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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6795). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-04-09T14:45:12 | 2024-04-09T14:53:33 | null | MEMBER | null | 1 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6794). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-04-09T11:13:19 | 2024-04-09T15:18:01 | null | MEMBER | null | 1 | {
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} | ...for faster dataset download when there are many many small files (e.g. imagefolder, audiofolder)
### Behcnmark
for example on [lhoestq/tmp-images-writer_batch_size](https://hf.co/datasets/lhoestq/tmp-images-writer_batch_size) (128 images)
| | duration of the download step in `load_dataset()` |
|--| ----------------------------------------------------------------------|
| Before | 58s |
| Now | 3s |
This should fix issues with the Dataset Viewer taking too much time to show up for imagefolder/audiofolder datasets.
### Implementation details
The main change is in the `DownloadManager`:
```diff
- download_func = partial(self._download, download_config=download_config)
+ download_func = partial(self._download_batched, download_config=download_config)
downloaded_path_or_paths = map_nested(
download_func,
url_or_urls,
map_tuple=True,
num_proc=download_config.num_proc,
desc="Downloading data files",
+ batched=True,
+ batch_size=-1,
)
```
and `_download_batched` is a multithreaded function.
I also had to tweak `map_nested` to support batching. In particular it slices the data correctly if the user also enables multiprocessing. | {
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} | [] | open | false | null | [] | null | [] | 2024-04-08T14:39:14 | 2024-04-08T14:39:14 | null | NONE | null | null | null | ### Describe the bug
I'd expect the following code to download just the validation split but instead I get all data on my disk (train, test and validation splits)
`
from datasets import load_dataset
dataset = load_dataset("imagenet-1k", split="validation", trust_remote_code=True)
`
Is it expected to work like that?
### Steps to reproduce the bug
1. Install the required libraries (python, datasets, huggingface_hub)
2. Login using huggingface cli
2. Run the code in the description
### Expected behavior
Just a single (validation) split should be downloaded.
### Environment info
python: 3.12.2
datasets: 2.18.0
huggingface_hub: 0.22.2 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6792). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005212 / 0.011353 (-0.006141) | 0.003536 / 0.011008 (-0.007472) | 0.063042 / 0.038508 (0.024534) | 0.032654 / 0.023109 (0.009545) | 0.242040 / 0.275898 (-0.033858) | 0.267735 / 0.323480 (-0.055745) | 0.003188 / 0.007986 (-0.004797) | 0.002697 / 0.004328 (-0.001631) | 0.050127 / 0.004250 (0.045877) | 0.045960 / 0.037052 (0.008908) | 0.260926 / 0.258489 (0.002437) | 0.293953 / 0.293841 (0.000112) | 0.028352 / 0.128546 (-0.100194) | 0.010558 / 0.075646 (-0.065088) | 0.208104 / 0.419271 (-0.211167) | 0.035889 / 0.043533 (-0.007644) | 0.246265 / 0.255139 (-0.008874) | 0.271819 / 0.283200 (-0.011381) | 0.018491 / 0.141683 (-0.123192) | 1.299274 / 1.452155 (-0.152881) | 1.205932 / 1.492716 (-0.286784) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095574 / 0.018006 (0.077568) | 0.306493 / 0.000490 (0.306003) | 0.000216 / 0.000200 (0.000016) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018304 / 0.037411 (-0.019107) | 0.061312 / 0.014526 (0.046786) | 0.074483 / 0.176557 (-0.102073) | 0.122231 / 0.737135 (-0.614905) | 0.075315 / 0.296338 (-0.221024) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275632 / 0.215209 (0.060423) | 2.696402 / 2.077655 (0.618747) | 1.418657 / 1.504120 (-0.085463) | 1.300014 / 1.541195 (-0.241181) | 1.299148 / 1.468490 (-0.169342) | 0.561893 / 4.584777 (-4.022884) | 2.410710 / 3.745712 (-1.335002) | 2.749058 / 5.269862 (-2.520803) | 1.712835 / 4.565676 (-2.852841) | 0.062278 / 0.424275 (-0.361997) | 0.005040 / 0.007607 (-0.002567) | 0.330352 / 0.226044 (0.104308) | 3.291274 / 2.268929 (1.022345) | 1.780987 / 55.444624 (-53.663638) | 1.514764 / 6.876477 (-5.361713) | 1.533892 / 2.142072 (-0.608181) | 0.632307 / 4.805227 (-4.172921) | 0.116011 / 6.500664 (-6.384653) | 0.041964 / 0.075469 (-0.033505) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982713 / 1.841788 (-0.859075) | 11.521597 / 8.074308 (3.447289) | 9.713063 / 10.191392 (-0.478329) | 0.132115 / 0.680424 (-0.548309) | 0.014564 / 0.534201 (-0.519637) | 0.294087 / 0.579283 (-0.285196) | 0.267399 / 0.434364 (-0.166965) | 0.327967 / 0.540337 (-0.212370) | 0.419279 / 1.386936 (-0.967657) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005098 / 0.011353 (-0.006255) | 0.003513 / 0.011008 (-0.007495) | 0.050121 / 0.038508 (0.011613) | 0.030842 / 0.023109 (0.007732) | 0.271323 / 0.275898 (-0.004575) | 0.293592 / 0.323480 (-0.029887) | 0.004225 / 0.007986 (-0.003761) | 0.002802 / 0.004328 (-0.001527) | 0.049035 / 0.004250 (0.044785) | 0.040748 / 0.037052 (0.003696) | 0.282542 / 0.258489 (0.024053) | 0.303779 / 0.293841 (0.009938) | 0.029213 / 0.128546 (-0.099333) | 0.010578 / 0.075646 (-0.065068) | 0.058053 / 0.419271 (-0.361219) | 0.032830 / 0.043533 (-0.010703) | 0.272226 / 0.255139 (0.017087) | 0.290485 / 0.283200 (0.007285) | 0.017968 / 0.141683 (-0.123714) | 1.166998 / 1.452155 (-0.285156) | 1.256354 / 1.492716 (-0.236362) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096126 / 0.018006 (0.078120) | 0.306303 / 0.000490 (0.305813) | 0.000246 / 0.000200 (0.000047) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022413 / 0.037411 (-0.014998) | 0.075008 / 0.014526 (0.060482) | 0.087703 / 0.176557 (-0.088854) | 0.127358 / 0.737135 (-0.609777) | 0.088817 / 0.296338 (-0.207521) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301103 / 0.215209 (0.085894) | 2.965441 / 2.077655 (0.887787) | 1.608075 / 1.504120 (0.103955) | 1.479214 / 1.541195 (-0.061981) | 1.492039 / 1.468490 (0.023549) | 0.574455 / 4.584777 (-4.010322) | 2.483234 / 3.745712 (-1.262478) | 2.795901 / 5.269862 (-2.473961) | 1.742034 / 4.565676 (-2.823642) | 0.064170 / 0.424275 (-0.360105) | 0.005572 / 0.007607 (-0.002035) | 0.349500 / 0.226044 (0.123456) | 3.482161 / 2.268929 (1.213232) | 1.950065 / 55.444624 (-53.494559) | 1.675270 / 6.876477 (-5.201207) | 1.674534 / 2.142072 (-0.467538) | 0.657478 / 4.805227 (-4.147749) | 0.117534 / 6.500664 (-6.383130) | 0.040880 / 0.075469 (-0.034589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.035276 / 1.841788 (-0.806511) | 12.035581 / 8.074308 (3.961273) | 10.127778 / 10.191392 (-0.063614) | 0.142289 / 0.680424 (-0.538134) | 0.014702 / 0.534201 (-0.519499) | 0.288206 / 0.579283 (-0.291077) | 0.282251 / 0.434364 (-0.152113) | 0.323479 / 0.540337 (-0.216858) | 0.419019 / 1.386936 (-0.967917) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f27d7b77c73412cfc50b24354bfd7a3e838202f \"CML watermark\")\n"
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} | It was reloading from the wrong cache dir because of a bug in `_check_legacy_cache2`. This function should not trigger if there are config_kwars like `sample_by=`
fix https://github.com/huggingface/datasets/issues/6758 | {
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"I realized I was passing a string column to this instead of float. Is it possible to add a warning or error to prevent users from falsely believing there's a bug?",
"Hello!\r\n\r\nI agree that we could add some safeguards around the type of `ds[column]`. At least for FAISS, we need the column to be made of embeddings as FAISS doesn't perform the embeddings itself.\r\n\r\nI can propose a PR sometime this week.",
"@Dref360 thanks for the initiative!"
] | 2024-04-08T01:57:03 | 2024-04-09T01:30:55 | null | NONE | null | null | null | ### Describe the bug
Calling `add_faiss_index` on a `Dataset` with a column argument raises a ValueError. The following is the trace
```python
214 def replacement_add(self, x):
215 """Adds vectors to the index.
216 The index must be trained before vectors can be added to it.
217 The vectors are implicitly numbered in sequence. When `n` vectors are
(...)
224 `dtype` must be float32.
225 """
--> 227 n, d = x.shape
228 assert d == self.d
229 x = np.ascontiguousarray(x, dtype='float32')
ValueError: not enough values to unpack (expected 2, got 1)
```
### Steps to reproduce the bug
1. Load any dataset like `ds = datasets.load_dataset("wikimedia/wikipedia", "20231101.en")["train"]`
2. Add an FAISS index on any column `ds.add_faiss_index('title')`
### Expected behavior
The index should be created
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-26-generic-x86_64-with-glibc2.35
- Python version: 3.9.19
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.2.0
- `faiss-cpu` version: 1.8.0 | {
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} | [] | open | false | null | [] | null | [] | 2024-04-07T19:25:39 | 2024-04-07T20:00:54 | null | NONE | null | null | null | ### Describe the bug
Hello,
I've been struggling with a problem using Huggingface datasets caused by PyArrow memory allocation. I finally managed to solve it, and thought to document it since similar issues have been raised here before (https://github.com/huggingface/datasets/issues/5710, https://github.com/huggingface/datasets/issues/6176).
In my case, I was trying to load ~70k dataset files from disk using `datasets.load_from_disk(data_path)` (meaning 70k repeated calls to load_from_disk). This triggered an (uninformative) exception around 64k loaded files:
```
File "pyarrow/io.pxi", line 1053, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 1000, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
OSError: Memory mapping file failed: Cannot allocate memory
```
Despite system RAM usage being very low. After a lot of digging around, I discovered that my Ubuntu machine had a limit on the maximum number of memory mapped files in `/proc/sys/vm/max_map_count` set to 65530, which was causing my data loader to crash. Increasing the limit in the file (`echo <new_mmap_size> | sudo tee /proc/sys/vm/max_map_count`) made the issue go away.
While this isn't a bug as such in either Datasets or PyArrow, this behavior can be very confusing to users. Maybe this should be mentioned in documentation? I suspect the other issues raised here about memory mapping OOM errors could actually be consequence of system configuration.
Br,
Lauri
### Steps to reproduce the bug
```
import numpy as np
import pyarrow as pa
import tqdm
# Write some data to disk
arr = pa.array(np.arange(100))
schema = pa.schema([
pa.field('nums', arr.type)
])
with pa.OSFile('arraydata.arrow', 'wb') as sink:
with pa.ipc.new_file(sink, schema=schema) as writer:
batch = pa.record_batch([arr], schema=schema)
writer.write(batch)
# Number of times to open the memory map
nums = 70000
# Read the data back
arrays = [pa.memory_map('arraydata.arrow', 'r') for _ in tqdm.tqdm(range(nums))]
```
### Expected behavior
No errors.
