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https://api.github.com/repos/huggingface/datasets/issues/4619 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4619/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4619/comments | https://api.github.com/repos/huggingface/datasets/issues/4619/events | https://github.com/huggingface/datasets/issues/4619 | 1,292,107,275 | I_kwDODunzps5NA_4L | 4,619 | np arrays get turned into native lists | {
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] | open | false | null | [] | null | [
"If you add the line `dataset2.set_format('np')` before calling `dataset2[0]['tmp']` it should return `np.ndarray`.\r\nI believe internally it will not store it as a list, it is only returning a list when you index it.\r\n\r\n```\r\nIn [1]: import datasets, numpy as np\r\nIn [2]: dataset = datasets.load_dataset(\"g... | 2022-07-02T17:54:57Z | 2022-07-03T20:27:07Z | null | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
When attaching an `np.array` field, it seems that it automatically gets turned into a list (see below). Why is this happening? Could it lose precision? Is there a way to make sure this doesn't happen?
## Steps to reproduce the bug
```python
>>> import datasets, numpy as np
>>> dataset = datasets.load_dataset("glue", "mrpc")["validation"]
Reusing dataset glue (...)
100%|███████████████████████████████████████████████| 3/3 [00:00<00:00, 1360.61it/s]
>>> dataset2 = dataset.map(lambda x: {"tmp": np.array([0.5])}, batched=False)
100%|██████████████████████████████████████████| 408/408 [00:00<00:00, 10819.97ex/s]
>>> dataset2[0]["tmp"]
[0.5]
>>> type(dataset2[0]["tmp"])
<class 'list'>
```
## Expected results
`dataset2[0]["tmp"]` should be an `np.ndarray`.
## Actual results
It's a list.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: mac, though I'm pretty sure it happens on a linux machine too
- Python version: 3.9.7
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/4903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4903/comments | https://api.github.com/repos/huggingface/datasets/issues/4903/events | https://github.com/huggingface/datasets/pull/4903 | 1,352,539,075 | PR_kwDODunzps494aud | 4,903 | Fix CI reporting | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-26T17:16:30Z | 2022-08-26T17:49:33Z | 2022-08-26T17:46:59Z | MEMBER | null | null | null | Fix CI so that it reports defaults (failed and error) besides the custom (xfailed and xpassed) in the test summary.
This PR fixes a regression introduced by:
- #4845
This introduced the reporting of xfailed and xpassed, but wrongly removed the reporting of the defaults failed and error. | {
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https://api.github.com/repos/huggingface/datasets/issues/4755 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4755/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4755/comments | https://api.github.com/repos/huggingface/datasets/issues/4755/events | https://github.com/huggingface/datasets/issues/4755 | 1,319,687,044 | I_kwDODunzps5OqNOE | 4,755 | Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size | {
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"I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:\r\n\r\n```\r\nimport datasets\r\nimport transformers\r\npretrained = 'deepset/tinyroberta-squad2'\r\ntokenizer = t... | 2022-07-27T14:54:11Z | 2023-12-13T19:34:43Z | null | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.
However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong. This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.
## Steps to reproduce the bug
1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field
## Expected results
`[0, 1, 2]`
## Actual results
`[0, 0, 0]`
Notes:
1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.
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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-06-20T14:28:35Z | 2023-06-21T13:39:50Z | 2023-06-21T13:32:22Z | COLLABORATOR | null | null | null | "Requested" in https://discuss.huggingface.co/t/utf-16-for-datasets/43828/3.
`pd.read_json` also has these parameters, so it makes sense to be consistent. | {
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https://api.github.com/repos/huggingface/datasets/issues/4698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4698/comments | https://api.github.com/repos/huggingface/datasets/issues/4698/events | https://github.com/huggingface/datasets/pull/4698 | 1,307,539,585 | PR_kwDODunzps47i9gN | 4,698 | Enable streaming dataset to use the "all" split | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4698). All of your documentation changes will be reflected on that endpoint.",
"@albertvillanova \r\nAdding the validation split causes these two `assert_called_once` assertions to fail with `AssertionError: Expected 'ArrowWrit... | 2022-07-18T07:47:39Z | 2023-01-19T10:11:38Z | null | CONTRIBUTOR | null | null | null | Fixes #4637 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4626 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4626/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4626/comments | https://api.github.com/repos/huggingface/datasets/issues/4626/events | https://github.com/huggingface/datasets/issues/4626 | 1,293,256,269 | I_kwDODunzps5NFYZN | 4,626 | Add non-commercial licensing info for datasets for which we removed tags | {
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"yep plus `license_details` also makes sense for this IMO"
] | 2022-07-04T14:32:43Z | 2022-07-08T14:27:29Z | null | MEMBER | null | null | {
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} | We removed several YAML tags saying that certain datasets can't be used for commercial purposes: https://github.com/huggingface/datasets/pull/4613#discussion_r911919753
Reason for this is that we only allow tags that are part of our [supported list of licenses](https://github.com/huggingface/datasets/blob/84fc3ad73c85de4eda5d152dfede7671491449cb/src/datasets/utils/resources/standard_licenses.tsv)
We should update the Licensing Information section of the concerned dataset cards, now that the non-commercial tag doesn't exist anymore for certain datasets | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7128 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7128/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7128/comments | https://api.github.com/repos/huggingface/datasets/issues/7128/events | https://github.com/huggingface/datasets/issues/7128 | 2,490,274,775 | I_kwDODunzps6UbpPX | 7,128 | Filter Large Dataset Entry by Entry | {
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"Hi ! you can do\r\n\r\n```python\r\nfiltered_dataset = dataset.filter(filter_function)\r\n```\r\n\r\non a subset:\r\n\r\n```python\r\nfiltered_subset = dataset.select(range(10_000)).filter(filter_function)\r\n```\r\n",
"Jumping on this as it seems relevant - when I use the `filter` method, it often results in an... | 2024-08-27T20:31:09Z | 2024-10-07T23:37:44Z | null | NONE | null | null | {
"completed": 0,
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} | ### Feature request
I am not sure if this is a new feature, but I wanted to post this problem here, and hear if others have ways of optimizing and speeding up this process.
Let's say I have a really large dataset that I cannot load into memory. At this point, I am only aware of `streaming=True` to load the dataset. Now, the dataset consists of many tables. Ideally, I would want to have some simple filtering criterion, such that I only see the "good" tables. Here is an example of what the code might look like:
```
dataset = load_dataset(
"really-large-dataset",
streaming=True
)
# And let's say we process the dataset bit by bit because we want intermediate results
dataset = islice(dataset, 10000)
# Define a function to filter the data
def filter_function(table):
if some_condition:
return True
else:
return False
# Use the filter function on your dataset
filtered_dataset = (ex for ex in dataset if filter_function(ex))
```
And then I work on the processed dataset, which would be magnitudes faster than working on the original. I would love to hear if the problem setup + solution makes sense to people, and if anyone has suggestions!
### Motivation
See description above
### Your contribution
Happy to make PR if this is a new feature | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6081 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6081/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6081/comments | https://api.github.com/repos/huggingface/datasets/issues/6081/events | https://github.com/huggingface/datasets/pull/6081 | 1,824,486,278 | PR_kwDODunzps5WjU0k | 6,081 | Deprecate `Dataset.export` | {
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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-07-27T14:22:18Z | 2023-07-28T11:09:54Z | 2023-07-28T11:01:04Z | COLLABORATOR | null | null | null | Deprecate `Dataset.export` that generates a TFRecord file from a dataset as this method is undocumented, and the usage seems low. Users should use [TFRecordWriter](https://www.tensorflow.org/api_docs/python/tf/io/TFRecordWriter#write) or the official [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) tutorial (on which this method is based) to write TFRecord files instead. | {
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https://api.github.com/repos/huggingface/datasets/issues/7022 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7022/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7022/comments | https://api.github.com/repos/huggingface/datasets/issues/7022/events | https://github.com/huggingface/datasets/issues/7022 | 2,388,064,650 | I_kwDODunzps6OVvmK | 7,022 | There is dead code after we require pyarrow >= 15.0.0 | {
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} | There are code lines specific for pyarrow versions < 15.0.0.
However, we require pyarrow >= 15.0.0 since the merge of PR:
- #6892
Those code lines are now dead code and should be removed. | {
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https://api.github.com/repos/huggingface/datasets/issues/6481 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6481/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6481/comments | https://api.github.com/repos/huggingface/datasets/issues/6481/events | https://github.com/huggingface/datasets/issues/6481 | 2,032,650,003 | I_kwDODunzps55J8cT | 6,481 | using torchrun, save_to_disk suddenly shows SIGTERM | {
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} | ### Describe the bug
When I run my code using the "torchrun" command, when the code reaches the "save_to_disk" part, suddenly I get the following warning and error messages:
Because the dataset is too large, the "save_to_disk" function splits it into 70 parts for saving. However, an error occurs suddenly when it reaches the 14th shard.
WARNING: torch.distributed.elastic.multiprocessing.api: Sending process 2224968 closing signal SIGTERM
ERROR: torch.distributed.elastic.multiprocessing.api: failed (exitcode: -7). traceback: Signal 7 (SIGBUS) received by PID 2224967.
### Steps to reproduce the bug
ds_shard = ds_shard.map(map_fn, *args, **kwargs)
ds_shard.save_to_disk(ds_shard_filepaths[rank])
Saving the dataset (14/70 shards): 20%|██ | 875350/4376702 [00:19<01:53, 30863.15 examples/s]
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 2224968 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -7) local_rank: 0 (pid: 2224967) of binary: /home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/python
Traceback (most recent call last):
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/bin/torchrun", line 8, in <module>
sys.exit(main())
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 794, in main
run(args)
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/bingxing2/home/scx6964/.conda/envs/ariya235/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
==========================================================
run.py FAILED
----------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
----------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-12-08_20:09:04
rank : 0 (local_rank: 0)
exitcode : -7 (pid: 2224967)
error_file: <N/A>
traceback : Signal 7 (SIGBUS) received by PID 2224967
### Expected behavior
I hope it can save successfully without any issues, but it seems there is a problem.
### Environment info
`datasets` version: 2.14.6
- Platform: Linux-4.19.90-24.4.v2101.ky10.aarch64-aarch64-with-glibc2.28
- Python version: 3.10.11
- Huggingface_hub version: 0.17.3
- PyArrow version: 14.0.0
- Pandas version: 2.1.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4881 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4881/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4881/comments | https://api.github.com/repos/huggingface/datasets/issues/4881/events | https://github.com/huggingface/datasets/issues/4881 | 1,348,495,777 | I_kwDODunzps5QYGmh | 4,881 | Language names and language codes: connecting to a big database (rather than slow enrichment of custom list) | {
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"Thanks for opening this discussion, @alexis-michaud.\r\n\r\nAs the language validation procedure is shared with other Hugging Face projects, I'm tagging them as well.\r\n\r\nCC: @huggingface/moon-landing ",
"on the Hub side, there is not fine grained validation we just check that `language:` contains an array of... | 2022-08-23T20:14:24Z | 2024-04-22T15:57:28Z | null | NONE | null | null | {
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} | **The problem:**
Language diversity is an important dimension of the diversity of datasets. To find one's way around datasets, being able to search by language name and by standardized codes appears crucial.
Currently the list of language codes is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json), right? At about 1,500 entries, it is roughly at 1/4th of the world's diversity of extant languages. (Probably less, as the list of 1,418 contains variants that are linguistically very close: 108 varieties of English, for instance.)
Looking forward to ever increasing coverage, how will the list of language names and language codes improve over time?
Enrichment of the custom list by HFT contributors (like [here](https://github.com/huggingface/datasets/pull/4880)) has several issues:
* progress is likely to be slow:
(input required from reviewers, etc.)
* the more contributors, the less consistency can be expected among contributions. No need to elaborate on how much confusion is likely to ensue as datasets accumulate.
* there is no information on which language relates with which: no encoding of the special closeness between the languages of the Northwestern Germanic branch (English+Dutch+German etc.), for instance. Information on phylogenetic closeness can be relevant to run experiments on transfer of technology from one language to its close relatives.
**A solution that seems desirable:**
Connecting to an established database that (i) aims at full coverage of the world's languages and (ii) has information on higher-level groupings, alternative names, etc.
It takes a lot of hard work to do such databases. Two important initiatives are [Ethnologue](https://www.ethnologue.com/) (ISO standard) and [Glottolog](https://glottolog.org/). Both have pros and cons. Glottolog contains references to Ethnologue identifiers, so adopting Glottolog entails getting the advantages of both sets of language codes.
Both seem technically accessible & 'developer-friendly'. Glottolog has a [GitHub repo](https://github.com/glottolog/glottolog). For Ethnologue, harvesting tools have been devised (see [here](https://github.com/lyy1994/ethnologue); I did not try it out).
In case a conversation with linguists seemed in order here, I'd be happy to participate ('pro bono', of course), & to rustle up more colleagues as useful, to help this useful development happen.
With appreciation of HFT, | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4856 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4856/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4856/comments | https://api.github.com/repos/huggingface/datasets/issues/4856/events | https://github.com/huggingface/datasets/issues/4856 | 1,339,779,957 | I_kwDODunzps5P22t1 | 4,856 | file missing when load_dataset with openwebtext on windows | {
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"I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip\r\nand put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd... | 2022-08-16T04:04:22Z | 2023-01-04T03:39:12Z | 2023-01-04T03:39:12Z | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip.
## Steps to reproduce the bug
```sh
python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base
```
or
```python
from datasets import load_dataset
load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None)
```
## Expected results
Loading is successful
## Actual results
Traceback (most recent call last):
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare
raise OSError(
OSError: Cannot find data file.
Original error:
[Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: windows
- Python version: 3.8.5
- PyArrow version: 9.0.0
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https://api.github.com/repos/huggingface/datasets/issues/5074 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5074/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5074/comments | https://api.github.com/repos/huggingface/datasets/issues/5074/events | https://github.com/huggingface/datasets/issues/5074 | 1,397,850,352 | I_kwDODunzps5TUYDw | 5,074 | Replace AssertionErrors with more meaningful errors | {
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"Hi, can I pick up this issue?",
"#self-assign",
"Looks like the top-level `datasource` directory was removed when https://github.com/huggingface/datasets/pull/4974 was merged, so there are 3 source files to fix."
] | 2022-10-05T14:03:55Z | 2022-10-07T14:33:11Z | 2022-10-07T14:33:11Z | COLLABORATOR | null | null | {
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} | Replace the AssertionErrors with more meaningful errors such as ValueError, TypeError, etc.
The files with AssertionErrors that need to be replaced:
```
src/datasets/arrow_reader.py
src/datasets/builder.py
src/datasets/utils/version.py
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5348 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5348/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5348/comments | https://api.github.com/repos/huggingface/datasets/issues/5348/events | https://github.com/huggingface/datasets/issues/5348 | 1,486,975,626 | I_kwDODunzps5YoXKK | 5,348 | The data downloaded in the download folder of the cache does not respect `umask` | {
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"note, that `datasets` already did some of that umask fixing in the past and also at the hub - the recent work on the hub about the same: https://github.com/huggingface/huggingface_hub/pull/1220\r\n\r\nAlso I noticed that each file has a .json counterpart and the latter always has the correct perms:\r\n\r\n```\r\n-... | 2022-12-09T15:46:27Z | 2022-12-09T17:21:26Z | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
For a project on a cluster we are several users to share the same cache for the datasets library. And we have a problem with the permissions on the data downloaded in the cache.
Indeed, it seems that the data is downloaded by giving read and write permissions only to the user launching the command (and no permissions to the group). In our case, those permissions don't respect the `umask` of this user, which was `0007`.
Traceback:
```
Using custom data configuration default
Downloading and preparing dataset text_caps/default to /gpfswork/rech/cnw/commun/datasets/HuggingFaceM4___text_caps/default/1.0.0/2b9ad220cd90fcf2bfb454645bc54364711b83d6d39401ffdaf8cc40882e9141...
Downloading data files: 100%|████████████████████| 3/3 [00:00<00:00, 921.62it/s]
---------------------------------------------------------------------------
PermissionError Traceback (most recent call last)
Cell In [3], line 1
----> 1 ds = load_dataset(dataset_name)
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1745 # Download and prepare data
-> 1746 builder_instance.download_and_prepare(
1747 download_config=download_config,
1748 download_mode=download_mode,
1749 ignore_verifications=ignore_verifications,
1750 try_from_hf_gcs=try_from_hf_gcs,
1751 use_auth_token=use_auth_token,
1752 )
1754 # Build dataset for splits
1755 keep_in_memory = (
1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1757 )
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1226 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File /gpfswork/rech/cnw/commun/modules/datasets_modules/datasets/HuggingFaceM4--TextCaps/2b9ad220cd90fcf2bfb454645bc54364711b83d6d39401ffdaf8cc40882e9141/TextCaps.py:125, in TextCapsDataset._split_generators(self, dl_manager)
123 def _split_generators(self, dl_manager):
124 # urls = _URLS[self.config.name] # TODO later
--> 125 data_dir = dl_manager.download_and_extract(_URLS)
126 gen_kwargs = {
127 split_name: {
128 f"{dir_name}_path": Path(data_dir[dir_name][split_name])
(...)
133 for split_name in ["train", "val", "test"]
134 }
136 for split_name in ["train", "val", "test"]:
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls)
415 def download_and_extract(self, url_or_urls):
416 """Download and extract given url_or_urls.
