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PR_kwDODunzps5RK40K

Load cached dataset as iterable

closed

CONTRIBUTOR

To be used to train models it allows to load an IterableDataset from the cached Arrow file. See https://github.com/huggingface/datasets/issues/5481

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I_kwDODunzps5mq1KQ

User access requests with manual review do not notify the dataset owner

closed

CONTRIBUTOR

### Describe the bug When a user access requests are enabled, and new requests are set to Manual Review, the dataset owner should be notified of the pending requests. However, instead, currently nothing happens, and so the dataset request can go unanswered for quite some time until the owner happens to check that particular dataset's Settings pane. ### Steps to reproduce the bug 1. Enable a dataset's user access requests 2. Set to Manual Review 3. Ask another HF user to request access to the dataset 4. Dataset owner is not notified ### Expected behavior The dataset owner should receive some kind of notification, perhaps in their HF site inbox, or by email, when a dataset access request is made and manual review is enabled. ### Environment info n/a

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PR_kwDODunzps5RKchn

Make split slicing consisten with list slicing

open

CONTRIBUTOR

Fix #1774, fix #5875 TODO: a test

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I_kwDODunzps5mqVXy

Token Alignment for input and output data over train and test batch/dataset.

open

NONE

`data` > DatasetDict({ train: Dataset({ features: ['input', 'output'], num_rows: 4500 }) test: Dataset({ features: ['input', 'output'], num_rows: 500 }) }) **# input (in-correct sentence)** `data['train'][0]['input']` **>>** 'We are meet sunday 10am12pmET in Crown Heights Brooklyn New York' **# output (correct sentence)** `data['train'][0]['output']` **>>** 'We meet Sundays 10am-12pmET in Crown Heights, Brooklyn, New York.' **I Want to align the output tokens with input** ``` `# tokenize both inputs and targets def tokenize_fn(batch): # tokenize the input sequence first # this populates input_ids, attention_mask, etc. tokenized_inputs = tokenizer( batch['input'] ) labels_batch = tokenizer.tokenize(batch['output']) # original targets aligned_labels_batch = [] for i, labels in enumerate(labels_batch): word_ids = tokenized_inputs[i].word_ids() aligned_labels_batch.append(align_targets(labels, word_ids)) # align_targets is another user defined function which is been called here # recall: the 'target' must be stored in key called 'labels' tokenized_inputs['labels'] = aligned_labels_batch return tokenized_inputs` ``` ``` data.map( tokenize_fn, batched=True, remove_columns=data['train'].column_names, ) ``` When this user defined function is mapped to every records of train and test batch am getting following error: **1.** **raise DatasetTransformationNotAllowedError( 3457 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it."** **2.** **TypeError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]]**

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I_kwDODunzps5mpixu

HuggingsFace dataset example give error

closed

NONE

### Describe the bug   ### Steps to reproduce the bug Use link as reference document written https://colab.research.google.com/github/huggingface/datasets/blob/main/notebooks/Overview.ipynb#scrollTo=biqDH9vpvSVz ```python # Now let's train our model device = 'cuda' if torch.cuda.is_available() else 'cpu' model.train().to(device) for i, batch in enumerate(dataloader): batch.to(device) outputs = model(**batch) loss = outputs.loss loss.backward() optimizer.step() model.zero_grad() print(f'Step {i} - loss: {loss:.3}') if i > 5: break ``` Error ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-44-7040b885f382>](https://localhost:8080/#) in <cell line: 5>() 5 for i, batch in enumerate(dataloader): 6 batch.to(device) ----> 7 outputs = model(**batch) 8 loss = outputs.loss 9 loss.backward() [/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs) 1499 or _global_backward_pre_hooks or _global_backward_hooks 1500 or _global_forward_hooks or _global_forward_pre_hooks): -> 1501 return forward_call(*args, **kwargs) 1502 # Do not call functions when jit is used 1503 full_backward_hooks, non_full_backward_hooks = [], [] TypeError: DistilBertForQuestionAnswering.forward() got an unexpected keyword argument 'token_type_ids' ``` https://github.com/huggingface/datasets/assets/1328316/5d8b1d61-9337-4d59-8423-4f37f834c156 ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free (my Google Drive just empty about 200 MB, but I don't think it cause problem)

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I_kwDODunzps5mlXKR

Use work-stealing algorithm when parallel computing

open

NONE

### Feature request when i used Dataset.map api to process data concurrently, i found that it gets slower and slower as it gets closer to completion. Then i read the source code of arrow_dataset.py and found that it shard the dataset and use multiprocessing pool to execute each shard.It may cause the slowest task to drag out the entire program's execution time,especially when processing huge dataset. ### Motivation using work-stealing algorithm instead of sharding and parallel computing to optimize performance. ### Your contribution just an idea.

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PR_kwDODunzps5RFjTL

Modify `is_remote_filesystem` to return True for FUSE-mounted paths

open

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I_kwDODunzps5mqBC7

A way to upload and visualize .mp4 files (millions of them) as part of a dataset

open

NONE

**Is your feature request related to a problem? Please describe.** I recently chose to use huggingface hub as the home for a large multi modal dataset I've been building. https://huggingface.co/datasets/Antreas/TALI It combines images, text, audio and video. Now, I could very easily upload a dataset made via datasets.Dataset.from_generator, as long as it did not include video files. I found that including .mp4 files in the entries would not auto-upload those files. Hence I tried to upload them myself. I quickly found out that uploading many small files is a very bad way to use git lfs, and that it would take ages, so, I resorted to using 7z to pack them all up. But then I had a new problem. My dataset had a size of 1.9TB. Trying to upload such a large file with the default huggingface_hub API always resulted in time outs etc. So I decided to split the large files into chunks of 5GB each and reupload. So, eventually it all worked out. But now the dataset can't be properly and natively used by the datasets API because of all the needed preprocessing -- and furthermore the hub is unable to visualize things. **Describe the solution you'd like** A native way to upload large datasets that include .mp4 or other video types. **Describe alternatives you've considered** Already explained earlier **Additional context** https://huggingface.co/datasets/Antreas/TALI

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I_kwDODunzps5mfjkM

`Dataset.to_tf_dataset` fails when strings cannot be encoded as `np.bytes_`

closed

CONTRIBUTOR

### Describe the bug When loading any dataset that contains a column with strings that are not ASCII-compatible, looping over those records raises the following exception e.g. for `é` character `UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128)`. ### Steps to reproduce the bug Running the following script will eventually fail, when reaching to the batch that contains non-ASCII compatible strings. ```python from datasets import load_dataset ds = load_dataset("imdb", split="train") tfds = ds.to_tf_dataset(batch_size=16) for batch in tfds: print(batch) >>> UnicodeEncodeError: 'ascii' codec can't encode character '\xe9' in position 0: ordinal not in range(128) ``` ### Expected behavior The following script to run properly, making sure that the strings are either `numpy.unicode_` or `numpy.string` instead of `numpy.bytes_` since some characters are not ASCII compatible and that would lead to an issue when applying the `map`. ```python from datasets import load_dataset ds = load_dataset("imdb", split="train") tfds = ds.to_tf_dataset(batch_size=16) for batch in tfds: print(batch) ``` ### Environment info - `datasets` version: 2.12.1.dev0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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PR_kwDODunzps5RAkYi

Fix string-encoding, make `batch_size` optional, and minor improvements in `Dataset.to_tf_dataset`

closed

CONTRIBUTOR

## What's in this PR? This PR addresses some minor fixes and general improvements in the `to_tf_dataset` method of `datasets.Dataset`, to convert a 🤗HuggingFace Dataset as a TensorFlow Dataset. The main bug solved in this PR comes with the string-encoding, since for safety purposes the internal conversion of `numpy.arrays` when `dtype` is unicode/string, is to convert it into `numpy.bytes`, more information in the docstring of https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#L210. That's triggered when using `tensorflow.numpy_function` as it's applying another type cast besides the one that `datasets` does, so the casting is applied at least twice per entry/batch. So this means that the definition of the `numpy.unicode_` dtype when the data in the batch is a string, is ignored, and replaced by `numpy.bytes_`. Besides that, some other minor things have been fixed: * Made `batch_size` an optional parameter in `to_tf_dataset` * Map the `tensorflow` output dtypes just once, and not in every `tf.function` call during `map` * Keep `numpy` formatting in the `datasets.Dataset` if already formatted like it, no need to format it again as `numpy` * Docstring indentation in `dataset_to_tf` and `multiprocess_dataset_to_tf` ## What's missing in this PR? I can include some integration tests if needed, to validate that `batch_size` is optional, and that the tensors in the TF-Dataset can be looped over with no issues as before.