### Environment info
datasets: 2.18.0
pyarrow: 15.0.0 | {
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"Default `writer_batch_size `is set to 1000 (see [map](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.map)).\r\nThe \"tmp1335llua\" is probably the temp file it creates while writing to disk.\r\nMaybe try lowering the `writer_batch_size`.\r\n\r\nFor multi-processing you should probably pass the `processor `as an argument (with e.g. partial) to the function or create it inside so that the sub-processes have access to it and maybe add `if __name__ == \"__main__\"` (not sure that's necessary?).\r\n",
"Hi @Modexus,\r\n\r\nThank you very much for the help! Yep after playing around with map, I managed to get the parallel processing to work by implementing it like you suggested.\r\n\r\nRegarding the temp files, it seems like the temp files just keep growing in size as the map continues. Eventually, once map finishes, the temp files are deleted, but they are instead saved as cache .arrow files. These cache files are absolutely gigantic (~ 30-50x the size of the initial dataset!).\r\n\r\nAfter playing around with the `prepare_dataset()` function above, it seems this issue is caused by the following line in the function, where the log-Mel spectrogram of the audio is calculated:\r\n\r\n`# compute log-Mel input features from input audio array\r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], \r\n sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n`\r\n\r\nWhen I remove this line, the final cache files are approximately the same size as the initial dataset.\r\n\r\nCan I check whether this is expected behavior with the whisper feature extractor? I cant imagine the spectrograms are that large!\r\n\r\nThank you so much for the help!"
] | 2024-04-07T02:52:06 | 2024-04-08T09:37:28 | null | NONE | null | null | null | ### Describe the bug
Map has been taking extremely long to preprocess my data.
It seems to process 1000 examples (which it does really fast in about 10 seconds), then it hangs for a good 1-2 minutes, before it moves on to the next batch of 1000 examples.
It also keeps eating up my hard drive space for some reason by creating a file named tmp1335llua that is over 300GB.
Trying to set num_proc to be >1 also gives me the following error: NameError: name 'processor' is not defined
Please advise on how I could optimise this?
### Steps to reproduce the bug
In general, I have been using map as per normal. Here is a snippet of my code:
````
########################### DATASET LOADING AND PREP #########################
def load_custom_dataset(split):
ds = []
if split == 'train':
for dset in args.train_datasets:
ds.append(load_from_disk(dset))
if split == 'test':
for dset in args.test_datasets:
ds.append(load_from_disk(dset))
ds_to_return = concatenate_datasets(ds)
ds_to_return = ds_to_return.shuffle(seed=22)
return ds_to_return
def prepare_dataset(batch):
# load and (possibly) resample audio data to 16kHz
audio = batch["audio"]
# compute log-Mel input features from input audio array
batch["input_features"] = processor.feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
# compute input length of audio sample in seconds
batch["input_length"] = len(audio["array"]) / audio["sampling_rate"]
# optional pre-processing steps
transcription = batch["sentence"]
if do_lower_case:
transcription = transcription.lower()
if do_remove_punctuation:
transcription = normalizer(transcription).strip()
# encode target text to label ids
batch["labels"] = processor.tokenizer(transcription).input_ids
return batch
print('DATASET PREPARATION IN PROGRESS...')
# case 3: combine_and_shuffle is true, only train provided
# load train datasets
train_set = load_custom_dataset('train')
# split dataset
raw_dataset = DatasetDict()
raw_dataset = train_set.train_test_split(test_size = args.test_size, shuffle=True, seed=42)
raw_dataset = raw_dataset.cast_column("audio", Audio(sampling_rate=args.sampling_rate))
print("Before Map:")
print(raw_dataset)
raw_dataset = raw_dataset.map(prepare_dataset, num_proc=1)
print("After Map:")
print(raw_dataset)
````
### Expected behavior
Based on the speed at which map is processing examples, I would expect a 5-6 hours completion for all mapping
However, because it hangs every 1000 examples, I instead roughly estimate it would take about 40 hours!
Moreover, i cant even finish the map because it keeps exponentially eating up my hard drive space
### Environment info
- `datasets` version: 2.18.0
- Platform: Windows-10-10.0.22631-SP0
- Python version: 3.10.14
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
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} | [] | open | false | null | [] | null | [] | 2024-04-06T11:45:23 | 2024-04-06T11:52:39 | null | NONE | null | null | null | ### Describe the bug
Hello,
I have a question regarding the map function in the Hugging Face datasets.
The situation is as follows: when I load a jsonl file using load_dataset(..., streaming=False), and then utilize the map function to process it, I specify that the returned example should be of type Torch.tensor. However, I noticed that after applying the map function, the datatype automatically changes to List, which leads to errors in my program.
I attempted to use load_dataset(..., streaming=True), and this issue no longer occurs. I'm not entirely clear on why this happens. Could you please provide some insights into this?
### Steps to reproduce the bug
1.dataset = load_dataset(xxx, streaming = False)
2. dataset.map(function), function will return torch.Tensor.
3. you will find the format of data in dataset is List.
### Expected behavior
I expected to receieve the format of data is torch.Tensor.
### Environment info
2.18.0 | {
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"From my current understanding, this timeout is only used when we need to get the results.\r\n\r\nOne of:\r\n1. All tasks are done\r\n2. One worker died\r\n\r\nYour function should work fine and it's definitely a bug if it doesn't."
] | 2024-04-06T06:25:39 | 2024-04-08T14:47:18 | null | CONTRIBUTOR | null | null | null | ### Describe the bug
```python
from datasets import Dataset
def worker(example):
while True:
continue
example['a'] = 100
return example
data = Dataset.from_list([{"a": 1}, {"a": 2}])
data = data.map(worker)
print(data[0])
```
I'm implementing a worker function whose runtime will depend on specific examples (e.g., while most examples take 0.01s in worker, several examples may take 50s).
Therefore, I would like to know how the current implementation will handle those subprocesses that require a long (e.g., >= 5min) or even infinite time.
I notice that the current implementation set a timeout of 0.05 second
https://github.com/huggingface/datasets/blob/c3ddb1ef00334a6f973679a51e783905fbc9ef0b/src/datasets/utils/py_utils.py#L674
However, this example code still gets stuck.
### Steps to reproduce the bug
run the example above
### Expected behavior
I want to set a default worker to handle these timeout cases, instead of getting stuck
### Environment info
main branch version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6786). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-04-05T17:00:46 | 2024-04-08T09:18:42 | null | NONE | null | 0 | {
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} | PR for issue #6782.
Makes `cast_storage` of the `Image` class faster by removing the slow call to `.pylist`.
Instead directly convert each `ListArray` item to either `Array2DExtensionType` or `Array3DExtensionType`.
This also preserves the `dtype` removing the warning if the array is already `uint8`. | {
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https://api.github.com/repos/huggingface/datasets/issues/6785 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6785/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6785/comments | https://api.github.com/repos/huggingface/datasets/issues/6785/events | https://github.com/huggingface/datasets/pull/6785 | 2,228,429,852 | PR_kwDODunzps5r3dCw | 6,785 | rename datasets-server to dataset-viewer | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6785). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005224 / 0.011353 (-0.006129) | 0.003938 / 0.011008 (-0.007070) | 0.063829 / 0.038508 (0.025321) | 0.030975 / 0.023109 (0.007865) | 0.265090 / 0.275898 (-0.010808) | 0.290994 / 0.323480 (-0.032486) | 0.003083 / 0.007986 (-0.004902) | 0.002810 / 0.004328 (-0.001518) | 0.048860 / 0.004250 (0.044609) | 0.044663 / 0.037052 (0.007611) | 0.272161 / 0.258489 (0.013672) | 0.306966 / 0.293841 (0.013125) | 0.028028 / 0.128546 (-0.100518) | 0.010616 / 0.075646 (-0.065031) | 0.211649 / 0.419271 (-0.207623) | 0.035906 / 0.043533 (-0.007626) | 0.251779 / 0.255139 (-0.003360) | 0.275543 / 0.283200 (-0.007657) | 0.017710 / 0.141683 (-0.123973) | 1.127015 / 1.452155 (-0.325139) | 1.173319 / 1.492716 (-0.319397) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090625 / 0.018006 (0.072619) | 0.301973 / 0.000490 (0.301483) | 0.000217 / 0.000200 (0.000017) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018868 / 0.037411 (-0.018543) | 0.062402 / 0.014526 (0.047876) | 0.074053 / 0.176557 (-0.102504) | 0.121484 / 0.737135 (-0.615652) | 0.078674 / 0.296338 (-0.217664) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277821 / 0.215209 (0.062612) | 2.761642 / 2.077655 (0.683987) | 1.452735 / 1.504120 (-0.051385) | 1.336303 / 1.541195 (-0.204891) | 1.343045 / 1.468490 (-0.125445) | 0.560917 / 4.584777 (-4.023860) | 2.353427 / 3.745712 (-1.392286) | 2.699067 / 5.269862 (-2.570795) | 1.704752 / 4.565676 (-2.860925) | 0.062668 / 0.424275 (-0.361607) | 0.005120 / 0.007607 (-0.002487) | 0.330455 / 0.226044 (0.104410) | 3.264604 / 2.268929 (0.995675) | 1.791940 / 55.444624 (-53.652685) | 1.526083 / 6.876477 (-5.350394) | 1.541429 / 2.142072 (-0.600643) | 0.630343 / 4.805227 (-4.174884) | 0.115189 / 6.500664 (-6.385475) | 0.041716 / 0.075469 (-0.033753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975008 / 1.841788 (-0.866779) | 11.326924 / 8.074308 (3.252616) | 9.810300 / 10.191392 (-0.381092) | 0.141068 / 0.680424 (-0.539356) | 0.013950 / 0.534201 (-0.520251) | 0.285691 / 0.579283 (-0.293592) | 0.257968 / 0.434364 (-0.176396) | 0.322976 / 0.540337 (-0.217361) | 0.411114 / 1.386936 (-0.975822) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005176 / 0.011353 (-0.006177) | 0.003631 / 0.011008 (-0.007377) | 0.050006 / 0.038508 (0.011498) | 0.030622 / 0.023109 (0.007513) | 0.277364 / 0.275898 (0.001466) | 0.299752 / 0.323480 (-0.023728) | 0.004110 / 0.007986 (-0.003876) | 0.002694 / 0.004328 (-0.001634) | 0.048966 / 0.004250 (0.044715) | 0.039634 / 0.037052 (0.002582) | 0.289959 / 0.258489 (0.031470) | 0.320689 / 0.293841 (0.026848) | 0.029285 / 0.128546 (-0.099261) | 0.010435 / 0.075646 (-0.065211) | 0.057432 / 0.419271 (-0.361840) | 0.032554 / 0.043533 (-0.010979) | 0.277354 / 0.255139 (0.022215) | 0.296872 / 0.283200 (0.013673) | 0.017338 / 0.141683 (-0.124344) | 1.134174 / 1.452155 (-0.317981) | 1.184695 / 1.492716 (-0.308021) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089953 / 0.018006 (0.071947) | 0.299372 / 0.000490 (0.298882) | 0.000212 / 0.000200 (0.000012) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021349 / 0.037411 (-0.016062) | 0.075167 / 0.014526 (0.060641) | 0.085910 / 0.176557 (-0.090647) | 0.124729 / 0.737135 (-0.612406) | 0.088313 / 0.296338 (-0.208025) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291939 / 0.215209 (0.076730) | 2.851077 / 2.077655 (0.773423) | 1.609382 / 1.504120 (0.105262) | 1.469656 / 1.541195 (-0.071539) | 1.490469 / 1.468490 (0.021979) | 0.570421 / 4.584777 (-4.014356) | 2.441438 / 3.745712 (-1.304274) | 2.756514 / 5.269862 (-2.513347) | 1.714202 / 4.565676 (-2.851474) | 0.063656 / 0.424275 (-0.360619) | 0.005640 / 0.007607 (-0.001967) | 0.336240 / 0.226044 (0.110196) | 3.355434 / 2.268929 (1.086505) | 1.947553 / 55.444624 (-53.497072) | 1.672776 / 6.876477 (-5.203700) | 1.685316 / 2.142072 (-0.456757) | 0.638849 / 4.805227 (-4.166378) | 0.116304 / 6.500664 (-6.384360) | 0.041588 / 0.075469 (-0.033881) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.026700 / 1.841788 (-0.815088) | 12.044628 / 8.074308 (3.970319) | 10.464007 / 10.191392 (0.272615) | 0.156169 / 0.680424 (-0.524255) | 0.015624 / 0.534201 (-0.518577) | 0.287233 / 0.579283 (-0.292050) | 0.270374 / 0.434364 (-0.163990) | 0.325255 / 0.540337 (-0.215083) | 0.412021 / 1.386936 (-0.974915) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f7f1718e3db54d7923ebe4383301fdd380c18b9 \"CML watermark\")\n"
] | 2024-04-05T16:37:05 | 2024-04-08T12:41:13 | 2024-04-08T12:35:02 | CONTRIBUTOR | null | 0 | {
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} | See https://github.com/huggingface/dataset-viewer/issues/2650
Tell me if it's OK, or if it's a breaking change that must be handled differently.