417
418 Is roughly equivalent to:
(...)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:324, in DownloadManager.download(self, url_or_urls)
321 self.downloaded_paths.update(dict(zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten())))
323 start_time = datetime.now()
--> 324 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
325 duration = datetime.now() - start_time
326 logger.info(f"Checksum Computation took {duration.total_seconds() // 60} min")
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:229, in DownloadManager._record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)
226 """Record size/checksum of downloaded files."""
227 for url, path in zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten()):
228 # call str to support PathLike objects
--> 229 self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(
230 path, record_checksum=self.record_checksums
231 )
File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/utils/info_utils.py:82, in get_size_checksum_dict(path, record_checksum)
80 if record_checksum:
81 m = sha256()
---> 82 with open(path, "rb") as f:
83 for chunk in iter(lambda: f.read(1 << 20), b""):
84 m.update(chunk)
PermissionError: [Errno 13] Permission denied: '/gpfswork/rech/cnw/commun/datasets/downloads/1e6aa6d23190c30885194fabb193dce3874d902d7636b66315ee8aaa584e80d6'
```
### Steps to reproduce the bug
I think the following will reproduce the bug.
Given 2 users belonging to the same group with `umask` set to `0007`
- first run with User 1:
```python
from datasets import load_dataset
ds_name = "HuggingFaceM4/VQAv2"
ds = load_dataset(ds_name)
```
- then run with User 2:
```python
from datasets import load_dataset
ds_name = "HuggingFaceM4/TextCaps"
ds = load_dataset(ds_name)
```
### Expected behavior
No `PermissionError`
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.65.1.el8_4.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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https://api.github.com/repos/huggingface/datasets/issues/5508 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5508/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5508/comments | https://api.github.com/repos/huggingface/datasets/issues/5508/events | https://github.com/huggingface/datasets/issues/5508 | 1,573,290,359 | I_kwDODunzps5dxoF3 | 5,508 | Saving a dataset after setting format to torch doesn't work, but only if filtering | {
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"Hey, I'm a research engineer working on language modelling wanting to contribute to open source. I was wondering if I could give it a shot?",
"Hi! This issue was fixed in https://github.com/huggingface/datasets/pull/4972, so please install `datasets>=2.5.0` to avoid it."
] | 2023-02-06T21:08:58Z | 2023-02-09T14:55:26Z | 2023-02-09T14:55:26Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Saving a dataset after setting format to torch doesn't work, but only if filtering
### Steps to reproduce the bug
```
a = Dataset.from_dict({"b": [1, 2]})
a.set_format('torch')
a.save_to_disk("test_save") # saves successfully
a.filter(None).save_to_disk("test_save_filter") # does not
>> [...] TypeError: Provided `function` which is applied to all elements of table returns a `dict` of types [<class 'torch.Tensor'>]. When using `batched=True`, make sure provided `function` returns a `dict` of types like `(<class 'list'>, <class 'numpy.ndarray'>)`.
# note: skipping the format change to torch lets this work.
### Expected behavior
Saving to work
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-6.1.9-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.9
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/7230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7230/comments | https://api.github.com/repos/huggingface/datasets/issues/7230/events | https://github.com/huggingface/datasets/pull/7230 | 2,589,531,942 | PR_kwDODunzps5-ttUV | 7,230 | Video support | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7230). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-15T18:17:29Z | 2024-10-24T16:39:51Z | 2024-10-24T16:39:50Z | MEMBER | null | null | null | (wip and experimental)
adding the `Video` type based on `VideoReader` from `decord`
```python
>>>from datasets import load_dataset
>>> ds = load_dataset("path/to/videos", split="train").with_format("torch")
>>> print(ds[0]["video"])
<decord.video_reader.VideoReader object at 0x337a47910>
>>> print(ds[0]["video"][0])
tensor([[[73, 73, 73],
[73, 73, 73],
[73, 73, 73],
...,
[23, 23, 23],
[23, 23, 23],
[23, 23, 23]]], dtype=torch.uint8)
```
the storage is the same as for audio and images: `{"path": pa.string(), "bytes": pa.binary()}` and I did a small to keep the hf:// URL in the "path" field if possible, this way the viewer can link to fiels on the hub if possible | {
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https://api.github.com/repos/huggingface/datasets/issues/5724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5724/comments | https://api.github.com/repos/huggingface/datasets/issues/5724/events | https://github.com/huggingface/datasets/issues/5724 | 1,659,938,135 | I_kwDODunzps5i8KVX | 5,724 | Error after shuffling streaming IterableDatasets with downloaded dataset | {
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"Moving `\"en\"` to the end of the path instead of passing it as a config name should fix the error:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('/path/to/your/data/dir/en', streaming=True, split='train')\r\ndataset = dataset.shuffle(buffer_size=10_000, seed=42)\r\nnext(iter(dataset))\r\n```\... | 2023-04-09T16:58:44Z | 2023-04-20T20:37:30Z | 2023-04-20T20:37:30Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I downloaded the C4 dataset, and used streaming IterableDatasets to read it. Everything went normal until I used `dataset = dataset.shuffle(seed=42, buffer_size=10_000)` to shuffle the dataset. Shuffled dataset will throw the following error when it is used by `next(iter(dataset))`:
```
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
for key, example in ex_iterable:
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 627, in __iter__
for x in self.ex_iterable:
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 138, in __iter__
yield from self.generate_examples_fn(**kwargs_with_shuffled_shards)
File "/data/miniconda3/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 763, in wrapper
for key, table in generate_tables_fn(**kwargs):
File "/data/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 101, in _generate_tables
batch = f.read(self.config.chunksize)
File "/data/miniconda3/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 372, in read_with_retries
out = read(*args, **kwargs)
File "/data/miniconda3/lib/python3.9/gzip.py", line 300, in read
return self._buffer.read(size)
File "/data/miniconda3/lib/python3.9/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/data/miniconda3/lib/python3.9/gzip.py", line 487, in read
if not self._read_gzip_header():
File "/data/miniconda3/lib/python3.9/gzip.py", line 435, in _read_gzip_header
raise BadGzipFile('Not a gzipped file (%r)' % magic)
gzip.BadGzipFile: Not a gzipped file (b've')
```
I found that there is no problem to use the dataset in this way without shuffling. Also, use `dataset = datasets.load_dataset('c4', 'en', split='train', streaming=True)`, which will download the dataset on-the-fly instead of loading from the local file, will also not have problems even after shuffle.
### Steps to reproduce the bug
1. Download C4 dataset from https://huggingface.co/datasets/allenai/c4
2.
```
import datasets
dataset = datasets.load_dataset('/path/to/your/data/dir', 'en', streaming=True, split='train')
dataset = dataset.shuffle(buffer_size=10_000, seed=42)
next(iter(dataset))
```
### Expected behavior
`next(iter(dataset))` should give me a sample from the dataset
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.4.32-1-tlinux4-0001-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/7500 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7500/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7500/comments | https://api.github.com/repos/huggingface/datasets/issues/7500/events | https://github.com/huggingface/datasets/issues/7500 | 2,974,841,921 | I_kwDODunzps6xUHxB | 7,500 | Make `with_format` correctly indicate that a `Dataset` is compatible with PyTorch's `Dataset` class | {
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"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
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"Does the torch `DataLoader` really require the dataset to be a subclass of `torch.utils.data.Dataset` ? Or is there a simpler type we could use ?\n\nPS: also note that a dataset without `with_format()` can also be used in a torch `DataLoader` . Calling `with_format(\"torch\")` simply makes the output of the datase... | 2025-04-06T09:56:09Z | 2025-04-15T12:57:39Z | null | NONE | null | null | {
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} | ### Feature request
Currently `datasets` does not correctly indicate to the Python type-checker (e.g. `pyright` / `Pylance`) that the output of `with_format` is compatible with PyTorch's `Dataloader` since it does not indicate that the HuggingFace `Dataset` is compatible with the PyTorch `Dataset` class. It would be great if we could get the typing to work nicely.
### Motivation
To avoid casting types in our Python code.
### Your contribution
I would be happy to contribute a PR if this is something that may be accepted and could work with the current approach.
This doesn't have to be for just PyTorch, but I imagine that the same thing would be useful for `tensorflow` and such, but we only have a need for PyTorch at this stage. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5799 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5799/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5799/comments | https://api.github.com/repos/huggingface/datasets/issues/5799/events | https://github.com/huggingface/datasets/issues/5799 | 1,686,334,572 | I_kwDODunzps5kg2xs | 5,799 | Files downloaded to cache do not respect umask | {
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} | As reported by @stas00, files downloaded to the cache do not respect umask:
```bash
$ ls -l /path/to/cache/datasets/downloads/
-rw------- 1 uername username 150M Apr 25 16:41 5e646c1d600f065adaeb134e536f6f2f296a6d804bd1f0e1fdcd20ee28c185c6
```
Related to:
- #2065 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-01T05:48:20Z | 2022-07-01T16:59:11Z | 2022-07-01T16:48:28Z | MEMBER | null | null | null | Currently, `MockDownloadManager.iter_archive` yields paths to archive members in an order given by `path.rglob("*")`, which migh not be the same order as in the original archive.
See issue in:
- https://github.com/huggingface/datasets/pull/4579#issuecomment-1172135027
This PR fixes the order of the members yielded by `MockDownloadManager.iter_archive` so that it is the same as in the original archive. | {
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https://api.github.com/repos/huggingface/datasets/issues/6672 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6672/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6672/comments | https://api.github.com/repos/huggingface/datasets/issues/6672/events | https://github.com/huggingface/datasets/pull/6672 | 2,138,732,288 | PR_kwDODunzps5nGAlw | 6,672 | Remove deprecated verbose parameter from CSV builder | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6672). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I am merging this PR (so that it is included in the next patch release) to remove the d... | 2024-02-16T14:26:21Z | 2024-02-19T09:26:34Z | 2024-02-19T09:20:22Z | MEMBER | null | null | null | Remove deprecated `verbose` parameter from CSV builder.
Note that the `verbose` parameter is deprecated since pandas 2.2.0. See:
- https://github.com/pandas-dev/pandas/pull/56556
- https://github.com/pandas-dev/pandas/pull/57450
Fix #6671. | {
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https://api.github.com/repos/huggingface/datasets/issues/7437 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7437/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7437/comments | https://api.github.com/repos/huggingface/datasets/issues/7437/events | https://github.com/huggingface/datasets/pull/7437 | 2,899,104,679 | PR_kwDODunzps6Nkhla | 7,437 | Use pyupgrade --py39-plus for remaining files | {
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https://api.github.com/repos/huggingface/datasets/issues/7071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7071/comments | https://api.github.com/repos/huggingface/datasets/issues/7071/events | https://github.com/huggingface/datasets/issues/7071 | 2,430,313,011 | I_kwDODunzps6Q26Iz | 7,071 | Filter hangs | {
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} | ### Describe the bug
When trying to filter my custom dataset, the process hangs, regardless of the lambda function used. It appears to be an issue with the way the Images are being handled. The dataset in question is a preprocessed version of https://huggingface.co/datasets/danaaubakirova/patfig where notably, I have converted the data to the Parquet format.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('lcolonn/patfig', split='test')
ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
```
Eventually I ctrl+C and I obtain this stack trace:
```
>>> ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
Filter: 0%| | 0/998 [00:00<?, ? examples/s]Filter: 0%| | 0/998 [00:35<?, ? examples/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/fingerprint.py", line 482, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3714, in filter
indices = self.map(
^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 602, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3161, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3552, in _map_single
batch = apply_function_on_filtered_inputs(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3421, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 6478, in get_indices_from_mask_function
num_examples = len(batch[next(iter(batch.keys()))])
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 273, in __getitem__
value = self.format(key)
^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 376, in format
return self.formatter.format_column(self.pa_table.select([key]))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 443, in format_column
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 219, in decode_column
return self.features.decode_column(column, column_name) if self.features else column
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in decode_column
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in <listcomp>
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 1351, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/PIL/ImageFile.py", line 293, in load
n, err_code = decoder.decode(b)
^^^^^^^^^^^^^^^^^
KeyboardInterrupt
```
Warning! This can even seem to cause some computers to crash.
### Expected behavior
Should return the filtered dataset
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6824/comments | https://api.github.com/repos/huggingface/datasets/issues/6824/events | https://github.com/huggingface/datasets/issues/6824 | 2,251,076,197 | I_kwDODunzps6GLLJl | 6,824 | Winogrande does not seem to be compatible with datasets version of 1.18.0 | {
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"Hi ! Do you mean 2.18 ? Can you try to update `fsspec` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U fsspec huggingface_hub\r\n```",
"Yes I meant 2.18, and it works after updating `fsspec` and `huggingface_hub`. Thanks!"
] | 2024-04-18T16:11:04Z | 2024-04-19T09:53:15Z | 2024-04-19T09:52:33Z | NONE | null | null | {
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} | ### Describe the bug
I get the following error when simply running `load_dataset('winogrande','winogrande_xl')`.
I do not have such an issue in the 1.17.0 version.
```Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2265, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 371, in __init__
self.config, self.config_id = self._create_builder_config(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 620, in _create_builder_config
builder_config._resolve_data_files(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 211, in _resolve_data_files
self.data_files = self.data_files.resolve(base_path, download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/data_files.py", line 799, in resolve
out[key] = data_files_patterns_list.resolve(base_path, download_config)
File "/usr/local/lib/python3.10/dist-packages/datasets/data_files.py", line 752, in resolve
resolve_pattern(
File "/usr/local/lib/python3.10/dist-packages/datasets/data_files.py", line 393, in resolve_pattern
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find 'hf://datasets/winogrande@ebf71e3c7b5880d019ecf6099c0b09311b1084f5/winogrande_xl/train/0000.parquet' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']```
### Steps to reproduce the bug
from datasets import load_dataset
datasets = load_dataset('winogrande','winogrande_xl')
### Expected behavior
```Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.06M/2.06M [00:00<00:00, 5.16MB/s]
Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 118k/118k [00:00<00:00, 360kB/s]
Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 85.9k/85.9k [00:00<00:00, 242kB/s]
Generating train split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 40398/40398 [00:00<00:00, 845491.12 examples/s]
Generating test split: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1767/1767 [00:00<00:00, 362501.11 examples/s]
Generating validation split: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 1267/1267 [00:00<00:00, 318768.11 examples/s]```
### Environment info
datasets version: 1.18.0
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https://api.github.com/repos/huggingface/datasets/issues/7146 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7146/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7146/comments | https://api.github.com/repos/huggingface/datasets/issues/7146/events | https://github.com/huggingface/datasets/pull/7146 | 2,519,820,162 | PR_kwDODunzps57KqRV | 7,146 | Set dev version | {
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} | [] | closed | false | null | [] | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7146). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-09-11T13:53:27Z | 2024-09-12T04:34:08Z | 2024-09-12T04:34:06Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7506/comments | https://api.github.com/repos/huggingface/datasets/issues/7506/events | https://github.com/huggingface/datasets/issues/7506 | 2,981,687,450 | I_kwDODunzps6xuPCa | 7,506 | HfHubHTTPError: 429 Client Error: Too Many Requests for URL when trying to access Fineweb-10BT on 4A100 GPUs using SLURM | {
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} | [] | open | false | null | [] | null | [
"Hi ! make sure to be logged in with your HF account (e.g. using `huggingface-cli login` or passing `token=` to `load_dataset()`), otherwise you'll get rate limited at one point"
] | 2025-04-09T06:32:04Z | 2025-04-15T13:04:31Z | null | NONE | null | null | {
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} | ### Describe the bug
I am trying to run some finetunings on 4 A100 GPUs using SLURM using axolotl training framework which in turn uses Huggingface's Trainer and Accelerate on [Fineweb-10BT](https://huggingface.co/datasets/HuggingFaceFW/fineweb), but I end up running into 429 Client Error: Too Many Requests for URL error when I call next(dataloader_iter). Funny is, that I can run some test fine tuning (for just 200 training steps) in 1 A100 GPU using SLURM. Is there any rate limiter set for querying dataset? I could run the fine tuning with the same settings (4 A100 GPUs in SLURM) last month.
