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https://github.com/huggingface/datasets/issues/4124
Image decoding often fails when transforming Image datasets
@albertvillanova Thanks for letting me know this is intended behavior. The docs are severely lacking on this, if I hadn't posted this here I would have never found out how I'm actually supposed to modify images in a Dataset object.
## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0
40
Image decoding often fails when transforming Image datasets ## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0 @albertvillanova Thanks for letting me know this is intended behavior. The docs are severely lacking on this, if I hadn't posted this here I would have never found out how I'm actually supposed to modify images in a Dataset object.
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0.023265332, 0.4771605432, 0.1239142567, -0.2152631283, 0.2992173135, 0.0961944684 ]
https://github.com/huggingface/datasets/issues/4124
Image decoding often fails when transforming Image datasets
@albertvillanova Secondly if you check the error message it shows that around 1999 images were successfully created, I'm pretty sure some of them were also flipped during the process. Back to my main contention, sometimes the decoding takes place other times it fails. I suppose to run `map` on any dataset all the examples should be invoked even if on some of them we end up doing nothing, is that right?
## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0
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Image decoding often fails when transforming Image datasets ## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0 @albertvillanova Secondly if you check the error message it shows that around 1999 images were successfully created, I'm pretty sure some of them were also flipped during the process. Back to my main contention, sometimes the decoding takes place other times it fails. I suppose to run `map` on any dataset all the examples should be invoked even if on some of them we end up doing nothing, is that right?
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https://github.com/huggingface/datasets/issues/4124
Image decoding often fails when transforming Image datasets
Hi @RafayAK! I've opened a PR with the fix, which adds a fallback to reattempt casting to PyArrow format with a more robust (but more expensive) procedure if the first attempt fails. Feel free to test it by installing `datasets` from the PR branch with the following command: ``` pip install git+https://github.com/huggingface/datasets.git@fix-4124 ```
## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0
53
Image decoding often fails when transforming Image datasets ## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0 Hi @RafayAK! I've opened a PR with the fix, which adds a fallback to reattempt casting to PyArrow format with a more robust (but more expensive) procedure if the first attempt fails. Feel free to test it by installing `datasets` from the PR branch with the following command: ``` pip install git+https://github.com/huggingface/datasets.git@fix-4124 ```
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0.023265332, 0.4771605432, 0.1239142567, -0.2152631283, 0.2992173135, 0.0961944684 ]
https://github.com/huggingface/datasets/issues/4124
Image decoding often fails when transforming Image datasets
@mariosasko Thanks a lot for looking into this, now the `map` function at least behaves as one would expect a function to behave. Looking forward to exploring Hugging Face more and even contributing πŸ˜ƒ. ```bash $ conda list | grep datasets datasets 2.0.1.dev0 pypi_0 pypi ``` ```python def preprocess_data(dataset): """ Helper funtion to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping functions that transforms some of the image up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` The output now show the function was applied successfully: ``` bash /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Downloading builder script: 5.61kB [00:00, 3.16MB/s] Downloading metadata: 4.21kB [00:00, 2.56MB/s] Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 10000/10000 [00:01<00:00, 5149.15ex/s] ```
## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0
245
Image decoding often fails when transforming Image datasets ## Describe the bug When transforming/modifying images in an image dataset using the `map` function the PIL images often fail to decode in time for the image transforms, causing errors. Using a debugger it is easy to see what the problem is, the Image decode invocation does not take place and the resulting image passed around is still raw bytes: ``` [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00 \x00\x00\x00 \x08\x02\x00\x00\x00\xfc\x18\xed\xa3\x00\x00\x08\x02IDATx\x9cEVIs[\xc7\x11\xeemf\xde\x82\x8d\x80\x08\x89"\xb5V\\\xb6\x94(\xe5\x9f\x90\xca5\x7f$\xa7T\xe5\x9f&9\xd9\x8a\\.\xdb\xa4$J\xa4\x00\x02x\xc0{\xb3t\xe7\x00\xca\x99\xd3\\f\xba\xba\xbf\xa5?|\xfa\xf4\xa2\xeb\xba\xedv\xa3f^\xf8\xd5\x0bY\xb6\x10\xb3\xaaDq\xcd\x83\x87\xdf5\xf3gZ\x1a\x04\x0f\xa0fp\xfa\xe0\xd4\x07?\x9dN\xc4\xb1\x99\xfd\xf2\xcb/\x97\x97\x97H\xa2\xaaf\x16\x82\xaf\xeb\xca{\xbf\xd9l.\xdf\x7f\xfa\xcb_\xff&\x88\x08\x00\x80H\xc0\x80@.;\x0f\x8c@#v\xe3\xe5\xfc\xd1\x9f\xee6q\xbf\xdf\xa6\x14\'\x93\xf1\xc3\xe5\xe3\xd1x\x14c\x8c1\xa5\x1c\x9dsM\xd3\xb4\xed\x08\x89SJ)\xa5\xedv\xbb^\xafNO\x97D\x84Hf .... ``` ## Steps to reproduce the bug ```python from datasets import load_dataset, Dataset import numpy as np # seeded NumPy random number generator for reprodducinble results. rng = np.random.default_rng(seed=0) test_dataset = load_dataset('cifar100', split="test") def preprocess_data(dataset): """ Helper function to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping function that transforms some of the images up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: the probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` ## Expected results The dataset should be transformed without problems. ## Actual results ``` /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 20%|β–ˆβ–‰ | 1999/10000 [00:00<00:01, 5560.44ex/s] Traceback (most recent call last): File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2326, in _map_single writer.write(example) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 441, in write self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py", line 55, in <module> my_test = my_test.map(generate_flipped_data) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1953, in map return self._map_single( File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 519, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 486, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/fingerprint.py", line 458, in wrapper out = func(self, *args, **kwargs) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2360, in _map_single writer.finalize() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 522, in finalize self.write_examples_on_file() File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 399, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 492, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 230, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/rafay/anaconda3/envs/pytorch_new/lib/python3.10/site-packages/datasets/arrow_writer.py", line 185, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert <PIL.Image.Image image mode=RGB size=32x32 at 0x7F56AEE61DE0> with type Image: did not recognize Python value type when inferring an Arrow data type Process finished with exit code 1 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux(Fedora 35) - Python version: 3.10 - PyArrow version: 7.0.0 @mariosasko Thanks a lot for looking into this, now the `map` function at least behaves as one would expect a function to behave. Looking forward to exploring Hugging Face more and even contributing πŸ˜ƒ. ```bash $ conda list | grep datasets datasets 2.0.1.dev0 pypi_0 pypi ``` ```python def preprocess_data(dataset): """ Helper funtion to pre-process HuggingFace Cifar-100 Dataset to remove fine_label and coarse_label columns and add is_flipped column Args: dataset: HuggingFace CIFAR-100 Dataset Object Returns: new_dataset: A Dataset object with "img" and "is_flipped" columns only """ # remove fine_label and coarse_label columns new_dataset = dataset.remove_columns(['fine_label', 'coarse_label']) # add the column for is_flipped new_dataset = new_dataset.add_column(name="is_flipped", column=np.zeros((len(new_dataset)), dtype=np.uint8)) return new_dataset def generate_flipped_data(example, p=0.5): """ A Dataset mapping functions that transforms some of the image up-side-down. If the probability value (p) is 0.5 approximately half the images will be flipped upside-down Args: example: An example from the dataset containing a Python dictionary with "img" and "is_flipped" key-value pair p: probability of flipping the image up-side-down, Default 0.5 Returns: example: A Dataset object """ # example['img'] = example['img'] if rng.random() > p: # the flip the image and set is_flipped column to 1 example['img'] = example['img'].transpose( 1) # ImageOps.flip(example['img']) #example['img'].transpose(Image.FLIP_TOP_BOTTOM) example['is_flipped'] = 1 return example my_test = preprocess_data(test_dataset) my_test = my_test.map(generate_flipped_data) ``` The output now show the function was applied successfully: ``` bash /home/rafay/anaconda3/envs/pytorch_new/bin/python /home/rafay/Documents/you_only_live_once/upside_down_detector/create_dataset.py Downloading builder script: 5.61kB [00:00, 3.16MB/s] Downloading metadata: 4.21kB [00:00, 2.56MB/s] Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) Reusing dataset cifar100 (/home/rafay/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142) 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 10000/10000 [00:01<00:00, 5149.15ex/s] ```
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0.023265332, 0.4771605432, 0.1239142567, -0.2152631283, 0.2992173135, 0.0961944684 ]
https://github.com/huggingface/datasets/issues/4123
Building C4 takes forever
Hi @StellaAthena, thanks for reporting. Please note, that our `datasets` library performs several operations in order to load a dataset, among them: - it downloads all the required files: for C4 "en", 378.69 GB of JSON GZIPped files - it parses their content to generate the dataset - it caches the dataset in an Arrow file: for C4 "en", this file size is 1.87 TB - it memory-maps the Arrow file If it suits your use case, you might load this dataset in streaming mode: - no Arrow file is generated - you can iterate over elements immediately (no need to wait to download all the entire files) ```python In [45]: from datasets import load_dataset ...: ds = load_dataset("c4", "en", split="train", streaming=True) ...: for item in ds: ...: print(item) ...: break ...: {'text': 'Beginners BBQ Class Taking Place in Missoula!\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z', 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/'} ``` I hope this is useful for your use case.
## Describe the bug C4-en is a 300 GB dataset. However, when I try to download it through the hub it takes over _six hours_ to generate the train/test split from the downloaded files. This is an absurd amount of time and an unnecessary waste of resources. ## Steps to reproduce the bug ```python c4 = datasets.load("c4", "en") ``` ## Expected results I would like to be able to download pre-split data. ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.13.0-35-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
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Building C4 takes forever ## Describe the bug C4-en is a 300 GB dataset. However, when I try to download it through the hub it takes over _six hours_ to generate the train/test split from the downloaded files. This is an absurd amount of time and an unnecessary waste of resources. ## Steps to reproduce the bug ```python c4 = datasets.load("c4", "en") ``` ## Expected results I would like to be able to download pre-split data. ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.13.0-35-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi @StellaAthena, thanks for reporting. Please note, that our `datasets` library performs several operations in order to load a dataset, among them: - it downloads all the required files: for C4 "en", 378.69 GB of JSON GZIPped files - it parses their content to generate the dataset - it caches the dataset in an Arrow file: for C4 "en", this file size is 1.87 TB - it memory-maps the Arrow file If it suits your use case, you might load this dataset in streaming mode: - no Arrow file is generated - you can iterate over elements immediately (no need to wait to download all the entire files) ```python In [45]: from datasets import load_dataset ...: ds = load_dataset("c4", "en", split="train", streaming=True) ...: for item in ds: ...: print(item) ...: break ...: {'text': 'Beginners BBQ Class Taking Place in Missoula!\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z', 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/'} ``` I hope this is useful for your use case.
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https://github.com/huggingface/datasets/issues/4122
medical_dialog zh has very slow _generate_examples
Hi @nbroad1881, thanks for reporting. Let me have a look to try to improve its performance.
## Describe the bug After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours. ## Steps to reproduce the bug The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud. ```python file_ids = [ "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E", "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX", "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc", "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J", "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu", "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP", "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c", "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S", "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU", "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD", "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH", ] for i in file_ids: url = f"https://drive.google.com/uc?id={i}" !gdown $url from datasets import load_dataset ds = load_dataset("medical_dialog", "zh", data_dir="./") ``` ## Expected results Faster load time ## Actual results `Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 @vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten
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medical_dialog zh has very slow _generate_examples ## Describe the bug After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours. ## Steps to reproduce the bug The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud. ```python file_ids = [ "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E", "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX", "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc", "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J", "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu", "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP", "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c", "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S", "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU", "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD", "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH", ] for i in file_ids: url = f"https://drive.google.com/uc?id={i}" !gdown $url from datasets import load_dataset ds = load_dataset("medical_dialog", "zh", data_dir="./") ``` ## Expected results Faster load time ## Actual results `Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 @vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten Hi @nbroad1881, thanks for reporting. Let me have a look to try to improve its performance.
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https://github.com/huggingface/datasets/issues/4122
medical_dialog zh has very slow _generate_examples
Thanks @nbroad1881 for reporting! I don't recall it taking so long. I will also have a look at this. @albertvillanova please let me know if I am doing something unnecessary or time consuming.
