments | https://api.github.com/repos/huggingface/datasets/issues/5395/events | https://github.com/huggingface/datasets/pull/5395 | 1,513,997,335 | PR_kwDODunzps5GXLUl | 5,395 | Temporarily pin pydantic test dependency | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2022-12-29T19:34:19 | 2022-12-30T06:36:57 | 2022-12-29T21:00:26 | MEMBER | null | false | {
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} | Temporarily pin `pydantic` until a permanent solution is found.
Fix #5394. | {
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https://api.github.com/repos/huggingface/datasets/issues/5394 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5394/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5394/comments | https://api.github.com/repos/huggingface/datasets/issues/5394/events | https://github.com/huggingface/datasets/issues/5394 | 1,513,976,229 | I_kwDODunzps5aPXGl | 5,394 | CI error: TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers' | {
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"I still getting the same error :\r\n\r\n`python -m spacy download fr_core_news_lg\r\n`.\r\n`import spacy`",
"@MFatnassi, this issue and the corresponding fix only affect our Continuous Integration testing environment.\r\n\r\nNote that `datasets` does not depend on `spacy`."
] | 2022-12-29T18:58:44 | 2022-12-30T10:40:51 | 2022-12-29T21:00:27 | MEMBER | null | null | null | ### Describe the bug
While installing the dependencies, the CI raises a TypeError:
```
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 183, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 142, in _get_module_details
return _get_module_details(pkg_main_name, error)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/runpy.py", line 109, in _get_module_details
__import__(pkg_name)
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/__init__.py", line 6, in <module>
from .errors import setup_default_warnings
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/errors.py", line 2, in <module>
from .compat import Literal
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/spacy/compat.py", line 3, in <module>
from thinc.util import copy_array
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/__init__.py", line 5, in <module>
from .config import registry
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/thinc/config.py", line 2, in <module>
import confection
File "/opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/confection/__init__.py", line 10, in <module>
from pydantic import BaseModel, create_model, ValidationError, Extra
File "pydantic/__init__.py", line 2, in init pydantic.__init__
File "pydantic/dataclasses.py", line 46, in init pydantic.dataclasses
# | None | Attribute is set to None. |
File "pydantic/main.py", line 121, in init pydantic.main
TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'
```
See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565
### Steps to reproduce the bug
```shell
pip install .[tests,metrics-tests]
python -m spacy download en_core_web_sm
```
### Expected behavior
No error.
### Environment info
See: https://github.com/huggingface/datasets/actions/runs/3793736481/jobs/6466356565 | {
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https://api.github.com/repos/huggingface/datasets/issues/5393 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5393/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5393/comments | https://api.github.com/repos/huggingface/datasets/issues/5393/events | https://github.com/huggingface/datasets/pull/5393 | 1,512,908,613 | PR_kwDODunzps5GTg0a | 5,393 | Finish deprecating the fs argument | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Thanks for the deprecation. Some minor suggested fixes below...\r\n> \r\n> Also note that the corresponding tests should be updated as well.\r\n\r\nThanks for the suggestions/typo fixes. I updated the failing test - passing locall... | 2022-12-28T15:33:17 | 2023-01-18T12:42:33 | 2023-01-18T12:35:32 | CONTRIBUTOR | null | false | {
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} | See #5385 for some discussion on this
The `fs=` arg was depcrecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in `2.8.0` (to be removed in `3.0.0`). There are a few other places where the `fs=` arg was still used (functions/methods in `datasets.info` and `datasets.load`). This PR adds a similar behavior, warnings and the `storage_options=` arg to these functions and methods.
One question: should the "deprecated" / "added" versions be `2.8.1` for the docs/warnings on these? Right now I'm going with "fs was deprecated in 2.8.0" but "storage_options= was added in 2.8.1" where appropriate.