### Steps to reproduce the bug
You would need a server installed with SLURM
1. Create conda environment
1.1 conda create -n example_env -c conda-forge gxx=11 python=3.10
1.2 conda activate example_env
1.3 pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
1.4 conda install nvidia/label/cuda-12.4.0::cuda-toolkit
1.5 Download flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
1.6 pip3 install packaging
1.7 pip3 install ninja
1.8 pip3 install mlflow
1.9 Clone https://github.com/calvintanama/axolotl.git
1.10 `cd` to `axolotl`
1.11 pip3 install -e '.[deepspeed]'
2. Run the training
2.1. Create a folder called `config_run` in axolotl directory
2.2. Copy `config/phi3_pruned_extra_pretrain_22_29_bottleneck_residual_8_a100_4.yaml` to `config_run`
2.3. Change yaml file in the `config_run` accordingly
2.4. Change directory and conda environment name in `jobs/train_phi3_22_29_bottleneck_residual_8_a100_4_temp.sh`
2.5. `jobs/train_phi3_22_29_bottleneck_residual_8_a100_4_temp.sh`
### Expected behavior
This should not cause any error, but gotten
```
File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/accelerate/data_loader.py", line 552, in __iter__
[rank3]: current_batch = next(dataloader_iter)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
[rank3]: data = self._next_data()
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 757, in _next_data
[rank3]: data = self._dataset_fetcher.fetch(index) # may raise StopIteration
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 33, in fetch
[rank3]: data.append(next(self.dataset_iter))
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/accelerate/data_loader.py", line 338, in __iter__
[rank3]: for element in self.dataset:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 2266, in __iter__
[rank3]: for key, example in ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1866, in __iter__
[rank3]: for key, example in self.ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1084, in __iter__
[rank3]: yield from self._iter()
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1263, in _iter
[rank3]: for key, transformed_example in outputs:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1258, in <genexpr>
[rank3]: outputs = (
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1244, in iter_outputs
[rank3]: for i, key_example in inputs_iterator:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1106, in iter_batched_inputs
[rank3]: for key, example in iterator:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1866, in __iter__
[rank3]: for key, example in self.ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1535, in __iter__
[rank3]: for x in self.ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 374, in __iter__
[rank3]: for key, pa_table in self.generate_tables_fn(**gen_kwags):
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 90, in _generate_tables
[rank3]: if parquet_fragment.row_groups:
[rank3]: File "pyarrow/_dataset_parquet.pyx", line 386, in pyarrow._dataset_parquet.ParquetFileFragment.row_groups.__get__
[rank3]: File "pyarrow/_dataset_parquet.pyx", line 393, in pyarrow._dataset_parquet.ParquetFileFragment.metadata.__get__
[rank3]: File "pyarrow/_dataset_parquet.pyx", line 382, in pyarrow._dataset_parquet.ParquetFileFragment.ensure_complete_metadata
[rank3]: File "pyarrow/error.pxi", line 89, in pyarrow.lib.check_status
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 827, in read_with_retries
[rank3]: out = read(*args, **kwargs)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 1013, in read
[rank3]: return super().read(length)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/fsspec/spec.py", line 1941, in read
[rank3]: out = self.cache._fetch(self.loc, self.loc + length)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/fsspec/caching.py", line 234, in _fetch
[rank3]: self.cache = self.fetcher(start, end) # new block replaces old
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 976, in _fetch_range
[rank3]: hf_raise_for_status(r)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 482, in hf_raise_for_status
[rank3]: raise _format(HfHubHTTPError, str(e), response) from e
[rank3]: huggingface_hub.errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/HuggingFaceFW/fineweb/resolve/0f039043b23fe1d4eed300b504aa4b4a68f1c7ba/sample/10BT/006_00000.parquet
```
### Environment info
- datasets 3.5.0
- torch 2.5.1
- transformers 4.46.2 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7352 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7352/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7352/comments | https://api.github.com/repos/huggingface/datasets/issues/7352/events | https://github.com/huggingface/datasets/pull/7352 | 2,767,763,850 | PR_kwDODunzps6GrBB5 | 7,352 | fsspec 2024.12.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7352). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-01-03T15:32:25Z | 2025-01-03T15:34:54Z | 2025-01-03T15:34:11Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6724/comments | https://api.github.com/repos/huggingface/datasets/issues/6724/events | https://github.com/huggingface/datasets/issues/6724 | 2,174,398,227 | I_kwDODunzps6Bmq8T | 6,724 | Dataset with loading script does not work in renamed repos | {
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} | [] | open | false | null | [] | null | [] | 2024-03-07T17:38:38Z | 2024-03-07T20:06:25Z | null | CONTRIBUTOR | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
My data repository was first called `BramVanroy/hplt-mono-v1-2` but I then renamed to use underscores instead of dashes. However, it seems that `datasets` retrieves the old repo name when it checks whether the repo contains data loading scripts in this line.
https://github.com/huggingface/datasets/blob/6fb6c834f008996c994b0a86c3808d0a33d44525/src/datasets/load.py#L1845
When I print `filename` it returns `hplt-mono-v1-2.py` but the files in the repo are of course `['.gitattributes', 'README.md', 'hplt_mono_v1_2.py']`. So the `filename` is the original reponame instead of the renamed one.
I am not sure if this is a caching issue or not or how I can resolve it.
### Steps to reproduce the bug
```
from datasets import load_dataset
ds = load_dataset(
"BramVanroy/hplt-mono-v1-2",
"ky",
trust_remote_code=True
)
```
### Expected behavior
That the most recent repo name is used when `filename` is generated.
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.34
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.2
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.10.0
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https://api.github.com/repos/huggingface/datasets/issues/5369 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5369/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5369/comments | https://api.github.com/repos/huggingface/datasets/issues/5369/events | https://github.com/huggingface/datasets/pull/5369 | 1,500,622,276 | PR_kwDODunzps5Fqaj- | 5,369 | Distributed support | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Alright all the tests are passing - this is ready for review",
"<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... | 2022-12-16T17:43:47Z | 2023-07-25T12:00:31Z | 2023-01-16T13:33:32Z | MEMBER | null | null | null | To split your dataset across your training nodes, you can use the new [`datasets.distributed.split_dataset_by_node`]:
```python
import os
from datasets.distributed import split_dataset_by_node
ds = split_dataset_by_node(ds, rank=int(os.environ["RANK"]), world_size=int(os.environ["WORLD_SIZE"]))
```
This works for both map-style datasets and iterable datasets.
The dataset is split for the node at rank `rank` in a pool of nodes of size `world_size`.
For map-style datasets:
Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset.
For iterable datasets:
If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`),
then the shards are evenly assigned across the nodes, which is the most optimized.
Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples.
This can also be combined with a `torch.utils.data.DataLoader` if you want each node to use multiple workers to load the data.
This also supports shuffling. At each epoch, the iterable dataset shards are reshuffled across all the nodes - you just have to call `iterable_ds.set_epoch(epoch_number)`.
TODO:
- [x] docs for usage in PyTorch
- [x] unit tests
- [x] integration tests with torch.distributed.launch
Related to https://github.com/huggingface/transformers/issues/20770
Close https://github.com/huggingface/datasets/issues/5360 | {
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https://api.github.com/repos/huggingface/datasets/issues/5186 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5186/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5186/comments | https://api.github.com/repos/huggingface/datasets/issues/5186/events | https://github.com/huggingface/datasets/issues/5186 | 1,432,045,011 | I_kwDODunzps5VW0XT | 5,186 | Incorrect error message when Dataset.from_sql fails and sqlalchemy not installed | {
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... | null | [
"Hi! The first `Dataset.from_sql` call also outputs the \"ImportError: Using URI string without sqlalchemy installed.\" message, but you also get \"During handling of the above exception another exception occurred: ...\" after which the ValueError is printed. I agree that this behavior makes it easy to miss the ori... | 2022-11-01T20:25:51Z | 2022-11-15T18:24:39Z | 2022-11-15T18:24:39Z | CONTRIBUTOR | null | null | {
"completed": 0,
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} | ### Describe the bug
When calling `Dataset.from_sql` (in my case, with sqlite3), it fails with a message ```ValueError: Please pass `features` or at least one example when writing data``` when I don't have `sqlalchemy` installed.
### Steps to reproduce the bug
Make a new sqlite db with `sqlite3` and `pandas` from a remote [URL](https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv).
```python
import sqlite3
import pandas as pd
from datasets import Dataset
conn = sqlite3.connect('us_covid_data.db')
df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv')
df.to_sql('states', conn, if_exists='replace')
```
Then if you try to query this DB like this:
```python
ds = Dataset.from_sql('''SELECT * from states WHERE state=="New York";''', "sqlite:///us_covid_data.db")
```
You run into the error I described above:
```ValueError: Please pass `features` or at least one example when writing data```
However, if you try to pass features, as the error suggests, then you get an error that tells you the underlying problem...
```python
from datasets import Dataset, Features, Value
features = Features({
'date': Value('date32'),
'label': Value('string'),
'fips': Value('int32'),
'cases': Value('int32'),
'deaths': Value('int32')
})
ds = Dataset.from_sql(
'''SELECT * from states WHERE state=="New York";''',
"sqlite:///us_covid_data.db",
features=features
)
```
Which results in the actual underlying error: `ImportError: Using URI string without sqlalchemy installed.`
### Expected behavior
Instead of `ValueError` about needing to pass features, we should provide the actual underlying error about not having SQLAlchemy installed when it isn't found in the environment.
### Environment info
- `datasets` version: 2.6.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 10.0.0
- Pandas version: 1.2.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/5995 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5995/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5995/comments | https://api.github.com/repos/huggingface/datasets/issues/5995/events | https://github.com/huggingface/datasets/pull/5995 | 1,777,088,925 | PR_kwDODunzps5UCvYJ | 5,995 | Support returning dataframe in map transform | {
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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-06-27T14:15:08Z | 2023-06-28T13:56:02Z | 2023-06-28T13:46:33Z | COLLABORATOR | null | null | null | Allow returning Pandas DataFrames in `map` transforms.
(Plus, raise an error in the non-batched mode if a returned PyArrow table/Pandas DataFrame has more than one row)
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https://api.github.com/repos/huggingface/datasets/issues/7037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7037/comments | https://api.github.com/repos/huggingface/datasets/issues/7037/events | https://github.com/huggingface/datasets/issues/7037 | 2,400,192,419 | I_kwDODunzps6PEAej | 7,037 | A bug of Dataset.to_json() function | {
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"Thanks for reporting, @LinglingGreat.\r\n\r\nI confirm this is a bug.",
"@albertvillanova I would like to take a shot at this if you aren't working on it currently. Let me know!"
] | 2024-07-10T09:11:22Z | 2024-09-22T13:16:07Z | null | NONE | null | null | {
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} | ### Describe the bug
When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again.
The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size).
### Steps to reproduce the bug
try this code:
```python
from datasets import load_dataset
import json
train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"]
output_path = "./harmless-base_hftojs.json"
print(len(train_dataset))
train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2)
with open(output_path, encoding="utf-8") as f:
data = json.loads(f.read())
```
it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709)
Extra square brackets have appeared here:
<img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc">
### Expected behavior
The code runs normally.
### Environment info
datasets=2.20.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5575 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5575/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5575/comments | https://api.github.com/repos/huggingface/datasets/issues/5575/events | https://github.com/huggingface/datasets/issues/5575 | 1,598,396,552 | I_kwDODunzps5fRZiI | 5,575 | Metadata for each column | {
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"Hi! Indeed it would be useful to support this. PyArrow natively supports schema-level and column-level metadata, so implementing this should be straightforward. The API I have in mind would work as follows:\r\n```python\r\ncol_feature = Value(\"string\", metadata=\"Some column-level metadata\")\r\n\r\nfeatures = F... | 2023-02-24T10:53:44Z | 2024-01-05T21:48:35Z | null | NONE | null | null | {
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} | ### Feature request
Being able to put some metadata for each column as a string or any other type.
### Motivation
I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata
### Your contribution
Maybe we could map another relational like database as the metadata? | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5073 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5073/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5073/comments | https://api.github.com/repos/huggingface/datasets/issues/5073/events | https://github.com/huggingface/datasets/pull/5073 | 1,397,832,183 | PR_kwDODunzps5AN3Gn | 5,073 | Restore saved format state in `load_from_disk` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-05T13:51:47Z | 2022-10-11T16:55:07Z | 2022-10-11T16:49:23Z | CONTRIBUTOR | null | null | null | Hello! @mariosasko
This pull request relates to issue #5050 and intends to add the format to datasets loaded from disk.
All I did was add a set_format in the Dataset.load_from_disk, as DatasetDict.load_from_disk relies on the first.
I don't know if I should add a test and where, so let me know if I should and I can work on that as well!
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https://api.github.com/repos/huggingface/datasets/issues/5129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5129/comments | https://api.github.com/repos/huggingface/datasets/issues/5129/events | https://github.com/huggingface/datasets/issues/5129 | 1,413,031,664 | I_kwDODunzps5UOSbw | 5,129 | unexpected `cast` or `class_encode_column` result after `rename_column` | {
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"Hi! Unfortunately, I can't reproduce this issue locally (in Python 3.7/3.10) or in Colab. I would assume this is due to a bug we fixed in the latest release, but your version is up-to-date, so I'm not sure if there is something we can do to help...",
"Hi, 方子东. I tried running the code with exact the same configu... | 2022-10-18T11:15:24Z | 2022-10-19T03:02:26Z | 2022-10-19T03:02:26Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ## Describe the bug
When invoke `cast` or `class_encode_column` to a colunm renamed by `rename_column` , it will convert all the variables in this column into one variable. I also run this script in version 2.5.2, this bug does not appear. So I switched to the older version.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("amazon_reviews_multi", "en")
data = dataset['train']
data = data.remove_columns(
[
"review_id",
"product_id",
"reviewer_id",
"review_title",
"language",
"product_category",
]
)
data = data.rename_column("review_body", "text")
data1 = data.class_encode_column("stars")
print(set(data1.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
data = data.rename_column("stars", "label")
print(set(data.data.columns[0]))
# output: {<pyarrow.Int32Scalar: 5>, <pyarrow.Int32Scalar: 4>, <pyarrow.Int32Scalar: 1>, <pyarrow.Int32Scalar: 3>, <pyarrow.Int32Scalar: 2>}
data2 = data.class_encode_column("label")
print(set(data2.data.columns[0]))
# output: {<pyarrow.Int64Scalar: 0>}
```
## Expected results
the last print should be:
{<pyarrow.Int64Scalar: 4>, <pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 3>, <pyarrow.Int64Scalar: 0>, <pyarrow.Int64Scalar: 1>}
## Actual results
but it output:
{<pyarrow.Int64Scalar: 0>}
## Environment info
- `datasets` version: 2.6.1
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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https://api.github.com/repos/huggingface/datasets/issues/4807 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4807/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4807/comments | https://api.github.com/repos/huggingface/datasets/issues/4807/events | https://github.com/huggingface/datasets/pull/4807 | 1,332,784,110 | PR_kwDODunzps483MSH | 4,807 | document fix in opus_gnome dataset | {
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"Duplicate:\r\n- #4806 "
] | 2022-08-09T06:38:13Z | 2022-08-09T07:28:03Z | 2022-08-09T07:28:03Z | CONTRIBUTOR | null | null | null | I fixed a issue #4805.
I changed `"gnome"` to `"opus_gnome"` in[ README.md](https://github.com/huggingface/datasets/tree/main/datasets/opus_gnome#dataset-summary). | {
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https://api.github.com/repos/huggingface/datasets/issues/5327 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5327/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5327/comments | https://api.github.com/repos/huggingface/datasets/issues/5327/events | https://github.com/huggingface/datasets/pull/5327 | 1,471,657,247 | PR_kwDODunzps5EE_3Q | 5,327 | Avoid unwanted behaviour when splits from script and metadata are not matching because of outdated metadata | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5327). All of your documentation changes will be reflected on that endpoint."
] | 2022-12-01T17:05:23Z | 2023-01-23T12:48:29Z | null | CONTRIBUTOR | null | null | null | will fix #5315 | null | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-04-12T08:52:35Z | 2023-04-13T11:01:24Z | 2023-04-13T10:54:13Z | MEMBER | null | null | null | This PR fixes the fixtures of our CI mock filesystems.
Before, we had to pass `clobber=True` to `fsspec.register_implementation` to overwrite the still present previously added "mock" filesystem. That meant that the mock filesystem fixture was not working properly, because the previously added "mock" filesystem, should have been deleted by the fixture.
This PR fixes the mock filesystem fixtures, so that the "mock" filesystem is properly deleted from the inner `fsspec` registry.
Tests were added to check the correct behavior of the mock filesystem fixtures.
Related to:
- #5733 | {
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https://api.github.com/repos/huggingface/datasets/issues/6223 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6223/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6223/comments | https://api.github.com/repos/huggingface/datasets/issues/6223/events | https://github.com/huggingface/datasets/pull/6223 | 1,885,710,696 | PR_kwDODunzps5Zxd5c | 6,223 | Update README.md | {
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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-09-07T11:33:20Z | 2023-09-13T22:32:31Z | 2023-09-13T22:23:42Z | CONTRIBUTOR | null | null | null | fixed a few typos | {
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https://api.github.com/repos/huggingface/datasets/issues/6265 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6265/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6265/comments | https://api.github.com/repos/huggingface/datasets/issues/6265/events | https://github.com/huggingface/datasets/pull/6265 | 1,915,651,566 | PR_kwDODunzps5bWDfc | 6,265 | Remove `apache_beam` import in `BeamBasedBuilder._save_info` | {
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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-09-27T13:56:34Z | 2023-09-28T18:34:02Z | 2023-09-28T18:23:35Z | COLLABORATOR | null | null | null | ... to avoid an `ImportError` raised in `BeamBasedBuilder._save_info` when `apache_beam` is not installed (e.g., when downloading the processed version of a dataset from the HF GCS)
Fix https://github.com/huggingface/datasets/issues/6260 | {
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https://api.github.com/repos/huggingface/datasets/issues/4652 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4652/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4652/comments | https://api.github.com/repos/huggingface/datasets/issues/4652/events | https://github.com/huggingface/datasets/issues/4652 | 1,296,697,498 | I_kwDODunzps5NSgia | 4,652 | Add Sentence Compression Dataset | {
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"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/sentence-compression)."