## Describe the bug After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours. ## Steps to reproduce the bug The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud. ```python file_ids = [ "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E", "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX", "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc", "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J", "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu", "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP", "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c", "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S", "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU", "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD", "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH", ] for i in file_ids: url = f"https://drive.google.com/uc?id={i}" !gdown $url from datasets import load_dataset ds = load_dataset("medical_dialog", "zh", data_dir="./") ``` ## Expected results Faster load time ## Actual results `Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 @vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten
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medical_dialog zh has very slow _generate_examples ## Describe the bug After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours. ## Steps to reproduce the bug The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud. ```python file_ids = [ "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E", "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX", "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc", "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J", "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu", "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP", "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c", "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S", "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU", "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD", "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH", ] for i in file_ids: url = f"https://drive.google.com/uc?id={i}" !gdown $url from datasets import load_dataset ds = load_dataset("medical_dialog", "zh", data_dir="./") ``` ## Expected results Faster load time ## Actual results `Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 @vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten Thanks @nbroad1881 for reporting! I don't recall it taking so long. I will also have a look at this. @albertvillanova please let me know if I am doing something unnecessary or time consuming.
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https://github.com/huggingface/datasets/issues/4122
medical_dialog zh has very slow _generate_examples
Hi @nbroad1881 and @vrindaprabhu, As a workaround for the performance of the parsing of the raw data files (this could be addressed in a subsequent PR), I have found that there are also processed data files, that do not require parsing. I have added these as new configurations `processed.en` and `processed.zh`: ```python ds = load_dataset("medical_dialog", "processed.zh") ```
## Describe the bug After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours. ## Steps to reproduce the bug The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud. ```python file_ids = [ "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E", "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX", "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc", "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J", "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu", "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP", "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c", "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S", "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU", "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD", "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH", ] for i in file_ids: url = f"https://drive.google.com/uc?id={i}" !gdown $url from datasets import load_dataset ds = load_dataset("medical_dialog", "zh", data_dir="./") ``` ## Expected results Faster load time ## Actual results `Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 @vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten
57
medical_dialog zh has very slow _generate_examples ## Describe the bug After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours. ## Steps to reproduce the bug The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud. ```python file_ids = [ "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E", "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX", "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc", "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J", "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu", "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP", "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c", "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S", "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU", "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD", "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH", ] for i in file_ids: url = f"https://drive.google.com/uc?id={i}" !gdown $url from datasets import load_dataset ds = load_dataset("medical_dialog", "zh", data_dir="./") ``` ## Expected results Faster load time ## Actual results `Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 @vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten Hi @nbroad1881 and @vrindaprabhu, As a workaround for the performance of the parsing of the raw data files (this could be addressed in a subsequent PR), I have found that there are also processed data files, that do not require parsing. I have added these as new configurations `processed.en` and `processed.zh`: ```python ds = load_dataset("medical_dialog", "processed.zh") ```
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https://github.com/huggingface/datasets/issues/4117
AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
Hi @arymbe, thanks for reporting. Unfortunately, I'm not able to reproduce your problem. Could you please write the complete stack trace? That way we will be able to see which package originates the exception.
## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance.
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AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance. Hi @arymbe, thanks for reporting. Unfortunately, I'm not able to reproduce your problem. Could you please write the complete stack trace? That way we will be able to see which package originates the exception.
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https://github.com/huggingface/datasets/issues/4117
AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
Hello, thank you for your fast replied. this is the complete error that I got --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Input In [27], in <module> ----> 1 from datasets import load_dataset venv/lib/python3.8/site-packages/datasets/__init__.py:39, in <module> 37 from .arrow_dataset import Dataset, concatenate_datasets 38 from .arrow_reader import ReadInstruction ---> 39 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder 40 from .combine import interleave_datasets 41 from .dataset_dict import DatasetDict, IterableDatasetDict venv/lib/python3.8/site-packages/datasets/builder.py:40, in <module> 32 from .arrow_reader import ( 33 HF_GCP_BASE_URL, 34 ArrowReader, (...) 37 ReadInstruction, 38 ) 39 from .arrow_writer import ArrowWriter, BeamWriter ---> 40 from .data_files import DataFilesDict, sanitize_patterns 41 from .dataset_dict import DatasetDict, IterableDatasetDict 42 from .features import Features venv/lib/python3.8/site-packages/datasets/data_files.py:297, in <module> 292 except FileNotFoundError: 293 raise FileNotFoundError(f"The directory at {base_path} doesn't contain any data file") from None 296 def _resolve_single_pattern_in_dataset_repository( --> 297 dataset_info: huggingface_hub.hf_api.DatasetInfo, 298 pattern: str, 299 allowed_extensions: Optional[list] = None, 300 ) -> List[PurePath]: 301 data_files_ignore = FILES_TO_IGNORE 302 fs = HfFileSystem(repo_info=dataset_info) AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance.
169
AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance. Hello, thank you for your fast replied. this is the complete error that I got --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Input In [27], in <module> ----> 1 from datasets import load_dataset venv/lib/python3.8/site-packages/datasets/__init__.py:39, in <module> 37 from .arrow_dataset import Dataset, concatenate_datasets 38 from .arrow_reader import ReadInstruction ---> 39 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder 40 from .combine import interleave_datasets 41 from .dataset_dict import DatasetDict, IterableDatasetDict venv/lib/python3.8/site-packages/datasets/builder.py:40, in <module> 32 from .arrow_reader import ( 33 HF_GCP_BASE_URL, 34 ArrowReader, (...) 37 ReadInstruction, 38 ) 39 from .arrow_writer import ArrowWriter, BeamWriter ---> 40 from .data_files import DataFilesDict, sanitize_patterns 41 from .dataset_dict import DatasetDict, IterableDatasetDict 42 from .features import Features venv/lib/python3.8/site-packages/datasets/data_files.py:297, in <module> 292 except FileNotFoundError: 293 raise FileNotFoundError(f"The directory at {base_path} doesn't contain any data file") from None 296 def _resolve_single_pattern_in_dataset_repository( --> 297 dataset_info: huggingface_hub.hf_api.DatasetInfo, 298 pattern: str, 299 allowed_extensions: Optional[list] = None, 300 ) -> List[PurePath]: 301 data_files_ignore = FILES_TO_IGNORE 302 fs = HfFileSystem(repo_info=dataset_info) AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
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-0.0257946104, 0.4158696234, -0.0157663189, -0.0044162716, 0.4041215181, -0.0740864575 ]
https://github.com/huggingface/datasets/issues/4117
AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
This is weird... It is long ago that the package `huggingface_hub` has a submodule called `hf_api`. Maybe you have a problem with your installed `huggingface_hub`... Could you please try to update it? ```shell pip install -U huggingface_hub ```
## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance.
38
AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance. This is weird... It is long ago that the package `huggingface_hub` has a submodule called `hf_api`. Maybe you have a problem with your installed `huggingface_hub`... Could you please try to update it? ```shell pip install -U huggingface_hub ```
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https://github.com/huggingface/datasets/issues/4117
AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
Yap, I've updated several times. Then, I've tried numeral combination of datasets and huggingface_hub versions. However, I think your point is right that there is a problem with my huggingface_hub installation. I'll try another way to find the solution. I'll update it later when I get the solution. Thank you :)
## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance.
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AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance. Yap, I've updated several times. Then, I've tried numeral combination of datasets and huggingface_hub versions. However, I think your point is right that there is a problem with my huggingface_hub installation. I'll try another way to find the solution. I'll update it later when I get the solution. Thank you :)
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https://github.com/huggingface/datasets/issues/4117
AttributeError: module 'huggingface_hub' has no attribute 'hf_api'
I'm sorry I can't reproduce your problem. Maybe you could try to create a new Python virtual environment and install all dependencies there from scratch. You can use either: - Python venv: https://docs.python.org/3/library/venv.html - or conda venv (if you are using conda): https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance.
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AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Describe the bug Could you help me please. I got this following error. AttributeError: module 'huggingface_hub' has no attribute 'hf_api' ## Steps to reproduce the bug when I imported the datasets # Sample code to reproduce the bug from datasets import list_datasets, load_dataset, list_metrics, load_metric ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: macOS-12.3-x86_64-i386-64bit - Python version: 3.8.9 - PyArrow version: 7.0.0 - Pandas version: 1.3.5 - Huggingface-hub: 0.5.0 - Transformers: 4.18.0 Thank you in advance. I'm sorry I can't reproduce your problem. Maybe you could try to create a new Python virtual environment and install all dependencies there from scratch. You can use either: - Python venv: https://docs.python.org/3/library/venv.html - or conda venv (if you are using conda): https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
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https://github.com/huggingface/datasets/issues/4115
ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
Maybe it would be nice to ignore private dirs like this one (ones starting with `.`) by default. CC @mariosasko
**Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually
20
ImageFolder add option to ignore some folders like '.ipynb_checkpoints' **Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually Maybe it would be nice to ignore private dirs like this one (ones starting with `.`) by default. CC @mariosasko
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https://github.com/huggingface/datasets/issues/4115
ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
Maybe we can add a `ignore_hidden_files` flag to the builder configs of our packaged loaders (to be consistent across all of them), wdyt @lhoestq @albertvillanova?
**Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually
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ImageFolder add option to ignore some folders like '.ipynb_checkpoints' **Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually Maybe we can add a `ignore_hidden_files` flag to the builder configs of our packaged loaders (to be consistent across all of them), wdyt @lhoestq @albertvillanova?
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https://github.com/huggingface/datasets/issues/4115
ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
I think they should always ignore them actually ! Not sure if adding a flag would be helpful
**Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually
18
ImageFolder add option to ignore some folders like '.ipynb_checkpoints' **Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually I think they should always ignore them actually ! Not sure if adding a flag would be helpful
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https://github.com/huggingface/datasets/issues/4115
ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
@lhoestq But what if the user explicitly requests those files via regex? `glob.glob` ignores hidden files (files starting with ".") by default unless they are explicitly requested, but fsspec's `glob` doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?
**Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually
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ImageFolder add option to ignore some folders like '.ipynb_checkpoints' **Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually @lhoestq But what if the user explicitly requests those files via regex? `glob.glob` ignores hidden files (files starting with ".") by default unless they are explicitly requested, but fsspec's `glob` doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?
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https://github.com/huggingface/datasets/issues/4115
ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
> @lhoestq But what if the user explicitly requests those files via regex? Usually hidden files are meant to be ignored. If they are data files, they must be placed outside a hidden directory in the first place right ? I think it's more sensible to explain this than adding a flag. > glob.glob ignores hidden files (files starting with ".") by default unless they are explicitly requested, but fsspec's glob doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo? After globbing using `fsspec`, we already ignore files that start with a `.` in `_resolve_single_pattern_locally` and `_resolve_single_pattern_in_dataset_repository`, I guess we can just account for parent directories as well ? We could open an issue on `fsspec` but I think they won't change this since it's an important breaking change for them.
**Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually
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ImageFolder add option to ignore some folders like '.ipynb_checkpoints' **Is your feature request related to a problem? Please describe.** I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large. **Describe the solution you'd like** maybe have an option `ignore` or something .gitignore style `dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")` **Describe alternatives you've considered** Could filter out manually > @lhoestq But what if the user explicitly requests those files via regex? Usually hidden files are meant to be ignored. If they are data files, they must be placed outside a hidden directory in the first place right ? I think it's more sensible to explain this than adding a flag. > glob.glob ignores hidden files (files starting with ".") by default unless they are explicitly requested, but fsspec's glob doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo? After globbing using `fsspec`, we already ignore files that start with a `.` in `_resolve_single_pattern_locally` and `_resolve_single_pattern_in_dataset_repository`, I guess we can just account for parent directories as well ? We could open an issue on `fsspec` but I think they won't change this since it's an important breaking change for them.
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https://github.com/huggingface/datasets/issues/4114
Allow downloading just some columns of a dataset
In the general case you can’t always reduce the quantity of data to download, since you can’t parse CSV or JSON data without downloading the whole files right ? ^^ However we could explore this case-by-case I guess
**Is your feature request related to a problem? Please describe.** Some people are interested in doing label analysis of a CV dataset without downloading all the images. Downloading the whole dataset does not always makes sense for this kind of use case **Describe the solution you'd like** Be able to just download some columns of a dataset, such as doing ```python load_dataset("huggan/wikiart",columns=["artist", "genre"]) ``` Although this might make things a bit complicated in terms of local caching of datasets.