@mariosasko | {
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https://api.github.com/repos/huggingface/datasets/issues/5392 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5392/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5392/comments | https://api.github.com/repos/huggingface/datasets/issues/5392/events | https://github.com/huggingface/datasets/pull/5392 | 1,512,712,529 | PR_kwDODunzps5GS2DF | 5,392 | Fix Colab notebook link | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2022-12-28T11:44:53 | 2023-01-03T15:36:14 | 2023-01-03T15:27:31 | MEMBER | null | false | {
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} | Fix notebook link to open in Colab. | {
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https://api.github.com/repos/huggingface/datasets/issues/5391 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5391/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5391/comments | https://api.github.com/repos/huggingface/datasets/issues/5391/events | https://github.com/huggingface/datasets/issues/5391 | 1,510,350,400 | I_kwDODunzps5aBh5A | 5,391 | Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it] | {
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"Hey @catswithbats! Super sorry for the late reply! This is happening because there is data with label length (504) that exceeds the model's max length (448). \r\n\r\nThere are two options here:\r\n1. Increase the model's `max_length` parameter: \r\n```python\r\nmodel.config.max_length = 512\r\n```\r\n2. Filter dat... | 2022-12-25T15:17:14 | 2023-06-25T22:27:56 | null | NONE | null | null | null | Done in a VM with a GPU (Ubuntu) following the [Whisper Event - PYTHON](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#python-script) instructions.
Attempted using [RuntimeError: he size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 - WEB](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010/10 ) - another person experiencing the same issue. But could not resolve the issue with the google/fleurs data. __Not clear what can be modified in the PY code to resolve the input data size mismatch, as the training data is already very small__.
Tried posting on Discord, @sanchit-gandhi and @vaibhavs10. Was hoping that the event is over and some input/help is now available. [Hugging Face - whisper-small-amet](https://huggingface.co/drmeeseeks/whisper-small-amet).
The paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. (Whisper small WER=120.2).
# ---> Initial Training Output
/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
[INFO|trainer.py:1641] 2022-12-18 05:23:28,799 >> ***** Running training *****
[INFO|trainer.py:1642] 2022-12-18 05:23:28,799 >> Num examples = 446
[INFO|trainer.py:1643] 2022-12-18 05:23:28,799 >> Num Epochs = 72
[INFO|trainer.py:1644] 2022-12-18 05:23:28,799 >> Instantaneous batch size per device = 16
[INFO|trainer.py:1645] 2022-12-18 05:23:28,799 >> Total train batch size (w. parallel, distributed & accumulation) = 32
[INFO|trainer.py:1646] 2022-12-18 05:23:28,799 >> Gradient Accumulation steps = 2
[INFO|trainer.py:1647] 2022-12-18 05:23:28,800 >> Total optimization steps = 1000
[INFO|trainer.py:1648] 2022-12-18 05:23:28,801 >> Number of trainable parameters = 241734912
# ---> Error
14% 9/65 [07:07<48:34, 52.04s/it][INFO|configuration_utils.py:523] 2022-12-18 05:03:07,941 >> Generate config GenerationConfig {
"begin_suppress_tokens": [
220,
50257
],
"bos_token_id": 50257,
"decoder_start_token_id": 50258,
"eos_token_id": 50257,
"max_length": 448,
"pad_token_id": 50257,
"transformers_version": "4.26.0.dev0",
"use_cache": false
}
Traceback (most recent call last):
File "run_speech_recognition_seq2seq_streaming.py", line 629, in <module>
main()
File "run_speech_recognition_seq2seq_streaming.py", line 578, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1534, in train
return inner_training_loop(
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1859, in _inner_training_loop
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2122, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 78, in evaluate
return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2818, in evaluate
output = eval_loop(
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 3000, in evaluation_loop
loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 213, in prediction_step
outputs = model(**inputs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1197, in forward
outputs = self.model(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1066, in forward
decoder_outputs = self.decoder(
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 873, in forward
hidden_states = inputs_embeds + positions
RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1
100% 1000/1000 [2:52:21<00:00, 10.34s/it]
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https://api.github.com/repos/huggingface/datasets/issues/5390 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5390/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5390/comments | https://api.github.com/repos/huggingface/datasets/issues/5390/events | https://github.com/huggingface/datasets/issues/5390 | 1,509,357,553 | I_kwDODunzps5Z9vfx | 5,390 | Error when pushing to the CI hub | {
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"Hmmm, git bisect tells me that the behavior is the same since https://github.com/huggingface/datasets/commit/67e65c90e9490810b89ee140da11fdd13c356c9c (3 Oct), i.e. https://github.com/huggingface/datasets/pull/4926",
"Maybe related to the discussions in https://github.com/huggingface/datasets/pull/5196",
"Maybe... | 2022-12-23T13:36:37 | 2022-12-23T20:29:02 | 2022-12-23T20:29:02 | CONTRIBUTOR | null | null | null | ### Describe the bug
Note that it's a special case where the Hub URL is "https://hub-ci.huggingface.co", which does not appear if we do the same on the Hub (https://huggingface.co).