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I_kwDODunzps5mfACT

Split dataset by node: index error when sharding iterable dataset

open

CONTRIBUTOR

### Describe the bug Context: we're splitting an iterable dataset by node and then passing it to a torch data loader with multiple workers When we iterate over it for 5 steps, we don't get an error When we instead iterate over it for 8 steps, we get an `IndexError` when fetching the data if we have too many workers ### Steps to reproduce the bug Here, we have 2 JAX processes (`jax.process_count() = 2`) which we split the dataset over. The dataset loading script can be found here: https://huggingface.co/datasets/distil-whisper/librispeech_asr/blob/c6a1e805cbfeed5057400ac5937327d7e30281b8/librispeech_asr.py#L310 <details> <summary> Code to reproduce </summary> ```python from datasets import load_dataset import jax from datasets.distributed import split_dataset_by_node from torch.utils.data import DataLoader from tqdm import tqdm # load an example dataset (https://huggingface.co/datasets/distil-whisper/librispeech_asr) dataset = load_dataset("distil-whisper/librispeech_asr", "all", split="train.clean.100", streaming=True) # just keep the text column -> no need to define a collator dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"}) # define some constants batch_size = 256 num_examples = 5 # works for 5 examples, doesn't for 8 num_workers = dataset_text.n_shards # try with multiple workers dataloader = DataLoader(dataset_text, batch_size=batch_size, num_workers=num_workers, drop_last=True) for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Multiple workers"): if i == num_examples: break # try splitting by node (we can't do this with `dataset_text` since `split_dataset_by_node` expects the Audio column for an ASR dataset) dataset = split_dataset_by_node(dataset, rank=jax.process_index(), world_size=jax.process_count()) # remove the text column again dataset_text = dataset.remove_columns(set(dataset.features.keys()) - {"text"}) dataloader = DataLoader(dataset_text, batch_size=16, num_workers=num_workers // 2, drop_last=True) for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Split by node"): if i == num_examples: break # too many workers dataloader = DataLoader(dataset_text, batch_size=256, num_workers=num_workers, drop_last=True) for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"): if i == num_examples: break ``` </details> <details> <summary> With 5 examples: </summary> ``` Multiple workers: 100%|███████████████████████████████████████████████████████████████████| 5/5 [00:16<00:00, 3.33s/it] Assigning 7 shards (or data sources) of the dataset to each node. Split by node: 100%|██████████████████████████████████████████████████████████████████████| 5/5 [00:13<00:00, 2.76s/it] Assigning 7 shards (or data sources) of the dataset to each node. Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary t o have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more files than 7. Too many workers: 100%|███████████████████████████████████████████████████████████████████| 5/5 [00:15<00:00, 3.03s/it] ``` </details> <details> <summary> With 7 examples: </summary> ``` Multiple workers: 100%|███████████████████████████████████████████████████████████████████| 8/8 [00:13<00:00, 1.71s/it] Assigning 7 shards (or data sources) of the dataset to each node. Split by node: 100%|██████████████████████████████████████████████████████████████████████| 8/8 [00:11<00:00, 1.38s/it] Assigning 7 shards (or data sources) of the dataset to each node. Too many dataloader workers: 14 (max is dataset.n_shards=7). Stopping 7 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=7. To enable more parallelism, please split the dataset in more files than 7. Too many workers: 88%|██████████████████████████████████████████████████████████▋ | 7/8 [00:13<00:01, 1.89s/it] Traceback (most recent call last): File "distil-whisper/test_librispeech.py", line 36, in <module> for i, batch in tqdm(enumerate(dataloader), total=num_examples, desc="Too many workers"): File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/tqdm/std.py", line 1178, in __iter__ for obj in iterable: File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1325, in _next_data return self._process_data(data) File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data data.reraise() File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/_utils.py", line 644, in reraise raise exception IndexError: Caught IndexError in DataLoader worker process 7. Original Traceback (most recent call last): File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop data = fetcher.fetch(index) File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch data.append(next(self.dataset_iter)) File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 986, in __iter__ yield from self._iter_pytorch(ex_iterable) File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 920, in _iter_pytorch for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers): File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 540, in shard_data_sources self.ex_iterable.shard_data_sources(worker_id, num_workers), File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 796, in shard_data_sources self.ex_iterable.shard_data_sources(worker_id, num_workers), File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 126, in shard_data_sources requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices]) File "/home/sanchitgandhi/datasets/src/datasets/utils/sharding.py", line 76, in _merge_gen_kwargs for key in gen_kwargs_list[0] IndexError: list index out of range ``` </details> ### Expected behavior Should pass for both 5 and 7 examples ### Environment info - `datasets` version: 2.12.1.dev0 - Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1

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I_kwDODunzps5mdzu1

load_dataset from s3 file system through streaming can't not iterate data

open

CONTRIBUTOR

### Describe the bug I have a JSON file in my s3 file system(minio), I can use load_dataset to get the file link, but I can't iterate it <img width="816" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/cc0778d3-36f3-45b5-ac68-4e7c664c2ed0"> <img width="1144" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/76872af3-8b3c-42ff-9f55-528c920a7af1"> we can change 4 lines to fix this bug, you can check whether it is ok for us. <img width="941" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/5a22155a-ece7-496c-8506-047e5c235cd3"> ### Steps to reproduce the bug 1. storage a file in you s3 file system 2. use load_dataset to read it through streaming 3. iterate it ### Expected behavior can iterate it successfully ### Environment info - `datasets` version: 2.12.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1

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I_kwDODunzps5mabXD

Prefetching for IterableDataset

open

NONE

### Feature request Add support for prefetching the next n batches through iterabledataset to reduce batch loading bottleneck in training loop. ### Motivation The primary motivation behind this is to use hardware accelerators alongside a streaming dataset. This is required when you are in a low ram or low disk space setting as well as quick iteration where you're iterating though different accelerator environments (e.x changing ec2 instances quickly to figure out batch/sec for a particular architecture). Currently, using the IterableDataset results in accelerators becoming basically useless due to the massive bottleneck induced by the dataset lazy loading/transform/mapping. I've considered two alternatives: PyTorch dataloader that handles this. However, I'm using jax, and I believe this is a piece of functionality that should live in the stream class. Replicating the "num_workers" part of the PyTorch DataLoader to eagerly load batches and apply the transform so Arrow caching will automatically cache results and make them accessible. ### Your contribution I may or may not have time to do this. Currently, I've written the basic multiprocessor approach to handle the eager DataLoader for my own use case with code that's not integrated to datasets. I'd definitely see this as being the default over the regular Dataset for most people given that they wouldn't have to wait on the datasets while also not worrying about performance.

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I_kwDODunzps5mZlrZ

Request for text deduplication feature

open

NONE

### Feature request It would be great if there would be support for high performance, highly scalable text deduplication algorithms as part of the datasets library. ### Motivation Motivated by this blog post https://huggingface.co/blog/dedup and this library https://github.com/google-research/deduplicate-text-datasets, but slightly frustrated by how its not very easy to work with these tools I am proposing this feature. ### Your contribution I would be happy to contribute to the development effort of this feature. would love to collaborate with others in the development effort.

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I_kwDODunzps5mZkdp

Incompatibility with DataLab

closed

NONE

### 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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I_kwDODunzps5mU9Za

Why split slicing doesn't behave like list slicing ?

open

NONE

### Describe the bug If I want to get the first 10 samples of my dataset, I can do : ``` ds = datasets.load_dataset('mnist', split='train[:10]') ``` But if I exceed the number of samples in the dataset, an exception is raised : ``` ds = datasets.load_dataset('mnist', split='train[:999999999]') ``` > ValueError: Requested slice [:999999999] incompatible with 60000 examples. ### Steps to reproduce the bug ``` ds = datasets.load_dataset('mnist', split='train[:999999999]') ``` ### Expected behavior I would expect it to behave like python lists (no exception raised, the whole list is kept) : ``` d = list(range(1000))[:999999] print(len(d)) # > 1000 ``` ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-12.6-arm64-arm-64bit - Python version: 3.9.12 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5mQ6QC

Using as_dataset on a "parquet" builder

closed

NONE

### Describe the bug I used a custom builder to ``download_and_prepare`` a dataset. The first (very minor) issue is that the doc seems to suggest ``download_and_prepare`` will return the dataset, while it does not ([builder.py](https://github.com/huggingface/datasets/blob/main/src/datasets/builder.py#L718-L738)). ``` >>> from datasets import load_dataset_builder >>> builder = load_dataset_builder("rotten_tomatoes") >>> ds = builder.download_and_prepare("./output_dir", file_format="parquet") ``` The main issue I am facing is loading the dataset from those parquet files. I used the `as_dataset` method suggested by the doc, however it returns: ` FileNotFoundError: [Errno 2] Failed to open local file 'output_dir/__main__-train-00000-of-00245.arrow'. Detail: [errno 2] No such file or directory. ` ### Steps to reproduce the bug 1. Create a custom builder of some sort: `builder = CustomBuilder()`. 2. Run `download_and_prepare` with the parquet format: `builder.download_and_prepare("./output_dir", file_format="parquet")`. 3. Run `dataset = builder.as_dataset()`. ### Expected behavior I guess I'd expect `as_dataset` to generate the dataset in arrow format if it has to, or to suggest an alternative way to load the dataset (I've also tried other methods with `load_dataset` to no avail, probably due to misunderstandings on my part). ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-1027-gcp-x86_64-with-glibc2.31 - Python version: 3.10.0 - Huggingface_hub version: 0.14.1 - PyArrow version: 8.0.0 - Pandas version: 1.5.3 ```

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I_kwDODunzps5mHmvc

Allow setting the environment variable for the lock file path

open

NONE

### Feature request Add an environment variable to replace the default lock file path. ### Motivation Usually, dataset path is a read-only path while the lock file needs to be modified each time. It would be convenient if the path can be reset individually. ### Your contribution ```/src/datasets/utils/filelock.py class UnixFileLock(BaseFileLock): def __init__(self, lock_file, timeout=-1, max_filename_length=None): #------------------- if os.getenv('DS_TMP_PATH'): file_name = str(lock_file).split('/')[-1] dataset_tmp_path = os.getenv('DS_TMP_PATH') lock_file = os.path.join(dataset_tmp_path, file_name) #------------------- max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax super().__init__(lock_file, timeout=timeout, max_filename_length=max_filename_length) ``` A simple demo is as upper. Thanks.

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PR_kwDODunzps5QrQ5o

Fix infer module for uppercase extensions

closed

MEMBER

Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`. Before, `None` module was returned.

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I_kwDODunzps5mE8qR

data configuration hash suffix depends on uncanonicalized data_dir

closed

CONTRIBUTOR

### Describe the bug I am working with the `recipe_nlg` dataset, which requires manual download. Once it's downloaded, I've noticed that the hash in the custom data configuration is different if I add a trailing `/` to my `data_dir`. It took me a while to notice that the hashes were different, and to understand that that was the cause of my dataset being processed anew instead of the cached version being used. ### Steps to reproduce the bug 1. Follow the steps to manually download the `recipe_nlg` dataset to `/data/recipenlg`. 2. Load it using `load_dataset`, once without a trailing slash and once with one: ```python >>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg") Using custom data configuration default-082278caeea85765 Downloading and preparing dataset recipe_nlg/default to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74... Dataset recipe_nlg downloaded and prepared to /home/kyle/.cache/huggingface/datasets/recipe_nlg/default-082278caeea85765/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74. Subsequent calls will reuse this data. 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.10s/it] DatasetDict({ train: Dataset({ features: ['id', 'title', 'ingredients', 'directions', 'link', 'source', 'ner'], num_rows: 2231142 }) }) >>> ds = load_dataset("recipe_nlg", data_dir="/data/recipenlg/") Using custom data configuration default-83e87680785d0493 Downloading and preparing dataset recipe_nlg/default to /home/user/.cache/huggingface/datasets/recipe_nlg/default-83e87680785d0493/1.0.0/aa4f120223637bedf7360cecb70a9bd108acfd64e38207ca90c9f385d21e5e74... Generating train split: 1%| | 12701/2231142 [00:04<13:15, 2790.25 examples/s ^C ``` 3. Observe that the hash suffix in the custom data configuration changes due to the altered string. ### Expected behavior I think I would expect the hash to remain constant if it actually points to the same location on disk. I would expect the use of `os.path.normpath` to canonicalize the paths. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5mDW56