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Allow downloading just some columns of a dataset **Is your feature request related to a problem? Please describe.** Some people are interested in doing label analysis of a CV dataset without downloading all the images. Downloading the whole dataset does not always makes sense for this kind of use case **Describe the solution you'd like** Be able to just download some columns of a dataset, such as doing ```python load_dataset("huggan/wikiart",columns=["artist", "genre"]) ``` Although this might make things a bit complicated in terms of local caching of datasets. In the general case you can’t always reduce the quantity of data to download, since you can’t parse CSV or JSON data without downloading the whole files right ? ^^ However we could explore this case-by-case I guess
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https://github.com/huggingface/datasets/issues/4114
Allow downloading just some columns of a dataset
Actually for csv pandas has `usecols` which allows loading a subset of columns in a more efficient way afaik, but yes, you're right this might be more complex than I thought.
**Is your feature request related to a problem? Please describe.** Some people are interested in doing label analysis of a CV dataset without downloading all the images. Downloading the whole dataset does not always makes sense for this kind of use case **Describe the solution you'd like** Be able to just download some columns of a dataset, such as doing ```python load_dataset("huggan/wikiart",columns=["artist", "genre"]) ``` Although this might make things a bit complicated in terms of local caching of datasets.
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Allow downloading just some columns of a dataset **Is your feature request related to a problem? Please describe.** Some people are interested in doing label analysis of a CV dataset without downloading all the images. Downloading the whole dataset does not always makes sense for this kind of use case **Describe the solution you'd like** Be able to just download some columns of a dataset, such as doing ```python load_dataset("huggan/wikiart",columns=["artist", "genre"]) ``` Although this might make things a bit complicated in terms of local caching of datasets. Actually for csv pandas has `usecols` which allows loading a subset of columns in a more efficient way afaik, but yes, you're right this might be more complex than I thought.
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https://github.com/huggingface/datasets/issues/4112
ImageFolder with Grayscale images dataset
Hi! Replacing: ```python transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") ``` with: ```python def transform_func(examples): examples["image"] = [transforms(img).to("cuda") for img in examples["image"]] return examples transformed_dataset = dataset.with_transform(transform_func) ``` should fix the issue. `datasets` doesn't support chaining of transforms (you can think of `set_format`/`with_format` as a predefined transform func for `set_transform`/`with_transforms`), so the last transform (in your case, `set_format`) takes precedence over the previous ones (in your case `with_format`). And the PyTorch formatter is not supported by the Image feature, hence the error (adding support for that is on our short-term roadmap).
Hi, I'm facing a problem with a grayscale images dataset I have uploaded [here](https://huggingface.co/datasets/ChainYo/rvl-cdip) (RVL-CDIP) I'm getting an error while I want to use images for training a model with PyTorch DataLoader. Here is the full traceback: ```bash AttributeError: Caught AttributeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1765, in __getitem__ return self._getitem( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1750, in _getitem formatted_output = format_table( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 532, in format_table return formatter(pa_table, query_type=query_type) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 281, in __call__ return self.format_row(pa_table) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 58, in format_row return self.recursive_tensorize(row) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 54, in recursive_tensorize return map_nested(self._recursive_tensorize, data_struct, map_list=False) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 314, in map_nested mapped = [ File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 315, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in _single_map_nested return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in <dictcomp> return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 251, in _single_map_nested return function(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 51, in _recursive_tensorize return self._tensorize(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 38, in _tensorize if np.issubdtype(value.dtype, np.integer): AttributeError: 'bytes' object has no attribute 'dtype' ``` I don't really understand why the image is still a bytes object while I used transformations on it. Here the code I used to upload the dataset (and it worked well): ```python train_dataset = load_dataset("imagefolder", data_dir="data/train") train_dataset = train_dataset["train"] test_dataset = load_dataset("imagefolder", data_dir="data/test") test_dataset = test_dataset["train"] val_dataset = load_dataset("imagefolder", data_dir="data/val") val_dataset = val_dataset["train"] dataset = DatasetDict({ "train": train_dataset, "val": val_dataset, "test": test_dataset }) dataset.push_to_hub("ChainYo/rvl-cdip") ``` Now here is the code I am using to get the dataset and prepare it for training: ```python img_size = 512 batch_size = 128 normalize = [(0.5), (0.5)] data_dir = "ChainYo/rvl-cdip" dataset = load_dataset(data_dir, split="train") transforms = transforms.Compose([ transforms.Resize(img_size), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize(*normalize) ]) transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") train_dataloader = torch.utils.data.DataLoader( transformed_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True ) ``` But this get me the error above. I don't understand why it's doing this kind of weird thing? Do I need to map something on the dataset? Something like this: ```python labels = dataset.features["label"].names num_labels = dataset.features["label"].num_classes def preprocess_data(examples): images = [ex.convert("RGB") for ex in examples["image"]] labels = [ex for ex in examples["label"]] return {"images": images, "labels": labels} features = Features({ "images": Image(decode=True, id=None), "labels": ClassLabel(num_classes=num_labels, names=labels) }) decoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, batched=True, batch_size=100) ```
89
ImageFolder with Grayscale images dataset Hi, I'm facing a problem with a grayscale images dataset I have uploaded [here](https://huggingface.co/datasets/ChainYo/rvl-cdip) (RVL-CDIP) I'm getting an error while I want to use images for training a model with PyTorch DataLoader. Here is the full traceback: ```bash AttributeError: Caught AttributeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1765, in __getitem__ return self._getitem( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1750, in _getitem formatted_output = format_table( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 532, in format_table return formatter(pa_table, query_type=query_type) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 281, in __call__ return self.format_row(pa_table) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 58, in format_row return self.recursive_tensorize(row) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 54, in recursive_tensorize return map_nested(self._recursive_tensorize, data_struct, map_list=False) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 314, in map_nested mapped = [ File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 315, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in _single_map_nested return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in <dictcomp> return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 251, in _single_map_nested return function(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 51, in _recursive_tensorize return self._tensorize(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 38, in _tensorize if np.issubdtype(value.dtype, np.integer): AttributeError: 'bytes' object has no attribute 'dtype' ``` I don't really understand why the image is still a bytes object while I used transformations on it. Here the code I used to upload the dataset (and it worked well): ```python train_dataset = load_dataset("imagefolder", data_dir="data/train") train_dataset = train_dataset["train"] test_dataset = load_dataset("imagefolder", data_dir="data/test") test_dataset = test_dataset["train"] val_dataset = load_dataset("imagefolder", data_dir="data/val") val_dataset = val_dataset["train"] dataset = DatasetDict({ "train": train_dataset, "val": val_dataset, "test": test_dataset }) dataset.push_to_hub("ChainYo/rvl-cdip") ``` Now here is the code I am using to get the dataset and prepare it for training: ```python img_size = 512 batch_size = 128 normalize = [(0.5), (0.5)] data_dir = "ChainYo/rvl-cdip" dataset = load_dataset(data_dir, split="train") transforms = transforms.Compose([ transforms.Resize(img_size), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize(*normalize) ]) transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") train_dataloader = torch.utils.data.DataLoader( transformed_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True ) ``` But this get me the error above. I don't understand why it's doing this kind of weird thing? Do I need to map something on the dataset? Something like this: ```python labels = dataset.features["label"].names num_labels = dataset.features["label"].num_classes def preprocess_data(examples): images = [ex.convert("RGB") for ex in examples["image"]] labels = [ex for ex in examples["label"]] return {"images": images, "labels": labels} features = Features({ "images": Image(decode=True, id=None), "labels": ClassLabel(num_classes=num_labels, names=labels) }) decoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, batched=True, batch_size=100) ``` Hi! Replacing: ```python transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") ``` with: ```python def transform_func(examples): examples["image"] = [transforms(img).to("cuda") for img in examples["image"]] return examples transformed_dataset = dataset.with_transform(transform_func) ``` should fix the issue. `datasets` doesn't support chaining of transforms (you can think of `set_format`/`with_format` as a predefined transform func for `set_transform`/`with_transforms`), so the last transform (in your case, `set_format`) takes precedence over the previous ones (in your case `with_format`). And the PyTorch formatter is not supported by the Image feature, hence the error (adding support for that is on our short-term roadmap).
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https://github.com/huggingface/datasets/issues/4112
ImageFolder with Grayscale images dataset
Ok thanks a lot for the code snippet! I love the way `datasets` is easy to use but it made it really long to pre-process all the images (400.000 in my case) before training anything. `ImageFolder` from pytorch is faster in my case but force me to have the images on my local machine. I don't know how to speed up the process without switching to `ImageFolder` :smile:
Hi, I'm facing a problem with a grayscale images dataset I have uploaded [here](https://huggingface.co/datasets/ChainYo/rvl-cdip) (RVL-CDIP) I'm getting an error while I want to use images for training a model with PyTorch DataLoader. Here is the full traceback: ```bash AttributeError: Caught AttributeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1765, in __getitem__ return self._getitem( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1750, in _getitem formatted_output = format_table( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 532, in format_table return formatter(pa_table, query_type=query_type) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 281, in __call__ return self.format_row(pa_table) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 58, in format_row return self.recursive_tensorize(row) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 54, in recursive_tensorize return map_nested(self._recursive_tensorize, data_struct, map_list=False) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 314, in map_nested mapped = [ File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 315, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in _single_map_nested return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in <dictcomp> return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 251, in _single_map_nested return function(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 51, in _recursive_tensorize return self._tensorize(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 38, in _tensorize if np.issubdtype(value.dtype, np.integer): AttributeError: 'bytes' object has no attribute 'dtype' ``` I don't really understand why the image is still a bytes object while I used transformations on it. Here the code I used to upload the dataset (and it worked well): ```python train_dataset = load_dataset("imagefolder", data_dir="data/train") train_dataset = train_dataset["train"] test_dataset = load_dataset("imagefolder", data_dir="data/test") test_dataset = test_dataset["train"] val_dataset = load_dataset("imagefolder", data_dir="data/val") val_dataset = val_dataset["train"] dataset = DatasetDict({ "train": train_dataset, "val": val_dataset, "test": test_dataset }) dataset.push_to_hub("ChainYo/rvl-cdip") ``` Now here is the code I am using to get the dataset and prepare it for training: ```python img_size = 512 batch_size = 128 normalize = [(0.5), (0.5)] data_dir = "ChainYo/rvl-cdip" dataset = load_dataset(data_dir, split="train") transforms = transforms.Compose([ transforms.Resize(img_size), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize(*normalize) ]) transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") train_dataloader = torch.utils.data.DataLoader( transformed_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True ) ``` But this get me the error above. I don't understand why it's doing this kind of weird thing? Do I need to map something on the dataset? Something like this: ```python labels = dataset.features["label"].names num_labels = dataset.features["label"].num_classes def preprocess_data(examples): images = [ex.convert("RGB") for ex in examples["image"]] labels = [ex for ex in examples["label"]] return {"images": images, "labels": labels} features = Features({ "images": Image(decode=True, id=None), "labels": ClassLabel(num_classes=num_labels, names=labels) }) decoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, batched=True, batch_size=100) ```
68
ImageFolder with Grayscale images dataset Hi, I'm facing a problem with a grayscale images dataset I have uploaded [here](https://huggingface.co/datasets/ChainYo/rvl-cdip) (RVL-CDIP) I'm getting an error while I want to use images for training a model with PyTorch DataLoader. Here is the full traceback: ```bash AttributeError: Caught AttributeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1765, in __getitem__ return self._getitem( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1750, in _getitem formatted_output = format_table( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 532, in format_table return formatter(pa_table, query_type=query_type) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 281, in __call__ return self.format_row(pa_table) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 58, in format_row return self.recursive_tensorize(row) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 54, in recursive_tensorize return map_nested(self._recursive_tensorize, data_struct, map_list=False) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 314, in map_nested mapped = [ File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 315, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in _single_map_nested return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in <dictcomp> return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 251, in _single_map_nested return function(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 51, in _recursive_tensorize return self._tensorize(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 38, in _tensorize if np.issubdtype(value.dtype, np.integer): AttributeError: 'bytes' object has no attribute 'dtype' ``` I don't really understand why the image is still a bytes object while I used transformations on it. Here the code I used to upload the dataset (and it worked well): ```python train_dataset = load_dataset("imagefolder", data_dir="data/train") train_dataset = train_dataset["train"] test_dataset = load_dataset("imagefolder", data_dir="data/test") test_dataset = test_dataset["train"] val_dataset = load_dataset("imagefolder", data_dir="data/val") val_dataset = val_dataset["train"] dataset = DatasetDict({ "train": train_dataset, "val": val_dataset, "test": test_dataset }) dataset.push_to_hub("ChainYo/rvl-cdip") ``` Now here is the code I am using to get the dataset and prepare it for training: ```python img_size = 512 batch_size = 128 normalize = [(0.5), (0.5)] data_dir = "ChainYo/rvl-cdip" dataset = load_dataset(data_dir, split="train") transforms = transforms.Compose([ transforms.Resize(img_size), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize(*normalize) ]) transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") train_dataloader = torch.utils.data.DataLoader( transformed_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True ) ``` But this get me the error above. I don't understand why it's doing this kind of weird thing? Do I need to map something on the dataset? Something like this: ```python labels = dataset.features["label"].names num_labels = dataset.features["label"].num_classes def preprocess_data(examples): images = [ex.convert("RGB") for ex in examples["image"]] labels = [ex for ex in examples["label"]] return {"images": images, "labels": labels} features = Features({ "images": Image(decode=True, id=None), "labels": ClassLabel(num_classes=num_labels, names=labels) }) decoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, batched=True, batch_size=100) ``` Ok thanks a lot for the code snippet! I love the way `datasets` is easy to use but it made it really long to pre-process all the images (400.000 in my case) before training anything. `ImageFolder` from pytorch is faster in my case but force me to have the images on my local machine. I don't know how to speed up the process without switching to `ImageFolder` :smile:
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https://github.com/huggingface/datasets/issues/4112
ImageFolder with Grayscale images dataset
You can pass `ignore_verifications=True` in `load_dataset` to skip checksum verification, which takes a lot of time if the number of files is large. We will consider making this the default behavior.