The call to `dataset.push_to_hub(` fails:
```
Pushing dataset shards to the dataset hub: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 1/1 [00:01<00:00, 1.93s/it]
Traceback (most recent call last):
File "reproduce_hubci.py", line 16, in <module>
dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True)
File "/home/slesage/hf/datasets/src/datasets/arrow_dataset.py", line 5025, in push_to_hub
HfApi(endpoint=config.HF_ENDPOINT).upload_file(
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1346, in upload_file
raise err
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1337, in upload_file
r.raise_for_status()
File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_DATASETS_SERVER_USER__/bug-16718047265472/upload/main/README.md
```
### Steps to reproduce the bug
```python
# reproduce.py
from datasets import Dataset
import time
USER = "__DUMMY_DATASETS_SERVER_USER__"
USER_TOKEN = "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"
dataset = Dataset.from_dict({"a": [1, 2, 3]})
repo_id = f"{USER}/bug-{int(time.time() * 10e3)}"
dataset.push_to_hub(repo_id=repo_id, private=False, token=USER_TOKEN, embed_external_files=True)
```
```bash
$ HF_ENDPOINT="https://hub-ci.huggingface.co" python reproduce.py
```
### Expected behavior
No error and the dataset should be uploaded to the Hub with the README file (which generates the error).
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-1026-aws-x86_64-with-glibc2.35
- Python version: 3.9.15
- PyArrow version: 7.0.0
- Pandas version: 1.5.2
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2022-12-23T13:26:31 | 2023-01-25T19:00:43 | 2023-01-24T16:33:38 | CONTRIBUTOR | null | false | {
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"merged_at": "2023-01-24T16:33:38"
} | Fix https://github.com/huggingface/datasets/issues/5387, fix https://github.com/huggingface/datasets/issues/4566 | {
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https://api.github.com/repos/huggingface/datasets/issues/5388 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5388/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5388/comments | https://api.github.com/repos/huggingface/datasets/issues/5388/events | https://github.com/huggingface/datasets/issues/5388 | 1,509,042,348 | I_kwDODunzps5Z8iis | 5,388 | Getting Value Error while loading a dataset.. | {
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"Hi! I can't reproduce this error locally (Mac) or in Colab. What version of `datasets` are you using?",
"Hi [mariosasko](https://github.com/mariosasko), the datasets version is '2.8.0'.",
"@valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n... | 2022-12-23T08:16:43 | 2022-12-29T08:36:33 | 2022-12-27T17:59:09 | NONE | null | null | null | ### Describe the bug
I am trying to load a dataset using Hugging Face Datasets load_dataset method. I am getting the value error as show below. Can someone help with this? I am using Windows laptop and Google Colab notebook.
```
WARNING:datasets.builder:Using custom data configuration default-a1d9e8eaedd958cd
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-12-5b4fdcb8e6d5>](https://localhost:8080/#) in <module>
6 )
7
----> 8 next(iter(law_dataset_streamed))
17 frames
[/usr/local/lib/python3.8/dist-packages/fsspec/core.py](https://localhost:8080/#) in get_compression(urlpath, compression)
485 compression = infer_compression(urlpath)
486 if compression is not None and compression not in compr:
--> 487 raise ValueError("Compression type %s not supported" % compression)
488 return compression
489
ValueError: Compression type zstd not supported
```
### Steps to reproduce the bug
```
!pip install zstandard
from datasets import load_dataset
lds = load_dataset(
"json",
data_files="https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst",
split="train",
streaming=True,
)
```
### Expected behavior
I expect an iterable object as the output 'lds' to be created.