Also note that the docs page is still https://huggingface.co/docs/datasets-server/, so I didn't change it.
And the API URL is still https://datasets-server.huggingface.co/ (and [might always be](https://github.com/huggingface/dataset-viewer/issues/2666)), so I let it too. | {
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https://api.github.com/repos/huggingface/datasets/issues/6784 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6784/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6784/comments | https://api.github.com/repos/huggingface/datasets/issues/6784/events | https://github.com/huggingface/datasets/pull/6784 | 2,228,390,504 | PR_kwDODunzps5r3UTj | 6,784 | Extract data on the fly in packaged builders | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6784). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-04-05T16:12:25 | 2024-04-08T23:33:24 | null | CONTRIBUTOR | null | 1 | {
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} | Instead of waiting for data files to be extracted in the packaged builders, we can prepend the compression prefix and extract them as they are being read (using `fsspec`). This saves disk space (deleting extracted archives is not set by default) and slightly speeds up dataset generation (less disk reads) | {
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https://api.github.com/repos/huggingface/datasets/issues/6783 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6783/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6783/comments | https://api.github.com/repos/huggingface/datasets/issues/6783/events | https://github.com/huggingface/datasets/issues/6783 | 2,228,179,466 | I_kwDODunzps6Ez1IK | 6,783 | AttributeError: module 'numpy' has no attribute 'object'. in Kaggle Notebook | {
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"Hi! You can fix this by updating the `datasets` package with `pip install -U datasets` and restarting the notebook.\r\n"
] | 2024-04-05T14:31:48 | 2024-04-08T16:11:01 | null | NONE | null | null | null | ### Describe the bug
# problem
I can't resample audio dataset in Kaggle Notebook. It looks like some code in `datasets` library use aliases that were deprecated in NumPy 1.20.
## code for resampling
```
from datasets import load_dataset, Audio
from transformers import AutoFeatureExtractor
from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer
minds = load_dataset("PolyAI/minds14", name="en-US", split="train")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
def preprocess_function(examples):
audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True
)
return inputs
dataset = dataset.map(preprocess_function, remove_columns="audio", batched=True, batch_size=100)
```
## the error I got
<details>
<summary>Click to expand</summary>
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[20], line 1
----> 1 dataset = dataset.map(preprocess_function, remove_columns="audio", batched=True, batch_size=100)
2 dataset
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1955, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1952 disable_tqdm = not logging.is_progress_bar_enabled()
1954 if num_proc is None or num_proc == 1:
-> 1955 return self._map_single(
1956 function=function,
1957 with_indices=with_indices,
1958 with_rank=with_rank,
1959 input_columns=input_columns,
1960 batched=batched,
1961 batch_size=batch_size,
1962 drop_last_batch=drop_last_batch,
1963 remove_columns=remove_columns,
1964 keep_in_memory=keep_in_memory,
1965 load_from_cache_file=load_from_cache_file,
1966 cache_file_name=cache_file_name,
1967 writer_batch_size=writer_batch_size,
1968 features=features,
1969 disable_nullable=disable_nullable,
1970 fn_kwargs=fn_kwargs,
1971 new_fingerprint=new_fingerprint,
1972 disable_tqdm=disable_tqdm,
1973 desc=desc,
1974 )
1975 else:
1977 def format_cache_file_name(cache_file_name, rank):
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:520, in transmit_tasks.<locals>.wrapper(*args, **kwargs)
518 self: "Dataset" = kwargs.pop("self")
519 # apply actual function
--> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
522 for dataset in datasets:
523 # Remove task templates if a column mapping of the template is no longer valid
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:487, in transmit_format.<locals>.wrapper(*args, **kwargs)
480 self_format = {
481 "type": self._format_type,
482 "format_kwargs": self._format_kwargs,
483 "columns": self._format_columns,
484 "output_all_columns": self._output_all_columns,
485 }
486 # apply actual function
--> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
489 # re-apply format to the output
File /opt/conda/lib/python3.10/site-packages/datasets/fingerprint.py:458, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
452 kwargs[fingerprint_name] = update_fingerprint(
453 self._fingerprint, transform, kwargs_for_fingerprint
454 )
456 # Call actual function
--> 458 out = func(self, *args, **kwargs)
460 # Update fingerprint of in-place transforms + update in-place history of transforms
462 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:2356, in Dataset._map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2354 writer.write_table(batch)
2355 else:
-> 2356 writer.write_batch(batch)
2357 if update_data and writer is not None:
2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py:507, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)
505 col_try_type = try_features[col] if try_features is not None and col in try_features else None
506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 507 arrays.append(pa.array(typed_sequence))
508 inferred_features[col] = typed_sequence.get_inferred_type()
509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
File /opt/conda/lib/python3.10/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()
File /opt/conda/lib/python3.10/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py:184, in TypedSequence.__arrow_array__(self, type)
182 out = numpy_to_pyarrow_listarray(data)
183 elif isinstance(data, list) and data and isinstance(first_non_null_value(data)[1], np.ndarray):
--> 184 out = list_of_np_array_to_pyarrow_listarray(data)
185 else:
186 trying_cast_to_python_objects = True
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1174, in list_of_np_array_to_pyarrow_listarray(l_arr, type)
1172 """Build a PyArrow ListArray from a possibly nested list of NumPy arrays"""
1173 if len(l_arr) > 0:
-> 1174 return list_of_pa_arrays_to_pyarrow_listarray(
1175 [numpy_to_pyarrow_listarray(arr, type=type) if arr is not None else None for arr in l_arr]
1176 )
1177 else:
1178 return pa.array([], type=type)
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1163, in list_of_pa_arrays_to_pyarrow_listarray(l_arr)
1160 null_indices = [i for i, arr in enumerate(l_arr) if arr is None]
1161 l_arr = [arr for arr in l_arr if arr is not None]
1162 offsets = np.cumsum(
-> 1163 [0] + [len(arr) for arr in l_arr], dtype=np.object
1164 ) # convert to dtype object to allow None insertion
1165 offsets = np.insert(offsets, null_indices, None)
1166 offsets = pa.array(offsets, type=pa.int32())
File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr)
319 warnings.warn(
320 f"In the future `np.{attr}` will be defined as the "
321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
323 if attr in __former_attrs__:
--> 324 raise AttributeError(__former_attrs__[attr])
326 if attr == 'testing':
327 import numpy.testing as testing
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
</details>
### Steps to reproduce the bug
Run above code in Kaggle Notebook.
### Expected behavior
I can resample audio data without fail.
### Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.15.133+-x86_64-with-glibc2.31
- Python version: 3.10.13
- PyArrow version: 11.0.0
- Pandas version: 2.2.1 | {
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"This may be a solution that only changes `cast_storage` of `Image`.\r\nHowever, I'm not totally sure that the assumptions hold that are made about the `ListArray`.\r\n\r\n```python\r\nelif pa.types.is_list(storage.type):\r\n from .features import Array3DExtensionType\r\n\r\n def get_shapes(arr):\r\n shape = ()\r\n while isinstance(arr, pa.ListArray):\r\n len_curr = len(arr)\r\n arr = arr.flatten()\r\n len_new = len(arr)\r\n shape = shape + (len_new // len_curr,)\r\n return shape\r\n\r\n def get_dtypes(arr):\r\n dtype = storage.type\r\n while hasattr(dtype, \"value_type\"):\r\n dtype = dtype.value_type\r\n return dtype\r\n\r\n arrays = []\r\n for i, is_null in enumerate(storage.is_null()):\r\n if not is_null.as_py():\r\n storage_part = storage.take([i])\r\n shape = get_shapes(storage_part)\r\n dtype = get_dtypes(storage_part)\r\n\r\n extension_type = Array3DExtensionType(shape=shape, dtype=str(dtype))\r\n array = pa.ExtensionArray.from_storage(extension_type, storage_part)\r\n arrays.append(array.to_numpy().squeeze(0))\r\n else:\r\n arrays.append(None)\r\n\r\n bytes_array = pa.array(\r\n [encode_np_array(arr)[\"bytes\"] if arr is not None else None for arr in arrays],\r\n type=pa.binary(),\r\n )\r\n path_array = pa.array([None] * len(storage), type=pa.string())\r\n storage = pa.StructArray.from_arrays(\r\n [bytes_array, path_array], [\"bytes\", \"path\"], mask=bytes_array.is_null()\r\n )\r\n```\r\n(Edited): to handle nulls\r\n\r\nNotably this doesn't change anything about the passing through of data or other things, just in the `Image` class.\r\nSeems quite fast:\r\n```bash\r\nFri Apr 5 17:55:51 2024 restats\r\n\r\n 63818 function calls (61995 primitive calls) in 0.812 seconds\r\n\r\n Ordered by: cumulative time\r\n List reduced from 1051 to 20 due to restriction <20>\r\n\r\n ncalls tottime percall cumtime percall filename:lineno(function)\r\n 47/1 0.000 0.000 0.810 0.810 {built-in method builtins.exec}\r\n 2/1 0.000 0.000 0.810 0.810 <string>:1(<module>)\r\n 2/1 0.000 0.000 0.809 0.809 arrow_dataset.py:594(wrapper)\r\n 2/1 0.000 0.000 0.809 0.809 arrow_dataset.py:551(wrapper)\r\n 2/1 0.000 0.000 0.809 0.809 arrow_dataset.py:2916(map)\r\n 3 0.000 0.000 0.807 0.269 arrow_dataset.py:3277(_map_single)\r\n 1 0.000 0.000 0.760 0.760 arrow_writer.py:589(finalize)\r\n 1 0.000 0.000 0.760 0.760 arrow_writer.py:423(write_examples_on_file)\r\n 1 0.000 0.000 0.759 0.759 arrow_writer.py:527(write_batch)\r\n 1 0.001 0.001 0.754 0.754 arrow_writer.py:161(__arrow_array__)\r\n 2/1 0.000 0.000 0.719 0.719 table.py:1800(wrapper)\r\n 1 0.000 0.000 0.719 0.719 table.py:1950(cast_array_to_feature)\r\n 1 0.006 0.006 0.718 0.718 image.py:209(cast_storage)\r\n 1 0.000 0.000 0.451 0.451 image.py:361(encode_np_array)\r\n 1 0.000 0.000 0.444 0.444 image.py:343(image_to_bytes)\r\n 1 0.000 0.000 0.413 0.413 Image.py:2376(save)\r\n 1 0.000 0.000 0.413 0.413 PngImagePlugin.py:1233(_save)\r\n 1 0.000 0.000 0.413 0.413 ImageFile.py:517(_save)\r\n 1 0.000 0.000 0.413 0.413 ImageFile.py:545(_encode_tile)\r\n 397 0.409 0.001 0.409 0.001 {method 'encode' of 'ImagingEncoder' objects}\r\n```",
"Also encounter this problem. Has been strugging with it for a long time..."