Hi, I'm facing a problem with a grayscale images dataset I have uploaded [here](https://huggingface.co/datasets/ChainYo/rvl-cdip) (RVL-CDIP) I'm getting an error while I want to use images for training a model with PyTorch DataLoader. Here is the full traceback: ```bash AttributeError: Caught AttributeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1765, in __getitem__ return self._getitem( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1750, in _getitem formatted_output = format_table( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 532, in format_table return formatter(pa_table, query_type=query_type) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 281, in __call__ return self.format_row(pa_table) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 58, in format_row return self.recursive_tensorize(row) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 54, in recursive_tensorize return map_nested(self._recursive_tensorize, data_struct, map_list=False) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 314, in map_nested mapped = [ File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 315, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in _single_map_nested return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in <dictcomp> return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 251, in _single_map_nested return function(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 51, in _recursive_tensorize return self._tensorize(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 38, in _tensorize if np.issubdtype(value.dtype, np.integer): AttributeError: 'bytes' object has no attribute 'dtype' ``` I don't really understand why the image is still a bytes object while I used transformations on it. Here the code I used to upload the dataset (and it worked well): ```python train_dataset = load_dataset("imagefolder", data_dir="data/train") train_dataset = train_dataset["train"] test_dataset = load_dataset("imagefolder", data_dir="data/test") test_dataset = test_dataset["train"] val_dataset = load_dataset("imagefolder", data_dir="data/val") val_dataset = val_dataset["train"] dataset = DatasetDict({ "train": train_dataset, "val": val_dataset, "test": test_dataset }) dataset.push_to_hub("ChainYo/rvl-cdip") ``` Now here is the code I am using to get the dataset and prepare it for training: ```python img_size = 512 batch_size = 128 normalize = [(0.5), (0.5)] data_dir = "ChainYo/rvl-cdip" dataset = load_dataset(data_dir, split="train") transforms = transforms.Compose([ transforms.Resize(img_size), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize(*normalize) ]) transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") train_dataloader = torch.utils.data.DataLoader( transformed_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True ) ``` But this get me the error above. I don't understand why it's doing this kind of weird thing? Do I need to map something on the dataset? Something like this: ```python labels = dataset.features["label"].names num_labels = dataset.features["label"].num_classes def preprocess_data(examples): images = [ex.convert("RGB") for ex in examples["image"]] labels = [ex for ex in examples["label"]] return {"images": images, "labels": labels} features = Features({ "images": Image(decode=True, id=None), "labels": ClassLabel(num_classes=num_labels, names=labels) }) decoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, batched=True, batch_size=100) ```
31
ImageFolder with Grayscale images dataset Hi, I'm facing a problem with a grayscale images dataset I have uploaded [here](https://huggingface.co/datasets/ChainYo/rvl-cdip) (RVL-CDIP) I'm getting an error while I want to use images for training a model with PyTorch DataLoader. Here is the full traceback: ```bash AttributeError: Caught AttributeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1765, in __getitem__ return self._getitem( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1750, in _getitem formatted_output = format_table( File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 532, in format_table return formatter(pa_table, query_type=query_type) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 281, in __call__ return self.format_row(pa_table) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 58, in format_row return self.recursive_tensorize(row) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 54, in recursive_tensorize return map_nested(self._recursive_tensorize, data_struct, map_list=False) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 314, in map_nested mapped = [ File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 315, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in _single_map_nested return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 267, in <dictcomp> return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 251, in _single_map_nested return function(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 51, in _recursive_tensorize return self._tensorize(data_struct) File "/home/chainyo/miniconda3/envs/gan-bird/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py", line 38, in _tensorize if np.issubdtype(value.dtype, np.integer): AttributeError: 'bytes' object has no attribute 'dtype' ``` I don't really understand why the image is still a bytes object while I used transformations on it. Here the code I used to upload the dataset (and it worked well): ```python train_dataset = load_dataset("imagefolder", data_dir="data/train") train_dataset = train_dataset["train"] test_dataset = load_dataset("imagefolder", data_dir="data/test") test_dataset = test_dataset["train"] val_dataset = load_dataset("imagefolder", data_dir="data/val") val_dataset = val_dataset["train"] dataset = DatasetDict({ "train": train_dataset, "val": val_dataset, "test": test_dataset }) dataset.push_to_hub("ChainYo/rvl-cdip") ``` Now here is the code I am using to get the dataset and prepare it for training: ```python img_size = 512 batch_size = 128 normalize = [(0.5), (0.5)] data_dir = "ChainYo/rvl-cdip" dataset = load_dataset(data_dir, split="train") transforms = transforms.Compose([ transforms.Resize(img_size), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize(*normalize) ]) transformed_dataset = dataset.with_transform(transforms) transformed_dataset.set_format(type="torch", device="cuda") train_dataloader = torch.utils.data.DataLoader( transformed_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True ) ``` But this get me the error above. I don't understand why it's doing this kind of weird thing? Do I need to map something on the dataset? Something like this: ```python labels = dataset.features["label"].names num_labels = dataset.features["label"].num_classes def preprocess_data(examples): images = [ex.convert("RGB") for ex in examples["image"]] labels = [ex for ex in examples["label"]] return {"images": images, "labels": labels} features = Features({ "images": Image(decode=True, id=None), "labels": ClassLabel(num_classes=num_labels, names=labels) }) decoded_dataset = dataset.map(preprocess_data, remove_columns=dataset.column_names, features=features, batched=True, batch_size=100) ``` You can pass `ignore_verifications=True` in `load_dataset` to skip checksum verification, which takes a lot of time if the number of files is large. We will consider making this the default behavior.
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0.0454468429, -0.128443718, -0.2307527363, 0.1417760849, -0.1530513018, 0.2092111856, 0.2500415742, -0.0464288406, 0.2442049086, -0.1752356887, 0.0224387459, 0.0872635171, -0.0487587973, -0.0773520693, -0.379309088, 0.1428935677, 0.2902626991, -0.3201149404, 0.197587952, -0.2589414418, -0.0545711629, 0.05995984, -0.1077675745, -0.0360558294, -0.3573840559, 0.2774815559, 0.0079270871, -0.0799010172, 0.2419312596, 0.0393378772, -0.2009781599, 0.4313525558, 0.2357802093, 0.0860425681, 0.0871711522, 0.0053940546, -0.1115887687, -0.0628932714, -0.1424941123, 0.252849251, 0.4057106674, -0.2829225361, 0.2793829143, -0.2250618637, 0.5416774154, 0.0412596911, -0.4253627956, -0.0741196349, 0.4103445113, 0.1175431386, -0.3084381521, -0.0355357677, 0.1603932679, -0.0324391872, -0.0636283681, 0.2351891696, -0.2564228773, 0.2688049078, -0.4622623622, 0.0892765373, 0.4003750384, -0.1793700904, -0.0920825154, 0.3410107493, -0.2817011178, 0.2245292664, 0.5956487656, 0.4211947024, 0.034299776, 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0.4479805529, 0.2066015899, -0.3605055213, 0.1420052201, -0.5662336349, 0.17935808, -0.3956069648, -0.1907967031, -0.212771818, 0.0853227079, 0.0581652485, 0.0181142781, 0.5102961659, 0.0966031998, 0.2582609951, 0.1857466996, -0.3883153796, -0.186390996, 0.2357792556, 0.2879166305, -0.0312007647, -0.2884857059, 0.0371026807, -0.0494870357, 0.0755612999, 0.0753765628, -0.062447954, -0.3076820374, -0.022643283, 0.3932262361, -0.0400664769, 0.6518452764, 0.0284376182, 0.1543140411, -0.1639033556, -0.0731625408, -0.2204556763, 0.0413555019, 0.3611475825, 0.4621957541, -0.1807102412, -0.1875404269, -0.545325458, 0.2212294638, -0.270527035, -0.1916170567, -0.4534896016, 0.3553599119, -0.1336776167, -0.0972774848, -0.0537538603, -0.0919022188, 0.0496811755, -0.0617893301, -0.5940253139, -0.3605217636, 0.5606501698, -0.4193266332, -0.4107775092, 0.1860111505, 0.0160743501, 0.1295608431, 0.3704101443, -0.2476652116, 0.1872053146, 0.0377702266, 0.0136742452, -0.1931713223, -0.1238076463, 0.2645735443, 0.1239132807, 0.1053142026, 0.4532472193, -0.3505420983, -0.1409757584, 0.0320620611, -0.0347930863 ]
https://github.com/huggingface/datasets/issues/4107
Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows
It's not related to the dataset viewer in itself. I can replicate the error with: ``` >>> import datasets as ds >>> d = ds.load_dataset('Pavithree/explainLikeImFive') Using custom data configuration Pavithree--explainLikeImFive-b68b6d8112cd8a51 Downloading and preparing dataset json/Pavithree--explainLikeImFive to /home/slesage/.cache/huggingface/datasets/json/Pavithree--explainLikeImFive-b68b6d8112cd8a51/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b... Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 305M/305M [00:03<00:00, 98.6MB/s] Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 17.9M/17.9M [00:00<00:00, 75.7MB/s] Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 11.9M/11.9M [00:00<00:00, 70.6MB/s] Downloading data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:05<00:00, 1.92s/it] Extracting data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:00<00:00, 1948.42it/s] Failed to read file '/home/slesage/.cache/huggingface/datasets/downloads/5fee9c8819754df277aee6f252e4db6897d785231c21938407b8862ca871d246' with error <class 'pyarrow.lib.ArrowInvalid'>: Exceeded maximum rows Traceback (most recent call last): File "/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py", line 293, in load return loads(fp.read(), File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py", line 357, in loads return _default_decoder.decode(s) File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 1 column 916 (char 915) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets/src/datasets/load.py", line 1691, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 694, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/slesage/hf/datasets/src/datasets/builder.py", line 1151, in _prepare_split for key, table in logging.tqdm( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__ for obj in iterable: File "/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py", line 146, in _generate_tables raise e File "/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py", line 122, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 246, in pyarrow._json.read_json File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Exceeded maximum rows ``` cc @lhoestq @albertvillanova @mariosasko
## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows **Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive* *This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error: Status code: 400 Exception: ArrowInvalid Message: Exceeded maximum rows When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error* Am I the one who added this dataset ? Yes
238
Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows ## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows **Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive* *This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error: Status code: 400 Exception: ArrowInvalid Message: Exceeded maximum rows When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error* Am I the one who added this dataset ? Yes It's not related to the dataset viewer in itself. I can replicate the error with: ``` >>> import datasets as ds >>> d = ds.load_dataset('Pavithree/explainLikeImFive') Using custom data configuration Pavithree--explainLikeImFive-b68b6d8112cd8a51 Downloading and preparing dataset json/Pavithree--explainLikeImFive to /home/slesage/.cache/huggingface/datasets/json/Pavithree--explainLikeImFive-b68b6d8112cd8a51/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b... Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 305M/305M [00:03<00:00, 98.6MB/s] Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 17.9M/17.9M [00:00<00:00, 75.7MB/s] Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 11.9M/11.9M [00:00<00:00, 70.6MB/s] Downloading data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:05<00:00, 1.92s/it] Extracting data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:00<00:00, 1948.42it/s] Failed to read file '/home/slesage/.cache/huggingface/datasets/downloads/5fee9c8819754df277aee6f252e4db6897d785231c21938407b8862ca871d246' with error <class 'pyarrow.lib.ArrowInvalid'>: Exceeded maximum rows Traceback (most recent call last): File "/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py", line 293, in load return loads(fp.read(), File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py", line 357, in loads return _default_decoder.decode(s) File "/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 1 column 916 (char 915) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets/src/datasets/load.py", line 1691, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 694, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/slesage/hf/datasets/src/datasets/builder.py", line 1151, in _prepare_split for key, table in logging.tqdm( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__ for obj in iterable: File "/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py", line 146, in _generate_tables raise e File "/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py", line 122, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 246, in pyarrow._json.read_json File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Exceeded maximum rows ``` cc @lhoestq @albertvillanova @mariosasko
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-0.2484668344, 0.3567450345, 0.1456316859, -0.0804798305, 0.1191764921, -0.0861398354 ]
https://github.com/huggingface/datasets/issues/4107
Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows
It seems that train.json is not a valid JSON Lines file: it has several JSON objects in the first line (the 915th character in the first line starts a new object, and there's no "\n") You need to have one JSON object per line
## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows **Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive* *This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error: Status code: 400 Exception: ArrowInvalid Message: Exceeded maximum rows When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error* Am I the one who added this dataset ? Yes
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Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows ## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows **Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive* *This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error: Status code: 400 Exception: ArrowInvalid Message: Exceeded maximum rows When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error* Am I the one who added this dataset ? Yes It seems that train.json is not a valid JSON Lines file: it has several JSON objects in the first line (the 915th character in the first line starts a new object, and there's no "\n") You need to have one JSON object per line
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-0.0674077198, -0.2138144672, 0.3591624498, 0.1556173414, -0.1686243713, 0.0569049157, -0.1682120413 ]
https://github.com/huggingface/datasets/issues/4107
Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows
I'm closing this issue. @Pavithree, please, feel free to re-open it if fixing the JSON file does not solve it.
## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows **Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive* *This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error: Status code: 400 Exception: ArrowInvalid Message: Exceeded maximum rows When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error* Am I the one who added this dataset ? Yes
20
Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows ## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows **Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive* *This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error: Status code: 400 Exception: ArrowInvalid Message: Exceeded maximum rows When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error* Am I the one who added this dataset ? Yes I'm closing this issue. @Pavithree, please, feel free to re-open it if fixing the JSON file does not solve it.
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https://github.com/huggingface/datasets/issues/4105
push to hub fails with huggingface-hub 0.5.0
Hi ! Indeed there was a breaking change in `huggingface_hub` 0.5.0 in `HfApi.create_repo`, which is called here in `datasets` by passing the org name in both the `repo_id` and the `organization` arguments: https://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369 I think we should fix that in `huggingface_hub`, will keep you posted. In the meantime please use `huggingface_hub` 0.4.0
## Describe the bug `ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id" ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("rubrix/news_test") ds.push_to_hub("<your-user>/news_test", token="<your-token>") ``` ## Expected results The dataset is successfully uploaded ## Actual results An error validation is raised: ```bash if repo_id and (name or organization): > raise ValueError( "Only pass `repo_id` and leave deprecated `name` and " "`organization` to be None." E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - `huggingface-hub`: 0.5 - Platform: macOS - Python version: 3.8.12 - PyArrow version: 6.0.0 cc @adrinjalali
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push to hub fails with huggingface-hub 0.5.0 ## Describe the bug `ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id" ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("rubrix/news_test") ds.push_to_hub("<your-user>/news_test", token="<your-token>") ``` ## Expected results The dataset is successfully uploaded ## Actual results An error validation is raised: ```bash if repo_id and (name or organization): > raise ValueError( "Only pass `repo_id` and leave deprecated `name` and " "`organization` to be None." E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - `huggingface-hub`: 0.5 - Platform: macOS - Python version: 3.8.12 - PyArrow version: 6.0.0 cc @adrinjalali Hi ! Indeed there was a breaking change in `huggingface_hub` 0.5.0 in `HfApi.create_repo`, which is called here in `datasets` by passing the org name in both the `repo_id` and the `organization` arguments: https://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369 I think we should fix that in `huggingface_hub`, will keep you posted. In the meantime please use `huggingface_hub` 0.4.0
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https://github.com/huggingface/datasets/issues/4105
push to hub fails with huggingface-hub 0.5.0
I'll be sending a fix for this later today on the `huggingface_hub` side. The error would be converted to a `FutureWarning` if `datasets` uses kwargs instead of positional, for example here: https://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369 to be: ``` python api.create_repo( name=dataset_name, token=token, repo_type="dataset", organization=organization, private=private, ) ``` But `name` and `organization` are deprecated in `huggingface_hub=0.5`, and people should pass `repo_id='org/name` instead. Note that `repo_id` was introduced in 0.5 and if `datasets` wants to support older `huggingface_hub` versions (which I encourage it to do), there needs to be a helper function to do that. It can be something like: ```python def create_repo( client, name: str, token: Optional[str] = None, organization: Optional[str] = None, private: Optional[bool] = None, repo_type: Optional[str] = None, exist_ok: Optional[bool] = False, space_sdk: Optional[str] = None, ) -> str: try: return client.create_repo( repo_id=f"{organization}/{name}", token=token, private=private, repo_type=repo_type, exist_ok=exist_ok, space_sdk=space_sdk, ) except TypeError: return client.create_repo( name=name, organization=organization, token=token, private=private, repo_type=repo_type, exist_ok=exist_ok, space_sdk=space_sdk, ) ``` in a `utils/_fixes.py` kinda file and and be used internally. I'll be sending a patch to `huggingface_hub` to convert the error reported in this issue to a `FutureWarning`.
## Describe the bug `ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id" ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("rubrix/news_test") ds.push_to_hub("<your-user>/news_test", token="<your-token>") ``` ## Expected results The dataset is successfully uploaded ## Actual results An error validation is raised: ```bash if repo_id and (name or organization): > raise ValueError( "Only pass `repo_id` and leave deprecated `name` and " "`organization` to be None." E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - `huggingface-hub`: 0.5 - Platform: macOS - Python version: 3.8.12 - PyArrow version: 6.0.0 cc @adrinjalali
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push to hub fails with huggingface-hub 0.5.0 ## Describe the bug `ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id" ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("rubrix/news_test") ds.push_to_hub("<your-user>/news_test", token="<your-token>") ``` ## Expected results The dataset is successfully uploaded ## Actual results An error validation is raised: ```bash if repo_id and (name or organization): > raise ValueError( "Only pass `repo_id` and leave deprecated `name` and " "`organization` to be None." E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - `huggingface-hub`: 0.5 - Platform: macOS - Python version: 3.8.12 - PyArrow version: 6.0.0 cc @adrinjalali I'll be sending a fix for this later today on the `huggingface_hub` side. The error would be converted to a `FutureWarning` if `datasets` uses kwargs instead of positional, for example here: https://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369 to be: ``` python api.create_repo( name=dataset_name, token=token, repo_type="dataset", organization=organization, private=private, ) ``` But `name` and `organization` are deprecated in `huggingface_hub=0.5`, and people should pass `repo_id='org/name` instead. Note that `repo_id` was introduced in 0.5 and if `datasets` wants to support older `huggingface_hub` versions (which I encourage it to do), there needs to be a helper function to do that. It can be something like: ```python def create_repo( client, name: str, token: Optional[str] = None, organization: Optional[str] = None, private: Optional[bool] = None, repo_type: Optional[str] = None, exist_ok: Optional[bool] = False, space_sdk: Optional[str] = None, ) -> str: try: return client.create_repo( repo_id=f"{organization}/{name}", token=token, private=private, repo_type=repo_type, exist_ok=exist_ok, space_sdk=space_sdk, ) except TypeError: return client.create_repo( name=name, organization=organization, token=token, private=private, repo_type=repo_type, exist_ok=exist_ok, space_sdk=space_sdk, ) ``` in a `utils/_fixes.py` kinda file and and be used internally. I'll be sending a patch to `huggingface_hub` to convert the error reported in this issue to a `FutureWarning`.
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https://github.com/huggingface/datasets/issues/4105
push to hub fails with huggingface-hub 0.5.0
We can definitely change `push_to_hub` to use `repo_id` in `datasets` and require `huggingface_hub>=0.5.0`. Let me open a PR :)
## Describe the bug `ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id" ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("rubrix/news_test") ds.push_to_hub("<your-user>/news_test", token="<your-token>") ``` ## Expected results The dataset is successfully uploaded ## Actual results An error validation is raised: ```bash if repo_id and (name or organization): > raise ValueError( "Only pass `repo_id` and leave deprecated `name` and " "`organization` to be None." E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - `huggingface-hub`: 0.5 - Platform: macOS - Python version: 3.8.12 - PyArrow version: 6.0.0 cc @adrinjalali
19
push to hub fails with huggingface-hub 0.5.0 ## Describe the bug `ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id" ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("rubrix/news_test") ds.push_to_hub("<your-user>/news_test", token="<your-token>") ``` ## Expected results The dataset is successfully uploaded ## Actual results An error validation is raised: ```bash if repo_id and (name or organization): > raise ValueError( "Only pass `repo_id` and leave deprecated `name` and " "`organization` to be None." E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - `huggingface-hub`: 0.5 - Platform: macOS - Python version: 3.8.12 - PyArrow version: 6.0.0 cc @adrinjalali We can definitely change `push_to_hub` to use `repo_id` in `datasets` and require `huggingface_hub>=0.5.0`. Let me open a PR :)
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https://github.com/huggingface/datasets/issues/4104
Add time series data - stock market
@INF800 happy to add this dataset! I will try to set a PR by the end of the day... if you can kindly point me to the dataset? Also, note we have a bunch of time series datasets checked in e.g. `electricity_load_diagrams` or `monash_tsf`, and ideally this dataset could also be in a similar format.
## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png)
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Add time series data - stock market ## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png) @INF800 happy to add this dataset! I will try to set a PR by the end of the day... if you can kindly point me to the dataset? Also, note we have a bunch of time series datasets checked in e.g. `electricity_load_diagrams` or `monash_tsf`, and ideally this dataset could also be in a similar format.
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https://github.com/huggingface/datasets/issues/4104
Add time series data - stock market
Thankyou. This is how raw data looks like before cleaning for an individual stocks: 1. https://github.com/INF800/marktech/tree/raw-data/f/data/raw 2. https://github.com/INF800/marktech/tree/raw-data/t/data/raw 3. https://github.com/INF800/marktech/tree/raw-data/rdfn/data/raw 4. https://github.com/INF800/marktech/tree/raw-data/irbt/data/raw 5. https://github.com/INF800/marktech/tree/raw-data/hll/data/raw 6. https://github.com/INF800/marktech/tree/raw-data/infy/data/raw 7. https://github.com/INF800/marktech/tree/raw-data/reli/data/raw 8. https://github.com/INF800/marktech/tree/raw-data/hdbk/data/raw > Scraping is automated using GitHub Actions. So, everyday we will see a new file added in the above links. I can rewrite the cleaning scripts to make sure it fits HF dataset standards. (P.S I am very much new to HF dataset) The data set above can be converted into univariate regression / multivariate regression / sequence to sequence generation dataset etc. So, do we have some kind of transformation modules that will read the dataset as some type of dataset (`GenericTimeData`) and convert it to other possible dataset relating to a specific ML task. **By having this kind of transformation module, I only have to add data once** and use transformation module whenever necessary Additionally, having some kind of versioning for the dataset will be really helpful because it will keep on updating - especially time series datasets
## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png)
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Add time series data - stock market ## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png) Thankyou. This is how raw data looks like before cleaning for an individual stocks: 1. https://github.com/INF800/marktech/tree/raw-data/f/data/raw 2. https://github.com/INF800/marktech/tree/raw-data/t/data/raw 3. https://github.com/INF800/marktech/tree/raw-data/rdfn/data/raw 4. https://github.com/INF800/marktech/tree/raw-data/irbt/data/raw 5. https://github.com/INF800/marktech/tree/raw-data/hll/data/raw 6. https://github.com/INF800/marktech/tree/raw-data/infy/data/raw 7. https://github.com/INF800/marktech/tree/raw-data/reli/data/raw 8. https://github.com/INF800/marktech/tree/raw-data/hdbk/data/raw > Scraping is automated using GitHub Actions. So, everyday we will see a new file added in the above links. I can rewrite the cleaning scripts to make sure it fits HF dataset standards. (P.S I am very much new to HF dataset) The data set above can be converted into univariate regression / multivariate regression / sequence to sequence generation dataset etc. So, do we have some kind of transformation modules that will read the dataset as some type of dataset (`GenericTimeData`) and convert it to other possible dataset relating to a specific ML task. **By having this kind of transformation module, I only have to add data once** and use transformation module whenever necessary Additionally, having some kind of versioning for the dataset will be really helpful because it will keep on updating - especially time series datasets
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https://github.com/huggingface/datasets/issues/4104
Add time series data - stock market
thanks @INF800 I'll have a look. I believe it should be possible to incorporate this into the time-series format.
## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png)
19
Add time series data - stock market ## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png) thanks @INF800 I'll have a look. I believe it should be possible to incorporate this into the time-series format.
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https://github.com/huggingface/datasets/issues/4104
Add time series data - stock market
@INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet. In any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with... Do you think you can make a version with just numerical data?
## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png)
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Add time series data - stock market ## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png) @INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet. In any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with... Do you think you can make a version with just numerical data?
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https://github.com/huggingface/datasets/issues/4104
Add time series data - stock market
> @INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet. > > In any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with... > > Do you think you can make a version with just numerical data? Will share the numeric data and conversion script within end of this week. I am on a business trip currently - it is in my desktop.
## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png)
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Add time series data - stock market ## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem ![image](https://user-images.githubusercontent.com/45640029/161904077-52fe97cb-3720-4e3f-98ee-7f6720a056e2.png) > @INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet. > > In any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with... > > Do you think you can make a version with just numerical data? Will share the numeric data and conversion script within end of this week. I am on a business trip currently - it is in my desktop.
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https://github.com/huggingface/datasets/issues/4101
How can I download only the train and test split for full numbers using load_dataset()?
Hi! Can you please specify the full name of the dataset? IIRC `full_numbers` is one of the configs of the `svhn` dataset, and its generation is slow due to data being stored in binary Matlab files. Even if you specify a specific split, `datasets` downloads all of them, but we plan to fix that soon and only download the requested split. If you are in a hurry, download the `svhn` script [here](`https://huggingface.co/datasets/svhn/blob/main/svhn.py`), remove [this code](https://huggingface.co/datasets/svhn/blob/main/svhn.py#L155-L162), and run: ```python from datasets import load_dataset dset = load_dataset("path/to/your/local/script.py", "full_numbers") ``` And to make loading easier in Colab, you can create a dataset repo on the Hub and upload the script there. Or push the script to Google Drive and mount the drive in Colab.
How can I download only the train and test split for full numbers using load_dataset()? I do not need the extra split and it will take 40 mins just to download in Colab. I have very short time in hand. Please help.
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How can I download only the train and test split for full numbers using load_dataset()? How can I download only the train and test split for full numbers using load_dataset()? I do not need the extra split and it will take 40 mins just to download in Colab. I have very short time in hand. Please help. Hi! Can you please specify the full name of the dataset? IIRC `full_numbers` is one of the configs of the `svhn` dataset, and its generation is slow due to data being stored in binary Matlab files. Even if you specify a specific split, `datasets` downloads all of them, but we plan to fix that soon and only download the requested split. If you are in a hurry, download the `svhn` script [here](`https://huggingface.co/datasets/svhn/blob/main/svhn.py`), remove [this code](https://huggingface.co/datasets/svhn/blob/main/svhn.py#L155-L162), and run: ```python from datasets import load_dataset dset = load_dataset("path/to/your/local/script.py", "full_numbers") ``` And to make loading easier in Colab, you can create a dataset repo on the Hub and upload the script there. Or push the script to Google Drive and mount the drive in Colab.
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https://github.com/huggingface/datasets/issues/4099
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)
Hi @andreybond, thanks for reporting. Unfortunately, I'm not able to able to reproduce your issue: ```python In [4]: from datasets import load_dataset ...: datasets = load_dataset("nielsr/XFUN", "xfun.ja") In [5]: datasets Out[5]: DatasetDict({ train: Dataset({ features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'], num_rows: 194 }) validation: Dataset({ features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'], num_rows: 71 }) }) ``` The only reason I can imagine this issue may arise is if your default encoding is not "UTF-8" (and it is ASCII instead). This is usually the case on Windows machines; but you say your environment is a Linux machine. Maybe you change your machine default encoding? Could you please check this? ```python In [6]: import sys In [7]: sys.getdefaultencoding() Out[7]: 'utf-8' ```
## Describe the bug Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("nielsr/XFUN", "xfun.ja") ``` ## Expected results Dataset should be downloaded without exceptions ## Actual results Stack trace (for the second-time execution): Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477... Downloading data files: 100% 2/2 [00:00<00:00, 88.48it/s] Extracting data files: 100% 2/2 [00:00<00:00, 79.60it/s] UnicodeDecodeErrorTraceback (most recent call last) <ipython-input-31-79c26bd1109c> in <module> 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja") /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 ) 605 --> 606 # By default, return all splits 607 if split is None: 608 split = {s: s for s in self.info.splits} /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 692 Args: 693 split: `datasets.Split` which subset of the data to read. --> 694 695 Returns: 696 `Dataset` /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) /usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self) 252 if not self.disable: 253 self.display(check_delay=False) --> 254 255 def __iter__(self): 256 try: /usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self) 1183 for obj in iterable: 1184 yield obj -> 1185 return 1186 1187 mininterval = self.mininterval ~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths) 140 logger.info("Generating examples from = %s", filepath) 141 with open(filepath[0], "r") as f: --> 142 data = json.load(f) 143 144 for doc in data["documents"]: /usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 294 295 """ --> 296 return loads(fp.read(), 297 cls=cls, object_hook=object_hook, 298 parse_float=parse_float, parse_int=parse_int, /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 (but reproduced with many previous versions) - Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu - Python version: 3.6.9 - PyArrow version: 6.0.1
123
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Describe the bug Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("nielsr/XFUN", "xfun.ja") ``` ## Expected results Dataset should be downloaded without exceptions ## Actual results Stack trace (for the second-time execution): Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477... Downloading data files: 100% 2/2 [00:00<00:00, 88.48it/s] Extracting data files: 100% 2/2 [00:00<00:00, 79.60it/s] UnicodeDecodeErrorTraceback (most recent call last) <ipython-input-31-79c26bd1109c> in <module> 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja") /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 ) 605 --> 606 # By default, return all splits 607 if split is None: 608 split = {s: s for s in self.info.splits} /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 692 Args: 693 split: `datasets.Split` which subset of the data to read. --> 694 695 Returns: 696 `Dataset` /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) /usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self) 252 if not self.disable: 253 self.display(check_delay=False) --> 254 255 def __iter__(self): 256 try: /usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self) 1183 for obj in iterable: 1184 yield obj -> 1185 return 1186 1187 mininterval = self.mininterval ~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths) 140 logger.info("Generating examples from = %s", filepath) 141 with open(filepath[0], "r") as f: --> 142 data = json.load(f) 143 144 for doc in data["documents"]: /usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 294 295 """ --> 296 return loads(fp.read(), 297 cls=cls, object_hook=object_hook, 298 parse_float=parse_float, parse_int=parse_int, /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 (but reproduced with many previous versions) - Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu - Python version: 3.6.9 - PyArrow version: 6.0.1 Hi @andreybond, thanks for reporting. Unfortunately, I'm not able to able to reproduce your issue: ```python In [4]: from datasets import load_dataset ...: datasets = load_dataset("nielsr/XFUN", "xfun.ja") In [5]: datasets Out[5]: DatasetDict({ train: Dataset({ features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'], num_rows: 194 }) validation: Dataset({ features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'], num_rows: 71 }) }) ``` The only reason I can imagine this issue may arise is if your default encoding is not "UTF-8" (and it is ASCII instead). This is usually the case on Windows machines; but you say your environment is a Linux machine. Maybe you change your machine default encoding? Could you please check this? ```python In [6]: import sys In [7]: sys.getdefaultencoding() Out[7]: 'utf-8' ```
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https://github.com/huggingface/datasets/issues/4099
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)
I opened a PR in the original dataset loading script: - microsoft/unilm#677 and fixed the corresponding dataset script on the Hub: - https://huggingface.co/datasets/nielsr/XFUN/commit/73ba5e026621e05fb756ae0f267eb49971f70ebd
## Describe the bug Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("nielsr/XFUN", "xfun.ja") ``` ## Expected results Dataset should be downloaded without exceptions ## Actual results Stack trace (for the second-time execution): Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477... Downloading data files: 100% 2/2 [00:00<00:00, 88.48it/s] Extracting data files: 100% 2/2 [00:00<00:00, 79.60it/s] UnicodeDecodeErrorTraceback (most recent call last) <ipython-input-31-79c26bd1109c> in <module> 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja") /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 ) 605 --> 606 # By default, return all splits 607 if split is None: 608 split = {s: s for s in self.info.splits} /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 692 Args: 693 split: `datasets.Split` which subset of the data to read. --> 694 695 Returns: 696 `Dataset` /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) /usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self) 252 if not self.disable: 253 self.display(check_delay=False) --> 254 255 def __iter__(self): 256 try: /usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self) 1183 for obj in iterable: 1184 yield obj -> 1185 return 1186 1187 mininterval = self.mininterval ~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths) 140 logger.info("Generating examples from = %s", filepath) 141 with open(filepath[0], "r") as f: --> 142 data = json.load(f) 143 144 for doc in data["documents"]: /usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 294 295 """ --> 296 return loads(fp.read(), 297 cls=cls, object_hook=object_hook, 298 parse_float=parse_float, parse_int=parse_int, /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 (but reproduced with many previous versions) - Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu - Python version: 3.6.9 - PyArrow version: 6.0.1
23
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Describe the bug Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("nielsr/XFUN", "xfun.ja") ``` ## Expected results Dataset should be downloaded without exceptions ## Actual results Stack trace (for the second-time execution): Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477... Downloading data files: 100% 2/2 [00:00<00:00, 88.48it/s] Extracting data files: 100% 2/2 [00:00<00:00, 79.60it/s] UnicodeDecodeErrorTraceback (most recent call last) <ipython-input-31-79c26bd1109c> in <module> 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja") /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 ) 605 --> 606 # By default, return all splits 607 if split is None: 608 split = {s: s for s in self.info.splits} /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 692 Args: 693 split: `datasets.Split` which subset of the data to read. --> 694 695 Returns: 696 `Dataset` /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) /usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self) 252 if not self.disable: 253 self.display(check_delay=False) --> 254 255 def __iter__(self): 256 try: /usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self) 1183 for obj in iterable: 1184 yield obj -> 1185 return 1186 1187 mininterval = self.mininterval ~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths) 140 logger.info("Generating examples from = %s", filepath) 141 with open(filepath[0], "r") as f: --> 142 data = json.load(f) 143 144 for doc in data["documents"]: /usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 294 295 """ --> 296 return loads(fp.read(), 297 cls=cls, object_hook=object_hook, 298 parse_float=parse_float, parse_int=parse_int, /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 (but reproduced with many previous versions) - Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu - Python version: 3.6.9 - PyArrow version: 6.0.1 I opened a PR in the original dataset loading script: - microsoft/unilm#677 and fixed the corresponding dataset script on the Hub: - https://huggingface.co/datasets/nielsr/XFUN/commit/73ba5e026621e05fb756ae0f267eb49971f70ebd
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https://github.com/huggingface/datasets/issues/4099
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)
import sys sys.getdefaultencoding() returned: 'utf-8' --------------------- I've just cloned master branch - your fix works! Thank you!