### Environment info
Windows laptop with Google Colab notebook | {
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"Hi! Our documentation builder does not support links to sections, hence the bug. This is the link it should point to https://huggingface.co/docs/datasets/v2.8.0/en/cache#improve-performance."
] | 2022-12-23T01:12:57 | 2023-01-24T16:33:40 | 2023-01-24T16:33:40 | NONE | null | null | null | ### Describe the bug
Trying to access https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/cache#improve-performance, the page is missing.
The link is in here : https://huggingface.co/docs/datasets/v2.8.0/en/package_reference/loading_methods#datasets.load_dataset.keep_in_memory
### Steps to reproduce the bug
Access the page and see it's missing.
### Expected behavior
Not missing page
### Environment info
Doesn't matter | {
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"Hi! \r\n\r\nThis behavior stems from the fact that we don't always embed image bytes in the underlying arrow table, which can lead to bad size estimation (we use the first 1000 table rows to [estimate](https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.... | 2022-12-22T21:50:58 | 2022-12-26T23:45:51 | 2022-12-26T23:45:51 | NONE | null | null | null | ### Describe the bug
`max_shard_size` parameter for `datasets.push_to_hub()` works unreliably with large files, generating shard files that are way past the specified limit.
In my private dataset, which contains unprocessed images of all sizes (up to `~100MB` per file), I've encountered cases where `max_shard_size='100MB'` results in shard files that are `>2GB` in size. Setting `max_shard_size` to another value, such as `1GB` or `500MB` does not fix this problem.
**The real problem is this:** When the shard file size grows too big, the entire dataset breaks because of #4721 and ultimately https://issues.apache.org/jira/browse/ARROW-5030. Since `max_shard_size` does not let one accurately control the size of the shard files, it becomes very easy to build a large dataset without any warnings that it will be broken -- even when you think you are mitigating this problem by setting `max_shard_size`.
```
File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/builder.py", line 1763, in _prepare_split_single
for _, table in generator:
File " /path/to/sd-test-suite-v1/venv/lib/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1323, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
```
### Steps to reproduce the bug
1. Clone [example repo](https://github.com/salieri/hf-dataset-shard-size-bug)
2. Follow steps in [README.md](https://github.com/salieri/hf-dataset-shard-size-bug/blob/main/README.md)
3. After uploading the dataset, you will see that the shard file size varies between `30MB` and `200MB` -- way beyond the `max_shard_size='75MB'` limit (example: `train-00003-of-00131...` is `155MB` in [here](https://huggingface.co/datasets/slri/shard-size-test/tree/main/data))
(Note that this example repo does not generate shard files that are so large that they would trigger #4721)
### Expected behavior
The shard file size should remain below or equal to `max_shard_size`.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.10.157-139.675.amzn2.aarch64-aarch64-with-glibc2.17
- Python version: 3.7.15
- PyArrow version: 10.0.1
- Pandas version: 1.3.5 | {
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"Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ",
"> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? Iโll look around for anywh... | 2022-12-22T21:00:45 | 2023-01-23T10:50:05 | 2023-01-23T10:50:04 | CONTRIBUTOR | null | null | null | ### Describe the bug
The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec:
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340
Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ?
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779
### Steps to reproduce the bug
n/a
### Expected behavior
n/a
### Environment info
n/a | {
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https://api.github.com/repos/huggingface/datasets/issues/5384 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5384/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5384/comments | https://api.github.com/repos/huggingface/datasets/issues/5384/events | https://github.com/huggingface/datasets/pull/5384 | 1,508,152,598 | PR_kwDODunzps5GDmR6 | 5,384 | Handle 0-dim tensors in `cast_to_python_objects` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2022-12-22T16:15:30 | 2023-01-13T16:10:15 | 2023-01-13T16:00:52 | CONTRIBUTOR | null | false | {
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} | Fix #5229 | {
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https://api.github.com/repos/huggingface/datasets/issues/5383 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5383/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5383/comments | https://api.github.com/repos/huggingface/datasets/issues/5383/events | https://github.com/huggingface/datasets/issues/5383 | 1,507,293,968 | I_kwDODunzps5Z13sQ | 5,383 | IterableDataset missing column_names, differs from Dataset interface | {
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"Another example is that `IterableDataset.map` does not have `fn_kwargs`, among other arguments. It makes it harder to convert code from Dataset to IterableDataset.",
"Hi! `fn_kwargs` was added to `IterableDataset.map` in `datasets 2.5.0`, so please update your installation (`pip install -U datasets`) to use it.\... | 2022-12-22T05:27:02 | 2023-03-13T19:03:33 | 2023-03-13T19:03:33 | NONE | null | null | null | ### Describe the bug
The documentation on [Stream](https://huggingface.co/docs/datasets/v1.18.2/stream.html) seems to imply that IterableDataset behaves just like a Dataset. However, examples like
```
dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, ...)