] | 2024-04-05T13:46:54 | 2024-04-05T21:04:43 | null | NONE | null | null | null | ### Describe the bug
Operations that save an image from a path into parquet are very slow.
I believe the reason for this is that the image data (`numpy`) is converted into `pyarrow` format but then back to python using `.pylist()` before being converted to a numpy array again.
`pylist` is already slow but used on a multi-dimensional numpy array such as an image it takes a very long time.
From the trace below we can see that `__arrow_array__` takes a long time.
It is currently also called in `get_inferred_type`, this should be removable #6781 but doesn't change the underyling issue.
The conversion to `pyarrow` and back also leads to the `numpy` array having type `int64` which causes a warning message because the image type excepts `uint8`.
However, originally the `numpy` image array was in `uint8`.
### Steps to reproduce the bug
```python
from PIL import Image
import numpy as np
import datasets
import cProfile
image = Image.fromarray(np.random.randint(0, 255, (2048, 2048, 3), dtype=np.uint8))
image.save("test_image.jpg")
ds = datasets.Dataset.from_dict(
{"image": ["test_image.jpg"]},
features=datasets.Features({"image": datasets.Image(decode=True)}),
)
# load as numpy array, e.g. for further processing with map
# same result as map returning numpy arrays
ds.set_format("numpy")
cProfile.run("ds.map(writer_batch_size=1, load_from_cache_file=False)", "restats")
```
```bash
Fri Apr 5 14:56:17 2024 restats
66817 function calls (64992 primitive calls) in 33.382 seconds
Ordered by: cumulative time
List reduced from 1073 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
46/1 0.000 0.000 33.382 33.382 {built-in method builtins.exec}
1 0.000 0.000 33.382 33.382 <string>:1(<module>)
1 0.000 0.000 33.382 33.382 arrow_dataset.py:594(wrapper)
1 0.000 0.000 33.382 33.382 arrow_dataset.py:551(wrapper)
1 0.000 0.000 33.379 33.379 arrow_dataset.py:2916(map)
4 0.000 0.000 33.327 8.332 arrow_dataset.py:3277(_map_single)
1 0.000 0.000 33.311 33.311 arrow_writer.py:465(write)
2 0.000 0.000 33.311 16.656 arrow_writer.py:423(write_examples_on_file)
1 0.000 0.000 33.311 33.311 arrow_writer.py:527(write_batch)
2 14.484 7.242 33.260 16.630 arrow_writer.py:161(__arrow_array__)
1 0.001 0.001 16.438 16.438 arrow_writer.py:121(get_inferred_type)
1 0.000 0.000 14.398 14.398 threading.py:637(wait)
1 0.000 0.000 14.398 14.398 threading.py:323(wait)
8 14.398 1.800 14.398 1.800 {method 'acquire' of '_thread.lock' objects}
4/2 0.000 0.000 4.337 2.169 table.py:1800(wrapper)
2 0.000 0.000 4.337 2.169 table.py:1950(cast_array_to_feature)
2 0.475 0.238 4.337 2.169 image.py:209(cast_storage)
9 2.583 0.287 2.583 0.287 {built-in method numpy.array}
2 0.000 0.000 1.284 0.642 image.py:319(encode_np_array)
2 0.000 0.000 1.246 0.623 image.py:301(image_to_bytes)
```
### Expected behavior
The `numpy` image data should be passed through as it will be directly consumed by `pillow` to convert it to bytes.
As an example one can replace `list_of_np_array_to_pyarrow_listarray(data)` in `__arrow_array__` with just `out = data` as a test.
We have to change `cast_storage` of the `Image` feature so it handles the passed through data (& if to handle type before)
```python
bytes_array = pa.array(
[encode_np_array(arr)["bytes"] if arr is not None else None for arr in storage],
type=pa.binary(),
)
```
Leading to the following:
```bash
Fri Apr 5 15:44:27 2024 restats
66419 function calls (64595 primitive calls) in 0.937 seconds
Ordered by: cumulative time
List reduced from 1023 to 20 due to restriction <20>
ncalls tottime percall cumtime percall filename:lineno(function)
47/1 0.000 0.000 0.935 0.935 {built-in method builtins.exec}
2/1 0.000 0.000 0.935 0.935 <string>:1(<module>)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:594(wrapper)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:551(wrapper)
2/1 0.000 0.000 0.934 0.934 arrow_dataset.py:2916(map)
4 0.000 0.000 0.933 0.233 arrow_dataset.py:3277(_map_single)
1 0.000 0.000 0.883 0.883 arrow_writer.py:466(write)
2 0.000 0.000 0.883 0.441 arrow_writer.py:424(write_examples_on_file)
1 0.000 0.000 0.882 0.882 arrow_writer.py:528(write_batch)
2 0.000 0.000 0.877 0.439 arrow_writer.py:161(__arrow_array__)
4/2 0.000 0.000 0.877 0.439 table.py:1800(wrapper)
2 0.000 0.000 0.877 0.439 table.py:1950(cast_array_to_feature)
2 0.009 0.005 0.877 0.439 image.py:209(cast_storage)
2 0.000 0.000 0.868 0.434 image.py:335(encode_np_array)
2 0.000 0.000 0.856 0.428 image.py:317(image_to_bytes)
2 0.000 0.000 0.822 0.411 Image.py:2376(save)
2 0.000 0.000 0.822 0.411 PngImagePlugin.py:1233(_save)
2 0.000 0.000 0.822 0.411 ImageFile.py:517(_save)
2 0.000 0.000 0.821 0.411 ImageFile.py:545(_encode_tile)
589 0.803 0.001 0.803 0.001 {method 'encode' of 'ImagingEncoder' objects}
```
This is of course only a test as it passes through all `numpy` arrays irrespective of if they should be an image.
Also I guess `cast_storage` is meant for casting `pyarrow` storage exclusively.
Converting to `pyarrow` array seems like a good solution as it also handles `pytorch` tensors etc., maybe there is a more efficient way to create a PIL image from a `pyarrow` array?
Not sure how this should be handled but I would be happy to help if there is a good solution.
### Environment info
- `datasets` version: 2.18.1.dev0
- Platform: Linux-6.7.11-200.fc39.x86_64-x86_64-with-glibc2.38
- Python version: 3.12.2
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.1
- `fsspec` version: 2024.3.1 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6781). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Close in favor of #6786."
] | 2024-04-05T13:21:05 | 2024-04-09T07:49:11 | 2024-04-09T07:49:11 | NONE | null | 0 | {
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} | Inferring the type seems to be unnecessary given that the pyarrow array has already been created.
Because pyarrow array creation is sometimes extremely slow this doubles the time write_batch takes. | {
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https://api.github.com/repos/huggingface/datasets/issues/6780 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6780/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6780/comments | https://api.github.com/repos/huggingface/datasets/issues/6780/events | https://github.com/huggingface/datasets/pull/6780 | 2,226,160,096 | PR_kwDODunzps5rvkyj | 6,780 | Fix CI | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6780). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005074 / 0.011353 (-0.006279) | 0.003395 / 0.011008 (-0.007614) | 0.062358 / 0.038508 (0.023849) | 0.031041 / 0.023109 (0.007932) | 0.244039 / 0.275898 (-0.031859) | 0.266361 / 0.323480 (-0.057119) | 0.003201 / 0.007986 (-0.004785) | 0.002609 / 0.004328 (-0.001719) | 0.049269 / 0.004250 (0.045018) | 0.045713 / 0.037052 (0.008661) | 0.264075 / 0.258489 (0.005586) | 0.295428 / 0.293841 (0.001587) | 0.027882 / 0.128546 (-0.100664) | 0.010424 / 0.075646 (-0.065222) | 0.208417 / 0.419271 (-0.210854) | 0.035728 / 0.043533 (-0.007805) | 0.246803 / 0.255139 (-0.008336) | 0.267169 / 0.283200 (-0.016031) | 0.019797 / 0.141683 (-0.121885) | 1.163299 / 1.452155 (-0.288856) | 1.196118 / 1.492716 (-0.296599) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.106091 / 0.018006 (0.088085) | 0.303970 / 0.000490 (0.303480) | 0.000219 / 0.000200 (0.000019) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017955 / 0.037411 (-0.019456) | 0.060539 / 0.014526 (0.046013) | 0.072884 / 0.176557 (-0.103673) | 0.119205 / 0.737135 (-0.617931) | 0.074072 / 0.296338 (-0.222266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.272676 / 0.215209 (0.057467) | 2.715169 / 2.077655 (0.637514) | 1.419090 / 1.504120 (-0.085030) | 1.303903 / 1.541195 (-0.237292) | 1.311903 / 1.468490 (-0.156587) | 0.562005 / 4.584777 (-4.022772) | 2.432817 / 3.745712 (-1.312896) | 2.770599 / 5.269862 (-2.499263) | 1.723043 / 4.565676 (-2.842633) | 0.064341 / 0.424275 (-0.359934) | 0.004923 / 0.007607 (-0.002684) | 0.330507 / 0.226044 (0.104463) | 3.240829 / 2.268929 (0.971901) | 1.787638 / 55.444624 (-53.656986) | 1.522971 / 6.876477 (-5.353506) | 1.529496 / 2.142072 (-0.612576) | 0.645768 / 4.805227 (-4.159459) | 0.116405 / 6.500664 (-6.384259) | 0.041524 / 0.075469 (-0.033945) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968515 / 1.841788 (-0.873272) | 11.628911 / 8.074308 (3.554603) | 9.495023 / 10.191392 (-0.696369) | 0.142219 / 0.680424 (-0.538204) | 0.013859 / 0.534201 (-0.520342) | 0.285727 / 0.579283 (-0.293556) | 0.276842 / 0.434364 (-0.157522) | 0.321247 / 0.540337 (-0.219090) | 0.409958 / 1.386936 (-0.976978) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005102 / 0.011353 (-0.006251) | 0.003213 / 0.011008 (-0.007796) | 0.049250 / 0.038508 (0.010742) | 0.030649 / 0.023109 (0.007540) | 0.276629 / 0.275898 (0.000731) | 0.297315 / 0.323480 (-0.026165) | 0.004198 / 0.007986 (-0.003787) | 0.002744 / 0.004328 (-0.001585) | 0.047899 / 0.004250 (0.043649) | 0.040596 / 0.037052 (0.003544) | 0.287248 / 0.258489 (0.028759) | 0.313573 / 0.293841 (0.019732) | 0.029067 / 0.128546 (-0.099480) | 0.010122 / 0.075646 (-0.065524) | 0.058869 / 0.419271 (-0.360402) | 0.033012 / 0.043533 (-0.010521) | 0.272995 / 0.255139 (0.017856) | 0.297102 / 0.283200 (0.013903) | 0.018209 / 0.141683 (-0.123474) | 1.157785 / 1.452155 (-0.294369) | 1.184999 / 1.492716 (-0.307717) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094228 / 0.018006 (0.076221) | 0.302055 / 0.000490 (0.301565) | 0.000221 / 0.000200 (0.000021) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022020 / 0.037411 (-0.015391) | 0.074970 / 0.014526 (0.060444) | 0.087682 / 0.176557 (-0.088875) | 0.126506 / 0.737135 (-0.610629) | 0.092046 / 0.296338 (-0.204293) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295634 / 0.215209 (0.080425) | 2.891554 / 2.077655 (0.813899) | 1.579963 / 1.504120 (0.075843) | 1.462924 / 1.541195 (-0.078271) | 1.463806 / 1.468490 (-0.004684) | 0.558371 / 4.584777 (-4.026406) | 2.513500 / 3.745712 (-1.232212) | 2.754146 / 5.269862 (-2.515716) | 1.762317 / 4.565676 (-2.803360) | 0.063965 / 0.424275 (-0.360310) | 0.005538 / 0.007607 (-0.002069) | 0.348114 / 0.226044 (0.122070) | 3.484558 / 2.268929 (1.215630) | 1.940002 / 55.444624 (-53.504623) | 1.658469 / 6.876477 (-5.218008) | 1.645777 / 2.142072 (-0.496295) | 0.639367 / 4.805227 (-4.165861) | 0.115605 / 6.500664 (-6.385059) | 0.040647 / 0.075469 (-0.034822) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.036002 / 1.841788 (-0.805786) | 12.286895 / 8.074308 (4.212587) | 10.146719 / 10.191392 (-0.044673) | 0.140867 / 0.680424 (-0.539557) | 0.015517 / 0.534201 (-0.518684) | 0.290126 / 0.579283 (-0.289157) | 0.298702 / 0.434364 (-0.135662) | 0.325518 / 0.540337 (-0.214819) | 0.412597 / 1.386936 (-0.974339) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3ddb1ef00334a6f973679a51e783905fbc9ef0b \"CML watermark\")\n"
] | 2024-04-04T17:45:04 | 2024-04-04T18:46:04 | 2024-04-04T18:23:34 | CONTRIBUTOR | null | 0 | {
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"merged_at": "2024-04-04T18:23:34Z",
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/6780"
} | Updates the `wmt_t2t` test to pin the `revision` to the version with a loading script (cc @albertvillanova).