## Describe the bug Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("nielsr/XFUN", "xfun.ja") ``` ## Expected results Dataset should be downloaded without exceptions ## Actual results Stack trace (for the second-time execution): Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477... Downloading data files: 100% 2/2 [00:00<00:00, 88.48it/s] Extracting data files: 100% 2/2 [00:00<00:00, 79.60it/s] UnicodeDecodeErrorTraceback (most recent call last) <ipython-input-31-79c26bd1109c> in <module> 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja") /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 ) 605 --> 606 # By default, return all splits 607 if split is None: 608 split = {s: s for s in self.info.splits} /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 692 Args: 693 split: `datasets.Split` which subset of the data to read. --> 694 695 Returns: 696 `Dataset` /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) /usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self) 252 if not self.disable: 253 self.display(check_delay=False) --> 254 255 def __iter__(self): 256 try: /usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self) 1183 for obj in iterable: 1184 yield obj -> 1185 return 1186 1187 mininterval = self.mininterval ~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths) 140 logger.info("Generating examples from = %s", filepath) 141 with open(filepath[0], "r") as f: --> 142 data = json.load(f) 143 144 for doc in data["documents"]: /usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 294 295 """ --> 296 return loads(fp.read(), 297 cls=cls, object_hook=object_hook, 298 parse_float=parse_float, parse_int=parse_int, /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 (but reproduced with many previous versions) - Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu - Python version: 3.6.9 - PyArrow version: 6.0.1
17
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Describe the bug Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("nielsr/XFUN", "xfun.ja") ``` ## Expected results Dataset should be downloaded without exceptions ## Actual results Stack trace (for the second-time execution): Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477... Downloading data files: 100% 2/2 [00:00<00:00, 88.48it/s] Extracting data files: 100% 2/2 [00:00<00:00, 79.60it/s] UnicodeDecodeErrorTraceback (most recent call last) <ipython-input-31-79c26bd1109c> in <module> 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja") /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 ) 605 --> 606 # By default, return all splits 607 if split is None: 608 split = {s: s for s in self.info.splits} /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 692 Args: 693 split: `datasets.Split` which subset of the data to read. --> 694 695 Returns: 696 `Dataset` /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) /usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self) 252 if not self.disable: 253 self.display(check_delay=False) --> 254 255 def __iter__(self): 256 try: /usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self) 1183 for obj in iterable: 1184 yield obj -> 1185 return 1186 1187 mininterval = self.mininterval ~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths) 140 logger.info("Generating examples from = %s", filepath) 141 with open(filepath[0], "r") as f: --> 142 data = json.load(f) 143 144 for doc in data["documents"]: /usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 294 295 """ --> 296 return loads(fp.read(), 297 cls=cls, object_hook=object_hook, 298 parse_float=parse_float, parse_int=parse_int, /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 (but reproduced with many previous versions) - Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu - Python version: 3.6.9 - PyArrow version: 6.0.1 import sys sys.getdefaultencoding() returned: 'utf-8' --------------------- I've just cloned master branch - your fix works! Thank you!
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https://github.com/huggingface/datasets/issues/4096
Add support for streaming Zarr stores for hosted datasets
Hi @jacobbieker, thanks for your request and study of possible alternatives. We are very interested in finding a way to make `datasets` useful to you. Looking at the Zarr docs, I saw that among its storage alternatives, there is the ZIP file format: https://zarr.readthedocs.io/en/stable/api/storage.html#zarr.storage.ZipStore This might be convenient for many reasons: - On the one hand, we avoid the Git issue with huge number of small files: chunks files are compressed into a single ZIP file - On the other hand, the ZIP file format is specially suited for streaming data because it allows random access to its component files (i.e. it supports random access to its chunks) Anyway, I think that a Python loading script will be necessary: you need to implement additional logic to select certain chunks (based on date or other criteria). Please, let me know if this makes sense to you.
**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
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Add support for streaming Zarr stores for hosted datasets **Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations. Hi @jacobbieker, thanks for your request and study of possible alternatives. We are very interested in finding a way to make `datasets` useful to you. Looking at the Zarr docs, I saw that among its storage alternatives, there is the ZIP file format: https://zarr.readthedocs.io/en/stable/api/storage.html#zarr.storage.ZipStore This might be convenient for many reasons: - On the one hand, we avoid the Git issue with huge number of small files: chunks files are compressed into a single ZIP file - On the other hand, the ZIP file format is specially suited for streaming data because it allows random access to its component files (i.e. it supports random access to its chunks) Anyway, I think that a Python loading script will be necessary: you need to implement additional logic to select certain chunks (based on date or other criteria). Please, let me know if this makes sense to you.
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https://github.com/huggingface/datasets/issues/4096
Add support for streaming Zarr stores for hosted datasets
Ah okay, I missed the option of zip files for zarr, I'll try that with our repos and see if it works! Thanks a lot!
**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
25
Add support for streaming Zarr stores for hosted datasets **Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations. Ah okay, I missed the option of zip files for zarr, I'll try that with our repos and see if it works! Thanks a lot!
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https://github.com/huggingface/datasets/issues/4096
Add support for streaming Zarr stores for hosted datasets
On behalf of the Zarr developers, let me say THANK YOU for working to support Zarr on HF! πŸ™ Zarr is a 100% open-source and community driven project (fiscally sponsored by NumFocus). We see it as an ideal format for ML training datasets, particularly in scientific domains. I think the solution of zipping the Zarr store is a reasonable way to balance the constraints of Git LFS with the structure of Zarr. It would be amazing to get something on the [Hugging Face Datasets Docs](https://huggingface.co/docs/datasets/index) about how to best work with Zarr. Let me know if there's a way I could help with that effort.
**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
105
Add support for streaming Zarr stores for hosted datasets **Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations. On behalf of the Zarr developers, let me say THANK YOU for working to support Zarr on HF! πŸ™ Zarr is a 100% open-source and community driven project (fiscally sponsored by NumFocus). We see it as an ideal format for ML training datasets, particularly in scientific domains. I think the solution of zipping the Zarr store is a reasonable way to balance the constraints of Git LFS with the structure of Zarr. It would be amazing to get something on the [Hugging Face Datasets Docs](https://huggingface.co/docs/datasets/index) about how to best work with Zarr. Let me know if there's a way I could help with that effort.
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https://github.com/huggingface/datasets/issues/4096
Add support for streaming Zarr stores for hosted datasets
Also just noting here that I was able to lazily open @jacobbieker's dataset over the internet from HF hub πŸš€ ! ```python import xarray as xr url = "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip" zip_url = 'zip:///::' + url ds = xr.open_dataset(zip_url, engine='zarr', chunks={}) ``` <img width="740" alt="image" src="https://user-images.githubusercontent.com/1197350/164508663-bc75cdc0-734d-44f4-9562-2877ecfdf433.png">
**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
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Add support for streaming Zarr stores for hosted datasets **Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations. Also just noting here that I was able to lazily open @jacobbieker's dataset over the internet from HF hub πŸš€ ! ```python import xarray as xr url = "https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip" zip_url = 'zip:///::' + url ds = xr.open_dataset(zip_url, engine='zarr', chunks={}) ``` <img width="740" alt="image" src="https://user-images.githubusercontent.com/1197350/164508663-bc75cdc0-734d-44f4-9562-2877ecfdf433.png">
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https://github.com/huggingface/datasets/issues/4096
Add support for streaming Zarr stores for hosted datasets
However, I wasn't able to get streaming working using the Datasets api: ```python from datasets import load_dataset ds = load_dataset("openclimatefix/mrms", streaming=True, split='train') item = next(iter(ds)) ``` <details> <summary>FileNotFoundError traceback</summary> ``` No config specified, defaulting to: mrms/2021 zip://::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip data/2016_001.zarr.zip zip://2016_001.zarr.zip::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [1], in <cell line: 3>() 1 from datasets import load_dataset 2 ds = load_dataset("openclimatefix/mrms", streaming=True, split='train') ----> 3 item = next(iter(ds)) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:497, in IterableDataset.__iter__(self) 496 def __iter__(self): --> 497 for key, example in self._iter(): 498 if self.features: 499 # we encode the example for ClassLabel feature types for example 500 encoded_example = self.features.encode_example(example) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:494, in IterableDataset._iter(self) 492 else: 493 ex_iterable = self._ex_iterable --> 494 yield from ex_iterable File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:87, in ExamplesIterable.__iter__(self) 86 def __iter__(self): ---> 87 yield from self.generate_examples_fn(**self.kwargs) File ~/.cache/huggingface/modules/datasets_modules/datasets/openclimatefix--mrms/2a6f697014d7eb3caf586ca137d47ca38785ae2fe36248611b021f8248b59936/mrms.py:150, in MRMS._generate_examples(self, filepath, split) 147 filepath = "[https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip](https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip%3C/span%3E%3Cspan) style="color:rgb(175,0,0)">" 148 # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. 149 # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. --> 150 with zarr.storage.FSStore(fsspec.open("zip::" + filepath, mode='r'), mode='r') as store: 151 data = xr.open_zarr(store) 152 for key, row in enumerate(data["time"].values): File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/zarr/storage.py:1120, in FSStore.__init__(self, url, normalize_keys, key_separator, mode, exceptions, dimension_separator, **storage_options) 1117 import fsspec 1118 self.normalize_keys = normalize_keys -> 1120 protocol, _ = fsspec.core.split_protocol(url) 1121 # set auto_mkdir to True for local file system 1122 if protocol in (None, "file") and not storage_options.get("auto_mkdir"): File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:514, in split_protocol(urlpath) 512 def split_protocol(urlpath): 513 """Return protocol, path pair""" --> 514 urlpath = stringify_path(urlpath) 515 if "://" in urlpath: 516 protocol, path = urlpath.split("://", 1) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/utils.py:315, in stringify_path(filepath) 313 return filepath 314 elif hasattr(filepath, "__fspath__"): --> 315 return filepath.__fspath__() 316 elif isinstance(filepath, pathlib.Path): 317 return str(filepath) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:98, in OpenFile.__fspath__(self) 96 def __fspath__(self): 97 # may raise if cannot be resolved to local file ---> 98 return self.open().__fspath__() File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:140, in OpenFile.open(self) 132 def open(self): 133 """Materialise this as a real open file without context 134 135 The file should be explicitly closed to avoid enclosed file (...) 138 been deleted; but a with-context is better style. 139 """ --> 140 out = self.__enter__() 141 closer = out.close 142 fobjects = self.fobjects.copy()[:-1] File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self) 100 def __enter__(self): 101 mode = self.mode.replace("t", "").replace("b", "") + "b" --> 103 f = self.fs.open(self.path, mode=mode) 105 self.fobjects = [f] 107 if self.compression is not None: File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/spec.py:1009, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs) 1007 else: 1008 ac = kwargs.pop("autocommit", not self._intrans) -> 1009 f = self._open( 1010 path, 1011 mode=mode, 1012 block_size=block_size, 1013 autocommit=ac, 1014 cache_options=cache_options, 1015 **kwargs, 1016 ) 1017 if compression is not None: 1018 from fsspec.compression import compr File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/implementations/zip.py:96, in ZipFileSystem._open(self, path, mode, block_size, autocommit, cache_options, **kwargs) 94 if mode != "rb": 95 raise NotImplementedError ---> 96 info = self.info(path) 97 out = self.zip.open(path, "r") 98 out.size = info["size"] File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/archive.py:42, in AbstractArchiveFileSystem.info(self, path, **kwargs) 40 return self.dir_cache[path + "/"] 41 else: ---> 42 raise FileNotFoundError(path) FileNotFoundError: ``` </details> Is this a bug? Or am I just doing it wrong...