```
will not work because `.column_names` does not exist on IterableDataset. I cannot find any clear explanation on why this is not available, is it an oversight? We do have `iterable_ds.features` available.
### Steps to reproduce the bug
See above
### Expected behavior
Dataset and IterableDataset would be expected to have the same interface, with any differences noted in the documentation.
### Environment info
n/a | {
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https://api.github.com/repos/huggingface/datasets/issues/5382 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5382/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5382/comments | https://api.github.com/repos/huggingface/datasets/issues/5382/events | https://github.com/huggingface/datasets/pull/5382 | 1,504,788,691 | PR_kwDODunzps5F4Q0V | 5,382 | Raise from disconnect error in xopen | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Could you review this small PR @albertvillanova ? :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric... | 2022-12-20T15:52:44 | 2023-01-26T09:51:13 | 2023-01-26T09:42:45 | MEMBER | null | false | {
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} | this way we can know the cause of the disconnect
related to https://github.com/huggingface/datasets/issues/5374 | {
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https://api.github.com/repos/huggingface/datasets/issues/5381 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5381/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5381/comments | https://api.github.com/repos/huggingface/datasets/issues/5381/events | https://github.com/huggingface/datasets/issues/5381 | 1,504,498,387 | I_kwDODunzps5ZrNLT | 5,381 | Wrong URL for the_pile dataset | {
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"Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.\r\n\r\nSoon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020"
] | 2022-12-20T12:40:14 | 2023-02-15T16:24:57 | 2023-02-15T16:24:57 | NONE | null | null | null | ### Describe the bug
When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error.
### Steps to reproduce the bug
Steps to reproduce:
Run:
```
from datasets import load_dataset
dataset = load_dataset("the_pile")
```
I get the output:
"name": "FileNotFoundError",
"message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'GRIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG', 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF', 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']"
### Expected behavior
`the_pile` dataset should be dowloaded.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/5380 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5380/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5380/comments | https://api.github.com/repos/huggingface/datasets/issues/5380/events | https://github.com/huggingface/datasets/issues/5380 | 1,504,404,043 | I_kwDODunzps5Zq2JL | 5,380 | Improve dataset `.skip()` speed in streaming mode | {
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"Hi! I agree `skip` can be inefficient to use in the current state.\r\n\r\nTo make it fast, we could use \"statistics\" stored in Parquet metadata and read only the chunks needed to form a dataset. \r\n\r\nAnd thanks to the \"datasets-server\" project, which aims to store the Parquet versions of the Hub datasets (o... | 2022-12-20T11:25:23 | 2023-03-08T10:47:12 | null | CONTRIBUTOR | null | null | null | ### Feature request
Add extra information to the `dataset_infos.json` file to include the number of samples/examples in each shard, for example in a new field `num_examples` alongside `num_bytes`. The `.skip()` function could use this information to ignore the download of a shard when in streaming mode, which AFAICT it should speed up the skipping process.
### Motivation
When resuming from a checkpoint after a crashed run, using `dataset.skip()` is very convenient to recover the exact state of the data and to not train again over the same examples (assuming same seed, no shuffling). However, I have noticed that for audio datasets in streaming mode this is very costly in terms of time, as shards need to be downloaded every time before skipping the right number of examples.
### Your contribution
I took a look already at the code, but it seems a change like this is way deeper than I am able to manage, as it touches the library in several parts. I could give it a try but might need some guidance on the internals. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the changes, looks good to me!",
"@stevhliu I have pushed some quality improvements both in terms of code and content. Would you be able to re-review? ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0... | 2022-12-20T05:32:11 | 2023-01-13T12:30:31 | 2023-01-13T12:23:34 | MEMBER | null | false | {
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} | This PR adds a guide for prepping datasets for depth estimation.