Behaviour difference between datasets.map and IterableDatasets.map

open

NONE

### Describe the bug All the examples in all the docs mentioned throughout huggingface datasets correspond to datasets object, and not IterableDatasets object. At one point of time, they might have been in sync, but the code for datasets version >=2.9.0 is very different as compared to the docs. I basically need to .map() a transform on images in an iterable dataset, which was made using a custom databuilder config. This works very good in map-styles datasets, but the .map() fails in IterableDatasets, show behvaiour as such: "pixel_values" key not found, KeyError in examples object/dict passed into transform function for map, which works fine with map style, even as batch. In iterable style, the object/dict passed into map() paramter callable function is completely different as what is mentioned in all examples. Please look into this. Thank you My databuilder class is inherited as such: def _info(self): print ("Config: ",self.config.__dict__.keys()) return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "labels": datasets.Sequence(datasets.Value("uint16")), # "labels_name": datasets.Value("string"), # "pixel_values": datasets.Array3D(shape=(3, 1280, 960), dtype="float32"), "pixel_values": datasets.Array3D(shape=(1280, 960, 3), dtype="uint8"), "image_s3_path": datasets.Value("string"), } ), supervised_keys=None, homepage="none", citation="", ) def _split_generators(self, dl_manager): records_train = list(db.mini_set.find({'split':'train'},{'image_s3_path':1, 'ocwen_template_name':1}))[:10000] records_val = list(db.mini_set.find({'split':'val'},{'image_s3_path':1, 'ocwen_template_name':1}))[:1000] # print (len(records),self.config.num_shards) # shard_size_train = len(records_train)//self.config.num_shards # sharded_records_train = [records_train[i:i+shard_size_train] for i in range(0,len(records_train),shard_size_train)] # shard_size_val = len(records_val)//self.config.num_shards # sharded_records_val = [records_val[i:i+shard_size_val] for i in range(0,len(records_val),shard_size_val)] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"records":records_train} # passing list of records, for sharding to take over ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"records":records_val} # passing list of records, for sharding to take over ), ] def _generate_examples(self, records): # print ("Generating examples for [{}] shards".format(len(shards))) # initiate_db_connection() # records = list(db.mini_set.find({'split':split},{'image_s3_path':1, 'ocwen_template_name':1}))[:10] id_ = 0 # for records in shards: for i,rec in enumerate(records): img_local_path = fetch_file(rec['image_s3_path'],self.config.buffer_dir) # t = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.squeeze() # print (t.shape, type(t),type(t[0][0][0])) # sys.exit() pvs = np.array(Image.open(img_local_path).resize((1280,960))) # image object is wxh, so resize as per that, numpy array of it is hxwxc, transposing to cxwxh # pvs = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.astype(np.float16).squeeze() # print (type(pvs[0][0][0])) lblids = self.config.processor.tokenizer('<s_class>'+rec['ocwen_template_name']+'</s_class>'+'</s>', add_special_tokens=False, padding=False, truncation=False, return_tensors="np")["input_ids"].squeeze(0) # take padding later, as per batch collating # print (len(lblids),type(lblids[0])) # print (type(pvs),pvs.shape,type(pvs[0][0][0]), type(lblids)) yield id_, {"labels":lblids,"pixel_values":pvs,"image_s3_path":rec['image_s3_path']} id_+=1 os.remove(img_local_path) and I load it inside my trainer script as such `ds = load_dataset("/tmp/DonutDS/dataset/", split="train", streaming=True) # iterable dataset, where .map() falls` or also as `ds = load_from_disk('/tmp/DonutDS/dataset/') #map style dataset` Thank you to the team for having such a great library, and for this bug fix in advance! ### Steps to reproduce the bug Above config can allow one to reproduce the said bug ### Expected behavior .map() should show some consistency b/w map-style and iterable-style datasets, or atleast the docs should address iterable-style datasets behaviour and examples. I honestly do not figure the use of such docs. ### Environment info datasets==2.9.0 transformers==4.26.0

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I_kwDODunzps5mCuTz

Image Encoding Issue when submitting a Parquet Dataset

closed

NONE

### Describe the bug Hello, I'd like to report an issue related to pushing a dataset represented as a Parquet file to a dataset repository using Dask. Here are the details: We attempted to load an example dataset in Parquet format from the Hugging Face (HF) filesystem using Dask with the following code snippet: ``` import dask.dataframe as dd df = dd.read_parquet("hf://datasets/lambdalabs/pokemon-blip-captions",index=False) ``` In this dataset, the "image" column is represented as a dictionary/struct with the format: ``` df = df.compute() df["image"].iloc[0].keys() -> dict_keys(['bytes', 'path']) ``` I think this is the format encoded by the [`Image`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Image) feature extractor from datasets to format suitable for Arrow. The next step was to push the dataset to a repository that I created: ``` dd.to_parquet(dask_df, path = "hf://datasets/philippemo/dummy_dataset/data") ``` However, after pushing the dataset using Dask, the "image" column is now represented as the encoded dictionary `(['bytes', 'path'])`, and the images are not properly visualized. You can find the dataset here: [Link to the problematic dataset](https://huggingface.co/datasets/philippemo/dummy_dataset). It's worth noting that both the original dataset and the one submitted with Dask have the same schema with minor alterations related to metadata: **[ Schema of original dummy example.](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/blob/main/data/train-00000-of-00001-566cc9b19d7203f8.parquet)** ``` image: struct<bytes: binary, path: null> child 0, bytes: binary child 1, path: null text: string ``` **[ Schema of pushed dataset with dask](https://huggingface.co/datasets/philippemo/dummy_dataset/blob/main/data/part.0.parquet)** ``` image: struct<bytes: binary, path: null> child 0, bytes: binary child 1, path: null text: string ``` This issue seems to be related to an encoding type that occurs when pushing a model to the hub. Normally, models should be represented as an HF dataset before pushing, but we are working with an example where we need to push large datasets using Dask. Could you please provide clarification on how to resolve this issue? Thank you! ### Reproduction To get the schema I downloaded the parquet files and used pyarrow.parquet to read the schema ``` import pyarrow.parquet pyarrow.parquet.read_schema(<path_to_parquet>, memory_map=True) ``` ### Logs _No response_ ### System info ```shell - huggingface_hub version: 0.14.1 - Platform: Linux-5.19.0-41-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Token path ?: /home/philippe/.cache/huggingface/token - Has saved token ?: True - Who am I ?: philippemo - Configured git credential helpers: cache - FastAI: N/A - Tensorflow: N/A - Torch: N/A - Jinja2: 3.1.2 - Graphviz: N/A - Pydot: N/A - Pillow: 9.4.0 - hf_transfer: N/A - gradio: N/A - ENDPOINT: https://huggingface.co - HUGGINGFACE_HUB_CACHE: /home/philippe/.cache/huggingface/hub - HUGGINGFACE_ASSETS_CACHE: /home/philippe/.cache/huggingface/assets - HF_TOKEN_PATH: /home/philippe/.cache/huggingface/token - HF_HUB_OFFLINE: False - HF_HUB_DISABLE_TELEMETRY: False - HF_HUB_DISABLE_PROGRESS_BARS: None - HF_HUB_DISABLE_SYMLINKS_WARNING: False - HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False - HF_HUB_DISABLE_IMPLICIT_TOKEN: False - HF_HUB_ENABLE_HF_TRANSFER: False ```

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I_kwDODunzps5l_m3q

Is it possible to change a cached file and 're-cache' it instead of re-generating?

closed

NONE

### Feature request Hi, I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours ### Motivation For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it. ### Your contribution For now, I can't help, sorry.

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PR_kwDODunzps5QizOn

Add logic for hashing modules/functions optimized with `torch.compile`

open

CONTRIBUTOR

Fix https://github.com/huggingface/datasets/issues/5839 PS: The `Pickler.save` method is becoming a bit messy, so I plan to refactor the pickler a bit at some point.

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I_kwDODunzps5l9Bzh

Issue with Sequence features

closed

NONE

### Describe the bug Sequences features sometimes causes errors when the specified length is not -1 ### Steps to reproduce the bug ```python import numpy as np from datasets import Features, ClassLabel, Sequence, Value, Dataset feats = Features(**{'target': ClassLabel(names=[0, 1]),'x': Sequence(feature=Value(dtype='float64',id=None), length=2, id=None)}) Dataset.from_dict({"target": np.ones(2000).astype(int), "x": np.random.rand(2000,2)},features = feats).flatten_indices() ``` Throws: ``` TypeError: Couldn't cast array of type fixed_size_list<item: double>[2] to Sequence(feature=Value(dtype='float64', id=None), length=2, id=None) ``` The same code works without any issues when `length = -1` EDIT: The error seems to happen only when the length of the dataset is bigger than 1000 for some reason ### Expected behavior No exception ### Environment info - `datasets` version: 2.10.1 - Python version: 3.9.5 - PyArrow version: 11.0.0 - Pandas version: 1.4.1

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PR_kwDODunzps5QiHnw

Deprecate task api

closed

CONTRIBUTOR

The task API is not well adopted in the ecosystem, so this PR deprecates it. The `train_eval_index` is a newer, more flexible solution that should be used instead (I think?). These are the projects that still use the task API : * the image classification example in Transformers: [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/pytorch/image-classification/run_image_classification_no_trainer.py#L262) and [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/tensorflow/image-classification/run_image_classification.py#L277) * autotrain: [here](https://github.com/huggingface/autotrain-backend/blob/455e274004b56f9377d64db4ab03671508fcc4cd/zeus/zeus/run/utils.py#L666) * api-inference-community: [here](https://github.com/huggingface/api-inference-community/blob/fb8fb29d577a5bf01c82944db745489a6d6ed3d4/manage.py#L64) (but the rest of the code does not call the `resolve_dataset` function) So we need to update these files after the merge. cc @lewtun