Additionally, it replaces the occurrences of the `lhoestq/test` repo id with `hf-internal-testing/dataset_with_script` and re-enables logging checks in the `Dataset.from_sql` tests. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6779). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005336 / 0.011353 (-0.006017) | 0.004052 / 0.011008 (-0.006956) | 0.063475 / 0.038508 (0.024967) | 0.032963 / 0.023109 (0.009854) | 0.243906 / 0.275898 (-0.031992) | 0.269048 / 0.323480 (-0.054432) | 0.003363 / 0.007986 (-0.004622) | 0.002802 / 0.004328 (-0.001527) | 0.049487 / 0.004250 (0.045236) | 0.046990 / 0.037052 (0.009938) | 0.260169 / 0.258489 (0.001680) | 0.289145 / 0.293841 (-0.004696) | 0.028030 / 0.128546 (-0.100517) | 0.010706 / 0.075646 (-0.064940) | 0.213640 / 0.419271 (-0.205632) | 0.035866 / 0.043533 (-0.007667) | 0.245106 / 0.255139 (-0.010033) | 0.269588 / 0.283200 (-0.013612) | 0.019791 / 0.141683 (-0.121892) | 1.117684 / 1.452155 (-0.334470) | 1.183389 / 1.492716 (-0.309327) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095736 / 0.018006 (0.077730) | 0.302586 / 0.000490 (0.302097) | 0.000220 / 0.000200 (0.000020) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018985 / 0.037411 (-0.018426) | 0.062097 / 0.014526 (0.047571) | 0.075617 / 0.176557 (-0.100939) | 0.120570 / 0.737135 (-0.616566) | 0.075949 / 0.296338 (-0.220390) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279597 / 0.215209 (0.064388) | 2.754319 / 2.077655 (0.676665) | 1.444147 / 1.504120 (-0.059973) | 1.328414 / 1.541195 (-0.212781) | 1.371073 / 1.468490 (-0.097417) | 0.553851 / 4.584777 (-4.030926) | 2.351694 / 3.745712 (-1.394018) | 2.860771 / 5.269862 (-2.409091) | 1.749664 / 4.565676 (-2.816013) | 0.061736 / 0.424275 (-0.362539) | 0.005073 / 0.007607 (-0.002534) | 0.329974 / 0.226044 (0.103930) | 3.300487 / 2.268929 (1.031558) | 1.812809 / 55.444624 (-53.631815) | 1.559018 / 6.876477 (-5.317458) | 1.628664 / 2.142072 (-0.513408) | 0.635757 / 4.805227 (-4.169471) | 0.116468 / 6.500664 (-6.384196) | 0.042641 / 0.075469 (-0.032828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972048 / 1.841788 (-0.869740) | 11.952721 / 8.074308 (3.878412) | 9.754274 / 10.191392 (-0.437118) | 0.132026 / 0.680424 (-0.548398) | 0.015352 / 0.534201 (-0.518849) | 0.290574 / 0.579283 (-0.288709) | 0.275384 / 0.434364 (-0.158980) | 0.330688 / 0.540337 (-0.209650) | 0.414868 / 1.386936 (-0.972068) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005412 / 0.011353 (-0.005941) | 0.003814 / 0.011008 (-0.007194) | 0.049988 / 0.038508 (0.011480) | 0.031617 / 0.023109 (0.008507) | 0.278975 / 0.275898 (0.003077) | 0.303540 / 0.323480 (-0.019940) | 0.004265 / 0.007986 (-0.003721) | 0.002804 / 0.004328 (-0.001525) | 0.049518 / 0.004250 (0.045268) | 0.041176 / 0.037052 (0.004123) | 0.291248 / 0.258489 (0.032759) | 0.317401 / 0.293841 (0.023560) | 0.029501 / 0.128546 (-0.099045) | 0.010392 / 0.075646 (-0.065255) | 0.057906 / 0.419271 (-0.361365) | 0.033056 / 0.043533 (-0.010477) | 0.280202 / 0.255139 (0.025063) | 0.298684 / 0.283200 (0.015484) | 0.018071 / 0.141683 (-0.123612) | 1.167691 / 1.452155 (-0.284464) | 1.211322 / 1.492716 (-0.281394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092325 / 0.018006 (0.074318) | 0.301209 / 0.000490 (0.300719) | 0.000221 / 0.000200 (0.000021) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021432 / 0.037411 (-0.015980) | 0.074556 / 0.014526 (0.060031) | 0.086049 / 0.176557 (-0.090508) | 0.125151 / 0.737135 (-0.611984) | 0.088279 / 0.296338 (-0.208059) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296755 / 0.215209 (0.081546) | 2.922650 / 2.077655 (0.844995) | 1.606031 / 1.504120 (0.101911) | 1.489692 / 1.541195 (-0.051502) | 1.530206 / 1.468490 (0.061716) | 0.577827 / 4.584777 (-4.006950) | 2.459716 / 3.745712 (-1.285997) | 2.825192 / 5.269862 (-2.444669) | 1.788110 / 4.565676 (-2.777566) | 0.064011 / 0.424275 (-0.360264) | 0.005616 / 0.007607 (-0.001991) | 0.341612 / 0.226044 (0.115568) | 3.455123 / 2.268929 (1.186194) | 1.961635 / 55.444624 (-53.482990) | 1.688107 / 6.876477 (-5.188370) | 1.725490 / 2.142072 (-0.416583) | 0.656011 / 4.805227 (-4.149216) | 0.117633 / 6.500664 (-6.383031) | 0.041386 / 0.075469 (-0.034083) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.025786 / 1.841788 (-0.816002) | 12.294598 / 8.074308 (4.220290) | 10.241136 / 10.191392 (0.049744) | 0.130577 / 0.680424 (-0.549847) | 0.016094 / 0.534201 (-0.518107) | 0.291193 / 0.579283 (-0.288090) | 0.273016 / 0.434364 (-0.161348) | 0.327553 / 0.540337 (-0.212784) | 0.418556 / 1.386936 (-0.968380) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3575036af2fd5cccff7fa60de30e2e444cf8a54e \"CML watermark\")\n"
] | 2024-04-04T17:02:51 | 2024-04-08T13:34:01 | 2024-04-08T13:27:44 | CONTRIBUTOR | null | 0 | {
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} | `diffusers` (https://github.com/huggingface/diffusers/pull/7116) and `huggingface_hub` (https://github.com/huggingface/huggingface_hub/pull/2072) also use `uv` to install their dependencies, so we can do the same here.
It seems to make the "Install dependencies" step in the `ubuntu` jobs 5-8x faster and 1.5-2x in the `windows` one.
Besides introducing `uv` in CI, this PR bumps the `tensorflow` minimal version requirement to align with Transformers and simplifies the SpaCy hashing tests (use blank language models instead of the pre-trained ones)
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"Hello!\r\n\r\nThis is due to how pandas write numpy arrays to csv. [Source](https://stackoverflow.com/questions/54753179/to-csv-saves-np-array-as-string-instead-of-as-a-list)\r\nTo fix this, you can convert them to list yourselves.\r\n\r\n```python\r\ndf = ds.to_pandas()\r\ndf['int'] = df['int'].apply(lambda arr: list(arr))\r\ndf.to_csv(index=False, '../output/temp.csv')\r\n```\r\n\r\nI think it would be good if `datasets` would do the conversion itself, but it's a breaking change and I would wait for the greenlight from someone from HF."
] | 2024-04-04T16:46:13 | 2024-04-08T15:24:41 | null | NONE | null | null | null | ### Describe the bug
The `to_csv()` method does not output commas in lists. So when the Dataset is loaded back in the data structure of the column with a list is not correct.
Here's an example:
Obviously, it's not as trivial as inserting commas in the list, since its a comma-separated file. But hopefully there's a way to export the list in a way that it'll be imported by `load_dataset()` correctly.
### Steps to reproduce the bug
Here's some code to reproduce the bug:
```python
from datasets import Dataset
ds = Dataset.from_dict(
{
"pokemon": ["bulbasaur", "squirtle"],
"type": ["grass", "water"]
}
)
def ascii_to_hex(text):
return [ord(c) for c in text]
ds = ds.map(lambda x: {"int": ascii_to_hex(x['pokemon'])})
ds.to_csv('../output/temp.csv')
```
temp.csv then contains:
```
### Expected behavior
ACTUAL OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[ 98 117 108 98 97 115 97 117 114]
squirtle,water,[115 113 117 105 114 116 108 101]
```
EXPECTED OUTPUT:
```
pokemon,type,int
bulbasaur,grass,[98, 117, 108, 98, 97, 115, 97, 117, 114]
squirtle,water,[115, 113, 117, 105, 114, 116, 108, 101]
```
or probably something more like this since it's a CSV file:
```
pokemon,type,int
bulbasaur,grass,"[98, 117, 108, 98, 97, 115, 97, 117, 114]"
squirtle,water,"[115, 113, 117, 105, 114, 116, 108, 101]"
```
### Environment info
### Package Version
Name: datasets
Version: 2.16.1
### Python
version: 3.10.12
### OS Info
PRETTY_NAME="Ubuntu 22.04.4 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.4 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
...