PR to add documentation images is up here: https://huggingface.co/datasets/huggingface/documentation-images/discussions/22 | {
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https://api.github.com/repos/huggingface/datasets/issues/5378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5378/comments | https://api.github.com/repos/huggingface/datasets/issues/5378/events | https://github.com/huggingface/datasets/issues/5378 | 1,503,887,508 | I_kwDODunzps5Zo4CU | 5,378 | The dataset "the_pile", subset "enron_emails" , load_dataset() failure | {
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"Thanks for reporting @shaoyuta. We are investigating it.\r\n\r\nWe are transferring the issue to \"the_pile\" Community tab on the Hub: https://huggingface.co/datasets/the_pile/discussions/4"
] | 2022-12-20T02:19:13 | 2022-12-20T07:52:54 | 2022-12-20T07:52:54 | NONE | null | null | null | ### Describe the bug
When run
"datasets.load_dataset("the_pile","enron_emails")" failure
### Steps to reproduce the bug
Run below code in python cli:
>>> import datasets
>>> datasets.load_dataset("the_pile","enron_emails")
### Expected behavior
Load dataset "the_pile", "enron_emails" successfully.
### Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.7.1
- Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.4.3
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https://api.github.com/repos/huggingface/datasets/issues/5377 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5377/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5377/comments | https://api.github.com/repos/huggingface/datasets/issues/5377/events | https://github.com/huggingface/datasets/pull/5377 | 1,503,477,833 | PR_kwDODunzps5Fz5lw | 5,377 | Add a parallel implementation of to_tf_dataset() | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Failing because the test server uses Py3.7 but the `SharedMemory` features require Py3.8! I forgot we still support 3.7 for another couple of months. I'm not sure exactly how to proceed, whether I should leave this PR until then, or ... | 2022-12-19T19:40:27 | 2023-01-25T16:28:44 | 2023-01-25T16:21:40 | MEMBER | null | false | {
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} | Hey all! Here's a first draft of the PR to add a multiprocessing implementation for `to_tf_dataset()`. It worked in some quick testing for me, but obviously I need to do some much more rigorous testing/benchmarking, and add some proper library tests.
The core idea is that we do everything using `multiprocessing` and `numpy`, and just wrap a `tf.data.Dataset` around the output. We could also rewrite the existing single-threaded implementation based on this code, which might simplify it a bit.
Checklist:
- [X] Add initial draft
- [x] Check that it works regardless of whether the `collate_fn` or dataset returns `tf` or `np` arrays
- [x] Check that it works with `tf.string` return data
- [x] Check indices are correctly reshuffled each epoch
- [x] Make sure workers don't try to initialize a GPU device!!
- [x] Check `fit()` with multiple epochs works fine and that the progress bar is correct
- [x] Check there are no memory leaks or zombie processes
- [x] Benchmark performance
- [x] Tweak params for dataset inference - can we speed things up there a bit?
- [x] Add tests to the library
- [x] Add a PR to `transformers` to expose the `num_workers` argument via `prepare_tf_dataset` (will merge after this one is released)
- [x] Stop TF console spam!! (almost)
- [x] Add a method for creating SHM that doesn't crash if it was left and still linked
- [x] Add a barrier for Py <= 3.7 because it doesn't support SharedMemory
- [x] Support string dtypes by converting them into fixed-width character arrays | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5376). All of your documentation changes will be reflected on that endpoint."
] | 2022-12-19T10:56:56 | 2022-12-19T11:01:55 | 2022-12-19T10:57:16 | MEMBER | null | false | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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"The data files are hosted on HF at https://huggingface.co/datasets/allenai/c4/tree/main\r\n\r\nYou have 200 runs streaming the same files in parallel. So this is probably a Hub limitation. Maybe rate limiting ? cc @julien-c \r\n\r\nMaybe you can also try to reduce the number of HTTP requests by increasing the bloc... | 2022-12-18T11:38:58 | 2022-12-19T16:33:31 | null | CONTRIBUTOR | null | null | null | ### Describe the bug
`streaming_download_manager` seems to disconnect if too many runs access the same underlying dataset ๐ง
The code works fine for me if I have ~100 runs in parallel, but disconnects once scaling to 200.