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I_kwDODunzps5l82V_

Slow iteration over Torch tensors

open

NONE

### Describe the bug I have a problem related to this [issue](https://github.com/huggingface/datasets/issues/5841): I get a way slower iteration when using a Torch dataloader if I use vanilla Numpy tensors or if I first apply a ToTensor transform to the input. In particular, it takes 5 seconds to iterate over the vanilla input and ~30s after the transformation. ### Steps to reproduce the bug Here is the minimum code to reproduce the problem ```python import numpy as np from datasets import Dataset, DatasetDict, load_dataset, Array3D, Image, Features from torch.utils.data import DataLoader from tqdm import tqdm import torchvision from torchvision.transforms import ToTensor, Normalize ################################# # Without transform ################################# train_dataset = load_dataset( 'cifar100', split='train', use_auth_token=True, ) train_dataset.set_format(type="numpy", columns=["img", "fine_label"]) train_loader= DataLoader( train_dataset, batch_size=100, pin_memory=False, shuffle=True, num_workers=8, ) for batch in tqdm(train_loader, desc="Loading data, no transform"): pass ################################# # With transform ################################# transform_func = torchvision.transforms.Compose([ ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std= [0.229, 0.224, 0.225]),] ) train_dataset = train_dataset.map( desc=f"Preprocessing samples", function=lambda x: {"img": transform_func(x["img"])}, ) train_dataset.set_format(type="numpy", columns=["img", "fine_label"]) train_loader= DataLoader( train_dataset, batch_size=100, pin_memory=False, shuffle=True, num_workers=8, ) for batch in tqdm(train_loader, desc="Loading data after transform"): pass ``` I have also tried converting the Image column to an Array3D ```python img_shape = train_dataset[0]["img"].shape features = train_dataset.features.copy() features["x"] = Array3D(shape=img_shape, dtype="float32") train_dataset = train_dataset.map( desc=f"Preprocessing samples", function=lambda x: {"x": np.array(x["img"], dtype=np.uint8)}, features=features, ) train_dataset.cast_column("x", Array3D(shape=img_shape, dtype="float32")) train_dataset.set_format(type="numpy", columns=["x", "fine_label"]) ``` but to no avail. Any clue? ### Expected behavior The iteration should take approximately the same time with or without the transformation, as it doesn't change the shape of the input. What may be the issue here? ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 ```

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PR_kwDODunzps5QhtlM

Use a new low-memory approach for tf dataset index shuffling

closed

MEMBER

This PR tries out a new approach to generating the index tensor in `to_tf_dataset`, which should reduce memory usage for very large datasets. I'll need to do some testing before merging it! Fixes #5855

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I_kwDODunzps5l7qzm

IndexError: list index out of range with data hosted on Zenodo

open

MEMBER

The dataset viewer sometimes raises an `IndexError`: ``` IndexError: list index out of range ``` See: - huggingface/datasets-server#1151 - https://huggingface.co/datasets/reddit/discussions/5 - huggingface/datasets-server#1118 - https://huggingface.co/datasets/krr-oxford/OntoLAMA/discussions/1 - https://huggingface.co/datasets/hyperpartisan_news_detection/discussions/3 - https://huggingface.co/datasets/um005/discussions/2 - https://huggingface.co/datasets/tapaco/discussions/2 - https://huggingface.co/datasets/common_language/discussions/3 - https://huggingface.co/datasets/pass/discussions/1 After investigation: - This happens with data files hosted on Zenodo - Indeed, there is an underlying 429 HTTP error: Too Many Requests Note that some time ago, it also happened with data files hosted on Google Drive. See: - #4581 - #4580 The reason then was that there was a 403 HTTP error: Forbidden

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PR_kwDODunzps5Qf55q

Better error message when combining dataset dicts instead of datasets

closed

MEMBER

close https://github.com/huggingface/datasets/issues/5851

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PR_kwDODunzps5QfojD

Minor tqdm optim

closed

MEMBER

Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`. On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts.

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PR_kwDODunzps5QfDLC

Raise TypeError when indexing a dataset with bool

closed

MEMBER

Fix #5858.

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I_kwDODunzps5l4liY

Throw an error when dataset improperly indexed

closed

NONE

### Describe the bug Pandas-style subset indexing on dataset does not throw an error, when maybe it should. Instead returns the first instance of the dataset regardless of index condition. ### Steps to reproduce the bug Steps to reproduce the behavior: 1. `squad = datasets.load_dataset("squad_v2", split="validation")` 2. `item = squad[squad['question'] == "Who was the Norse leader?"]` or `it = squad[squad['id'] == '56ddde6b9a695914005b962b']` 3. returns the first item in the dataset, which does not satisfy the above conditions: `{'id': '56ddde6b9a695914005b9628', 'title': 'Normans', 'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse ("Norman" comes from "Norseman") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries.', 'question': 'In what country is Normandy located?', 'answers': {'text': ['France', 'France', 'France', 'France'], 'answer_start': [159, 159, 159, 159]}}` ### Expected behavior Should either throw an error message, or return the dataset item that satisfies the condition. ### Environment info - `datasets` version: 2.9.0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.3

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I_kwDODunzps5l4kEe

Adding chemistry dataset/models in huggingface

closed

NONE

### Feature request Huggingface is really amazing platform for open science. In addition to computer vision, video and NLP, would it be of interest to add chemistry/materials science dataset/models in Huggingface? Or, if its already done, can you provide some pointers. We have been working on a comprehensive benchmark on this topic: [JARVIS-Leaderboard](https://pages.nist.gov/jarvis_leaderboard/) and I am wondering if we could contribute/integrate this project as a part of huggingface. ### Motivation Similar to the main stream AI field, there is need of large scale benchmarks/models/infrastructure for chemistry/materials data. ### Your contribution We can start adding datasets as our [benchmarks](https://github.com/usnistgov/jarvis_leaderboard/tree/main/jarvis_leaderboard/benchmarks) should be easily convertible to the dataset format.

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I_kwDODunzps5l4JnC

Error loading natural_questions

closed

NONE

### Describe the bug When try to load natural_questions through datasets == 2.12.0 with python == 3.8.9: ```python import datasets datasets.load_dataset('natural_questions',beam_runner='DirectRunner') ``` It failed with following info: `pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs` ### Steps to reproduce the bug In python console: ```python import datasets datasets.load_dataset('natural_questions',beam_runner='DirectRunner') ``` Then the trace is: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/load.py", line 1797, in load_dataset builder_instance.download_and_prepare( File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 890, in download_and_prepare self._download_and_prepare( File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/builder.py", line 2019, in _download_and_prepare num_examples, num_bytes = beam_writer.finalize(metrics.query(m_filter)) File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 694, in finalize shard_num_bytes, _ = parquet_to_arrow(source, destination) File "/home/nlp/.cache/pypoetry/virtualenvs/drg-W3LF4Ol9-py3.8/lib/python3.8/site-packages/datasets/arrow_writer.py", line 737, in parquet_to_arrow for record_batch in parquet_file.iter_batches(): File "pyarrow/_parquet.pyx", line 1323, in iter_batches File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs ``` ### Expected behavior load natural_question questions ### Environment info ``` - `datasets` version: 2.12.0 - Platform: Linux-3.10.0-1160.42.2.el7.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.9 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.1 ```

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I_kwDODunzps5l2f0v

`to_tf_dataset` consumes too much memory

closed

NONE

### Describe the bug Hi, I'm using `to_tf_dataset` to convert a _large_ dataset to `tf.data.Dataset`. I observed that the data loading *before* training took a lot of time and memory, even with `batch_size=1`. After some digging, i believe the reason lies in the shuffle behavior. The [source code](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/tf_utils.py#L185) uses `len(dataset)` as the `buffer_size`, which may load all the data into the memory, and the [tf.data doc](https://www.tensorflow.org/guide/data#randomly_shuffling_input_data) also states that "While large buffer_sizes shuffle more thoroughly, they can take a lot of memory, and significant time to fill". ### Steps to reproduce the bug ```python from datasets import Dataset def gen(): # some large data for i in range(50000000): yield {"data": i} ds = Dataset.from_generator(gen, cache_dir="./huggingface") tf_ds = ds.to_tf_dataset( batch_size=64, shuffle=False, # no shuffle drop_remainder=False, prefetch=True, ) # fast and memory friendly 🤗 for batch in tf_ds: ... tf_ds_shuffle = ds.to_tf_dataset( batch_size=64, shuffle=True, drop_remainder=False, prefetch=True, ) # slow and memory hungry for simple iteration 😱 for batch in tf_ds_shuffle: ... ``` ### Expected behavior Shuffling should not load all the data into the memory. Would adding a `buffer_size` parameter in the `to_tf_dataset` API alleviate the problem? ### Environment info - `datasets` version: 2.11.0 - Platform: Linux-5.17.1-051701-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5l2eck

Can not load audiofolder dataset on kaggle

closed

NONE

### Describe the bug It's crash log: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/audiofolder/audiofolder.py or any data file in the same directory. Couldn't find 'audiofolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/audiofolder/audiofolder.py ### Steps to reproduce the bug  common_voice = load_dataset("audiofolder", data_dir="/kaggle/working/data") ### Expected behavior load dataset without error. It works ok on colab, but on kaggle it happends. ### Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.15.109+-x86_64-with-glibc2.31 - Python version: 3.10.10 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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PR_kwDODunzps5QaZLP

[docs] Redirects, migrated from nginx

closed

MEMBER

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PR_kwDODunzps5QZ1lj

Iterable torch formatting

closed

MEMBER

Used the TorchFormatter to get torch tensors in iterable dataset with format set to "torch". It uses the data from Arrow if possible, otherwise applies recursive_tensorize. When set back to format_type=None, cast_to_python_objects is used. requires https://github.com/huggingface/datasets/pull/5821 close https://github.com/huggingface/datasets/issues/5793

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PR_kwDODunzps5QZALv

Make packaged builders skip non-supported file formats

open

MEMBER

This PR makes packaged builders skip non-supported file formats: - Csv builder skips non-CSV files - Analogously for the other builders Fix #5849.

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I_kwDODunzps5lxysX

CSV datasets should only read the CSV data files in the repo

closed

MEMBER

When a no-script dataset has many CSV files and a JPG file, the library infers to use the Csv builder, but tries to read as CSV all files in the repo, also the JPG file. I think the Csv builder should filter out non-CSV files when reading. An analogue solution should be implemented for other packaged builders. Related to: - https://huggingface.co/datasets/abidlabs/img2text/discussions/1 - https://github.com/gradio-app/gradio/pull/3973#issuecomment-1545409061 CC: @abidlabs @severo

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PR_kwDODunzps5QYa1B

Add `accelerate` as metric's test dependency to fix CI error

closed

CONTRIBUTOR

The `frugalscore` metric uses Transformers' Trainer, which requires `accelerate` (as of recently). Fixes the following [CI error](https://github.com/huggingface/datasets/actions/runs/4950900048/jobs/8855148703?pr=5845).