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"Hi! `metadata.jsonl` (or `metadata.csv`) is the only allowed name for the `imagefolder`'s metadata files.",
"@mariosasko hey i tried with metadata.jsonl also and it still doesn't get the right columns",
"@mariosasko it says metadata.csv not found\r\n<img width=\"1150\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/81643693/3754980c-6185-4413-88fa-b499bcdd4195\">\r\n\r\ndataset = load_dataset('/dataset',metadata.csv) \r\n\r\n| workspace\r\n|| source code\r\n| dataset\r\n| |-- images\r\n| |-- metadata.csv\r\n| |-- metadata.jsonl\r\n| |-- padded_images\r\n\r\nExample of metadata.jsonl file\r\n{\"caption\": \"a drawing depicts a full shot of a black t-shirt with a triangular pattern on the front there is a white label on the left side of the triangle\", \"image\": \"images/212734.png\", \"gaussian_padded_image\": \"padded_images/p_212734.png\"}\r\n{\"caption\": \"an eye-level full shot of a large elephant and a baby elephant standing in a watering hole on the left side is a small elephant with its head turned to the right of dry land, trees, and bushes\", \"image\": \"images/212735.png\", \"gaussian_padded_image\": \"padded_images/p_212735.png\"}\r\n",
"Loading more than one image per row with `imagefolder` is not supported currently. You can subscribe to https://github.com/huggingface/datasets/issues/5760 to see when it will be.\r\n\r\nInstead, you can load the dataset with `Dataset.from_generator`:\r\n```python\r\nimport json\r\nfrom datasets import Dataset, Value, Image, Features\r\n\r\ndef gen():\r\n with open(\"./dataset/metadata.jsonl\") as f:\r\n for line in f:\r\n line = json.loads(line)\r\n yield {\"caption\": line[\"caption\"], \"image\": os.path.join(\"./dataset\", line[\"image\"], \"gaussian_padded_image\": os.path.join(\"./dataset\", line[\"gaussian_padded_image\"]))}\r\n\r\nfeatures = Features({\"caption\": Value(\"string\"), \"image\": Image(), \"gaussian_padded_image\": Image()})\r\ndataset = Dataset.from_generator(gen, features=features)\r\n```\r\n(E.g., if you want to share this dataset on the Hub, you can call `dataset.push_to_hub(...)` afterward)",
"hi Thanks for sharing this, Actually I was trying with a webdataset format of the data as well and it did'nt work. Could you share how i can create Dataset object from webdataset format of this data?"
] | 2024-04-04T06:31:53 | 2024-04-05T21:14:48 | null | NONE | null | null | null | ### Describe the bug
Hi I have the following directory structure:
|--dataset
| |-- images
| |-- metadata1000.csv
| |-- metadata1000.jsonl
| |-- padded_images
Example of metadata1000.jsonl file
{"caption": "a drawing depicts a full shot of a black t-shirt with a triangular pattern on the front there is a white label on the left side of the triangle", "image": "images/212734.png", "gaussian_padded_image": "padded_images/p_212734.png"}
{"caption": "an eye-level full shot of a large elephant and a baby elephant standing in a watering hole on the left side is a small elephant with its head turned to the right of dry land, trees, and bushes", "image": "images/212735.png", "gaussian_padded_image": "padded_images/p_212735.png"}
.
.
.
I'm trying to use dataset = load_dataset("imagefolder", data_dir='/dataset/', split='train') to load the the dataset, however it is not able to load according to the fields in the metadata1000.jsonl .
please assist to load the data properly
also getting
```
File "/workspace/train_trans_vae.py", line 1089, in <module>
print(get_metadata_patterns('/dataset/'))
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 499, in get_metadata_patterns
raise FileNotFoundError(f"The directory at {base_path} doesn't contain any metadata file") from None
FileNotFoundError: The directory at /dataset/ doesn't contain any metadata file
```
when trying
```
from datasets.data_files import get_metadata_patterns
print(get_metadata_patterns('/dataset/'))
```
### Steps to reproduce the bug
dataset Version: 2.18.0
make a similar jsonl and similar directory format
### Expected behavior
creates a dataset object with the column names, caption,image,gaussian_padded_image
### Environment info
dataset Version: 2.18.0 | {
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"Same problem.",
"Hi! You should be able to fix this by passing `remove_unused_columns=False` to the `transformers` `TrainingArguments` as explained in https://github.com/huggingface/peft/issues/1299.\r\n\r\n(I'm not familiar with Vertex AI, but I'd assume `remove_unused_columns` can be passed as a flag to the docker container) ",
"I had the same problem, but I spent a whole day trying different combination with my own dataset with the example data set and found the reason: the example data is multi-turn conversation between human and assistant, so # Humman or # Assistant appear at least twice. If your own custom data only has single turn conversation, it might end up with the same error. What you can do is repeat your single turn conversation twice in your training data (keep the key 'text' the same) and maybe it works. I guess the reason is the specific way processing the data requires and counts multi-turn only (single turn will be discarded so it ends up with no training data), but since I am using Google Vertex AI, I don't have direct access to the underlying code so that was just my guess. ",
"> Hi! You should be able to fix this by passing `remove_unused_columns=False` to the `transformers` `TrainingArguments` as explained in [huggingface/peft#1299](https://github.com/huggingface/peft/issues/1299).\r\n> \r\n> (I'm not familiar with Vertex AI, but I'd assume `remove_unused_columns` can be passed as a flag to the docker container)\r\n\r\n@mariosasko Thanks for the response and suggestion. \r\nWhen I set `remove_unused_columns` as `False` , I end up getting different error (will post the error soon). \r\nEither the Vertex-AI does not support `remove_unused_columns` or my dataset is completely wrong. \r\n\r\nThank you, \r\nKK",
"> I had the same problem, but I spent a whole day trying different combination with my own dataset with the example data set and found the reason: the example data is multi-turn conversation between human and assistant, so # Humman or # Assistant appear at least twice. If your own custom data only has single turn conversation, it might end up with the same error. What you can do is repeat your single turn conversation twice in your training data (keep the key 'text' the same) and maybe it works. I guess the reason is the specific way processing the data requires and counts multi-turn only (single turn will be discarded so it ends up with no training data), but since I am using Google Vertex AI, I don't have direct access to the underlying code so that was just my guess.\r\n\r\n@cyberyu Thanks for your suggestions. \r\nI have tried the approach you suggested, copied the same conversation in each jsonl element so every jsonl item has 2 `HUMAN` and `ASSISTANT`. \r\nHowever in my case, the issue persists. I am gonna give few more tries, and post the results here. \r\nYou can find my dataset [here](https://huggingface.co/datasets/kk2491/test/tree/main) \r\n\r\nThank you, \r\nKK ",
"> > I had the same problem, but I spent a whole day trying different combination with my own dataset with the example data set and found the reason: the example data is multi-turn conversation between human and assistant, so # Humman or # Assistant appear at least twice. If your own custom data only has single turn conversation, it might end up with the same error. What you can do is repeat your single turn conversation twice in your training data (keep the key 'text' the same) and maybe it works. I guess the reason is the specific way processing the data requires and counts multi-turn only (single turn will be discarded so it ends up with no training data), but since I am using Google Vertex AI, I don't have direct access to the underlying code so that was just my guess.\r\n> \r\n> @cyberyu Thanks for your suggestions. I have tried the approach you suggested, copied the same conversation in each jsonl element so every jsonl item has 2 `HUMAN` and `ASSISTANT`. However in my case, the issue persists. I am gonna give few more tries, and post the results here. You can find my dataset [here](https://huggingface.co/datasets/kk2491/test/tree/main)\r\n> \r\n> Thank you, KK\r\n\r\nI think another reason is your training sample length is too short. I saw a relevant report (https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/16) stating that the processing code might have a bug discarding sequence length short than max_seq_length, which is 512. Not sure the Vertex AI backend code has fixed that bug or not. So I tried to add some garbage content in your data, and extended the length longer than 512 for a single turn, and repeated twice. You can copy the following line as 5 repeated lines as your training data jsonl file of five samples (no eval or test needed, for speed up, set evaluation step to 5 and training step to 10,), and it will pass.\r\n\r\n{\"text\":\"### Human: You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You will handle customers queries and provide effective help message. Please provide response to 'Can Interplai software optimize routes for minimizing package handling and transfer times in distribution centers'? ### Assistant: Yes, Interplai software can optimize routes for distribution centers by streamlining package handling processes, minimizing transfer times between loading docks and storage areas, and optimizing warehouse layouts for efficient order fulfillment. ### Human: You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You are a helpful AI Assistant familiar with customer service. You will handle customers queries and provide effective help message. Please provide response to 'Can Interplai software optimize routes for minimizing package handling and transfer times in distribution centers'? ### Assistant: Yes, Interplai software can optimize routes for distribution centers by streamlining package handling processes, minimizing transfer times between loading docks and storage areas, and optimizing warehouse layouts for efficient order fulfillment.\"}\r\n",
"@cyberyu **Thank you so much, You saved my day (+ so many days)**. \r\nI tried the example you provided above, and the training is successfully completed in Vertex-AI (through GUI). \r\nI never thought there would be constraints on the length of the samples and also on the number of turns. \r\nI will update my complete dataset and see update here once the training is completed. \r\n\r\nThank you, \r\nKK "
] | 2024-04-03T17:06:30 | 2024-04-08T01:24:35 | null | NONE | null | null | null | ### Describe the bug
I am trying to fine-tune llama2-7b model in GCP. The notebook I am using for this can be found [here](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama2_peft_finetuning.ipynb).
When I use the dataset given in the example, the training gets successfully completed (example dataset can be found [here](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)).
However when I use my own dataset which is in the same format as the example dataset, I get the below error (my dataset can be found [here](https://huggingface.co/datasets/kk2491/finetune_dataset_002)).
I see the files are being read correctly from the logs:
### Steps to reproduce the bug
1. Clone the [vertex-ai-samples](https://github.com/GoogleCloudPlatform/vertex-ai-samples) repository.
2. Run the [llama2-7b peft fine-tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_llama2_peft_finetuning.ipynb).
3. Change the dataset `kk2491/finetune_dataset_002`
### Expected behavior
The training should complete successfully, and model gets deployed to an endpoint.
### Environment info
Python version : Python 3.10.12
Dataset : https://huggingface.co/datasets/kk2491/finetune_dataset_002
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When I create a dataset, it gets stuck while generating cached data.
The image format is PNG, and it will not get stuck when the image format is jpeg.
After debugging, I know that it is because of the `pa.array` operation in [arrow_writer](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_writer.py#L553), but i don't why.
### Steps to reproduce the bug
```
from datasets import Dataset
def generator(lines):
for line in lines:
img = Image.open(open(line["url"], "rb"))
# print(img.format) # "PNG"
yield {
"image": img,
}
lines = open(dataset_path, "r")
dataset = Dataset.from_generator(
generator,
gen_kwargs={"lines": lines}
)
```
### Expected behavior
Generating split done.
### Environment info
datasets 2.13.0 | {
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"The caching works as expected when I try to reproduce this locally or on Colab...",
"hi @mariosasko , Thank you for checking. I also tried running this again just now, and it seems like the `load_dataset()` caches properly (though I'll double check later).\r\n\r\nI think the issue might be in the caching of the function output for `territories.map(lambda row: {'Claimants': row['Claimants'].split(';')})`. My current run re-ran this, even though I have run this many times before, and as demonstrated by loading from cache, the loaded dataset is the same.\r\n\r\nI wonder if the issue stems from using CSV output. Do you recommend changing to Parquet, and if so, is there an easy way to take the already uploaded data on the Hub and reformat?",
"This issue seems similar to https://github.com/huggingface/datasets/issues/6184 (`dill` serializes objects defined outside the `__main__` module by reference). You should be able to work around this limitation by defining the lambdas outside of `load_borderlines_hf` (as module variables) and then setting their `__module__` attribute's value to `None` to force serializing them by value, e.g., like this: \r\n```python\r\nsplit_Claimants_row = lambda row: {'Claimants': row['Claimants'].split(';')}\r\nsplit_Claimants_row.__module__ = None\r\n```",
"Thank you, I'll give this a try. Your fix makes sense to me, so this issue can be closed for now.\r\n\r\nUnrelated comment -- for \"Downloads last month\" on the hub page, I'm assuming for this project that each downloaded CSV is 1 download? The dataset consists of 51 CSVs, so I'm trying to see why it's incrementing so quickly (1125 2 days ago, 1246 right now).",
"This doc explains how we count \"Downloads last month\": https://huggingface.co/docs/hub/datasets-download-stats"
] | 2024-04-02T17:23:22 | 2024-04-08T18:43:45 | 2024-04-08T18:43:45 | NONE | null | null | null | ### Describe the bug
Hi, I have a dataset on the hub [here](https://huggingface.co/datasets/manestay/borderlines). It has 1k+ downloads, which I sure is mostly just me and my colleagues working with it. It should have far fewer, since I'm using the same machine with a properly set up HF_HOME variable. However, whenever I run the below function `load_borderlines_hf`, it downloads the entire dataset from the hub and then does the other logic:
https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80
Let me know what I'm doing wrong here, or if it's a bug with the `datasets` library itself. On the hub I have my data stored in CSVs, but several columns are lists, so that's why I have the code to map splitting on `;`. I looked into dataset loading scripts, but it seemed difficult to set up. I have verified that other `datasets` and `models` on my system are using the cache properly (e.g. I have a 13B parameter model and large datasets, but those are cached and don't redownload).