Possibly related:
- https://github.com/huggingface/datasets/pull/3100
- https://github.com/huggingface/datasets/pull/3050
### Steps to reproduce the bug
Running
```python
c4 = datasets.load_dataset("c4", "en", split="train", streaming=True).skip(args.start).take(args.end-args.start)
df = pd.DataFrame(c4, index=None)
```
with different start & end arguments on 200 CPUs in parallel yields:
```
WARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4.
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [1/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [2/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [3/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [4/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [5/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [6/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [7/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [8/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [9/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [10/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [11/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [12/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [13/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [14/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [15/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [16/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [17/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [18/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [19/20]
WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [20/20]
โญโโโโโโโโโโโโโโโโโโโโโ Traceback (most recent call last) โโโโโโโโโโโโโโโโโโโโโโโฎ
โ /pfs/lustrep4/scratch/project_462000119/muennighoff/dec-2022-tasky/inference โ
โ _c4.py:68 in <module> โ
โ โ
โ 65 โ model.eval() โ
โ 66 โ โ
โ 67 โ c4 = datasets.load_dataset("c4", "en", split="train", streaming=Tru โ
โ โฑ 68 โ df = pd.DataFrame(c4, index=None) โ
โ 69 โ texts = df["text"].to_list() โ
โ 70 โ preds = batch_inference(texts, batch_size=args.batch_size) โ
โ 71 โ
โ โ
โ /opt/cray/pe/python/3.9.12.1/lib/python3.9/site-packages/pandas/core/frame.p โ
โ y:684 in __init__ โ
โ โ
โ 681 โ โ # For data is list-like, or Iterable (will consume into list โ
โ 682 โ โ elif is_list_like(data): โ
โ 683 โ โ โ if not isinstance(data, (abc.Sequence, ExtensionArray)): โ
โ โฑ 684 โ โ โ โ data = list(data) โ
โ 685 โ โ โ if len(data) > 0: โ
โ 686 โ โ โ โ if is_dataclass(data[0]): โ
โ 687 โ โ โ โ โ data = dataclasses_to_dicts(data) โ
โ โ
โ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ โ
โ lib/python3.9/site-packages/datasets/iterable_dataset.py:751 in __iter__ โ
โ โ
โ 748 โ โ yield from ex_iterable.shard_data_sources(shard_idx) โ
โ 749 โ โ
โ 750 โ def __iter__(self): โ
โ โฑ 751 โ โ for key, example in self._iter(): โ
โ 752 โ โ โ if self.features: โ
โ 753 โ โ โ โ # `IterableDataset` automatically fills missing colum โ
โ 754 โ โ โ โ # This is done with `_apply_feature_types`. โ
โ โ
โ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ โ
โ lib/python3.9/site-packages/datasets/iterable_dataset.py:741 in _iter โ
โ โ
โ 738 โ โ โ ex_iterable = self._ex_iterable.shuffle_data_sources(self โ
โ 739 โ โ else: โ
โ 740 โ โ โ ex_iterable = self._ex_iterable โ
โ โฑ 741 โ โ yield from ex_iterable โ
โ 742 โ โ
โ 743 โ def _iter_shard(self, shard_idx: int): โ
โ 744 โ โ if self._shuffling: โ
โ โ
โ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ โ
โ lib/python3.9/site-packages/datasets/iterable_dataset.py:617 in __iter__ โ
โ โ
โ 614 โ โ self.n = n โ
โ 615 โ โ
โ 616 โ def __iter__(self): โ