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I_kwDODunzps5luOc6

Streaming IterableDataset not working with translation pipeline

open

NONE

### Describe the bug I'm trying to use a streaming dataset for translation inference to avoid downloading the training data. I'm using a pipeline and a dataset, and following the guidance in the tutorial. Instead I get an exception that IterableDataset has no len(). ### Steps to reproduce the bug CODE: ``` from transformers import pipeline from transformers.pipelines.pt_utils import KeyDataset from datasets import load_dataset ds = load_dataset(path="wmt14", name="fr-en", split="test", streaming=True) bs=1 mt = pipeline("translation_en_to_fr", model="t5-base", batch_size=bs) #print(mt("hello")) THIS WORKS ks = KeyDataset(ds, "translation") print(f"{ks}") xx= mt(ks) for x in xx: print(x) ``` RUN: ``` (watnlp) [jlquinn@bertdev01 hf]$ python ende.t5.pipe.py 2023-05-11 16:48:08.817572: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2023-05-11 16:48:08.821388: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2023-05-11 16:48:08.821407: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. <transformers.pipelines.pt_utils.KeyDataset object at 0x7f61ed5da9d0> Traceback (most recent call last): File "/home/jlquinn/models/hf/ende.t5.pipe.py", line 11, in <module> for x in xx: File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__ item = next(self.iterator) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 111, in __next__ item = next(self.iterator) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in __next__ data = self._next_data() File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 720, in _next_data index = self._next_index() # may raise StopIteration File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 671, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 247, in __iter__ for idx in self.sampler: File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 76, in __iter__ return iter(range(len(self.data_source))) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 13, in __len__ return len(self.dataset) File "/home/jlquinn/miniconda3/envs/watnlp/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 289, in __len__ return len(self.dataset) TypeError: object of type 'IterableDataset' has no len() ``` ### Expected behavior I'm expecting french translations of the english test set to be printed. ### Environment info Run on CPU with no GPU. RHEL 8.7 x86_64 python 3.9.0 transformers 4.17.0 datasets 2.0.0 tokenizers 0.12.1 ``` (watnlp) [jlquinn@bertdev01 hf]$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.0.0 - Platform: Linux-4.18.0-372.19.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.0 - PyArrow version: 8.0.0 - Pandas version: 1.4.4 ```

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I_kwDODunzps5lzJfo

Error message not clear in interleaving datasets

closed

NONE

### System Info standard env ### Who can help? _No response_ ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction I'm trying to interleave 'sciq', 'wiki' and the 'pile-enron' dataset. I think the error I made was that I loaded the train split of one, but for the other but the error is not too helpful- ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/home/suryahari/Vornoi/save_model_ops.py](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/Vornoi/save_model_ops.py) in line 3 [41](file:///home/suryahari/Vornoi/save_model_ops.py?line=40) # %% ----> [43](file:///home/suryahari/Vornoi/save_model_ops.py?line=42) dataset = interleave_datasets(datasets, stopping_strategy="all_exhausted") File [~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124), in interleave_datasets(datasets, probabilities, seed, info, split, stopping_strategy) [122](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=121) for dataset in datasets[1:]: [123](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=122) if (map_style and not isinstance(dataset, Dataset)) or (iterable and not isinstance(dataset, IterableDataset)): --> [124](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=123) raise ValueError( [125](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=124) f"Unable to interleave a {type(datasets[0])} with a {type(dataset)}. Expected a list of Dataset objects or a list of IterableDataset objects." [126](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=125) ) [127](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=126) if stopping_strategy not in ["first_exhausted", "all_exhausted"]: [128](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=127) raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.") ValueError: Unable to interleave a with a . Expected a list of Dataset objects or a list of IterableDataset objects. ``` ### Expected behavior the error message should hopefully be more clear

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I_kwDODunzps5ls-iK

load_dataset('bigcode/the-stack-dedup', streaming=True) very slow!

open

NONE

### Describe the bug Running ``` import datasets ds = datasets.load_dataset('bigcode/the-stack-dedup', streaming=True) ``` takes about 2.5 minutes! I would expect this to be near instantaneous. With other datasets, the runtime is one or two seconds. ### Environment info - `datasets` version: 2.11.0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.10.10 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0

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PR_kwDODunzps5QUMjS

Add `date_format` param to the CSV reader

closed

CONTRIBUTOR

Adds the `date_format` param introduced in Pandas 2.0 to the CSV reader and improves its type hints.

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I_kwDODunzps5lrhZk

TypeError: Couldn't cast array of type struct<answer: struct<unanswerable: bool, answerType: string, free_form_answer: string, evidence: list<item: string>, evidenceAnnotate: list<item: string>, highlighted_evidence: list<item: string>>> to ...

open

NONE

### Describe the bug TypeError: Couldn't cast array of type struct<answer: struct<unanswerable: bool, answerType: string, free_form_answer: string, evidence: list<item: string>, evidenceAnnotate: list<item: string>, highlighted_evidence: list<item: string>>> to {'answer': {'unanswerable': Value(dtype='bool', id=None), 'answerType': Value(dtype='string', id=None), 'free_form_answer': Value(dtype='string', id=None), 'evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'evidenceAnnotate': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'highlighted_evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'unanswerable': Value(dtype='bool', id=None), 'answerType': Value(dtype='string', id=None), 'free_form_answer': Value(dtype='string', id=None), 'evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'evidenceAnnotate': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'highlighted_evidence': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} When I use _load_dataset()_ I get the error `from datasets import load_dataset datafiles = {'train': './data/train.json', 'validation': './data/validation.json', 'test': './data/test.json'} raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache") ` Detailed error information is as follows： Traceback (most recent call last): File "C:/Users/CHENJIALEI/Desktop/NLPCC2023/NLPCC23_SciMRC-main/test2.py", line 9, in <module> raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache") File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\load.py", line 1747, in load_dataset builder_instance.download_and_prepare( File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 814, in download_and_prepare self._download_and_prepare( File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 905, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\builder.py", line 1521, in _prepare_split writer.write_table(table) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\arrow_writer.py", line 540, in write_table pa_table = table_cast(pa_table, self._schema) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2069, in table_cast return cast_table_to_schema(table, schema) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2031, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 2031, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1740, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1740, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1867, in cast_array_to_feature casted_values = _c(array.values, feature[0]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper return func(array, *args, **kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1862, in cast_array_to_feature arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1862, in <listcomp> arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper return func(array, *args, **kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1867, in cast_array_to_feature casted_values = _c(array.values, feature[0]) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1742, in wrapper return func(array, *args, **kwargs) File "D:\Environment\anaconda3\envs\test\lib\site-packages\datasets\table.py", line 1913, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") It is successful when I load the data separately `raw_data = load_dataset("json", data_files="./data/train.json", cache_dir="./cache")` ### Steps to reproduce the bug 1.from datasets import load_dataset 2.datafiles = {'train': './data/train.json', 'validation': './data/validation.json', 'test': './data/test.json'} 3.raw_data = load_dataset("json", data_files=datafiles, cache_dir="./cache") ### Expected behavior Successfully load dataset ### Environment info datasets == 2.6.1 pyarrow == 8.0.0 python == 3.8 platform:windows11

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I_kwDODunzps5lpJvv

Abusurdly slow on iteration

closed

NONE

### Describe the bug I am attempting to iterate through an image dataset, but I am encountering a significant slowdown in the iteration speed. In order to investigate this issue, I conducted the following experiment: ```python a=torch.randn(100,224) a=torch.stack([a] * 10000) a.shape # %% ds=Dataset.from_dict({"tensor":a}) for i in tqdm(ds.with_format("numpy")): pass for i in tqdm(ds.with_format("torch")): pass ``` I noticed that the dataset in numpy format performs significantly faster than the one in torch format. My hypothesis is that the dataset undergoes a transformation process of torch->python->numpy(torch) in the background, which might be causing the slowdown. Is there any way to expedite the process by bypassing such transformations? Furthermore, if I increase the size of a to an image shape, like: ```python a=torch.randn(3,224,224) ``` the iteration speed becomes absurdly slow, around 100 iterations per second, whereas the speed with numpy format is approximately 250 iterations per second. This level of speed would be unacceptable for large image datasets, as it could take several hours just to iterate through a single epoch. ### Steps to reproduce the bug ```python a=torch.randn(100,224) a=torch.stack([a] * 10000) a.shape # %% ds=Dataset.from_dict({"tensor":a}) for i in tqdm(ds.with_format("numpy")): pass for i in tqdm(ds.with_format("torch")): pass ``` ### Expected behavior iteration faster ### Environment info - `datasets` version: 2.11.0 - Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.10 - Python version: 3.8.16 - Huggingface_hub version: 0.13.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0

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I_kwDODunzps5lo3i1

load model error.

closed

NONE

### Describe the bug I had trained one model use deepspeed, when I load the final load I get the follow error: OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer. my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/ ### Steps to reproduce the bug 。。。 ### Expected behavior 。。。 ### Environment info 。。。

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I_kwDODunzps5lqAbK

Remove columns in interable dataset

closed

NONE

### Feature request Right now, remove_columns() produces a NotImplementedError for iterable style datasets ### Motivation It would be great to have the same functionality irrespective of whether one is using an iterable or a map-style dataset ### Your contribution hope and courage.

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I_kwDODunzps5lqBY3

Can't add iterable datasets to a Dataset Dict.

closed

NONE

### System Info standard env ### Who can help? _No response_ ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction Get the following error: TypeError: Values in `DatasetDict` should be of type `Dataset` but got type '<class 'datasets.iterable_dataset.IterableDataset'>' ### Expected behavior should be able to add iterable datasets to a dataset dict

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I_kwDODunzps5lmXDe

Make models/functions optimized with `torch.compile` hashable

open

CONTRIBUTOR

As reported in https://github.com/huggingface/datasets/issues/5819, hashing functions/transforms that reference a model, or a function, optimized with `torch.compile` currently fails due to them not being picklable (the concrete error can be found in the linked issue). The solutions to consider: 1. hashing/pickling the original, uncompiled version of a compiled model/function (attributes `_orig_mod`/`_torchdynamo_orig_callable`) (less precise than the 2nd option as it ignores the other params of `torch.compute`) 2. wait for https://github.com/pytorch/pytorch/issues/101107 to be resolved

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I_kwDODunzps5lhO9g

Streaming support for `load_from_disk`

closed

NONE

### Feature request Support for streaming datasets stored in object stores in `load_from_disk`. ### Motivation The `load_from_disk` function supports fetching datasets stored in object stores such as `s3`. In many cases, the datasets that are stored in object stores are very large and being able to stream the data from the buckets becomes essential. ### Your contribution I'd be happy to contribute this feature if I could get the guidance on how to do so.