] | 2022-07-07T02:13:46Z | 2022-07-14T02:11:48Z | 2022-07-14T02:11:48Z | NONE | null | null | {
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} | ## Adding a Dataset
- **Name:** *Sentence Compression*
- **Description:** *Large corpus of uncompressed and compressed sentences from news articles.*
- **Paper:** *https://www.aclweb.org/anthology/D13-1155/*
- **Data:** *https://github.com/google-research-datasets/sentence-compression/tree/master/data*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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https://api.github.com/repos/huggingface/datasets/issues/4907 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4907/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4907/comments | https://api.github.com/repos/huggingface/datasets/issues/4907/events | https://github.com/huggingface/datasets/issues/4907 | 1,353,808,348 | I_kwDODunzps5QsXnc | 4,907 | None Type error for swda datasets | {
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"Thanks for reporting @hannan72 ! I couldn't reproduce the error on my side, can you share the full stack trace please ?",
"Thanks a lot for your response @lhoestq \r\nThe problem is solved accidentally today and I don't know exactly why it was happened yesterday.\r\nThe issue can be closed.",
"Ok, let us know ... | 2022-08-29T07:05:20Z | 2022-08-30T14:43:41Z | 2022-08-30T14:43:41Z | NONE | null | null | {
"completed": 0,
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} | ## Describe the bug
I got `'NoneType' object is not callable` error while calling the swda datasets.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("swda")
```
## Expected results
Run without error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Python version: 3.8.10
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"Indeed, it makes sense to do this by default. \r\n\r\nIn the meantime, you can use `.with_transform` to transpose the images when accessing them:\r\n\r\n```python\r\nimport PIL.ImageOps\r\n\r\ndef exif_transpose_transform(batch):\r\n batch[\"image\"] = [PIL.ImageOps.exif_transpose(image) for image in batch[\"imag... | 2023-09-21T08:11:46Z | 2024-03-19T15:29:43Z | 2024-03-19T15:29:43Z | NONE | null | null | {
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} | ### Feature request
I noticed that some of my images loaded using PIL have some metadata related to exif that can rotate them when loading.
Since the dataset.features.Image uses PIL for loading, the loaded image may be rotated (width and height will be inverted) thus for tasks as object detection and layoutLM this can create some inconsistencies (between input bboxes and input images).
For now there is no option in datasets.features.Image to specify that. We need to do the following when preparing examples (when preparing images for training, test or inference):
```
from PIL import Image, ImageOps
pil = ImageOps.exif_transpose(pil)
```
reference: https://stackoverflow.com/a/63950647/5720150
Is it possible to add this by default to the datasets.feature.Image ? or to add the option to do the ImageOps.exif_transpose?
Thank you
### Motivation
Prevent having inverted data related to exif metadata that may affect object detection tasks
### Your contribution
Changing in datasets.featrues.Image I can help with that. | {
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https://api.github.com/repos/huggingface/datasets/issues/5112 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5112/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5112/comments | https://api.github.com/repos/huggingface/datasets/issues/5112/events | https://github.com/huggingface/datasets/issues/5112 | 1,409,143,409 | I_kwDODunzps5T_dJx | 5,112 | Bug with filtered indices | {
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"The issue is here:\r\nhttps://github.com/huggingface/datasets/blob/3ad9644b9a2e4558dd1d0f1e43c67658674e6228/src/datasets/arrow_dataset.py#L2964",
"@PartiallyTyped, @Muennighoff: the issue is fixed.\r\n\r\nWe are planning to make a patch release today.",
"Thanks a lot for the swift response! For a brief moment ... | 2022-10-14T10:35:47Z | 2022-10-14T13:55:03Z | 2022-10-14T12:11:45Z | MEMBER | null | null | {
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} | ## Describe the bug
As reported by @PartiallyTyped (and by @Muennighoff):
- https://github.com/huggingface/datasets/issues/5111#issuecomment-1278652524
There is an issue with the indices of a filtered dataset.
## Steps to reproduce the bug
```python
ds = Dataset.from_dict({"num": [0, 1, 2, 3]})
ds = ds.filter(lambda num: num % 2 == 0, input_columns="num", batch_size=2)
assert all(item["num"] % 2 == 0 for item in ds)
```
## Expected results
The indices of the filtered dataset should correspond to the examples with "language" equals to "english".
## Actual results
Indices to items with other languages are included in the filtered dataset indices
## Preliminar investigation
It seems a bug introduced by:
- #5030
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"Hi! I agree it makes sense for `IterableDataset.map` to support the `batch_size=None` case. This should be super easy to fix.",
"@mariosasko as this is something simple maybe I can include it as part of https://github.com/huggingface/datasets/pull/5311? Let me know :+1:",
"#self-assign",
"Feel free to close ... | 2022-12-01T15:43:42Z | 2022-12-07T15:54:43Z | 2022-12-07T15:54:42Z | CONTRIBUTOR | null | null | {
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} | ### Feature request
Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too.
### Motivation
Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice.
One is that load_dataset(...) can return either IterableDataset or Dataset. mypy will then complain if batch_size=None even if we know it is Dataset. Of course we can do:
assert isinstance(d, datasets.DatasetDict)
But it is a mild inconvenience. What's more annoying is that whenever we use something like e.g. `combine_datasets(...)`, we end up with the union again, and so have to do the assert again.
Another is that we could actually end up with an IterableDataset small enough for memory in normal/correct usage, e.g. by filtering a massive IterableDataset.
For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this.
### Your contribution
Not this time. | {
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https://api.github.com/repos/huggingface/datasets/issues/5749 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5749/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5749/comments | https://api.github.com/repos/huggingface/datasets/issues/5749/events | https://github.com/huggingface/datasets/issues/5749 | 1,668,016,321 | I_kwDODunzps5ja-jB | 5,749 | AttributeError: 'Version' object has no attribute 'match' | {
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"I got the same error, and the official website for visual genome is down. Did you solve this problem? ",
"I am in the same situation now :( ",
"Thanks for reporting, @gulnaz-zh.\r\n\r\nI am investigating it.",
"The host server is down: https://visualgenome.org/\r\n\r\nWe are contacting the dataset authors.",... | 2023-04-14T10:48:06Z | 2023-06-30T11:31:17Z | 2023-04-18T12:57:08Z | NONE | null | null | {
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} | ### Describe the bug
When I run
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
AttributeError: 'Version' object has no attribute 'match'
### Steps to reproduce the bug
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
### Expected behavior
This is error trace:
Downloading and preparing dataset visual_genome/region_descriptions_v1.2.0 to C:/Users/Acer/.cache/huggingface/datasets/visual_genome/region_descriptions_v1.2.0/1.2.0/136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3...
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
File ~\.conda\envs\aai\Lib\site-packages\datasets\load.py:1791, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
1788 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1790 # Download and prepare data
-> 1791 builder_instance.download_and_prepare(
1792 download_config=download_config,
1793 download_mode=download_mode,
1794 verification_mode=verification_mode,
1795 try_from_hf_gcs=try_from_hf_gcs,
1796 num_proc=num_proc,
1797 storage_options=storage_options,
1798 )
1800 # Build dataset for splits
1801 keep_in_memory = (
1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1803 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:891, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_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)
889 if num_proc is not None:
890 prepare_split_kwargs["num_proc"] = num_proc
--> 891 self._download_and_prepare(
892 dl_manager=dl_manager,
893 verification_mode=verification_mode,
894 **prepare_split_kwargs,
895 **download_and_prepare_kwargs,
896 )
897 # Sync info
898 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:1651, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1650 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1651 super()._download_and_prepare(
1652 dl_manager,
1653 verification_mode,
1654 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
1655 or verification_mode == VerificationMode.ALL_CHECKS,
1656 **prepare_splits_kwargs,
1657 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:964, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
962 split_dict = SplitDict(dataset_name=self.name)
963 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 964 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
966 # Checksums verification
967 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:377, in VisualGenome._split_generators(self, dl_manager)
375 def _split_generators(self, dl_manager):
376 # Download image meta datas.
--> 377 image_metadatas_dir = dl_manager.download_and_extract(self.config.image_metadata_url)
378 image_metadatas_file = os.path.join(
379 image_metadatas_dir, _get_decompressed_filename_from_url(self.config.image_metadata_url)
380 )
382 # Download annotations
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:328, in VisualGenomeConfig.image_metadata_url(self)
326 @property
327 def image_metadata_url(self):
--> 328 if not self.version.match(_LATEST_VERSIONS["image_metadata"]):
329 logger.warning(
330 f"Latest image metadata version is {_LATEST_VERSIONS['image_metadata']}. Trying to generate a dataset of version: {self.version}. Please double check that image data are unchanged between the two versions."
331 )
332 return f"{_BASE_ANNOTATION_URL}/image_data.json.zip"
### Environment info
datasets 2.11.0
python 3.11.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/7293 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7293/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7293/comments | https://api.github.com/repos/huggingface/datasets/issues/7293/events | https://github.com/huggingface/datasets/pull/7293 | 2,664,592,054 | PR_kwDODunzps6CIjS- | 7,293 | Updated inconsistent output in documentation examples for `ClassLabel` | {
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"Updated! 😄 ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7293). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq, can you help with this failing test please? 🙏 "
] | 2024-11-16T16:20:57Z | 2024-12-06T11:33:33Z | 2024-12-06T11:32:01Z | MEMBER | null | null | null | fix #7129
@stevhliu | {
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https://api.github.com/repos/huggingface/datasets/issues/4719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4719/comments | https://api.github.com/repos/huggingface/datasets/issues/4719/events | https://github.com/huggingface/datasets/issues/4719 | 1,309,854,492 | I_kwDODunzps5OEssc | 4,719 | Issue loading TheNoob3131/mosquito-data dataset | {
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"I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.",
"Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Da... | 2022-07-19T17:47:37Z | 2022-07-20T06:46:57Z | 2022-07-20T06:46:02Z | NONE | null | null | {
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So my dataset is public in the Huggingface Hub, but when I try to load it using the load_dataset command, it shows that it is downloading the files, but throws a ValueError. When I went to my directory to see if the files were downloaded, the folder was blank.
Here is the error below:
ValueError Traceback (most recent call last)
Input In [8], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("TheNoob3131/mosquito-data", split="train")
File ~\Anaconda3\lib\site-packages\datasets\load.py:1679, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1676 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1678 # Download and prepare data
-> 1679 builder_instance.download_and_prepare(
1680 download_config=download_config,
1681 download_mode=download_mode,
1682 ignore_verifications=ignore_verifications,
1683 try_from_hf_gcs=try_from_hf_gcs,
1684 use_auth_token=use_auth_token,
1685 )
1687 # Build dataset for splits
1688 keep_in_memory = (
1689 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1690 )
Is the dataset in the wrong format or is there some security permission that I should enable? | {
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https://api.github.com/repos/huggingface/datasets/issues/5671 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5671/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5671/comments | https://api.github.com/repos/huggingface/datasets/issues/5671/events | https://github.com/huggingface/datasets/issues/5671 | 1,640,840,012 | I_kwDODunzps5hzTtM | 5,671 | How to use `load_dataset('glue', 'cola')` | {
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"Sounds like an issue with incompatible `transformers` dependencies versions.\r\n\r\nCan you try to update `transformers` ?\r\n\r\nEDIT: I checked the `transformers` dependencies and it seems like you need `tokenizers>=0.10.1,<0.11` with `transformers==4.5.1`\r\n\r\nEDIT2: this old version of `datasets` seems to im... | 2023-03-26T09:40:34Z | 2023-03-28T07:43:44Z | 2023-03-28T07:43:43Z | NONE | null | null | {
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} | ### Describe the bug
I'm new to use HuggingFace datasets but I cannot use `load_dataset('glue', 'cola')`.
- I was stacked by the following problem:
```python
from datasets import load_dataset
cola_dataset = load_dataset('glue', 'cola')
---------------------------------------------------------------------------
InvalidVersion Traceback (most recent call last)
File <timed exec>:1
(Omit because of long error message)
File /usr/local/lib/python3.8/site-packages/packaging/version.py:197, in Version.__init__(self, version)
195 match = self._regex.search(version)
196 if not match:
--> 197 raise InvalidVersion(f"Invalid version: '{version}'")
199 # Store the parsed out pieces of the version
200 self._version = _Version(
201 epoch=int(match.group("epoch")) if match.group("epoch") else 0,
202 release=tuple(int(i) for i in match.group("release").split(".")),
(...)
208 local=_parse_local_version(match.group("local")),
209 )
InvalidVersion: Invalid version: '0.10.1,<0.11'
```
- You can check this full error message in my repository: [MLOps-Basics/week_0_project_setup/experimental_notebooks/data_exploration.ipynb](https://github.com/makinzm/MLOps-Basics/blob/eabab4b837880607d9968d3fa687c70177b2affd/week_0_project_setup/experimental_notebooks/data_exploration.ipynb)
### Steps to reproduce the bug
- This is my repository to reproduce: [MLOps-Basics/week_0_project_setup](https://github.com/makinzm/MLOps-Basics/tree/eabab4b837880607d9968d3fa687c70177b2affd/week_0_project_setup)
1. cd `/DockerImage` and command `docker build . -t week0`
2. cd `/` and command `docker-compose up`
3. Run `experimental_notebooks/data_exploration.ipynb`
----
Just to be sure, I wrote down Dockerfile and requirements.txt
- Dockerfile
```Dockerfile
FROM python:3.8
WORKDIR /root/working
RUN apt-get update && \
apt-get install -y python3-dev python3-pip python3-venv && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
COPY requirements.txt .
RUN pip3 install --no-cache-dir jupyter notebook && pip install --no-cache-dir -r requirements.txt
CMD ["bash"]
```
- requirements.txt
```txt
pytorch-lightning==1.2.10
datasets==1.6.2
transformers==4.5.1
scikit-learn==0.24.2
```
### Expected behavior
There is no bug to implement `load_dataset('glue', 'cola')`
### Environment info
I already wrote it. | {
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https://api.github.com/repos/huggingface/datasets/issues/5211 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5211/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5211/comments | https://api.github.com/repos/huggingface/datasets/issues/5211/events | https://github.com/huggingface/datasets/pull/5211 | 1,438,544,617 | PR_kwDODunzps5CVgBx | 5,211 | Update Overview.ipynb google colab | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"WDYT @albertvillanova ?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5211). All of your documentation changes will be reflected on that endpoint."
] | 2022-11-07T15:23:52Z | 2022-11-29T15:59:48Z | 2022-11-29T15:54:17Z | MEMBER | null | null | null | - removed metrics stuff
- added image example
- added audio example (with ffmpeg instructions)
- updated the "add a new dataset" section | {
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https://api.github.com/repos/huggingface/datasets/issues/4958 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4958/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4958/comments | https://api.github.com/repos/huggingface/datasets/issues/4958/events | https://github.com/huggingface/datasets/issues/4958 | 1,367,695,376 | I_kwDODunzps5RhWAQ | 4,958 | ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.4.0/datasets/jsonl/jsonl.py | {
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"I have solved this problem... The extension of the file should be `.json` not `.jsonl`"
] | 2022-09-09T11:29:55Z | 2022-09-09T11:38:44Z | 2022-09-09T11:38:44Z | NONE | null | null | {
"completed": 0,
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} | Hi,
When I use load_dataset from local jsonl files, below error happens, and I type the link into the browser prompting me `404: Not Found`. I download the other `.py` files using the same method and it works. It seems that the server is missing the appropriate file, or it is a problem with the code version.
```
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x2b08342004c0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))
```
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https://api.github.com/repos/huggingface/datasets/issues/5611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5611/comments | https://api.github.com/repos/huggingface/datasets/issues/5611/events | https://github.com/huggingface/datasets/pull/5611 | 1,611,197,906 | PR_kwDODunzps5LW2Lx | 5,611 | add Dataset.to_list | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, thanks for working on this! `Table.to_pylist` requires PyArrow 7.0+, and our minimal version requirement is 6.0, so we need to bump the version requirement to avoid CI failure. I'll do this in a separate PR.",
"<details>\n<summ... | 2023-03-06T11:21:57Z | 2023-03-27T13:34:19Z | 2023-03-27T13:26:38Z | CONTRIBUTOR | null | null | null | close https://github.com/huggingface/datasets/issues/5606
This PR is for adding the `Dataset.to_list` method.
Thank you in advance.
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https://api.github.com/repos/huggingface/datasets/issues/6317 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6317/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6317/comments | https://api.github.com/repos/huggingface/datasets/issues/6317/events | https://github.com/huggingface/datasets/issues/6317 | 1,951,965,668 | I_kwDODunzps50WKHk | 6,317 | sentiment140 dataset unavailable | {
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"Thanks for reporting. We are investigating the issue.",
"We have opened an issue in the corresponding Hub dataset: https://huggingface.co/datasets/sentiment140/discussions/3\r\n\r\nLet's continue the discussion there."
] | 2023-10-19T11:25:21Z | 2023-10-19T13:04:56Z | 2023-10-19T13:04:56Z | NONE | null | null | {
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"percent_completed": 0,
"total": 0
} | ### Describe the bug
loading the dataset using load_dataset("sentiment140") returns the following error
ConnectionError: Couldn't reach http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip (error 403)
### Steps to reproduce the bug
Run the following code (version should not matter).
```
from datasets import load_dataset
data = load_dataset("sentiment140")
```
### Expected behavior
The dataset should be loaded just like any other.
The main issue is that it is no longer hosted by stanford. It is still available from a [Google Drive Link](https://docs.google.com/file/d/0B04GJPshIjmPRnZManQwWEdTZjg/edit).
### Environment info
- `datasets` version: 2.14.5
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.10.8
- Huggingface_hub version: 0.17.3
- PyArrow version: 13.0.0
- Pandas version: 2.1.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5245 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5245/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5245/comments | https://api.github.com/repos/huggingface/datasets/issues/5245/events | https://github.com/huggingface/datasets/issues/5245 | 1,450,376,433 | I_kwDODunzps5Wcvzx | 5,245 | Unable to rename columns in streaming dataset | {
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"Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!",
"Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also trie... | 2022-11-15T21:04:41Z | 2022-11-28T12:53:24Z | 2022-11-28T12:53:24Z | NONE | null | null | {
"completed": 0,
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"total": 0
} | ### Describe the bug
Trying to rename column in a streaming datasets, destroys the features object.