โ โฑ 617 โ โ yield from islice(self.ex_iterable, self.n) โ
โ 618 โ โ
โ 619 โ def shuffle_data_sources(self, generator: np.random.Generator) -> โ
โ 620 โ โ """Doesn't shuffle the wrapped examples iterable since it wou โ
โ โ
โ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ โ
โ lib/python3.9/site-packages/datasets/iterable_dataset.py:594 in __iter__ โ
โ โ
โ 591 โ โ
โ 592 โ def __iter__(self): โ
โ 593 โ โ #ex_iterator = iter(self.ex_iterable) โ
โ โฑ 594 โ โ yield from islice(self.ex_iterable, self.n, None) โ
โ 595 โ โ #for _ in range(self.n): โ
โ 596 โ โ # next(ex_iterator) โ
โ 597 โ โ #yield from islice(ex_iterator, self.n, None) โ
โ โ
โ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ โ
โ lib/python3.9/site-packages/datasets/iterable_dataset.py:106 in __iter__ โ
โ โ
โ 103 โ โ self.kwargs = kwargs โ
โ 104 โ โ
โ 105 โ def __iter__(self): โ
โ โฑ 106 โ โ yield from self.generate_examples_fn(**self.kwargs) โ
โ 107 โ โ
โ 108 โ def shuffle_data_sources(self, generator: np.random.Generator) -> โ
โ 109 โ โ return ShardShuffledExamplesIterable(self.generate_examples_f โ
โ โ
โ /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/d โ
โ f532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01/c4.py:89 in โ
โ _generate_examples โ
โ โ
โ 86 โ โ for filepath in filepaths: โ
โ 87 โ โ โ logger.info("generating examples from = %s", filepath) โ
โ 88 โ โ โ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8" โ
โ โฑ 89 โ โ โ โ for line in f: โ
โ 90 โ โ โ โ โ if line: โ
โ 91 โ โ โ โ โ โ example = json.loads(line) โ
โ 92 โ โ โ โ โ โ yield id_, example โ
โ โ
โ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:313 in read1 โ
โ โ
โ 310 โ โ โ
โ 311 โ โ if size < 0: โ
โ 312 โ โ โ size = io.DEFAULT_BUFFER_SIZE โ
โ โฑ 313 โ โ return self._buffer.read1(size) โ
โ 314 โ โ
โ 315 โ def peek(self, n): โ
โ 316 โ โ self._check_not_closed() โ
โ โ
โ /opt/cray/pe/python/3.9.12.1/lib/python3.9/_compression.py:68 in readinto โ
โ โ
โ 65 โ โ
โ 66 โ def readinto(self, b): โ
โ 67 โ โ with memoryview(b) as view, view.cast("B") as byte_view: โ
โ โฑ 68 โ โ โ data = self.read(len(byte_view)) โ
โ 69 โ โ โ byte_view[:len(data)] = data โ
โ 70 โ โ return len(data) โ
โ 71 โ
โ โ
โ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:493 in read โ
โ โ
โ 490 โ โ โ โ self._new_member = False โ
โ 491 โ โ โ โ
โ 492 โ โ โ # Read a chunk of data from the file โ
โ โฑ 493 โ โ โ buf = self._fp.read(io.DEFAULT_BUFFER_SIZE) โ
โ 494 โ โ โ โ
โ 495 โ โ โ uncompress = self._decompressor.decompress(buf, size) โ
โ 496 โ โ โ if self._decompressor.unconsumed_tail != b"": โ
โ โ
โ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:96 in read โ
โ โ
โ 93 โ โ โ read = self._read โ
โ 94 โ โ โ self._read = None โ
โ 95 โ โ โ return self._buffer[read:] + \ โ
โ โฑ 96 โ โ โ โ self.file.read(size-self._length+read) โ
โ 97 โ โ
โ 98 โ def prepend(self, prepend=b''): โ
โ 99 โ โ if self._read is None: โ
โ โ
โ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ โ
โ lib/python3.9/site-packages/datasets/download/streaming_download_manager.py: โ
โ 365 in read_with_retries โ
โ โ
โ 362 โ โ โ โ ) โ
โ 363 โ โ โ โ time.sleep(config.STREAMING_READ_RETRY_INTERVAL) โ
โ 364 โ โ else: โ
โ โฑ 365 โ โ โ raise ConnectionError("Server Disconnected") โ
โ 366 โ โ return out โ
โ 367 โ โ
โ 368 โ file_obj.read = read_with_retries โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
ConnectionError: Server Disconnected
```
### Expected behavior
There should be no disconnect I think.
### Environment info
```
datasets=2.7.0
Python 3.9.12
``` | {
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