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I_kwDODunzps5lggUo

Use DeepSpeed load myself " .csv " dataset.

open

NONE

### Describe the bug When I use DeepSpeed train a model with my own " XXX.csv" dataset I got the follow question: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py", line 1767, in load_dataset builder_instance = load_dataset_builder( File "/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py", line 1498, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/fm001/.conda/envs/hzl/lib/python3.8/site-packages/datasets/load.py", line 1217, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/fm001/hzl/Data/qa.csv/qa.csv.py or any data file in the same directory. ### Steps to reproduce the bug my code is : from datasets import load_dataset mydata = load_dataset("/home/fm001/hzl/Data/qa.csv") ### Expected behavior 。。。 ### Environment info 。。。

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PR_kwDODunzps5QIgzu

[docs] Custom decoding transforms

closed

MEMBER

Adds custom decoding transform solution to the docs to fix #5782.

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https://github.com/huggingface/datasets/pull/5835

PR_kwDODunzps5QHquR

Always set nullable fields in the writer

closed

MEMBER

This fixes loading of e.g. parquet data with non-nullable fields. Indeed `datasets.Features` doesn't support non-nullable fields, which can lead to data not concatenable due to arrow schema mismatch.

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I_kwDODunzps5leU78

Is uint8 supported?

closed

NONE

### Describe the bug I expect the dataset to store the data in the `uint8` data type, but it's returning `int64` instead. While I've found that `datasets` doesn't yet support float16 (https://github.com/huggingface/datasets/issues/4981), I'm wondering if this is the case for other data types as well. Is there a way to store vector data as `uint8` and then upload it to the hub? ### Steps to reproduce the bug ```python from datasets import Features, Dataset, Sequence, Value import numpy as np dataset = Dataset.from_dict( {"vector": [np.array([0, 1, 2], dtype=np.uint8)]}, features=Features({"vector": Sequence(Value("uint8"))}) ).with_format("numpy") print(dataset[0]["vector"].dtype) ``` ### Expected behavior Expected: `uint8` Actual: `int64` ### Environment info - `datasets` version: 2.12.0 - Platform: macOS-12.1-x86_64-i386-64bit - Python version: 3.8.12 - Huggingface_hub version: 0.12.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5ldr3q

Unable to push dataset - `create_pr` problem

open

NONE

### Describe the bug I can't upload to the hub the dataset I manually created locally (Image dataset). I have a problem when using the method `.push_to_hub` which asks for a `create_pr` attribute which is not compatible. ### Steps to reproduce the bug here what I have: ```python dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts") ``` Output: ```python Pushing split train to the Hub. Pushing dataset shards to the dataset hub: 0%| | 0/2 [00:00<?, ?it/s] Creating parquet from Arrow format: 0%| | 0/3 [00:00<?, ?ba/s] Creating parquet from Arrow format: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 12.70ba/s] Pushing dataset shards to the dataset hub: 0%| | 0/2 [00:01<?, ?it/s] --------------------------------------------------------------------------- HTTPError Traceback (most recent call last) File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py:259, in hf_raise_for_status(response, endpoint_name) 258 try: --> 259 response.raise_for_status() 260 except HTTPError as e: File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/requests/models.py:1021, in Response.raise_for_status(self) 1020 if http_error_msg: -> 1021 raise HTTPError(http_error_msg, response=self) HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/agomberto/FrenchCensus-handwritten-texts/commit/main The above exception was the direct cause of the following exception: HfHubHTTPError Traceback (most recent call last) Cell In[7], line 1 ----> 1 dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts") File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/dataset_dict.py:1583, in DatasetDict.push_to_hub(self, repo_id, private, token, branch, max_shard_size, num_shards, embed_external_files) 1581 logger.warning(f"Pushing split {split} to the Hub.") 1582 # The split=key needs to be removed before merging -> 1583 repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( 1584 repo_id, 1585 split=split, 1586 private=private, 1587 token=token, 1588 branch=branch, 1589 max_shard_size=max_shard_size, 1590 num_shards=num_shards.get(split), 1591 embed_external_files=embed_external_files, 1592 ) 1593 total_uploaded_size += uploaded_size 1594 total_dataset_nbytes += dataset_nbytes File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/arrow_dataset.py:5275, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, max_shard_size, num_shards, embed_external_files) 5273 shard.to_parquet(buffer) 5274 uploaded_size += buffer.tell() -> 5275 _retry( 5276 api.upload_file, 5277 func_kwargs={ 5278 "path_or_fileobj": buffer.getvalue(), 5279 "path_in_repo": shard_path_in_repo, 5280 "repo_id": repo_id, 5281 "token": token, 5282 "repo_type": "dataset", 5283 "revision": branch, 5284 }, 5285 exceptions=HTTPError, 5286 status_codes=[504], 5287 base_wait_time=2.0, 5288 max_retries=5, 5289 max_wait_time=20.0, 5290 ) 5291 shards_path_in_repo.append(shard_path_in_repo) 5293 # Cleanup to remove unused files File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/utils/file_utils.py:285, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 283 except exceptions as err: 284 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): --> 285 raise err 286 else: 287 sleep_time = min(max_wait_time, base_wait_time * 2**retry) # Exponential backoff File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/datasets/utils/file_utils.py:282, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time) 280 while True: 281 try: --> 282 return func(*func_args, **func_kwargs) 283 except exceptions as err: 284 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes): File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:120, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs) 117 if check_use_auth_token: 118 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs) --> 120 return fn(*args, **kwargs) File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/hf_api.py:2998, in HfApi.upload_file(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, commit_message, commit_description, create_pr, parent_commit) 2990 commit_message = ( 2991 commit_message if commit_message is not None else f"Upload {path_in_repo} with huggingface_hub" 2992 ) 2993 operation = CommitOperationAdd( 2994 path_or_fileobj=path_or_fileobj, 2995 path_in_repo=path_in_repo, 2996 ) -> 2998 commit_info = self.create_commit( 2999 repo_id=repo_id, 3000 repo_type=repo_type, 3001 operations=[operation], 3002 commit_message=commit_message, 3003 commit_description=commit_description, 3004 token=token, 3005 revision=revision, 3006 create_pr=create_pr, 3007 parent_commit=parent_commit, 3008 ) 3010 if commit_info.pr_url is not None: 3011 revision = quote(_parse_revision_from_pr_url(commit_info.pr_url), safe="") File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:120, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs) 117 if check_use_auth_token: 118 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs) --> 120 return fn(*args, **kwargs) File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/hf_api.py:2548, in HfApi.create_commit(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit) 2546 try: 2547 commit_resp = get_session().post(url=commit_url, headers=headers, data=data, params=params) -> 2548 hf_raise_for_status(commit_resp, endpoint_name="commit") 2549 except RepositoryNotFoundError as e: 2550 e.append_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE) File ~/miniconda3/envs/hwocr/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py:301, in hf_raise_for_status(response, endpoint_name) 297 raise BadRequestError(message, response=response) from e 299 # Convert `HTTPError` into a `HfHubHTTPError` to display request information 300 # as well (request id and/or server error message) --> 301 raise HfHubHTTPError(str(e), response=response) from e HfHubHTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/agomberto/FrenchCensus-handwritten-texts/commit/main (Request ID: Root=1-645a66bf-255ad91602a6404e6cb70fba) Forbidden: pass `create_pr=1` as a query parameter to create a Pull Request ``` And then when I do ```python dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts", create_pr=1) ``` I get ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[8], line 1 ----> 1 dataset.push_to_hub("agomberto/FrenchCensus-handwritten-texts", create_pr=1) TypeError: push_to_hub() got an unexpected keyword argument 'create_pr' ``` ### Expected behavior I would like to have the dataset updloaded [here](https://huggingface.co/datasets/agomberto/FrenchCensus-handwritten-texts). ### Environment info ```bash - `datasets` version: 2.12.0 - Platform: macOS-13.3.1-arm64-arm-64bit - Python version: 3.8.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 1.5.3 ```