### Steps to reproduce the bug
The following code illustrates the error:
```
from datasets import load_dataset
dataset = load_dataset('mc4', 'en', streaming=True, split='train')
dataset.info.features
# {'text': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
dataset = dataset.rename_column("text", "content")
dataset.info.features
# This returned object is now None!
```
### Expected behavior
This should just alter the renamed column.
### Environment info
datasets 2.6.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/7061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7061/comments | https://api.github.com/repos/huggingface/datasets/issues/7061/events | https://github.com/huggingface/datasets/issues/7061 | 2,423,786,881 | I_kwDODunzps6QeA2B | 7,061 | Custom Dataset | Still Raise Error while handling errors in _generate_examples | {
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} | [] | open | false | null | [] | null | [] | 2024-07-22T21:18:12Z | 2024-09-09T14:48:07Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution.
```
def _generate_examples(self, filepaths):
errors=[]
id_ = 0
for filepath in filepaths:
try:
with open(filepath, 'r') as f:
for line in f:
json_obj = json.loads(line)
yield id_, json_obj
id_ += 1
except Exception as exc:
logger.error(f"error occur at filepath: {filepath}")
errors.append(error)
```
seems the logger.error is printed but still exception is raised the the run is exit.
```
Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841
ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl
Traceback (most recent call last):
File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples
json_obj = json.loads(line)
File "myenv/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "myenv/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3)
Generating train split: 0 examples [00:06, ? examples/s]>
RemoteTraceback:
"""
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize
raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in
_write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, 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
"""
The above exception was the direct cause of the following exception:
│ │
│ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. │
│ py:1377 in <listcomp> │
│ │
│ 1374 │ │ │ │ if all(async_result.ready() for async_result in async_results) and queue │
│ 1375 │ │ │ │ │ break │
│ 1376 │ │ # we get the result in case there's an error to raise │
│ ❱ 1377 │ │ [async_result.get() for async_result in async_results] │
│ 1378 │
│ │
│ ╭──────────────────────────────── locals ─────────────────────────────────╮ │
│ │ .0 = <list_iterator object at 0x7f2cc1f0ce20> │ │
│ │ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ ╰─────────────────────────────────────────────────────────────────────────╯ │
│ │
│ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 │
│ in get │
│ │
│ 768 │ │ if self._success: │
│ 769 │ │ │ return self._value │
│ 770 │ │ else: │
│ ❱ 771 │ │ │ raise self._value │
│ 772 │ │
│ 773 │ def _set(self, i, obj): │
│ 774 │ │ self._success, self._value = obj │
│ │
│ ╭────────────────────────────── locals ──────────────────────────────╮ │
│ │ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ │ timeout = None │ │
│ ╰────────────────────────────────────────────────────────────────────╯ │
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
same as above
### Expected behavior
should handle error and continue reading remaining files
### Environment info
python 3.9 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6795 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6795/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6795/comments | https://api.github.com/repos/huggingface/datasets/issues/6795/events | https://github.com/huggingface/datasets/pull/6795 | 2,233,618,719 | PR_kwDODunzps5sJAC8 | 6,795 | Add CLI function to convert script-dataset to Parquet | {
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} | [] | closed | false | null | [] | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6795). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@huggingface/datasets once this PR is merged, I would suggest making a release. Do you ... | 2024-04-09T14:45:12Z | 2024-04-17T08:41:23Z | 2024-04-12T15:27:04Z | MEMBER | null | null | null | Close #6690. | {
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https://api.github.com/repos/huggingface/datasets/issues/7048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7048/comments | https://api.github.com/repos/huggingface/datasets/issues/7048/events | https://github.com/huggingface/datasets/issues/7048 | 2,408,487,547 | I_kwDODunzps6Pjpp7 | 7,048 | ImportError: numpy.core.multiarray when using `filter` | {
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"Could you please check your `numpy` version?",
"I got this issue while using numpy version 2.0. \r\n\r\nI solved it by switching back to numpy 1.26.0 :) ",
"We recently added support for numpy 2.0, but it is not released yet.",
"Ok I see, thanks! I think we can close this issue for now as switching back to v... | 2024-07-15T11:21:04Z | 2024-07-16T10:11:25Z | 2024-07-16T10:11:25Z | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
I can't apply the filter method on my dataset.
### Steps to reproduce the bug
The following snippet generates a bug:
```python
from datasets import load_dataset
ami = load_dataset('kamilakesbi/ami', 'ihm')
ami['train'].filter(
lambda example: example["file_name"] == 'EN2001a'
)
```
I get the following error:
`ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).`
### Expected behavior
It should work properly!
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5891 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5891/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5891/comments | https://api.github.com/repos/huggingface/datasets/issues/5891/events | https://github.com/huggingface/datasets/pull/5891 | 1,722,384,135 | PR_kwDODunzps5RKchn | 5,891 | Make split slicing consistent with list slicing | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5891). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-05-23T16:04:33Z | 2024-01-31T16:00:26Z | 2024-01-31T15:54:17Z | COLLABORATOR | null | null | null | Fix #1774, fix #5875
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https://api.github.com/repos/huggingface/datasets/issues/5876 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5876/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5876/comments | https://api.github.com/repos/huggingface/datasets/issues/5876/events | https://github.com/huggingface/datasets/issues/5876 | 1,717,978,985 | I_kwDODunzps5mZkdp | 5,876 | Incompatibility with DataLab | {
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"Indeed, `clobber=True` (with a warning if the existing protocol will be overwritten) should fix the issue, but maybe a better solution is to register our compression filesystem before the script is executed and unregister them afterward. WDYT @lhoestq @albertvillanova?",
"I think we should use clobber and show a... | 2023-05-20T01:39:11Z | 2023-05-25T06:42:34Z | 2023-05-25T06:42:34Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
Hello,
I am currently working on a project where both [DataLab](https://github.com/ExpressAI/DataLab) and [datasets](https://github.com/huggingface/datasets) are subdependencies.
I noticed that I cannot import both libraries, as they both register FileSystems in `fsspec`, expecting the FileSystems not being registered before.
When running the code below, I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\__init__.py", line 28, in <module>
from datalabs.arrow_dataset import concatenate_datasets, Dataset
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_dataset.py", line 60, in <module>
from datalabs.arrow_writer import ArrowWriter, OptimizedTypedSequence
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\arrow_writer.py", line 28, in <module>
from datalabs.features import (
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\__init__.py", line 2, in <module>
from datalabs.features.audio import Audio
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\features\audio.py", line 21, in <module>
from datalabs.utils.streaming_download_manager import xopen
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\utils\streaming_download_manager.py", line 16, in <module>
from datalabs.filesystems import COMPRESSION_FILESYSTEMS
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\datalabs\filesystems\__init__.py", line 37, in <module>
fsspec.register_implementation(fs_class.protocol, fs_class)
File "C:\Users\Bened\anaconda3\envs\ner-eval-dashboard2\lib\site-packages\fsspec\registry.py", line 51, in register_implementation
raise ValueError(
ValueError: Name (bz2) already in the registry and clobber is False
```
I think as simple solution would be to just set `clobber=True` in https://github.com/huggingface/datasets/blob/main/src/datasets/filesystems/__init__.py#L28. This allows the register to discard previous registrations. This should work, as the datalabs FileSystems are copies of the datasets FileSystems. However, I don't know if it is guaranteed to be compatible with other libraries that might use the same protocols.
I am linking the symmetric issue on [DataLab](https://github.com/ExpressAI/DataLab/issues/425) as ideally the issue is solved in both libraries the same way. Otherwise, it could lead to different behaviors depending on which library gets imported first.
### Steps to reproduce the bug
1. Run `pip install datalabs==0.4.15 datasets==2.12.0`
2. Run the following python code:
```
import datalabs
import datasets
```
### Expected behavior
It should be possible to import both libraries without getting a Value Error
### Environment info
datalabs==0.4.15
datasets==2.12.0
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https://api.github.com/repos/huggingface/datasets/issues/6540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6540/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6540/comments | https://api.github.com/repos/huggingface/datasets/issues/6540/events | https://github.com/huggingface/datasets/issues/6540 | 2,058,965,157 | I_kwDODunzps56uVCl | 6,540 | Extreme inefficiency for `save_to_disk` when merging datasets | {
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"Concatenating datasets doesn't create any indices mapping - so flattening indices is not needed (unless you shuffle the dataset).\r\nCan you share the snippet of code you are using to merge your datasets and save them to disk ?"
] | 2023-12-29T00:44:35Z | 2023-12-30T15:05:48Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
Hi, I tried to merge in total 22M sequences of data, where each sequence is of maximum length 2000. I found that merging these datasets and then `save_to_disk` is extremely slow because of flattening the indices. Wondering if you have any suggestions or guidance on this. Thank you very much!
### Steps to reproduce the bug
The source data is too big to demonstrate
### Expected behavior
The source data is too big to demonstrate
### Environment info
python 3.9.0
datasets 2.7.0
pytorch 2.0.0
tokenizers 0.13.1
transformers 4.31.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6416 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6416/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6416/comments | https://api.github.com/repos/huggingface/datasets/issues/6416/events | https://github.com/huggingface/datasets/pull/6416 | 1,992,954,723 | PR_kwDODunzps5fbA4H | 6,416 | Rename audio_classificiation.py to audio_classification.py | {
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"Oh good catch. Can you also rename it in `src/datasets/tasks/__init__.py` ?",
"Fixed! \r\n\r\n(I think, tough word to spell right TBH)",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show... | 2023-11-14T15:15:29Z | 2023-11-15T11:59:32Z | 2023-11-15T11:53:20Z | CONTRIBUTOR | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6018 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6018/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6018/comments | https://api.github.com/repos/huggingface/datasets/issues/6018/events | https://github.com/huggingface/datasets/pull/6018 | 1,799,411,999 | PR_kwDODunzps5VOmKY | 6,018 | test1 | {
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"We no longer host datasets in this repo. You should use the HF Hub instead."
] | 2023-07-11T17:25:49Z | 2023-07-20T10:11:41Z | 2023-07-20T10:11:41Z | NONE | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5769 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5769/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5769/comments | https://api.github.com/repos/huggingface/datasets/issues/5769/events | https://github.com/huggingface/datasets/issues/5769 | 1,673,441,182 | I_kwDODunzps5jvq-e | 5,769 | Tiktoken tokenizers are not pickable | {
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"Thanks for reporting, @markovalexander.\r\n\r\nUnfortunately, I'm not able to reproduce the issue: the `tiktoken` tokenizer can be used within `Dataset.map`, both in my local machine and in a Colab notebook: https://colab.research.google.com/drive/1DhJroZgk0sNFJ2Mrz-jYgrmh9jblXaCG?usp=sharing\r\n\r\nAre you sure y... | 2023-04-18T16:07:40Z | 2023-05-04T18:55:57Z | 2023-05-04T18:55:57Z | NONE | null | null | {
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} | ### Describe the bug
Since tiktoken tokenizer is not pickable, it is not possible to use it inside `dataset.map()` with multiprocessing enabled. However, you [made](https://github.com/huggingface/datasets/issues/5536) tiktoken's tokenizers pickable in `datasets==2.10.0` for caching. For some reason, this logic does not work in dataset processing and raises `TypeError: cannot pickle 'builtins.CoreBPE' object`
### Steps to reproduce the bug
```
from datasets import load_dataset
import tiktoken
dataset = load_dataset("stas/openwebtext-10k")
enc = tiktoken.get_encoding("gpt2")
tokenized = dataset.map(
process,
remove_columns=['text'],
desc="tokenizing the OWT splits",
num_proc=2,
)
def process(example):
ids = enc.encode(example['text'])
ids.append(enc.eot_token)
out = {'ids': ids, 'len': len(ids)}
return out
```
### Expected behavior
starts processing dataset
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.0-1021-oracle-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.13.4
- PyArrow version: 9.0.0
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6194 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6194/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6194/comments | https://api.github.com/repos/huggingface/datasets/issues/6194/events | https://github.com/huggingface/datasets/issues/6194 | 1,872,598,223 | I_kwDODunzps5vnZTP | 6,194 | Support custom fingerprinting with `Dataset.from_generator` | {
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"The `fingerprint` parameter serves a slightly different purpose - we use it to inject a new fingerprint after transforming a `Dataset` (computed from the previous fingerprint + transform + transform args), e.g., to be able to compute the cache file for a transform. There is no concept of `fingerprint` before a `Da... | 2023-08-29T22:43:13Z | 2024-12-22T01:14:39Z | null | NONE | null | null | {
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} | ### Feature request
When using `Dataset.from_generator`, the generator is hashed when building the fingerprint. Similar to `.map`, it would be interesting to let the user bypass this hashing by accepting a `fingerprint` argument to `.from_generator`.
### Motivation
Using the `.from_generator` constructor with a non-picklable generator fails. By accepting a `fingerprint` argument to `.from_generator`, the user would have the opportunity to manually fingerprint the dataset and thus bypass the crash.
### Your contribution
If validated, I can try to submit a PR for this. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5646 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5646/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5646/comments | https://api.github.com/repos/huggingface/datasets/issues/5646/events | https://github.com/huggingface/datasets/pull/5646 | 1,627,838,762 | PR_kwDODunzps5MOqjj | 5,646 | Allow self as key in `Features` | {
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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-03-16T16:17:03Z | 2023-03-16T17:21:58Z | 2023-03-16T17:14:50Z | COLLABORATOR | null | null | null | Fix #5641 | {
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https://api.github.com/repos/huggingface/datasets/issues/5127 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5127/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5127/comments | https://api.github.com/repos/huggingface/datasets/issues/5127/events | https://github.com/huggingface/datasets/pull/5127 | 1,411,897,544 | PR_kwDODunzps5A8m-Q | 5,127 | [WIP] WebDataset export | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5127). All of your documentation changes will be reflected on that endpoint.",
"Should we close this PR?"
] | 2022-10-17T16:50:22Z | 2024-01-11T06:27:04Z | 2024-01-08T14:25:43Z | MEMBER | null | null | null | I added a first draft of the `IterableDataset.to_wds` method.
You can use it to savea dataset loaded in streamign mode as a webdataset locally.
The API can be further improved to allow to export to a cloud storage like the HF Hub.
I also included sharding with a default max shard size of 500MB (uncompressed), and it is single-processed fo rnow.
Choosing the number of shards is not implemented yet - though if we know the size of the `IterableDataset` this is probably doable`.
For example
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="train", streaming=True)
>>> ds.to_wds("output_dir", compress=True)
>>> import webdataset as wds
>>> ds = wds.WebDataset("output_dir/rotten_tomatoes-train-000000.tar.gz").decode()
>>> next(iter(ds))
{'__key__': '0',
'__url__': 'output_dir/rotten_tomatoes-train-000000.tar.gz',
'label.cls': 1,
'text.txt': 'the rock is destined to be the 21st century\'s new ..., jean-claud van damme or steven segal .'}
```
### Implementation details
The WebDataset format is made of TAR archives containing a series of files per example. For example one pair of `image.jpg` and `label.cls` for image classification.
WebDataset automatically decodes serialized data based on the extension of the files, and output a dictionary. For example `{"image.png": np.array(...), "label.cls": 0}` if you choose the numpy decoding.
To use the automatic decoding, I store each field of each example as a file with its corresponding extension (jpg, json, cls, etc.)
While this is useful to end up with a dictionary with one key per column and appropriate decoding, it can create huge TAR archives if the dataset is made of small samples of text - probably because of useless TAR metadata for each file. This also makes loading super slow: iterating on SQuAD takes 50sec vs 7sec using `datasets` in streaming mode.
I haven't taken a look at alternatives for text datasets made out of small samples, but for image datasets this can already be used to run some benchmarks. | {
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https://api.github.com/repos/huggingface/datasets/issues/7072 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7072/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7072/comments | https://api.github.com/repos/huggingface/datasets/issues/7072/events | https://github.com/huggingface/datasets/issues/7072 | 2,430,577,916 | I_kwDODunzps6Q36z8 | 7,072 | nm | {
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https://api.github.com/repos/huggingface/datasets/issues/6380 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6380/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6380/comments | https://api.github.com/repos/huggingface/datasets/issues/6380/events | https://github.com/huggingface/datasets/pull/6380 | 1,974,741,221 | PR_kwDODunzps5edaO6 | 6,380 | Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET | {
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} | [] | open | false | null | [] | null | [] | 2023-11-02T17:28:23Z | 2023-11-02T17:31:19Z | null | NONE | null | null | null | This PR proposes a (slightly hacky) fix for an Issue that can occur when downloading large dataset parts over unstable connections.
The underlying issue is also being discussed in https://github.com/huggingface/datasets/issues/5594.
Issue Symptoms & Behaviour:
- Download of a large archive file during dataset download via HTTP-GET fails.
- An silent net exception (which I was unable to identify) is thrown within the `tqdm` download progress.
- Due to missing exception catch code, the above process just continues processing, assuming `http_get` completed successfully.
- Pending Archive file gets renamed to remove the `.incomplete` extension, despite not all data has been downloaded.