__EDIT: __ as pointed out in the discussion below, it may be the `map()` calls that aren't being cached properly. Supposing the `load_dataset()` retrieve from the cache, then it should be the case that the `map()` calls also retrieve from the cached output. But the `map()` commands re-execute sometimes.
### Steps to reproduce the bug
1. Copy and paste the function from [here](https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80) (lines 80-100)
2. Run it in Python `load_borderlines_hf(None)`
3. It completes successfully, downloading from HF hub, then doing the mapping logic etc.
4. If you run it again after some time, it will re-download, ignoring the cache
### Expected behavior
Re-running the code, which calls `datasets.load_dataset('manestay/borderlines', 'territories')`, should use the cached version
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.21-150500.55.7-default-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6772). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005728 / 0.011353 (-0.005624) | 0.003809 / 0.011008 (-0.007199) | 0.062930 / 0.038508 (0.024422) | 0.032320 / 0.023109 (0.009211) | 0.251072 / 0.275898 (-0.024826) | 0.275397 / 0.323480 (-0.048083) | 0.003314 / 0.007986 (-0.004671) | 0.002869 / 0.004328 (-0.001460) | 0.049070 / 0.004250 (0.044819) | 0.049282 / 0.037052 (0.012229) | 0.263546 / 0.258489 (0.005057) | 0.291471 / 0.293841 (-0.002370) | 0.028462 / 0.128546 (-0.100084) | 0.010528 / 0.075646 (-0.065119) | 0.211249 / 0.419271 (-0.208023) | 0.036840 / 0.043533 (-0.006693) | 0.250038 / 0.255139 (-0.005101) | 0.268883 / 0.283200 (-0.014317) | 0.021417 / 0.141683 (-0.120266) | 1.139754 / 1.452155 (-0.312400) | 1.197319 / 1.492716 (-0.295397) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094191 / 0.018006 (0.076185) | 0.302413 / 0.000490 (0.301923) | 0.000220 / 0.000200 (0.000020) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018490 / 0.037411 (-0.018922) | 0.063361 / 0.014526 (0.048835) | 0.075854 / 0.176557 (-0.100702) | 0.121499 / 0.737135 (-0.615637) | 0.075982 / 0.296338 (-0.220356) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286030 / 0.215209 (0.070821) | 2.778487 / 2.077655 (0.700832) | 1.440963 / 1.504120 (-0.063157) | 1.326217 / 1.541195 (-0.214977) | 1.359228 / 1.468490 (-0.109262) | 0.566999 / 4.584777 (-4.017778) | 2.453344 / 3.745712 (-1.292368) | 2.841448 / 5.269862 (-2.428413) | 1.825197 / 4.565676 (-2.740479) | 0.062301 / 0.424275 (-0.361974) | 0.004948 / 0.007607 (-0.002659) | 0.334578 / 0.226044 (0.108534) | 3.302327 / 2.268929 (1.033399) | 1.799808 / 55.444624 (-53.644817) | 1.529693 / 6.876477 (-5.346783) | 1.564684 / 2.142072 (-0.577389) | 0.632891 / 4.805227 (-4.172336) | 0.116594 / 6.500664 (-6.384070) | 0.042695 / 0.075469 (-0.032774) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.999994 / 1.841788 (-0.841794) | 12.767365 / 8.074308 (4.693057) | 10.550439 / 10.191392 (0.359047) | 0.133437 / 0.680424 (-0.546986) | 0.015252 / 0.534201 (-0.518949) | 0.293285 / 0.579283 (-0.285998) | 0.274773 / 0.434364 (-0.159590) | 0.328718 / 0.540337 (-0.211619) | 0.428021 / 1.386936 (-0.958915) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005538 / 0.011353 (-0.005815) | 0.003738 / 0.011008 (-0.007271) | 0.050179 / 0.038508 (0.011671) | 0.032441 / 0.023109 (0.009332) | 0.294721 / 0.275898 (0.018823) | 0.322616 / 0.323480 (-0.000864) | 0.004255 / 0.007986 (-0.003731) | 0.002913 / 0.004328 (-0.001416) | 0.049044 / 0.004250 (0.044794) | 0.042361 / 0.037052 (0.005309) | 0.304162 / 0.258489 (0.045673) | 0.332757 / 0.293841 (0.038916) | 0.029355 / 0.128546 (-0.099191) | 0.010546 / 0.075646 (-0.065100) | 0.058213 / 0.419271 (-0.361058) | 0.032648 / 0.043533 (-0.010885) | 0.298241 / 0.255139 (0.043102) | 0.313710 / 0.283200 (0.030510) | 0.017836 / 0.141683 (-0.123847) | 1.135050 / 1.452155 (-0.317104) | 1.178277 / 1.492716 (-0.314439) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094387 / 0.018006 (0.076381) | 0.301955 / 0.000490 (0.301466) | 0.000220 / 0.000200 (0.000020) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023135 / 0.037411 (-0.014276) | 0.078109 / 0.014526 (0.063583) | 0.087519 / 0.176557 (-0.089037) | 0.127815 / 0.737135 (-0.609320) | 0.090107 / 0.296338 (-0.206231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289149 / 0.215209 (0.073940) | 2.832354 / 2.077655 (0.754699) | 1.574003 / 1.504120 (0.069883) | 1.449190 / 1.541195 (-0.092005) | 1.465798 / 1.468490 (-0.002692) | 0.561953 / 4.584777 (-4.022824) | 2.445788 / 3.745712 (-1.299924) | 2.882453 / 5.269862 (-2.387409) | 1.813267 / 4.565676 (-2.752409) | 0.063163 / 0.424275 (-0.361112) | 0.005785 / 0.007607 (-0.001822) | 0.340125 / 0.226044 (0.114081) | 3.355370 / 2.268929 (1.086442) | 1.924226 / 55.444624 (-53.520398) | 1.643242 / 6.876477 (-5.233234) | 1.650149 / 2.142072 (-0.491924) | 0.654818 / 4.805227 (-4.150409) | 0.114968 / 6.500664 (-6.385696) | 0.042044 / 0.075469 (-0.033425) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.024867 / 1.841788 (-0.816921) | 12.656140 / 8.074308 (4.581832) | 10.927014 / 10.191392 (0.735622) | 0.155929 / 0.680424 (-0.524495) | 0.015356 / 0.534201 (-0.518845) | 0.289834 / 0.579283 (-0.289449) | 0.280889 / 0.434364 (-0.153475) | 0.331490 / 0.540337 (-0.208847) | 0.418037 / 1.386936 (-0.968899) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ad3467e9b138d1a9b87b661828a71139f4e46ece \"CML watermark\")\n"
] | 2024-04-02T15:41:28 | 2024-04-02T16:28:45 | 2024-04-02T16:17:46 | CONTRIBUTOR | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/6772.diff",
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"merged_at": "2024-04-02T16:17:46Z",
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} | Use more consistent wording in `remove_columns` to explain why it's faster than `map` and update `remove_columns`/`rename_columns` docstrings to fix in-place calls.
Reported in https://github.com/huggingface/datasets/issues/6700 | {
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https://api.github.com/repos/huggingface/datasets/issues/6771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6771/comments | https://api.github.com/repos/huggingface/datasets/issues/6771/events | https://github.com/huggingface/datasets/issues/6771 | 2,220,131,457 | I_kwDODunzps6EVISB | 6,771 | Datasets FileNotFoundError when trying to generate examples. | {
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"Hi! I've opened a PR in the repo to fix this issue: https://huggingface.co/datasets/RitchieP/VerbaLex_voice/discussions/6",
"@mariosasko Thanks for the PR and help! Guess I could close the issue for now. Appreciate the help!"
] | 2024-04-02T10:24:57 | 2024-04-04T14:22:03 | 2024-04-04T14:22:03 | NONE | null | null | null | ### Discussed in https://github.com/huggingface/datasets/discussions/6768
<div type='discussions-op-text'>
<sup>Originally posted by **RitchieP** April 1, 2024</sup>
Currently, I have a dataset hosted on Huggingface with a custom script [here](https://huggingface.co/datasets/RitchieP/VerbaLex_voice).
I'm loading my dataset as below.
```py
from datasets import load_dataset, IterableDatasetDict
dataset = IterableDatasetDict()
dataset["train"] = load_dataset("RitchieP/VerbaLex_voice", "ar", split="train", use_auth_token=True, streaming=True)
dataset["test"] = load_dataset("RitchieP/VerbaLex_voice", "ar", split="test", use_auth_token=True, streaming=True)
```
And when I try to see the data I have loaded with
```py
list(dataset["train"].take(1))
```
And it gives me this stack trace
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[2], line 1
----> 1 list(dataset["train"].take(1))
File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:1388, in IterableDataset.__iter__(self)
1385 yield formatter.format_row(pa_table)
1386 return
-> 1388 for key, example in ex_iterable:
1389 if self.features:
1390 # `IterableDataset` automatically fills missing columns with None.
1391 # This is done with `_apply_feature_types_on_example`.