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I_kwDODunzps5ldIYo

404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased

closed

NONE

### Describe the bug Running [Bert-Large-Cased](https://huggingface.co/bert-large-cased) model causes `HTTPError`, with the following traceback- ``` HTTPError Traceback (most recent call last) <ipython-input-6-5c580443a1ad> in <module> ----> 1 tokenizer = BertTokenizer.from_pretrained('bert-large-cased') ~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs) 1646 # At this point pretrained_model_name_or_path is either a directory or a model identifier name 1647 fast_tokenizer_file = get_fast_tokenizer_file( -> 1648 pretrained_model_name_or_path, revision=revision, use_auth_token=use_auth_token 1649 ) 1650 additional_files_names = { ~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in get_fast_tokenizer_file(path_or_repo, revision, use_auth_token) 3406 """ 3407 # Inspect all files from the repo/folder. -> 3408 all_files = get_list_of_files(path_or_repo, revision=revision, use_auth_token=use_auth_token) 3409 tokenizer_files_map = {} 3410 for file_name in all_files: ~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/file_utils.py in get_list_of_files(path_or_repo, revision, use_auth_token) 1685 token = None 1686 model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info( -> 1687 path_or_repo, revision=revision, token=token 1688 ) 1689 return [f.rfilename for f in model_info.siblings] ~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/huggingface_hub/hf_api.py in model_info(self, repo_id, revision, token) 246 ) 247 r = requests.get(path, headers=headers) --> 248 r.raise_for_status() 249 d = r.json() 250 return ModelInfo(**d) ~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/requests/models.py in raise_for_status(self) 951 952 if http_error_msg: --> 953 raise HTTPError(http_error_msg, response=self) 954 955 def close(self): HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased ``` I have also tried running in offline mode, as [discussed here](https://huggingface.co/docs/transformers/installation#offline-mode) ``` HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 ``` ### Steps to reproduce the bug 1. `from transformers import BertTokenizer, BertModel` 2. `tokenizer = BertTokenizer.from_pretrained('bert-large-cased')` ### Expected behavior Run without the HTTP error. ### Environment info | # Name | Version | Build | Channel | | |--------------------|------------|-----------------------------|---------|---| | _libgcc_mutex | 0.1 | main | | | | _openmp_mutex | 4.5 | 1_gnu | | | | _pytorch_select | 0.1 | cpu_0 | | | | appdirs | 1.4.4 | pypi_0 | pypi | | | backcall | 0.2.0 | pypi_0 | pypi | | | blas | 1.0 | mkl | | | | bzip2 | 1.0.8 | h7b6447c_0 | | | | ca-certificates | 2021.7.5 | h06a4308_1 | | | | certifi | 2021.5.30 | py37h06a4308_0 | | | | cffi | 1.14.6 | py37h400218f_0 | | | | charset-normalizer | 2.0.3 | pypi_0 | pypi | | | click | 8.0.1 | pypi_0 | pypi | | | colorama | 0.4.4 | pypi_0 | pypi | | | cudatoolkit | 11.1.74 | h6bb024c_0 | nvidia | | | cycler | 0.11.0 | pypi_0 | pypi | | | decorator | 5.0.9 | pypi_0 | pypi | | | docker-pycreds | 0.4.0 | pypi_0 | pypi | | | docopt | 0.6.2 | pypi_0 | pypi | | | dominate | 2.6.0 | pypi_0 | pypi | | | ffmpeg | 4.3 | hf484d3e_0 | pytorch | | | filelock | 3.0.12 | pypi_0 | pypi | | | fonttools | 4.38.0 | pypi_0 | pypi | | | freetype | 2.10.4 | h5ab3b9f_0 | | | | gitdb | 4.0.7 | pypi_0 | pypi | | | gitpython | 3.1.18 | pypi_0 | pypi | | | gmp | 6.2.1 | h2531618_2 | | | | gnutls | 3.6.15 | he1e5248_0 | | | | huggingface-hub | 0.0.12 | pypi_0 | pypi | | | humanize | 3.10.0 | pypi_0 | pypi | | | idna | 3.2 | pypi_0 | pypi | | | importlib-metadata | 4.6.1 | pypi_0 | pypi | | | intel-openmp | 2019.4 | 243 | | | | ipdb | 0.13.9 | pypi_0 | pypi | | | ipython | 7.25.0 | pypi_0 | pypi | | | ipython-genutils | 0.2.0 | pypi_0 | pypi | | | jedi | 0.18.0 | pypi_0 | pypi | | | joblib | 1.0.1 | pypi_0 | pypi | | | jpeg | 9b | h024ee3a_2 | | | | jsonpickle | 1.5.2 | pypi_0 | pypi | | | kiwisolver | 1.4.4 | pypi_0 | pypi | | | lame | 3.100 | h7b6447c_0 | | | | lcms2 | 2.12 | h3be6417_0 | | | | ld_impl_linux-64 | 2.35.1 | h7274673_9 | | | | libffi | 3.3 | he6710b0_2 | | | | libgcc-ng | 9.3.0 | h5101ec6_17 | | | | libgomp | 9.3.0 | h5101ec6_17 | | | | libiconv | 1.15 | h63c8f33_5 | | | | libidn2 | 2.3.2 | h7f8727e_0 | | | | libmklml | 2019.0.5 | 0 | | | | libpng | 1.6.37 | hbc83047_0 | | | | libstdcxx-ng | 9.3.0 | hd4cf53a_17 | | | | libtasn1 | 4.16.0 | h27cfd23_0 | | | | libtiff | 4.2.0 | h85742a9_0 | | | | libunistring | 0.9.10 | h27cfd23_0 | | | | libuv | 1.40.0 | h7b6447c_0 | | | | libwebp-base | 1.2.0 | h27cfd23_0 | | | | lz4-c | 1.9.3 | h2531618_0 | | | | matplotlib | 3.5.3 | pypi_0 | pypi | | | matplotlib-inline | 0.1.2 | pypi_0 | pypi | | | mergedeep | 1.3.4 | pypi_0 | pypi | | | mkl | 2020.2 | 256 | | | | mkl-service | 2.3.0 | py37he8ac12f_0 | | | | mkl_fft | 1.3.0 | py37h54f3939_0 | | | | mkl_random | 1.1.1 | py37h0573a6f_0 | | | | msgpack | 1.0.2 | pypi_0 | pypi | | | munch | 2.5.0 | pypi_0 | pypi | | | ncurses | 6.2 | he6710b0_1 | | | | nettle | 3.7.3 | hbbd107a_1 | | | | ninja | 1.10.2 | hff7bd54_1 | | | | nltk | 3.8.1 | pypi_0 | pypi | | | numpy | 1.19.2 | py37h54aff64_0 | | | | numpy-base | 1.19.2 | py37hfa32c7d_0 | | | | olefile | 0.46 | py37_0 | | | | openh264 | 2.1.0 | hd408876_0 | | | | openjpeg | 2.3.0 | h05c96fa_1 | | | | openssl | 1.1.1k | h27cfd23_0 | | | | packaging | 21.0 | pypi_0 | pypi | | | pandas | 1.3.1 | pypi_0 | pypi | | | parso | 0.8.2 | pypi_0 | pypi | | | pathtools | 0.1.2 | pypi_0 | pypi | | | pexpect | 4.8.0 | pypi_0 | pypi | | | pickleshare | 0.7.5 | pypi_0 | pypi | | | pillow | 8.3.1 | py37h2c7a002_0 | | | | pip | 21.1.3 | py37h06a4308_0 | | | | prompt-toolkit | 3.0.19 | pypi_0 | pypi | | | protobuf | 4.21.12 | pypi_0 | pypi | | | psutil | 5.8.0 | pypi_0 | pypi | | | ptyprocess | 0.7.0 | pypi_0 | pypi | | | py-cpuinfo | 8.0.0 | pypi_0 | pypi | | | pycparser | 2.20 | py_2 | | | | pygments | 2.9.0 | pypi_0 | pypi | | | pyparsing | 2.4.7 | pypi_0 | pypi | | | python | 3.7.10 | h12debd9_4 | | | | python-dateutil | 2.8.2 | pypi_0 | pypi | | | pytorch | 1.9.0 | py3.7_cuda11.1_cudnn8.0.5_0 | pytorch | | | pytz | 2021.1 | pypi_0 | pypi | | | pyyaml | 5.4.1 | pypi_0 | pypi | | | readline | 8.1 | h27cfd23_0 | | | | regex | 2022.10.31 | pypi_0 | pypi | | | requests | 2.26.0 | pypi_0 | pypi | | | sacred | 0.8.2 | pypi_0 | pypi | | | sacremoses | 0.0.45 | pypi_0 | pypi | | | scikit-learn | 0.24.2 | pypi_0 | pypi | | | scipy | 1.7.0 | pypi_0 | pypi | | | sentry-sdk | 1.15.0 | pypi_0 | pypi | | | setproctitle | 1.3.2 | pypi_0 | pypi | | | setuptools | 52.0.0 | py37h06a4308_0 | | | | six | 1.16.0 | pyhd3eb1b0_0 | | | | smmap | 4.0.0 | pypi_0 | pypi | | | sqlite | 3.36.0 | hc218d9a_0 | | | | threadpoolctl | 2.2.0 | pypi_0 | pypi | | | tk | 8.6.10 | hbc83047_0 | | | | tokenizers | 0.10.3 | pypi_0 | pypi | | | toml | 0.10.2 | pypi_0 | pypi | | | torchaudio | 0.9.0 | py37 | pytorch | | | torchvision | 0.10.0 | py37_cu111 | pytorch | | | tqdm | 4.61.2 | pypi_0 | pypi | | | traitlets | 5.0.5 | pypi_0 | pypi | | | transformers | 4.9.1 | pypi_0 | pypi | | | typing-extensions | 3.10.0.0 | hd3eb1b0_0 | | | | typing_extensions | 3.10.0.0 | pyh06a4308_0 | | | | urllib3 | 1.26.14 | pypi_0 | pypi | | | wandb | 0.13.10 | pypi_0 | pypi | | | wcwidth | 0.2.5 | pypi_0 | pypi | | | wheel | 0.36.2 | pyhd3eb1b0_0 | | | | wrapt | 1.12.1 | pypi_0 | pypi | | | xz | 5.2.5 | h7b6447c_0 | | | | zipp | 3.5.0 | pypi_0 | pypi | | | zlib | 1.2.11 | h7b6447c_3 | | | | zstd | 1.4.9 | haebb681_0 | | |

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I_kwDODunzps5lb55L

[Bug]504 Server Error when loading dataset which was already cached

open

NONE

### Describe the bug I have already cached the dataset using: ``` dataset = load_dataset("databricks/databricks-dolly-15k", cache_dir="/mnt/data/llm/datasets/databricks-dolly-15k") ``` After that, I tried to load it again using the same machine, I got this error: ``` Traceback (most recent call last): File "/mnt/home/llm/pythia/train.py", line 16, in <module> dataset = load_dataset("databricks/databricks-dolly-15k", File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1773, in load_dataset builder_instance = load_dataset_builder( File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1502, in load_dataset_builder dataset_module = dataset_module_factory( File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory raise e1 from None File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1186, in dataset_module_factory raise e File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/datasets/load.py", line 1160, in dataset_module_factory dataset_info = hf_api.dataset_info( File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 120, in _inner_fn return fn(*args, **kwargs) File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 1667, in dataset_info hf_raise_for_status(r) File "/mnt/data/conda/envs/pythia_ft/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 301, in hf_raise_for_status raise HfHubHTTPError(str(e), response=response) from e huggingface_hub.utils._errors.HfHubHTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/databricks/databricks-dolly-15k ``` ### Steps to reproduce the bug 1. cache the databrick-dolly-15k dataset using load_dataset, setting a cache_dir 2. use load_dataset again, setting the same cache_dir ### Expected behavior Dataset loaded succuessfully. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-4.18.0-372.16.1.el8_6.x86_64-x86_64-with-glibc2.27 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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PR_kwDODunzps5QEFEi

Debug windows #2

closed

NONE

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I_kwDODunzps5lU02t

(mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'))

closed

NONE

### Describe the bug M2 MBP can't run ```python from datasets import load_dataset jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy') ``` ### Steps to reproduce the bug 1. Use M2 MBP 2. Python 3.10.10 from pyenv 3. Run ``` from datasets import load_dataset jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy') ``` ### Expected behavior Be able to run normally ### Environment info ``` from datasets import load_dataset jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy') ``` OSX: 13.2 CPU: M2