- Also, for reasons I did not investigate, there seems to be no real integrity check for the downloaded files; or it does not detect this problem. This is especially problematic, since the downloader script won't retry downloading this archive after CRC-Checking, even if it is being manually restarted / executed again after running into errors on extraction.
Fix proposal: Adding a retry mechanic for HTTP-GET downloads, which adds the following behaviour:
- Download Progress Thread checks for download size validity in case the HTTP connection starves mid download. If the check fails, a RuntimeError is thrown
- Cache Downloader code with retry mechanic monitors for an exception thrown by the download progress thread, and retries download with updated `resume_size`.
- Cache Downloader will not mark incomplete files which have thrown an exception during download, and exceeded retries, as complete. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6633 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6633/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6633/comments | https://api.github.com/repos/huggingface/datasets/issues/6633/events | https://github.com/huggingface/datasets/pull/6633 | 2,110,124,475 | PR_kwDODunzps5lknz9 | 6,633 | dataset viewer requires no-script | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6633). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-01-31T13:41:54Z | 2024-01-31T14:05:04Z | 2024-01-31T13:59:01Z | COLLABORATOR | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6571 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6571/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6571/comments | https://api.github.com/repos/huggingface/datasets/issues/6571/events | https://github.com/huggingface/datasets/issues/6571 | 2,072,111,000 | I_kwDODunzps57geeY | 6,571 | Make DatasetDict.column_names return a list instead of dict | {
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} | Currently, `DatasetDict.column_names` returns a dict, with each split name as keys and the corresponding list of column names as values.
However, by construction, all splits have the same column names.
I think it makes more sense to return a single list with the column names, which is the same for all the split keys. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6690 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6690/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6690/comments | https://api.github.com/repos/huggingface/datasets/issues/6690/events | https://github.com/huggingface/datasets/issues/6690 | 2,150,800,065 | I_kwDODunzps6AMprB | 6,690 | Add function to convert a script-dataset to Parquet | {
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https://api.github.com/repos/huggingface/datasets/issues/6146 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6146/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6146/comments | https://api.github.com/repos/huggingface/datasets/issues/6146/events | https://github.com/huggingface/datasets/issues/6146 | 1,848,417,366 | I_kwDODunzps5uLJxW | 6,146 | DatasetGenerationError when load glue benchmark datasets from `load_dataset` | {
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"I've tried clear the .cache file, doesn't work.",
"This issue happens on AWS sagemaker",
"This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?",... | 2023-08-13T05:17:56Z | 2023-08-26T22:09:09Z | 2023-08-26T22:09:09Z | NONE | null | null | {
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} | ### Describe the bug
Package version: datasets-2.14.4
When I run the codes:
```
from datasets import load_dataset
dataset = load_dataset("glue", "ax")
```
I got the following errors:
---------------------------------------------------------------------------
SchemaInferenceError Traceback (most recent call last)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1948 num_shards = shard_id + 1
-> 1949 num_examples, num_bytes = writer.finalize()
1950 writer.close()
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream)
597 self.stream.close()
--> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
599 logger.debug(
600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}."
601 )
SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[5], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("glue", "ax")
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
2135 # Download and prepare data
-> 2136 builder_instance.download_and_prepare(
2137 download_config=download_config,
2138 download_mode=download_mode,
2139 verification_mode=verification_mode,
2140 try_from_hf_gcs=try_from_hf_gcs,
2141 num_proc=num_proc,
2142 storage_options=storage_options,
2143 )
2145 # Build dataset for splits
2146 keep_in_memory = (
2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2148 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_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)
952 if num_proc is not None:
953 prepare_split_kwargs["num_proc"] = num_proc
--> 954 self._download_and_prepare(
955 dl_manager=dl_manager,
956 verification_mode=verification_mode,
957 **prepare_split_kwargs,
958 **download_and_prepare_kwargs,
959 )
960 # Sync info
961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
1045 split_dict.add(split_generator.split_info)
1047 try:
1048 # Prepare split will record examples associated to the split
-> 1049 self._prepare_split(split_generator, **prepare_split_kwargs)
1050 except OSError as e:
1051 raise OSError(
1052 "Cannot find data file. "
1053 + (self.manual_download_instructions or "")
1054 + "\nOriginal error:\n"
1055 + str(e)
1056 ) from None
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1811 job_id = 0
1812 with pbar:
-> 1813 for job_id, done, content in self._prepare_split_single(
1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1815 ):
1816 if done:
1817 result = content
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1957 e = e.__context__
-> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
### Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("glue", "ax")
### Expected behavior
When generating the train split:
Generating train split:
0/0 [00:00<?, ? examples/s]
It raise the error:
DatasetGenerationError: An error occurred while generating the dataset
### Environment info
datasets-2.14.4.
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https://api.github.com/repos/huggingface/datasets/issues/6991 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6991/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6991/comments | https://api.github.com/repos/huggingface/datasets/issues/6991/events | https://github.com/huggingface/datasets/pull/6991 | 2,367,711,094 | PR_kwDODunzps5zPoQs | 6,991 | Unblock NumPy 2.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6991). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@albertvillanova Any chance we could get this in before the next release? Everything d... | 2024-06-22T09:19:53Z | 2024-12-25T17:57:34Z | 2024-07-12T12:04:53Z | CONTRIBUTOR | null | null | null | Fixes https://github.com/huggingface/datasets/issues/6980 | {
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https://api.github.com/repos/huggingface/datasets/issues/4535 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4535/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4535/comments | https://api.github.com/repos/huggingface/datasets/issues/4535/events | https://github.com/huggingface/datasets/pull/4535 | 1,278,365,039 | PR_kwDODunzps46BnXq | 4,535 | Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays` | {
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"Also, I had a doubt while checking the code related to the indices... \r\n\r\n@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just us... | 2022-06-21T12:18:49Z | 2022-06-27T16:25:09Z | 2022-06-27T16:14:36Z | MEMBER | null | null | null | Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`.
This is useful so as to tweak the `batch_size` according to the VM specifications. | {
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https://api.github.com/repos/huggingface/datasets/issues/4998 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4998/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4998/comments | https://api.github.com/repos/huggingface/datasets/issues/4998/events | https://github.com/huggingface/datasets/pull/4998 | 1,379,466,717 | PR_kwDODunzps4_Ryp3 | 4,998 | Don't add a tag on the Hub on release | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-20T13:54:57Z | 2022-09-20T14:11:46Z | 2022-09-20T14:08:54Z | MEMBER | null | null | null | Datasets with no namespace on the Hub have tags to redirect to the version of datasets where they come from.
I’m about to remove them all because I think it looks bad/unexpected in the UI and it’s not actually useful
Therefore I'm also disabling tagging.
Note that the CI job will be completely removed in https://github.com/huggingface/datasets/pull/4974 anyway | {
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https://api.github.com/repos/huggingface/datasets/issues/5028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5028/comments | https://api.github.com/repos/huggingface/datasets/issues/5028/events | https://github.com/huggingface/datasets/issues/5028 | 1,386,272,533 | I_kwDODunzps5SoNcV | 5,028 | passing parameters to the method passed to Dataset.from_generator() | {
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"Hi! Yes, you can either use the `gen_kwargs` param in `Dataset.from_generator` (`ds = Dataset.from_generator(gen, gen_kwargs={\"param1\": val})`) or wrap the generator function with `functools.partial`\r\n(`ds = Dataset.from_generator(functools.partial(gen, param1=\"val\"))`) to pass custom parameters to it.\r\n"
... | 2022-09-26T15:20:06Z | 2022-10-03T13:00:00Z | 2022-10-03T13:00:00Z | NONE | null | null | {
"completed": 0,
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} | Big thanks for providing dataset creation via a generator.
I want to ask whether there is any way that parameters can be passed to the method Dataset.from_generator() method, like as follows.
```
from datasets import Dataset
def gen(param1):
for idx in len(custom_dataset):
yield custom_dataset[idx] + param1
ds = Dataset.from_generator(gen(param1))
```
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https://api.github.com/repos/huggingface/datasets/issues/4963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4963/comments | https://api.github.com/repos/huggingface/datasets/issues/4963/events | https://github.com/huggingface/datasets/issues/4963 | 1,368,201,188 | I_kwDODunzps5RjRfk | 4,963 | Dataset without script does not support regular JSON data file | {
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"Hi @julien-c,\r\n\r\nOut of the box, we only support JSON lines (NDJSON) data files, but your data file is a regular JSON file. The reason is we use `pyarrow.json.read_json` and this only supports line-delimited JSON. "
] | 2022-09-09T18:45:33Z | 2022-09-20T15:40:07Z | 2022-09-20T15:40:07Z | MEMBER | null | null | {
"completed": 0,
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} | ### Link
https://huggingface.co/datasets/julien-c/label-studio-my-dogs
### Description
<img width="1115" alt="image" src="https://user-images.githubusercontent.com/326577/189422048-7e9c390f-bea7-4521-a232-43f049ccbd1f.png">
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/6735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6735/comments | https://api.github.com/repos/huggingface/datasets/issues/6735/events | https://github.com/huggingface/datasets/pull/6735 | 2,189,132,932 | PR_kwDODunzps5px84g | 6,735 | Add `mode` parameter to `Image` feature | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6735). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-03-15T17:21:12Z | 2024-03-18T15:47:48Z | 2024-03-18T15:41:33Z | COLLABORATOR | null | null | null | Fix https://github.com/huggingface/datasets/issues/6675 | {
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https://api.github.com/repos/huggingface/datasets/issues/6823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6823/comments | https://api.github.com/repos/huggingface/datasets/issues/6823/events | https://github.com/huggingface/datasets/issues/6823 | 2,250,775,569 | I_kwDODunzps6GKBwR | 6,823 | Loading problems of Datasets with a single shard | {
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"Has there been a PR to resolve this already?",
"The problem rises from using a wrong api.\r\nWhen loading a save_to_disk dataset, **load_from_disk** (instead of load_dataset) is what should be used.\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndst.save_to_disk(\"cache\")\r\ndst = load_from_disk... | 2024-04-18T13:59:00Z | 2024-11-25T05:40:09Z | null | NONE | null | null | {
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} | ### Describe the bug
When saving a dataset on disk and it has a single shard it is not loaded as when it is saved in multiple shards. I installed the latest version of datasets via pip.
### Steps to reproduce the bug
The code below reproduces the behavior. All works well when the range of the loop is 10000 but it fails when it is 1000.
```
from PIL import Image
import numpy as np
from datasets import Dataset, DatasetDict, load_dataset
def load_image():
# Generate random noise image
noise = np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8)
return Image.fromarray(noise)
def create_dataset():
input_images = []
output_images = []
text_prompts = []
for _ in range(10000): # this is the problematic parameter
input_images.append(load_image())
output_images.append(load_image())
text_prompts.append('test prompt')
data = {'input_image': input_images, 'output_image': output_images, 'text_prompt': text_prompts}
dataset = Dataset.from_dict(data)
return DatasetDict({'train': dataset})
dataset = create_dataset()
print('dataset before saving')
print(dataset)
print(dataset['train'].column_names)
dataset.save_to_disk('test_ds')
print('dataset after loading')
dataset_loaded = load_dataset('test_ds')
print(dataset_loaded)
print(dataset_loaded['train'].column_names)
```
The output for 1000 iterations is:
```
dataset before saving
DatasetDict({
train: Dataset({
features: ['input_image', 'output_image', 'text_prompt'],
num_rows: 1000
})
})
['input_image', 'output_image', 'text_prompt']
Saving the dataset (1/1 shards): 100%|█| 1000/1000 [00:00<00:00, 5156.00 example
dataset after loading
Generating train split: 1 examples [00:00, 230.52 examples/s]
DatasetDict({
train: Dataset({
features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],
num_rows: 1
})
})
['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split']
```
For 10000 iteration (8 shards) it is correct:
```
dataset before saving
DatasetDict({
train: Dataset({
features: ['input_image', 'output_image', 'text_prompt'],
num_rows: 10000
})
})
['input_image', 'output_image', 'text_prompt']
Saving the dataset (8/8 shards): 100%|█| 10000/10000 [00:01<00:00, 6237.68 examp
dataset after loading
Generating train split: 10000 examples [00:00, 10773.16 examples/s]
DatasetDict({
train: Dataset({
features: ['input_image', 'output_image', 'text_prompt'],
num_rows: 10000
})
})
['input_image', 'output_image', 'text_prompt']
```
### Expected behavior
The procedure should work for a dataset with one shrad the same as for one with multiple shards
### Environment info
- `datasets` version: 2.18.0
- Platform: macOS-14.1-arm64-arm-64bit
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0
Edit: I looked in the source code of load.py in datasets. I should have used "load_from_disk" and it indeed works that way. But ideally load_dataset would have raisen an error the same way as if I call a path:
```
if Path(path, config.DATASET_STATE_JSON_FILENAME).exists():
raise ValueError(
"You are trying to load a dataset that was saved using `save_to_disk`. "
"Please use `load_from_disk` instead."
)
```
nevertheless I find it interesting that it works just well and without a warning if there are multiple shards. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6190 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6190/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6190/comments | https://api.github.com/repos/huggingface/datasets/issues/6190/events | https://github.com/huggingface/datasets/issues/6190 | 1,871,582,175 | I_kwDODunzps5vjhPf | 6,190 | `Invalid user token` even when correct user token is passed! | {
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"This is because `download_config.use_auth_token` is deprecated - you should use `download_config.token` instead",
"Works! Thanks for the quick fix! <3"
] | 2023-08-29T12:37:03Z | 2023-08-29T13:01:10Z | 2023-08-29T13:01:09Z | MEMBER | null | null | {
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} | ### Describe the bug
I'm working on a dataset which comprises other datasets on the hub.
URL: https://huggingface.co/datasets/open-asr-leaderboard/datasets-test-only
Note: Some of the sub-datasets in this metadataset require explicit access.
All the other datasets work fine, except, `common_voice`.
### Steps to reproduce the bug
https://github.com/Vaibhavs10/scratchpad/blob/main/cv_datasets_bug_repro.ipynb
### Expected behavior
It should work if the provided access token is valid (as it does for all the other datasets)
### Environment info
datasets version -> 2.14.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6053/comments | https://api.github.com/repos/huggingface/datasets/issues/6053/events | https://github.com/huggingface/datasets/issues/6053 | 1,812,635,902 | I_kwDODunzps5sCqD- | 6,053 | Change package name from "datasets" to something less generic | {
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"This would break a lot of existing code, so we can't really do this.",
"I encountered this issue while working on a large project with 6+ years history. We have a submodule named datasets in the backend, and face a big challenge incorporating huggingface datasets into the project, especially considering django a... | 2023-07-19T19:53:28Z | 2024-11-20T21:22:36Z | 2023-10-03T16:04:09Z | NONE | null | null | {
"completed": 0,
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} | ### Feature request
I'm repeatedly finding myself in situations where I want to have a package called `datasets.py` or `evaluate.py` in my code and can't because those names are being taken up by Huggingface packages. While I can understand how (even from the user's perspective) it's aesthetically pleasing to have nice terse library names, ultimately a library hogging simple names like this is something I find short-sighted, impractical and at my most irritable, frankly rude.
My preference would be a pattern like what you get with all the other big libraries like numpy or pandas:
```
import huggingface as hf
# hf.transformers, hf.datasets, hf.evaluate
```
or things like
```
import huggingface.transformers as tf
# tf.load_model(), etc
```
If this isn't possible for some technical reason, at least just call the packages something like `hf_transformers` and so on.
I realize this is a very big change that's probably been discussed internally already, but I'm making this issue and sister issues on each huggingface project just to start the conversation and begin tracking community feeling on the matter, since I suspect I'm not the only one who feels like this.
Sorry if this has been requested already on this issue tracker, I couldn't find anything looking for terms like "package name".
Sister issues:
- [transformers](https://github.com/huggingface/transformers/issues/24934)
- **datasets**
- [evaluate](https://github.com/huggingface/evaluate/issues/476)
### Motivation
Not taking up package names the user is likely to want to use.
### Your contribution
No - more a matter of internal discussion among core library authors. | {
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https://api.github.com/repos/huggingface/datasets/issues/6506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6506/comments | https://api.github.com/repos/huggingface/datasets/issues/6506/events | https://github.com/huggingface/datasets/issues/6506 | 2,044,975,038 | I_kwDODunzps5549e- | 6,506 | Incorrect test set labels for RTE and CoLA datasets via load_dataset | {
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"As this is a specific issue of the \"glue\" dataset, I have transferred it to the dataset Discussion page: https://huggingface.co/datasets/glue/discussions/15\r\n\r\nLet's continue the discussion there!"
] | 2023-12-16T22:06:08Z | 2023-12-21T09:57:57Z | 2023-12-21T09:57:57Z | NONE | null | null | {
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} | ### Describe the bug
The test set labels for the RTE and CoLA datasets when loading via datasets load_dataset are all -1.
Edit: It appears this is also the case for every other dataset except for MRPC (stsb, sst2, qqp, mnli (both matched and mismatched), qnli, wnli, ax). Is this intended behavior to safeguard the test set for evaluation purposes?