1392 example = _apply_feature_types_on_example(
1393 example, self.features, token_per_repo_id=self._token_per_repo_id
1394 )
File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:1044, in TakeExamplesIterable.__iter__(self)
1043 def __iter__(self):
-> 1044 yield from islice(self.ex_iterable, self.n)
File /opt/conda/lib/python3.10/site-packages/datasets/iterable_dataset.py:234, in ExamplesIterable.__iter__(self)
233 def __iter__(self):
--> 234 yield from self.generate_examples_fn(**self.kwargs)
File ~/.cache/huggingface/modules/datasets_modules/datasets/RitchieP--VerbaLex_voice/9465eaee58383cf9d7c3e14111d7abaea56398185a641b646897d6df4e4732f7/VerbaLex_voice.py:127, in VerbaLexVoiceDataset._generate_examples(self, local_extracted_archive_paths, archives, meta_path)
125 for i, audio_archive in enumerate(archives):
126 print(audio_archive)
--> 127 for path, file in audio_archive:
128 _, filename = os.path.split(path)
129 if filename in metadata:
File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:869, in _IterableFromGenerator.__iter__(self)
868 def __iter__(self):
--> 869 yield from self.generator(*self.args, **self.kwargs)
File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:919, in ArchiveIterable._iter_from_urlpath(cls, urlpath, download_config)
915 @classmethod
916 def _iter_from_urlpath(
917 cls, urlpath: str, download_config: Optional[DownloadConfig] = None
918 ) -> Generator[Tuple, None, None]:
--> 919 compression = _get_extraction_protocol(urlpath, download_config=download_config)
920 # Set block_size=0 to get faster streaming
921 # (e.g. for hf:// and https:// it uses streaming Requests file-like instances)
922 with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
File /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:400, in _get_extraction_protocol(urlpath, download_config)
398 urlpath, storage_options = _prepare_path_and_storage_options(urlpath, download_config=download_config)
399 try:
--> 400 with fsspec.open(urlpath, **(storage_options or {})) as f:
401 return _get_extraction_protocol_with_magic_number(f)
402 except FileNotFoundError:
File /opt/conda/lib/python3.10/site-packages/fsspec/core.py:100, in OpenFile.__enter__(self)
97 def __enter__(self):
98 mode = self.mode.replace("t", "").replace("b", "") + "b"
--> 100 f = self.fs.open(self.path, mode=mode)
102 self.fobjects = [f]
104 if self.compression is not None:
File /opt/conda/lib/python3.10/site-packages/fsspec/spec.py:1307, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)
1305 else:
1306 ac = kwargs.pop("autocommit", not self._intrans)
-> 1307 f = self._open(
1308 path,
1309 mode=mode,
1310 block_size=block_size,
1311 autocommit=ac,
1312 cache_options=cache_options,
1313 **kwargs,
1314 )
1315 if compression is not None:
1316 from fsspec.compression import compr
File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:180, in LocalFileSystem._open(self, path, mode, block_size, **kwargs)
178 if self.auto_mkdir and "w" in mode:
179 self.makedirs(self._parent(path), exist_ok=True)
--> 180 return LocalFileOpener(path, mode, fs=self, **kwargs)
File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:302, in LocalFileOpener.__init__(self, path, mode, autocommit, fs, compression, **kwargs)
300 self.compression = get_compression(path, compression)
301 self.blocksize = io.DEFAULT_BUFFER_SIZE
--> 302 self._open()
File /opt/conda/lib/python3.10/site-packages/fsspec/implementations/local.py:307, in LocalFileOpener._open(self)
305 if self.f is None or self.f.closed:
306 if self.autocommit or "w" not in self.mode:
--> 307 self.f = open(self.path, mode=self.mode)
308 if self.compression:
309 compress = compr[self.compression]
FileNotFoundError: [Errno 2] No such file or directory: '/kaggle/working/h'
```
After looking into the stack trace, and referring to the source codes, it looks like its trying to access a directory in the notebook's environment and I don't understand why.
Not sure if its a bug in Datasets library, so I'm opening a discussions first. Feel free to ask for more information if needed. Appreciate any help in advance!</div>
Hi, referring to the discussion title above, after further digging, I think it's an issue within the datasets library. But not quite sure where it is.
If you require any more info or actions from me, please let me know. Appreciate any help in advance! | {
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"You should be able to fix this by updating `huggingface_hub` with `pip install -U huggingface_hub`. We use this package under the hood to resolve the Hub's files."
] | 2024-04-01T20:17:48 | 2024-04-03T13:42:29 | null | NONE | null | null | null | ### Describe the bug
`Datasets==2.18.0` is not compatible with `fsspec==2023.12.2`.
I have to downgrade fsspec to `fsspec==2023.10.0` to make `Datasets==2.18.0` work properly.
### Steps to reproduce the bug
To reproduce the bug:
1. Make sure that `Datasets==2.18.0` and `fsspec==2023.12.2`.
2. Run the following code:
```
from datasets import load_dataset
dataset = load_dataset("trec")
```
3. Then one will get the following error message:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2556, in load_dataset
builder_instance = load_dataset_builder(
File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2265, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 371, in __init__
self.config, self.config_id = self._create_builder_config(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 620, in _create_builder_config
builder_config._resolve_data_files(
File "/opt/conda/lib/python3.10/site-packages/datasets/builder.py", line 211, in _resolve_data_files
self.data_files = self.data_files.resolve(base_path, download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 799, in resolve
out[key] = data_files_patterns_list.resolve(base_path, download_config)
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 752, in resolve
resolve_pattern(
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 393, in resolve_pattern
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find 'hf://datasets/trec@65752bf53af25bc935a0dce92fb5b6c930728450/default/train/0000.parquet' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.geoparquet', '.gpq', '.arrow', '.txt', '.tar', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
```
4. Similar issue also found for the following code:
```
dataset = load_dataset("sst", "default")
```
### Expected behavior
If the dataset is loaded correctly, one will have:
```
>>> print(dataset)
DatasetDict({
train: Dataset({
features: ['text', 'coarse_label', 'fine_label'],
num_rows: 5452
})
test: Dataset({
features: ['text', 'coarse_label', 'fine_label'],
num_rows: 500
})
})
>>>
```
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.2.0-35-generic-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.1
- Pandas version: 2.2.1
- `fsspec` version: 2023.12.2 | {
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] | open | false | null | [] | null | [] | 2024-04-01T13:18:47 | 2024-04-01T13:36:58 | null | NONE | null | null | null | ### Feature request
Hi thanks for the library! I would like to have a huggingface Dataset, and one of its column is custom (non-serializable) Python objects. For example, a minimal code:
```
class MyClass:
pass
dataset = datasets.Dataset.from_list([
dict(a=MyClass(), b='hello'),
])
```
It gives error:
```
ArrowInvalid: Could not convert <__main__.MyClass object at 0x7a852830d050> with type MyClass: did not recognize Python value type when inferring an Arrow data type
```
I guess it is because Dataset forces to convert everything into arrow format. However, is there any ways to make the scenario work? Thanks!
### Motivation
(see above)
### Your contribution
Yes, I am happy to PR!
Cross-posted: https://discuss.huggingface.co/t/datasets-with-custom-python-objects/79050?u=fzyzcjy
EDIT: possibly related https://github.com/huggingface/datasets/issues/5766 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6767). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005526 / 0.011353 (-0.005827) | 0.003839 / 0.011008 (-0.007169) | 0.064027 / 0.038508 (0.025519) | 0.032316 / 0.023109 (0.009206) | 0.250707 / 0.275898 (-0.025191) | 0.269222 / 0.323480 (-0.054258) | 0.004335 / 0.007986 (-0.003651) | 0.002703 / 0.004328 (-0.001626) | 0.049621 / 0.004250 (0.045370) | 0.047499 / 0.037052 (0.010446) | 0.262362 / 0.258489 (0.003873) | 0.292765 / 0.293841 (-0.001076) | 0.028661 / 0.128546 (-0.099885) | 0.010835 / 0.075646 (-0.064811) | 0.208910 / 0.419271 (-0.210362) | 0.036624 / 0.043533 (-0.006909) | 0.247448 / 0.255139 (-0.007691) | 0.270593 / 0.283200 (-0.012607) | 0.018988 / 0.141683 (-0.122695) | 1.141224 / 1.452155 (-0.310931) | 1.204944 / 1.492716 (-0.287772) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096324 / 0.018006 (0.078318) | 0.292495 / 0.000490 (0.292006) | 0.000232 / 0.000200 (0.000032) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018379 / 0.037411 (-0.019032) | 0.065216 / 0.014526 (0.050690) | 0.074071 / 0.176557 (-0.102486) | 0.120793 / 0.737135 (-0.616343) | 0.075882 / 0.296338 (-0.220456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286354 / 0.215209 (0.071145) | 2.800766 / 2.077655 (0.723111) | 1.474126 / 1.504120 (-0.029994) | 1.358232 / 1.541195 (-0.182963) | 1.400639 / 1.468490 (-0.067851) | 0.578354 / 4.584777 (-4.006423) | 2.454441 / 3.745712 (-1.291271) | 2.927003 / 5.269862 (-2.342859) | 1.826127 / 4.565676 (-2.739550) | 0.063049 / 0.424275 (-0.361226) | 0.005010 / 0.007607 (-0.002597) | 0.342174 / 0.226044 (0.116129) | 3.415900 / 2.268929 (1.146971) | 1.854096 / 55.444624 (-53.590528) | 1.568626 / 6.876477 (-5.307851) | 1.660138 / 2.142072 (-0.481934) | 0.664059 / 4.805227 (-4.141168) | 0.120496 / 6.500664 (-6.380168) | 0.044664 / 0.075469 (-0.030805) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988434 / 1.841788 (-0.853353) | 12.525563 / 8.074308 (4.451255) | 10.016862 / 10.191392 (-0.174530) | 0.134043 / 0.680424 (-0.546381) | 0.014349 / 0.534201 (-0.519852) | 0.287173 / 0.579283 (-0.292110) | 0.266499 / 0.434364 (-0.167865) | 0.325425 / 0.540337 (-0.214912) | 0.418772 / 1.386936 (-0.968164) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005675 / 0.011353 (-0.005678) | 0.004238 / 0.011008 (-0.006770) | 0.051048 / 0.038508 (0.012540) | 0.033428 / 0.023109 (0.010319) | 0.283406 / 0.275898 (0.007508) | 0.309321 / 0.323480 (-0.014159) | 0.004354 / 0.007986 (-0.003631) | 0.003101 / 0.004328 (-0.001228) | 0.049369 / 0.004250 (0.045119) | 0.043252 / 0.037052 (0.006200) | 0.293097 / 0.258489 (0.034608) | 0.324392 / 0.293841 (0.030551) | 0.030524 / 0.128546 (-0.098022) | 0.010977 / 0.075646 (-0.064669) | 0.058546 / 0.419271 (-0.360726) | 0.033295 / 0.043533 (-0.010238) | 0.284929 / 0.255139 (0.029790) | 0.302925 / 0.283200 (0.019726) | 0.018586 / 0.141683 (-0.123097) | 1.156552 / 1.452155 (-0.295602) | 1.208856 / 1.492716 (-0.283860) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096938 / 0.018006 (0.078932) | 0.305375 / 0.000490 (0.304886) | 0.000227 / 0.000200 (0.000027) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022658 / 0.037411 (-0.014754) | 0.078125 / 0.014526 (0.063599) | 0.087892 / 0.176557 (-0.088665) | 0.127745 / 0.737135 (-0.609390) | 0.089806 / 0.296338 (-0.206533) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292434 / 0.215209 (0.077225) | 2.862329 / 2.077655 (0.784674) | 1.607948 / 1.504120 (0.103828) | 1.487179 / 1.541195 (-0.054016) | 1.542234 / 1.468490 (0.073744) | 0.579446 / 4.584777 (-4.005331) | 2.478549 / 3.745712 (-1.267163) | 2.923493 / 5.269862 (-2.346369) | 1.833161 / 4.565676 (-2.732515) | 0.064289 / 0.424275 (-0.359986) | 0.005638 / 0.007607 (-0.001969) | 0.350111 / 0.226044 (0.124067) | 3.436035 / 2.268929 (1.167107) | 1.970592 / 55.444624 (-53.474032) | 1.717474 / 6.876477 (-5.159002) | 1.753150 / 2.142072 (-0.388922) | 0.660495 / 4.805227 (-4.144732) | 0.119302 / 6.500664 (-6.381362) | 0.042633 / 0.075469 (-0.032836) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.018761 / 1.841788 (-0.823027) | 12.859834 / 8.074308 (4.785525) | 10.547789 / 10.191392 (0.356397) | 0.131986 / 0.680424 (-0.548438) | 0.016469 / 0.534201 (-0.517732) | 0.288585 / 0.579283 (-0.290698) | 0.270499 / 0.434364 (-0.163865) | 0.325801 / 0.540337 (-0.214537) | 0.416551 / 1.386936 (-0.970385) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7599f15537b094bfd18de5af7bb2a482c06d7a0e \"CML watermark\")\n"
] | 2024-03-31T16:13:37 | 2024-04-02T14:14:02 | 2024-04-02T14:01:18 | CONTRIBUTOR | null | 0 | {
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"merged_at": "2024-04-02T14:01:18Z",
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} | Fixed the issue #6755 on the typo mistake | {
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