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I_kwDODunzps5lSEeb

Stream data concatenation issue

closed

NONE

### Describe the bug I am not able to concatenate the augmentation of the stream data. I am using the latest version of dataset. ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string', id=None), 'audio': {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'path': Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'transcript': Value(dtype='string', id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'path': Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null"). ### Steps to reproduce the bug dataset = load_dataset("tobiolatunji/afrispeech-200", "all", streaming=True).shuffle(seed=42) dataset_cln = dataset.remove_columns(['speaker_id', 'path', 'age_group', 'gender', 'accent', 'domain', 'country', 'duration']) dataset_cln = dataset_cln.cast_column("audio", Audio(sampling_rate=16000)) from audiomentations import AddGaussianNoise,Compose,Gain,OneOf,PitchShift,PolarityInversion,TimeStretch augmentation = Compose([ AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=0.2) ]) def augment_dataset(batch): audio = batch["audio"] audio["array"] = augmentation(audio["array"], sample_rate=audio["sampling_rate"]) return batch augmented_dataset_cln = dataset_cln['train'].map(augment_dataset) dataset_cln['train'] = interleave_datasets([dataset_cln['train'], augmented_dataset_cln]) dataset_cln['train'] = dataset_cln['train'].shuffle(seed=42) ### Expected behavior I should be able to merge as sampling rate is same. ### Environment info import datasets import transformers import accelerate print(datasets.__version__) print(transformers.__version__) print(torch.__version__) print(evaluate.__version__) print(accelerate.__version__) 2.12.0 4.28.1 2.0.0 0.4.0 0.18.0

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I_kwDODunzps5lQwXu

load json dataset interrupt when dtype cast problem occured

open

NONE

### Describe the bug i have a json like this: [ {"id": 1, "name": 1}, {"id": 2, "name": "Nan"}, {"id": 3, "name": 3}, .... ] ，which have several problematic rows data like row 2, then i load it with datasets.load_dataset('json', data_files=['xx.json'], split='train'), it will report like this: Generating train split: 0 examples [00:00, ? examples/s]Failed to read file 'C:\Users\gawinjunwu\Downloads\test\data\a.json' with error <class 'pyarrow.lib.ArrowInvalid'>: Could not convert '2' with type str: tried to convert to int64 Traceback (most recent call last): File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 1858, in _prepare_split_single for _, table in generator: File "D:\Python3.9\lib\site-packages\datasets\packaged_modules\json\json.py", line 146, in _generate_tables raise ValueError(f"Not able to read records in the JSON file at {file}.") from None ValueError: Not able to read records in the JSON file at C:\Users\gawinjunwu\Downloads\test\data\a.json. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "c:\Users\gawinjunwu\Downloads\test\scripts\a.py", line 4, in <module> ds = load_dataset('json', data_dir='data', split='train') File "D:\Python3.9\lib\site-packages\datasets\load.py", line 1797, in load_dataset builder_instance.download_and_prepare( File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 890, in download_and_prepare self._download_and_prepare( File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 985, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 1746, in _prepare_split for job_id, done, content in self._prepare_split_single( File "D:\Python3.9\lib\site-packages\datasets\builder.py", line 1891, 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. Could datasets skip those problematic data row? ### Steps to reproduce the bug prepare a json file like this: [ {"id": 1, "name": 1}, {"id": 2, "name": "Nan"}, {"id": 3, "name": 3} ] then use datasets.load_dataset('json', dir_files=['xxx.json']) to load the json file ### Expected behavior skip the problematic data row and load row1 and row3 ### Environment info python3.9

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https://github.com/huggingface/datasets/pull/5826

PR_kwDODunzps5P5FYZ

Support working_dir in from_spark

closed

CONTRIBUTOR

Accept `working_dir` as an argument to `Dataset.from_spark`. Setting a non-NFS working directory for Spark workers to materialize to will improve write performance.

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I_kwDODunzps5lKyl7

FileNotFound even though exists

closed

CONTRIBUTOR

### Describe the bug I'm trying to download https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl which works fine in my webbrowser, but somehow not with datasets. Am I doing sth wrong? ``` Downloading builder script: 100% 2.82k/2.82k [00:00<00:00, 64.2kB/s] Downloading readme: 100% 12.6k/12.6k [00:00<00:00, 585kB/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-2-4b45446a91d5>](https://localhost:8080/#) in <cell line: 4>() 2 lang = "ur" 3 fname = "xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl" ----> 4 dataset = load_dataset("bigscience/xP3", data_files=f"{lang}/{fname}") 6 frames [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions) 291 if allowed_extensions is not None: 292 error_msg += f" with any supported extension {list(allowed_extensions)}" --> 293 raise FileNotFoundError(error_msg) 294 return sorted(out) 295 FileNotFoundError: Unable to find 'https://huggingface.co/datasets/bigscience/xP3/resolve/main/ur/xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl' at /content/https:/huggingface.co/datasets/bigscience/xP3/resolve/main ``` ### Steps to reproduce the bug ``` !pip install -q datasets from datasets import load_dataset lang = "ur" fname = "xp3_facebook_flores_spa_Latn-urd_Arab_devtest_ab-spa_Latn-urd_Arab.jsonl" dataset = load_dataset("bigscience/xP3", data_files=f"{lang}/{fname}") ``` ### Expected behavior Correctly downloads ### Environment info latest versions

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PR_kwDODunzps5P1rIZ

Fix incomplete docstring for `BuilderConfig`

closed

CONTRIBUTOR

Fixes #5820 Also fixed a couple of typos I spotted

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I_kwDODunzps5lJosV

[2.12.0] DatasetDict.save_to_disk not saving to S3

closed

NONE

### Describe the bug When trying to save a `DatasetDict` to a private S3 bucket using `save_to_disk`, the artifacts are instead saved locally, and not in the S3 bucket. I have tried using the deprecated `fs` as well as the `storage_options` arguments and I get the same results. ### Steps to reproduce the bug 1. Create a DatsetDict `dataset` 2. Create a S3FileSystem object `s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)` 3. Save using `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", storage_options=s3.storage_options)` or `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", fs=s3)` 4. Check the corresponding S3 bucket and verify nothing has been uploaded 5. Check the path at f"{s3_bucket}/{s3_dir}/{dataset_name}" and verify that files have been saved there ### Expected behavior Artifacts are uploaded at the f"{s3_bucket}/{s3_dir}/{dataset_name}" S3 location. ### Environment info - `datasets` version: 2.12.0 - Platform: macOS-13.3.1-x86_64-i386-64bit - Python version: 3.11.2 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1

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I_kwDODunzps5lIHps

Audio Dataset with_format torch problem

closed

NONE

### Describe the bug Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets ``` audio_dataset = \ (Dataset .from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()}) .cast_column("audio", Audio(sampling_rate=16_000)) .with_format('numpy')) audio_dataset[0]["audio"] ``` works, but ``` audio_dataset = \ (Dataset .from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()}) .cast_column("audio", Audio(sampling_rate=16_000)) .with_format('torch')) audio_dataset[0]["audio"] ``` does not instead I get ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[54], line 1 ----> 1 audio_dataset[0]["audio"] File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key) 2152 def __getitem__(self, key): # noqa: F811 2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2154 return self._getitem( 2155 key, 2156 ) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs) 2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 2139 formatted_output = format_table( 2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 2141 ) 2142 return formatted_output File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:58, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: 57 row = self.numpy_arrow_extractor().extract_row(pa_table) ---> 58 return self.recursive_tensorize(row) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:54, in TorchFormatter.recursive_tensorize(self, data_struct) 53 def recursive_tensorize(self, data_struct: dict): ---> 54 return map_nested(self._recursive_tensorize, data_struct, map_list=False) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:356, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc) 354 num_proc = 1 355 if num_proc <= 1 or len(iterable) <= num_proc: --> 356 mapped = [ 357 _single_map_nested((function, obj, types, None, True, None)) 358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 359 ] 360 else: 361 split_kwds = [] # We organize the splits ourselve (contiguous splits) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:357, in <listcomp>(.0) 354 num_proc = 1 355 if num_proc <= 1 or len(iterable) <= num_proc: 356 mapped = [ --> 357 _single_map_nested((function, obj, types, None, True, None)) 358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 359 ] 360 else: 361 split_kwds = [] # We organize the splits ourselve (contiguous splits) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in _single_map_nested(args) 306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc) 308 if isinstance(data_struct, dict): --> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} 310 else: 311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in <dictcomp>(.0) 306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc) 308 if isinstance(data_struct, dict): --> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} 310 else: 311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:293, in _single_map_nested(args) 291 # Singleton first to spare some computation 292 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 293 return function(data_struct) 295 # Reduce logging to keep things readable in multiprocessing with tqdm 296 if rank is not None and logging.get_verbosity() < logging.WARNING: File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:51, in TorchFormatter._recursive_tensorize(self, data_struct) 49 if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects 50 return [self.recursive_tensorize(substruct) for substruct in data_struct] ---> 51 return self._tensorize(data_struct) File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:38, in TorchFormatter._tensorize(self, value) 35 import torch 37 default_dtype = {} ---> 38 if np.issubdtype(value.dtype, np.integer): 39 default_dtype = {"dtype": torch.int64} 40 elif np.issubdtype(value.dtype, np.floating): AttributeError: 'NoneType' object has no attribute 'dtype' ``` ### Steps to reproduce the bug 1. Download some audio dataset in this case I used Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets 2. Try the Code from above ### Expected behavior It should work for torch ### Environment info pytorch: 2.0.0 datasets: 2.3.2 numpy: 1.21.6 Python: 3.8 Linux

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https://github.com/huggingface/datasets/pull/5821

PR_kwDODunzps5PzHLU

IterableDataset Arrow formatting

closed

MEMBER

Adding an optional `.iter_arrow` to examples iterable. This allows to use Arrow formatting in map/filter. This will also be useful for torch formatting, since we can reuse the TorchFormatter that converts Arrow data to torch tensors Related to https://github.com/huggingface/datasets/issues/5793 and https://github.com/huggingface/datasets/issues/3444 Required for https://github.com/huggingface/datasets/pull/5852 ### Example: Speed x10 in map ```python from datasets import Dataset import pyarrow.compute as pc import time ds = Dataset.from_dict({"a": range(100_000)}) ids = ds.to_iterable_dataset() ids = ids.map(lambda x: {"a": [a + 10 for a in x["a"]]}, batched=True) _start = time.time() print(f"Python ({sum(1 for _ in ids)} items):\t{(time.time() - _start) * 1000:.1f}ms") # Python (100000 items): 695.7ms ids = ds.to_iterable_dataset().with_format("arrow") ids = ids.map(lambda t: t.set_column(0, "a", pc.add(t[0], 10)), batched=True) ids = ids.with_format(None) _start = time.time() print(f"Arrow ({sum(1 for _ in ids)} items):\t{(time.time() - _start) * 1000:.1f}ms)") # Arrow (100000 items): 81.0ms) ``` ### Implementation details I added an optional `iter_arrow` method to examples iterable. If an example iterable has this method, then it can be used to iterate on the examples by batch of arrow tables.

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I_kwDODunzps5lFUVL

Incomplete docstring for `BuilderConfig`

closed

CONTRIBUTOR