### Steps to reproduce the bug
!pip install datasets
from datasets import load_dataset
rte_data = load_dataset('glue', 'rte')
cola_data = load_dataset('glue', 'cola')
print(rte_data['test'][0:30]['label'])
print(cola_data['test'][0:30]['label'])
Output:
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
The non-label test data seems to be fine:
e.g. rte_data['test'][1] is:
{'sentence1': "Authorities in Brazil say that more than 200 people are being held hostage in a prison in the country's remote, Amazonian-jungle state of Rondonia.",
'sentence2': 'Authorities in Brazil hold 200 people as hostage.',
'label': -1,
'idx': 1}
Training and validation data are also fine:
e.g. rte_data['train][0] is:
{'sentence1': 'No Weapons of Mass Destruction Found in Iraq Yet.',
'sentence2': 'Weapons of Mass Destruction Found in Iraq.',
'label': 1,
'idx': 0}
### Expected behavior
Expected the labels to be binary 0/1 values; Got all -1s instead
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4566 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4566/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4566/comments | https://api.github.com/repos/huggingface/datasets/issues/4566/events | https://github.com/huggingface/datasets/issues/4566 | 1,284,397,594 | I_kwDODunzps5Mjloa | 4,566 | Document link #load_dataset_enhancing_performance points to nowhere | {
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"Hi! This is indeed the link the docstring should point to. Are you interested in submitting a PR to fix this?",
"https://github.com/huggingface/datasets/blame/master/docs/source/cache.mdx#L93\r\n\r\nThere seems already an anchor here. Somehow it doesn't work. I am not very familiar with how this online documenta... | 2022-06-25T01:18:19Z | 2023-01-24T16:33:40Z | 2023-01-24T16:33:40Z | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
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} | ## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
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https://api.github.com/repos/huggingface/datasets/issues/5672 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5672/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5672/comments | https://api.github.com/repos/huggingface/datasets/issues/5672/events | https://github.com/huggingface/datasets/issues/5672 | 1,641,005,322 | I_kwDODunzps5hz8EK | 5,672 | Pushing dataset to hub crash | {
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"Hi ! It's been fixed by https://github.com/huggingface/datasets/pull/5598. We're doing a new release tomorrow with the fix and you'll be able to push your 100k images ;)\r\n\r\nBasically `push_to_hub` used to fail if the remote repository already exists and has a README.md without dataset_info in the YAML tags.\r\... | 2023-03-26T17:42:13Z | 2023-03-30T08:11:05Z | 2023-03-30T08:11:05Z | NONE | null | null | {
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} | ### Describe the bug
Uploading a dataset with `push_to_hub()` fails without error description.
### Steps to reproduce the bug
Hey there,
I've built a image dataset of 100k images + text pair as described here https://huggingface.co/docs/datasets/image_dataset#imagefolder
Now I'm trying to push it to the hub but I'm running into issues. First, I tried doing it via git directly, I added all the files in git lfs and pushed but I got hit with an error saying huggingface only accept up to 10k files in a folder.
So I'm now trying with the `push_to_hub()` func as follow:
```python
from datasets import load_dataset
import os
dataset = load_dataset("imagefolder", data_dir="./data", split="train")
dataset.push_to_hub("tzvc/organization-logos", token=os.environ.get('HF_TOKEN'))
```
But again, this produces an error:
```
Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 100212/100212 [00:00<00:00, 439108.61it/s]
Downloading and preparing dataset imagefolder/default to /home/contact_theochampion/.cache/huggingface/datasets/imagefolder/default-20567ffc703aa314/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...
Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 100211/100211 [00:00<00:00, 149323.73it/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15947.92it/s]
Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2245.34it/s]
Dataset imagefolder downloaded and prepared to /home/contact_theochampion/.cache/huggingface/datasets/imagefolder/default-20567ffc703aa314/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f. Subsequent calls will reuse this data.
Resuming upload of the dataset shards.
Pushing dataset shards to the dataset hub: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:31<00:00, 2.24s/it]
Downloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 118/118 [00:00<00:00, 225kB/s]
Traceback (most recent call last):
File "/home/contact_theochampion/organization-logos/push_to_hub.py", line 5, in <module>
dataset.push_to_hub("tzvc/organization-logos", token=os.environ.get('HF_TOKEN'))
File "/home/contact_theochampion/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5245, in push_to_hub
repo_info = dataset_infos[next(iter(dataset_infos))]
StopIteration
```
What could be happening here ?
### Expected behavior
The dataset is pushed to the hub
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.10.0-21-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5175/comments | https://api.github.com/repos/huggingface/datasets/issues/5175/events | https://github.com/huggingface/datasets/issues/5175 | 1,428,696,231 | I_kwDODunzps5VKCyn | 5,175 | Loading an external NER dataset | {
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} | I need to use huggingface datasets to load a custom dataset similar to conll2003 but with more entities and each the files contain only two columns: word and ner tag.
I tried this code snnipet that I found here as an answer to a similar issue:
from datasets import Dataset
INPUT_COLUMNS = "ID Text NER".split()
def read_conll(file):
example = {col: [] for col in INPUT_COLUMNS}
idx = 0
with open(file) as f:
for line in f:
if line.startswith("-DOCSTART-") or line == "\n" or not line:
if example[next(iter(example))]:
yield idx, example
idx += 1
example = {col: [] for col in INPUT_COLUMNS}
else:
row_cols = line.split()
for i, col in enumerate(example):
example[col] = row_cols[i].rstrip()
train = Dataset.from_generator(read_conll, gen_kwargs={"file": "some_path"})
But the following error happened:
ValueError: Please pass `features` or at least one example when writing data | {
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https://api.github.com/repos/huggingface/datasets/issues/5589 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5589/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5589/comments | https://api.github.com/repos/huggingface/datasets/issues/5589/events | https://github.com/huggingface/datasets/pull/5589 | 1,603,535,704 | PR_kwDODunzps5K9K1i | 5,589 | Revert "pass the dataset features to the IterableDataset.from_generator" | {
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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... | 2023-02-28T17:52:04Z | 2023-09-24T10:07:33Z | 2023-03-21T14:18:18Z | MEMBER | null | null | null | This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily)
It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it
cc @mariosasko @Hubert-Bonisseur | {
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"I encountered this same issue when loading a customized dataset for ORPO training, in which there were three columns and two of them were lists. \r\nI debugged and found that it might be caused by the type-infer mechanism and because in some chunks one of the columns is always an empty list ([]), it was regarded a... | 2024-04-26T14:11:44Z | 2024-05-15T12:06:59Z | null | NONE | null | null | {
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} | ### Describe the bug
dataset = load_dataset("Doraemon-AI/text-to-neo4j-cypher-chinese")
got exception:
Generating train split: 1834 examples [00:00, 5227.98 examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2295, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2254, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2254, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2018, in cast_array_to_feature
casted_array_values = _c(array.values, feature[0])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/table.py", line 2115, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<m.name: string, x.name: string, p.name: string, n.name: string, h.name: string, name: string, c: int64, collect(r.name): list<item: string>, q.name: string, rel.name: string, count(p): int64, 1: int64, p.location: string, max(n.name): null, mn.name: string, p.time: int64, min(q.name): string>
to
{'q.name': Value(dtype='string', id=None), 'mn.name': Value(dtype='string', id=None), 'x.name': Value(dtype='string', id=None), 'p.name': Value(dtype='string', id=None), 'n.name': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'm.name': Value(dtype='string', id=None), 'h.name': Value(dtype='string', id=None), 'count(p)': Value(dtype='int64', id=None), 'rel.name': Value(dtype='string', id=None), 'c': Value(dtype='int64', id=None), 'collect(r.name)': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '1': Value(dtype='int64', id=None), 'p.location': Value(dtype='string', id=None), 'substring(h.name,0,5)': Value(dtype='string', id=None), 'p.time': Value(dtype='int64', id=None)}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/llm/train-2.py", line 150, in <module>
dataset = load_dataset("Doraemon-AI/text-to-neo4j-cypher-chinese")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/datasets/load.py", line 2609, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.11/dist-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
### Steps to reproduce the bug
dataset = load_dataset("Doraemon-AI/text-to-neo4j-cypher-chinese")
### Expected behavior
no exception
### Environment info
python 3.11
datasets 2.19.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6582 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6582/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6582/comments | https://api.github.com/repos/huggingface/datasets/issues/6582/events | https://github.com/huggingface/datasets/pull/6582 | 2,076,072,101 | PR_kwDODunzps5jxpry | 6,582 | Fix for Incorrect ex_iterable used with multi num_worker | {
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"A toy example to reveal the bug.\r\n\r\n```python\r\n\"\"\"\r\nDATASETS_VERBOSITY=debug torchrun --nproc-per-node 2 main.py \r\n\"\"\"\r\nimport torch.utils.data\r\nimport torch.distributed\r\nimport datasets.distributed\r\nimport datasets\r\n\r\n# num shards = 4\r\nshards = [(0, 100), (100, 200), (200, 300), (300... | 2024-01-11T08:49:43Z | 2024-03-01T19:09:14Z | 2024-03-01T19:02:33Z | CONTRIBUTOR | null | null | null | Corrects an issue where `self._ex_iterable` was erroneously used instead of `ex_iterable`, when both Distributed Data Parallel (DDP) and multi num_worker are used concurrently. This improper usage led to the generation of incorrect `shards_indices`, subsequently causing issues with the control flow responsible for worker creation. The fix ensures the appropriate iterable is used, thus providing a more accurate determination of whether a new worker should be instantiated or not. | {
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https://api.github.com/repos/huggingface/datasets/issues/6710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6710/comments | https://api.github.com/repos/huggingface/datasets/issues/6710/events | https://github.com/huggingface/datasets/pull/6710 | 2,164,781,564 | PR_kwDODunzps5oe4ov | 6,710 | Persist IterableDataset epoch in workers | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6710). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-03-02T12:08:50Z | 2024-07-01T17:51:25Z | 2024-07-01T17:45:30Z | MEMBER | null | null | null | Use shared memory for the IterableDataset epoch.
This way calling `ds.set_epoch()` in the main process will update the epoch in the DataLoader workers as well.
This is useful especially because the epoch is used to compute the `effective_seed` used for shuffling.
I used torch's shared memory in case users want to send dataset copies without shared memory using pickle. I also find it easier to use than using `multiprocessing.shared_memory` than requires unlinking only in the main process, or `mp.Value` that is not picklable.
close https://github.com/huggingface/datasets/issues/6673
cc @rwightman | {
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https://api.github.com/repos/huggingface/datasets/issues/4712 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4712/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4712/comments | https://api.github.com/repos/huggingface/datasets/issues/4712/events | https://github.com/huggingface/datasets/pull/4712 | 1,309,177,302 | PR_kwDODunzps47ohdr | 4,712 | Highlight non-commercial license in amazon_reviews_multi dataset card | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-19T08:36:20Z | 2022-07-27T16:09:40Z | 2022-07-27T15:57:41Z | CONTRIBUTOR | null | null | null | Highlight that the licence granted by Amazon only covers non-commercial research use. | {
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https://api.github.com/repos/huggingface/datasets/issues/6954 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6954/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6954/comments | https://api.github.com/repos/huggingface/datasets/issues/6954/events | https://github.com/huggingface/datasets/pull/6954 | 2,333,530,558 | PR_kwDODunzps5xbWtU | 6,954 | Remove default `trust_remote_code=True` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6954). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"yay! 🎉 ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<detai... | 2024-06-04T13:22:56Z | 2024-06-17T16:32:24Z | 2024-06-07T12:20:29Z | MEMBER | null | null | null | TODO:
- [x] fix tests | {
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https://api.github.com/repos/huggingface/datasets/issues/7513 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7513/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7513/comments | https://api.github.com/repos/huggingface/datasets/issues/7513/events | https://github.com/huggingface/datasets/issues/7513 | 2,994,678,437 | I_kwDODunzps6yfyql | 7,513 | MemoryError while creating dataset from generator | {
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"Upd: created a PR that can probably solve the problem: #7514",
"Hi ! We need to take the generator into account for the cache. The generator is hashed to make the dataset fingerprint used by the cache. This way you can reload the Dataset from the cache without regenerating in subsequent `from_generator` calls.\n... | 2025-04-15T01:02:02Z | 2025-04-23T19:37:08Z | null | NONE | null | null | {
"completed": 0,
"percent_completed": 0,
"total": 0
} | ### Describe the bug
# TL:DR
`Dataset.from_generator` function passes all of its arguments to `BuilderConfig.create_config_id`, including `generator` function itself. `BuilderConfig.create_config_id` function tries to hash all the args, which can take a large amount of time or even cause MemoryError if the dataset processed in a generator function is large enough.
Maybe we should pop `generator` from `config_kwargs_to_add_to_suffix` before hashing to avoid it.
# Full description
I have a pretty large spatial imagery dataset that is generated from two xbatcher.BatchGenerators via custom `dataset_generator` function that looks like this if simplified:
```
def dataset_generator():
for index in samples:
data_dict = {
"key": index,
"x": x_batches[index].data,
"y": y_batches[index].data,
}
yield data_dict
```
Then I use `datasets.Dataset.from_generator` to generate the dataset itself.
```
# Create dataset
ds = datasets.Dataset.from_generator(
dataset_generator,
features=feat,
cache_dir=(output / ".cache"),
)
```
It works nicely with pretty small data, but if the dataset is huge and barely fits in memory, it crashes with memory error:
<details>
<summary>Full stack trace</summary>
```
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\remote_sensing_processor\segmentation\semantic\tiles.py:248](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/remote_sensing_processor/segmentation/semantic/tiles.py#line=247), in generate_tiles(x, y, output, tile_size, shuffle, split, x_dtype, y_dtype, x_nodata, y_nodata)
245 yield data_dict
247 # Create dataset
--> 248 ds = datasets.Dataset.from_generator(
249 dataset_generator,
250 features=feat,
251 cache_dir=(output / ".cache"),
252 )
254 # Save dataset
255 ds.save_to_disk(output / name)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\arrow_dataset.py:1105](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/arrow_dataset.py#line=1104), in Dataset.from_generator(generator, features, cache_dir, keep_in_memory, gen_kwargs, num_proc, split, **kwargs)
1052 """Create a Dataset from a generator.
1053
1054 Args:
(...) 1101 ```
1102 """
1103 from .io.generator import GeneratorDatasetInputStream
-> 1105 return GeneratorDatasetInputStream(
1106 generator=generator,
1107 features=features,
1108 cache_dir=cache_dir,
1109 keep_in_memory=keep_in_memory,
1110 gen_kwargs=gen_kwargs,
1111 num_proc=num_proc,
1112 split=split,
1113 **kwargs,
1114 ).read()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\io\generator.py:29](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/io/generator.py#line=28), in GeneratorDatasetInputStream.__init__(self, generator, features, cache_dir, keep_in_memory, streaming, gen_kwargs, num_proc, split, **kwargs)
9 def __init__(
10 self,
11 generator: Callable,
(...) 19 **kwargs,
20 ):
21 super().__init__(
22 features=features,
23 cache_dir=cache_dir,
(...) 27 **kwargs,
28 )
---> 29 self.builder = Generator(
30 cache_dir=cache_dir,
31 features=features,
32 generator=generator,
33 gen_kwargs=gen_kwargs,
34 split=split,
35 **kwargs,
36 )
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:343](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=342), in DatasetBuilder.__init__(self, cache_dir, dataset_name, config_name, hash, base_path, info, features, token, repo_id, data_files, data_dir, storage_options, writer_batch_size, **config_kwargs)
341 config_kwargs["data_dir"] = data_dir
342 self.config_kwargs = config_kwargs
--> 343 self.config, self.config_id = self._create_builder_config(
344 config_name=config_name,
345 custom_features=features,
346 **config_kwargs,
347 )
349 # prepare info: DatasetInfo are a standardized dataclass across all datasets
350 # Prefill datasetinfo
351 if info is None:
352 # TODO FOR PACKAGED MODULES IT IMPORTS DATA FROM src/packaged_modules which doesn't make sense
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:604](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=603), in DatasetBuilder._create_builder_config(self, config_name, custom_features, **config_kwargs)
598 builder_config._resolve_data_files(
599 base_path=self.base_path,
600 download_config=DownloadConfig(token=self.token, storage_options=self.storage_options),
601 )
603 # compute the config id that is going to be used for caching
--> 604 config_id = builder_config.create_config_id(
605 config_kwargs,
606 custom_features=custom_features,
607 )
608 is_custom = (config_id not in self.builder_configs) and config_id != "default"
609 if is_custom:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\builder.py:187](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/builder.py#line=186), in BuilderConfig.create_config_id(self, config_kwargs, custom_features)
185 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
186 else:
--> 187 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
189 if custom_features is not None:
190 m = Hasher()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\fingerprint.py:188](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/fingerprint.py#line=187), in Hasher.hash(cls, value)
186 @classmethod
187 def hash(cls, value: Any) -> str:
--> 188 return cls.hash_bytes(dumps(value))
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:109](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=108), in dumps(obj)
107 """Pickle an object to a string."""
108 file = BytesIO()
--> 109 dump(obj, file)
110 return file.getvalue()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:103](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=102), in dump(obj, file)
101 def dump(obj, file):
102 """Pickle an object to a file."""
--> 103 Pickler(file, recurse=True).dump(obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:420](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=419), in Pickler.dump(self, obj)
418 def dump(self, obj): #NOTE: if settings change, need to update attributes
419 logger.trace_setup(self)
--> 420 StockPickler.dump(self, obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:484](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=483), in _Pickler.dump(self, obj)
482 if self.proto >= 4:
483 self.framer.start_framing()
--> 484 self.save(obj)
485 self.write(STOP)
486 self.framer.end_framing()
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1985](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1984), in save_function(pickler, obj)
1982 if state_dict:
1983 state = state, state_dict
-> 1985 _save_with_postproc(pickler, (_create_function, (
1986 obj.__code__, globs, obj.__name__, obj.__defaults__,
1987 closure
1988 ), state), obj=obj, postproc_list=postproc_list)
1990 # Lift closure cell update to earliest function (#458)
1991 if _postproc:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1117](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1116), in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
1115 continue
1116 else:
-> 1117 pickler.save_reduce(*reduction)
1118 # pop None created by calling preprocessing step off stack
1119 pickler.write(POP)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:690](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=689), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
688 else:
689 save(func)
--> 690 save(args)
691 write(REDUCE)
693 if obj is not None:
694 # If the object is already in the memo, this means it is
695 # recursive. In this case, throw away everything we put on the
696 # stack, and fetch the object back from the memo.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:905](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=904), in _Pickler.save_tuple(self, obj)
903 if n <= 3 and self.proto >= 2:
904 for element in obj:
--> 905 save(element)
906 # Subtle. Same as in the big comment below.
907 if id(obj) in memo:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:690](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=689), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
688 else:
689 save(func)
--> 690 save(args)
691 write(REDUCE)
693 if obj is not None:
694 # If the object is already in the memo, this means it is
695 # recursive. In this case, throw away everything we put on the
696 # stack, and fetch the object back from the memo.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:920](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=919), in _Pickler.save_tuple(self, obj)
918 write(MARK)
919 for element in obj:
--> 920 save(element)
922 if id(obj) in memo:
923 # Subtle. d was not in memo when we entered save_tuple(), so
924 # the process of saving the tuple's elements must have saved
(...) 928 # could have been done in the "for element" loop instead, but
929 # recursive tuples are a rare thing.
930 get = self.get(memo[id(obj)][0])
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1019](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1018), in _Pickler._batch_setitems(self, items)
1017 k, v = tmp[0]
1018 save(k)
-> 1019 save(v)
1020 write(SETITEM)
1021 # else tmp is empty, and we're done
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:1217](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=1216), in save_module_dict(pickler, obj)
1214 if is_dill(pickler, child=False) and pickler._session:
1215 # we only care about session the first pass thru
1216 pickler._first_pass = False
-> 1217 StockPickler.save_dict(pickler, obj)
1218 logger.trace(pickler, "# D2")
1219 return
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:990](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=989), in _Pickler.save_dict(self, obj)
987 self.write(MARK + DICT)
989 self.memoize(obj)
--> 990 self._batch_setitems(obj.items())
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:83](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=82), in Pickler._batch_setitems(self, items)
80 from datasets.fingerprint import Hasher
82 items = sorted(items, key=lambda x: Hasher.hash(x[0]))
---> 83 dill.Pickler._batch_setitems(self, items)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:1014](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=1013), in _Pickler._batch_setitems(self, items)
1012 for k, v in tmp:
1013 save(k)
-> 1014 save(v)
1015 write(SETITEMS)
1016 elif n:
[... skipping similar frames: Pickler.save at line 70 (1 times), Pickler.save at line 414 (1 times)]
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:601](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=600), in _Pickler.save(self, obj, save_persistent_id)
597 raise PicklingError("Tuple returned by %s must have "
598 "two to six elements" % reduce)
600 # Save the reduce() output and finally memoize the object
--> 601 self.save_reduce(obj=obj, *rv)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:715](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=714), in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
713 if state is not None:
714 if state_setter is None:
--> 715 save(state)
716 write(BUILD)
717 else:
718 # If a state_setter is specified, call it instead of load_build
719 # to update obj's with its previous state.
720 # First, push state_setter and its tuple of expected arguments
721 # (obj, state) onto the stack.
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:920](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=919), in _Pickler.save_tuple(self, obj)
918 write(MARK)
919 for element in obj:
--> 920 save(element)
922 if id(obj) in memo:
923 # Subtle. d was not in memo when we entered save_tuple(), so
924 # the process of saving the tuple's elements must have saved
(...) 928 # could have been done in the "for element" loop instead, but
929 # recursive tuples are a rare thing.
930 get = self.get(memo[id(obj)][0])
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\datasets\utils\_dill.py:70](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/datasets/utils/_dill.py#line=69), in Pickler.save(self, obj, save_persistent_id)
68 if obj_type is FunctionType:
69 obj = getattr(obj, "_torchdynamo_orig_callable", obj)
---> 70 dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\site-packages\dill\_dill.py:414](file:///C:/ProgramData/miniforge3/envs/geo/Lib/site-packages/dill/_dill.py#line=413), in Pickler.save(self, obj, save_persistent_id)
412 msg = "Can't pickle %s: attribute lookup builtins.generator failed" % GeneratorType
413 raise PicklingError(msg)
--> 414 StockPickler.save(self, obj, save_persistent_id)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:558](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=557), in _Pickler.save(self, obj, save_persistent_id)
556 f = self.dispatch.get(t)
557 if f is not None:
--> 558 f(self, obj) # Call unbound method with explicit self
559 return
561 # Check private dispatch table if any, or else
562 # copyreg.dispatch_table
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:809](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=808), in _Pickler.save_bytes(self, obj)
806 self.save_reduce(codecs.encode,
807 (str(obj, 'latin1'), 'latin1'), obj=obj)
808 return
--> 809 self._save_bytes_no_memo(obj)
810 self.memoize(obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:797](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=796), in _Pickler._save_bytes_no_memo(self, obj)
795 self._write_large_bytes(BINBYTES8 + pack("<Q", n), obj)
796 elif n >= self.framer._FRAME_SIZE_TARGET:
--> 797 self._write_large_bytes(BINBYTES + pack("<I", n), obj)
798 else:
799 self.write(BINBYTES + pack("<I", n) + obj)
File [C:\ProgramData\miniforge3\envs\geo\Lib\pickle.py:254](file:///C:/ProgramData/miniforge3/envs/geo/Lib/pickle.py#line=253), in _Framer.write_large_bytes(self, header, payload)
247 # Perform direct write of the header and payload of the large binary
248 # object. Be careful not to concatenate the header and the payload
249 # prior to calling 'write' as we do not want to allocate a large
250 # temporary bytes object.
251 # We intentionally do not insert a protocol 4 frame opcode to make
252 # it possible to optimize file.read calls in the loader.
253 write(header)
--> 254 write(payload)
MemoryError:
```
</details>
Memory error is an expected type of error in such case, but when I started digging down, I found out that it occurs in a kinda unexpected place - in `create_config_id` function. It tries to hash `config_kwargs_to_add_to_suffix`, including generator function itself.
I modified the `BuilderConfig.create_config_id` code like this to check which values are hashed and how much time it takes to hash them and ran it on a toy dataset:
```
print(config_kwargs_to_add_to_suffix)
start_time = time.time()
if all(isinstance(v, (str, bool, int, float)) for v in config_kwargs_to_add_to_suffix.values()):
suffix = ",".join(
str(k) + "=" + urllib.parse.quote_plus(str(v)) for k, v in config_kwargs_to_add_to_suffix.items()
)
if len(suffix) > 32: # hash if too long
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
else:
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
end_time = time.time()
print(f"Execution time: {end_time - start_time:.4f} seconds")
print(suffix)
```
In my case the content of `config_kwargs_to_add_to_suffix` was like this:
```
{'features': {'key': Value(dtype='int64', id=None), 'x': Array3D(shape=(44, 128, 128), dtype='float32', id=None), 'y_class': Array2D(shape=(128, 128), dtype='int32', id=None)}, 'gen_kwargs': None, 'generator': <function generate_tiles.<locals>.dataset_generator at 0x00000139D10D7920>, 'split': NamedSplit('train')}
```
Also I noticed that hashing took a significant amount of time - 43.1482 seconds, while the overall function execution (with data loading, batching and saving dataset) took 2min 45s. The output of `create_config_id` is just a dataset id, so, it is inappropirately large amount of time.
But when I added `config_kwargs_to_add_to_suffix.pop("generator", None)`, the hashing took only 0.0060 seconds.
Maybe we shouldn't hash the generator function, as it can be really computationally and memory expensive.
### Steps to reproduce the bug
This is a simplified example of a workflow I used to generate dataset. But I think that you can use almost any workflow to reproduce that bug.
```
import pystac
import pystac_client
import planetary_computer
import numpy as np
import xarray as xr
import rioxarray as rxr
import dask
import xbatcher
import datasets
# Loading a dataset, in our case - single Landsat image
catalog = pystac_client.Client.open(
"https://planetarycomputer.microsoft.com/api/stac/v1",
modifier=planetary_computer.sign_inplace,
)
brazil = [-60.2, -3.31]
time_of_interest = "2021-06-01/2021-08-31"
search = catalog.search(collections=["landsat-c2-l2"], intersects={"type": "Point", "coordinates": brazil}, datetime=time_of_interest)
items = search.item_collection()
item = min(items, key=lambda item: pystac.extensions.eo.EOExtension.ext(item).cloud_cover)
# Getting x data
bands = []
for band in ["red", "green", "blue", "nir08", "coastal", "swir16", "swir22", "lwir11"]:
with rxr.open_rasterio(item.assets[band].href, chunks=True, lock=True) as raster:
raster = raster.to_dataset('band')
#print(raster)
raster = raster.rename({1: band})
bands.append(raster)
x = xr.merge(bands).squeeze().to_array("band").persist()
# Getting y data
with rxr.open_rasterio(item.assets['qa_pixel'].href, chunks=True, lock=True) as raster:
y = raster.squeeze().persist()
# Setting up batches generators
x_batches = xbatcher.BatchGenerator(ds=x, input_dims={"x": 256, "y": 256})
y_batches = xbatcher.BatchGenerator(ds=y, input_dims={"x": 256, "y": 256})
# Filtering samples that contain only nodata
samples = list(range(len(x_batches)))
samples_filtered = []
for i in samples:
if not np.array_equal(np.unique(x_batches[i]), np.array([0.])) and not np.array_equal(np.unique(y_batches[i]), np.array([0])):
samples_filtered.append(i)
samples = samples_filtered
np.random.shuffle(samples)
# Setting up features
feat = {
"key": datasets.Value(dtype="int64"),
"x": datasets.Array3D(dtype="float32", shape=(4, 256, 256)),
"y": datasets.Array2D(dtype="int32", shape=(256, 256))
}
feat = datasets.Features(feat)
# Setting up a generator
def dataset_generator():
for index in samples:
data_dict = {
"key": index,
"x": x_batches[index].data,
"y": y_batches[index].data,
}
yield data_dict
# Create dataset
ds = datasets.Dataset.from_generator(
dataset_generator,
features=feat,
cache_dir="temp/cache",
)
```
Please, try adding `config_kwargs_to_add_to_suffix.pop("generator", None)` to `BuilderConfig.create_config_id` and then measuring how much time it takes to run
```
if all(isinstance(v, (str, bool, int, float)) for v in config_kwargs_to_add_to_suffix.values()):
suffix = ",".join(
str(k) + "=" + urllib.parse.quote_plus(str(v)) for k, v in config_kwargs_to_add_to_suffix.items()
)
if len(suffix) > 32: # hash if too long
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
else:
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
```
code block with and without `config_kwargs_to_add_to_suffix.pop("generator", None)`
In my case the difference was 3.3828 seconds without popping generator function and 0.0010 seconds with popping.
### Expected behavior
Much faster hashing and no MemoryErrors
### Environment info
- `datasets` version: 3.5.0
- Platform: Windows-11-10.0.26100-SP0
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.1
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.12.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6575 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6575/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6575/comments | https://api.github.com/repos/huggingface/datasets/issues/6575/events | https://github.com/huggingface/datasets/pull/6575 | 2,072,617,406 | PR_kwDODunzps5jl1V6 | 6,575 | [IterableDataset] Fix `drop_last_batch`in map after shuffling or sharding | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6575). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>... | 2024-01-09T15:35:31Z | 2024-01-11T16:16:54Z | 2024-01-11T16:10:30Z | MEMBER | null | null | null | It was not taken into account e.g. when passing to a DataLoader with num_workers>0
Fix https://github.com/huggingface/datasets/issues/6565 | {
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https://api.github.com/repos/huggingface/datasets/issues/5035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5035/comments | https://api.github.com/repos/huggingface/datasets/issues/5035/events | https://github.com/huggingface/datasets/pull/5035 | 1,388,914,476 | PR_kwDODunzps4_wVie | 5,035 | Fix typos in load docstrings and comments | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-28T08:05:07Z | 2022-09-28T17:28:40Z | 2022-09-28T17:26:15Z | MEMBER | null | null | null | Minor fix of typos in load docstrings and comments | {
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https://api.github.com/repos/huggingface/datasets/issues/6645 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6645/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6645/comments | https://api.github.com/repos/huggingface/datasets/issues/6645/events | https://github.com/huggingface/datasets/issues/6645 | 2,122,956,818 | I_kwDODunzps5-icAS | 6,645 | Support fsspec 2024.2 | {
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"I'd be very grateful. This upper bound banished me straight into dependency hell today. :("
] | 2024-02-07T12:45:29Z | 2024-02-29T15:12:19Z | 2024-02-29T15:12:19Z | MEMBER | null | null | {
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First, we should address:
- #6644 | {
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https://api.github.com/repos/huggingface/datasets/issues/6490 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6490/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6490/comments | https://api.github.com/repos/huggingface/datasets/issues/6490/events | https://github.com/huggingface/datasets/issues/6490 | 2,037,204,892 | I_kwDODunzps55bUec | 6,490 | `load_dataset(...,save_infos=True)` not working without loading script | {
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"Also, once the README.md exists in the python environment it is used when loading another dataset in the same format (e.g. json) since it always resolves the path to the same directory.\r\nThe consequence here is any other dataset won't load because of infos mismatch.\r\nTo reproduce this aspect:\r\n1. Do a `load_... | 2023-12-12T08:09:18Z | 2023-12-12T08:36:22Z | null | NONE | null | null | {
"completed": 0,
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} | ### Describe the bug
It seems that saving a dataset infos back into the card file is not working for datasets without a loading script.
After tracking the problem a bit it looks like saving the infos uses `Builder.get_imported_module_dir()` as its destination directory.
Internally this is a call to `inspect.getfile()` but since the actual builder class used is dynamically created (cf. `datasets.load.configure_builder_class`) this method actually return te path to the parent builder class (e.g. `datasets.packaged_modules.json.JSON`).
### Steps to reproduce the bug
1. Have a local dataset without any loading script
2. Make sure there are no dataset infos in the README.md
3. Load with `save_infos=True`
4. No change in the dataset README.md
5. A new README.md file is created in the directory of the parent builder class (e.g. for json in `.../site-packages/datasets/packaged_modules/json/README.md`)
### Expected behavior
The dataset README.md should be updated and no file should be created in the python environment.
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.3
- `fsspec` version: 2023.6.0
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https://api.github.com/repos/huggingface/datasets/issues/5365 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5365/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5365/comments | https://api.github.com/repos/huggingface/datasets/issues/5365/events | https://github.com/huggingface/datasets/pull/5365 | 1,498,422,466 | PR_kwDODunzps5Fi6ZD | 5,365 | fix: image array should support other formats than uint8 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, thanks for working on this! \r\n\r\nI agree that the current type-casting (always cast to `np.uint8` as Tensorflow Datasets does) is a bit too harsh. However, not all dtypes are supported in `Image.fromarray` (e.g. np.int64), so ... | 2022-12-15T13:17:50Z | 2023-01-26T18:46:45Z | 2023-01-26T18:39:36Z | CONTRIBUTOR | null | null | null | Currently images that are provided as ndarrays, but not in `uint8` format are going to loose data. Namely, for example in a depth image where the data is in float32 format, the type-casting to uint8 will basically make the whole image blank.
`PIL.Image.fromarray` [does support mode `F`](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes).
although maybe some further metadata could be supplied via the [Image](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Image) object. | {
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https://api.github.com/repos/huggingface/datasets/issues/6129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6129/comments | https://api.github.com/repos/huggingface/datasets/issues/6129/events | https://github.com/huggingface/datasets/pull/6129 | 1,841,563,517 | PR_kwDODunzps5Xcmuw | 6,129 | Release 2.14.4 | {
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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==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-08-08T15:43:56Z | 2023-08-08T16:08:22Z | 2023-08-08T15:49:06Z | MEMBER | null | null | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6889 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6889/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6889/comments | https://api.github.com/repos/huggingface/datasets/issues/6889/events | https://github.com/huggingface/datasets/pull/6889 | 2,287,720,539 | PR_kwDODunzps5u_hW- | 6,889 | fix bug #6877 | {
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"@loicmagne, @KennethEnevoldsen",
"Can you give more details on why this fix works ?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6889). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",... | 2024-05-09T13:38:40Z | 2024-05-13T13:35:32Z | 2024-05-13T13:35:32Z | NONE | null | null | null | fix bug #6877 due to maybe f becomes invaild after yield process
the results are below:
Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:01<00:00, 420.41it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 26148.48it/s]
Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 409731.44it/s]
Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 289720.84it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 26663.42it/s]
Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 434056.21it/s]
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:00<00:00, 13222.33files/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [00:04<00:00, 180.67files/s]
Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 828/828 [01:35<00:00, 8.70files/s]
Generating train split: 1571592 examples [00:08, 176736.09 examples/s]
Generating test split: 85533 examples [00:01, 48224.56 examples/s]
Generating validation split: 86246 examples [00:01, 50164.16 examples/s]
Fix https://github.com/huggingface/datasets/issues/6877.
CC: @natolambert
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"merged_at": null,
"patch_url": "https://github.com/huggingface/datasets/pull/6889.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6889"
} |
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