**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
511
Add support for streaming Zarr stores for hosted datasets **Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations. However, I wasn't able to get streaming working using the Datasets api: ```python from datasets import load_dataset ds = load_dataset("openclimatefix/mrms", streaming=True, split='train') item = next(iter(ds)) ``` <details> <summary>FileNotFoundError traceback</summary> ``` No config specified, defaulting to: mrms/2021 zip://::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip data/2016_001.zarr.zip zip://2016_001.zarr.zip::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Input In [1], in <cell line: 3>() 1 from datasets import load_dataset 2 ds = load_dataset("openclimatefix/mrms", streaming=True, split='train') ----> 3 item = next(iter(ds)) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:497, in IterableDataset.__iter__(self) 496 def __iter__(self): --> 497 for key, example in self._iter(): 498 if self.features: 499 # we encode the example for ClassLabel feature types for example 500 encoded_example = self.features.encode_example(example) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:494, in IterableDataset._iter(self) 492 else: 493 ex_iterable = self._ex_iterable --> 494 yield from ex_iterable File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:87, in ExamplesIterable.__iter__(self) 86 def __iter__(self): ---> 87 yield from self.generate_examples_fn(**self.kwargs) File ~/.cache/huggingface/modules/datasets_modules/datasets/openclimatefix--mrms/2a6f697014d7eb3caf586ca137d47ca38785ae2fe36248611b021f8248b59936/mrms.py:150, in MRMS._generate_examples(self, filepath, split) 147 filepath = "[https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip](https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip%3C/span%3E%3Cspan) style="color:rgb(175,0,0)">" 148 # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. 149 # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. --> 150 with zarr.storage.FSStore(fsspec.open("zip::" + filepath, mode='r'), mode='r') as store: 151 data = xr.open_zarr(store) 152 for key, row in enumerate(data["time"].values): File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/zarr/storage.py:1120, in FSStore.__init__(self, url, normalize_keys, key_separator, mode, exceptions, dimension_separator, **storage_options) 1117 import fsspec 1118 self.normalize_keys = normalize_keys -> 1120 protocol, _ = fsspec.core.split_protocol(url) 1121 # set auto_mkdir to True for local file system 1122 if protocol in (None, "file") and not storage_options.get("auto_mkdir"): File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:514, in split_protocol(urlpath) 512 def split_protocol(urlpath): 513 """Return protocol, path pair""" --> 514 urlpath = stringify_path(urlpath) 515 if "://" in urlpath: 516 protocol, path = urlpath.split("://", 1) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/utils.py:315, in stringify_path(filepath) 313 return filepath 314 elif hasattr(filepath, "__fspath__"): --> 315 return filepath.__fspath__() 316 elif isinstance(filepath, pathlib.Path): 317 return str(filepath) File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:98, in OpenFile.__fspath__(self) 96 def __fspath__(self): 97 # may raise if cannot be resolved to local file ---> 98 return self.open().__fspath__() File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:140, in OpenFile.open(self) 132 def open(self): 133 """Materialise this as a real open file without context 134 135 The file should be explicitly closed to avoid enclosed file (...) 138 been deleted; but a with-context is better style. 139 """ --> 140 out = self.__enter__() 141 closer = out.close 142 fobjects = self.fobjects.copy()[:-1] File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self) 100 def __enter__(self): 101 mode = self.mode.replace("t", "").replace("b", "") + "b" --> 103 f = self.fs.open(self.path, mode=mode) 105 self.fobjects = [f] 107 if self.compression is not None: File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/spec.py:1009, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs) 1007 else: 1008 ac = kwargs.pop("autocommit", not self._intrans) -> 1009 f = self._open( 1010 path, 1011 mode=mode, 1012 block_size=block_size, 1013 autocommit=ac, 1014 cache_options=cache_options, 1015 **kwargs, 1016 ) 1017 if compression is not None: 1018 from fsspec.compression import compr File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/implementations/zip.py:96, in ZipFileSystem._open(self, path, mode, block_size, autocommit, cache_options, **kwargs) 94 if mode != "rb": 95 raise NotImplementedError ---> 96 info = self.info(path) 97 out = self.zip.open(path, "r") 98 out.size = info["size"] File /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/archive.py:42, in AbstractArchiveFileSystem.info(self, path, **kwargs) 40 return self.dir_cache[path + "/"] 41 else: ---> 42 raise FileNotFoundError(path) FileNotFoundError: ``` </details> Is this a bug? Or am I just doing it wrong...
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https://github.com/huggingface/datasets/issues/4096
Add support for streaming Zarr stores for hosted datasets
I'm still messing around with that dataset, so the data might have moved. I currently have each year of MRMS precipitation rate data as it's own zarr, but as they are quite large (on order of 100GB each) I'm working to split them into single days, and as such they are still being moved around, I was just trying to get a proof of concept working originally.
**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
67
Add support for streaming Zarr stores for hosted datasets **Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations. I'm still messing around with that dataset, so the data might have moved. I currently have each year of MRMS precipitation rate data as it's own zarr, but as they are quite large (on order of 100GB each) I'm working to split them into single days, and as such they are still being moved around, I was just trying to get a proof of concept working originally.
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https://github.com/huggingface/datasets/issues/4096
Add support for streaming Zarr stores for hosted datasets
I've mostly finished rearranging the data now and uploading some more, so this works now: ```python import datasets ds = datasets.load_dataset("openclimatefix/mrms", streaming=True, split="train") item = next(iter(ds)) print(item.keys()) print(item["timestamp"]) ``` The MRMS data now goes most of 2016-2022, with quite a few gaps I'm working on filling in
**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
47
Add support for streaming Zarr stores for hosted datasets **Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations. I've mostly finished rearranging the data now and uploading some more, so this works now: ```python import datasets ds = datasets.load_dataset("openclimatefix/mrms", streaming=True, split="train") item = next(iter(ds)) print(item.keys()) print(item["timestamp"]) ``` The MRMS data now goes most of 2016-2022, with quite a few gaps I'm working on filling in
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https://github.com/huggingface/datasets/issues/4093
elena-soare/crawled-ecommerce: missing dataset
By the way, the error on our part is due to the huge size of every row (~90MB). The dataset viewer does not support such big dataset rows for the moment. Anyway, we're working to give a hint about this in the dataset viewer.
elena-soare/crawled-ecommerce **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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elena-soare/crawled-ecommerce: missing dataset elena-soare/crawled-ecommerce **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No By the way, the error on our part is due to the huge size of every row (~90MB). The dataset viewer does not support such big dataset rows for the moment. Anyway, we're working to give a hint about this in the dataset viewer.
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https://github.com/huggingface/datasets/issues/4093
elena-soare/crawled-ecommerce: missing dataset
Fixed. See https://huggingface.co/datasets/elena-soare/crawled-ecommerce/viewer/elena-soare--crawled-ecommerce/train. <img width="1552" alt="Capture d’écran 2022-04-12 aΜ€ 11 23 51" src="https://user-images.githubusercontent.com/1676121/162929722-2e2b80e2-154a-4b61-87bd-e341bd6c46e6.png"> Thanks for reporting!
elena-soare/crawled-ecommerce **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
16
elena-soare/crawled-ecommerce: missing dataset elena-soare/crawled-ecommerce **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No Fixed. See https://huggingface.co/datasets/elena-soare/crawled-ecommerce/viewer/elena-soare--crawled-ecommerce/train. <img width="1552" alt="Capture d’écran 2022-04-12 aΜ€ 11 23 51" src="https://user-images.githubusercontent.com/1676121/162929722-2e2b80e2-154a-4b61-87bd-e341bd6c46e6.png"> Thanks for reporting!
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https://github.com/huggingface/datasets/issues/4091
Build a Dataset One Example at a Time Without Loading All Data Into Memory
Hi! Yes, the problem with `add_item` is that it keeps examples in memory, so you are left with these options: * writing a dataset loading script in which you iterate over `custom_example_dict_streamer` and yield the examples (in `_generate examples`) * storing the data in a JSON/CSV/Parquet/TXT file and using `Dataset.from_{format}` * using `add_item` + `save_to_disk` on smaller chunks: ```python from datasets import Dataset, concatenate_datasets MAX_SAMPLES_IN_MEMORY = 1000 samples_in_dset = 0 dset = Dataset.from_dict({"col1": [], "col2": []}) # empty dataset path_to_save_dir = "path/to/save/dir" num_chunks = 0 for example_dict in custom_example_dict_streamer("/path/to/raw/data"): dset = dset.add_item(example_dict) samples_in_dset += 1 if samples_in_dset == MAX_SAMPLES_IN_MEMORY: samples_in_dset = 0 dset.save_to_disk(f"{path_to_save_dir}{num_chunks}") num_chunks =+ 1 dset = Dataset.from_dict({"col1": [], "col2": []}) # empty dataset if samples_in_dset > 0: dset.save_to_disk(f"{path_to_save_dir}{num_chunks}") num_chunks =+ 1 loaded_dsets = [] # memory-mapped for chunk_num in range(num_chunks): dset = Dataset.load_from_disk(f"{path_to_save_dir}{chunk_num}") loaded_dsets.append(dset) final_dset = concatenate_datasets(dset) ``` If you still have issues with this approach, you can try to delete unused datasets with `gc.collect()` to free some memory.
**Is your feature request related to a problem? Please describe.** I have a very large dataset stored on disk in a custom format. I have some custom code that reads one data example at a time and yields it in the form of a dictionary. I want to construct a `Dataset` with all examples, and then save it to disk. I later want to load the saved `Dataset` and use it like any other HuggingFace dataset, get splits, wrap it in a PyTorch `DataLoader`, etc. **Crucially, I do not ever want to materialize all the data in memory while building the dataset.** **Describe the solution you'd like** I would like to be able to do something like the following. Notice how each example is read and then immediately added to the dataset. We do not store all the data in memory when constructing the `Dataset`. If it helps, I will know the schema of my dataset before hand. ``` # Initialize an empty Dataset, possibly from a known schema. dataset = Dataset() # Read in examples one by one using a custom data streamer. for example_dict in custom_example_dict_streamer("/path/to/raw/data"): # Add this example to the dict but do not store it in memory. dataset.add_item(example_dict) # Save the final dataset to disk as an Arrow-backed dataset. dataset.save_to_disk("/path/to/dataset") ... # I'd like to be able to later `load_from_disk` and use the loaded Dataset # just like any other memory-mapped pyarrow-backed HuggingFace dataset... loaded_dataset = Dataset.load_from_disk("/path/to/dataset") loaded_dataset.set_format(type="torch", columnns=["foo", "bar", "baz"]) dataloader = torch.utils.data.DataLoader(loaded_dataset, batch_size=16) ... ``` **Describe alternatives you've considered** I initially tried to read all the data into memory, construct a Pandas DataFrame and then call `Dataset.from_pandas`. This would not work as it requires storing all the data in memory. It seems that there is an `add_item` method already -- I tried to implement something like the desired API written above, but I've not been able to initialize an empty `Dataset` (this seems to require several layers of constructing `datasets.table.Table` which requires constructing a `pyarrow.lib.Table`, etc). I also considered writing my data to multiple sharded CSV files or JSON files and then using `from_csv` or `from_json`. I'd prefer not to do this because (1) I'd prefer to avoid the intermediate step of creating these temp CSV/JSON files and (2) I'm not sure if `from_csv` and `from_json` use memory-mapping. Do you have any suggestions on how I'd be able to achieve this use case? Does something already exist to support this? Thank you very much in advance!
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Build a Dataset One Example at a Time Without Loading All Data Into Memory **Is your feature request related to a problem? Please describe.** I have a very large dataset stored on disk in a custom format. I have some custom code that reads one data example at a time and yields it in the form of a dictionary. I want to construct a `Dataset` with all examples, and then save it to disk. I later want to load the saved `Dataset` and use it like any other HuggingFace dataset, get splits, wrap it in a PyTorch `DataLoader`, etc. **Crucially, I do not ever want to materialize all the data in memory while building the dataset.** **Describe the solution you'd like** I would like to be able to do something like the following. Notice how each example is read and then immediately added to the dataset. We do not store all the data in memory when constructing the `Dataset`. If it helps, I will know the schema of my dataset before hand. ``` # Initialize an empty Dataset, possibly from a known schema. dataset = Dataset() # Read in examples one by one using a custom data streamer. for example_dict in custom_example_dict_streamer("/path/to/raw/data"): # Add this example to the dict but do not store it in memory. dataset.add_item(example_dict) # Save the final dataset to disk as an Arrow-backed dataset. dataset.save_to_disk("/path/to/dataset") ... # I'd like to be able to later `load_from_disk` and use the loaded Dataset # just like any other memory-mapped pyarrow-backed HuggingFace dataset... loaded_dataset = Dataset.load_from_disk("/path/to/dataset") loaded_dataset.set_format(type="torch", columnns=["foo", "bar", "baz"]) dataloader = torch.utils.data.DataLoader(loaded_dataset, batch_size=16) ... ``` **Describe alternatives you've considered** I initially tried to read all the data into memory, construct a Pandas DataFrame and then call `Dataset.from_pandas`. This would not work as it requires storing all the data in memory. It seems that there is an `add_item` method already -- I tried to implement something like the desired API written above, but I've not been able to initialize an empty `Dataset` (this seems to require several layers of constructing `datasets.table.Table` which requires constructing a `pyarrow.lib.Table`, etc). I also considered writing my data to multiple sharded CSV files or JSON files and then using `from_csv` or `from_json`. I'd prefer not to do this because (1) I'd prefer to avoid the intermediate step of creating these temp CSV/JSON files and (2) I'm not sure if `from_csv` and `from_json` use memory-mapping. Do you have any suggestions on how I'd be able to achieve this use case? Does something already exist to support this? Thank you very much in advance! Hi! Yes, the problem with `add_item` is that it keeps examples in memory, so you are left with these options: * writing a dataset loading script in which you iterate over `custom_example_dict_streamer` and yield the examples (in `_generate examples`) * storing the data in a JSON/CSV/Parquet/TXT file and using `Dataset.from_{format}` * using `add_item` + `save_to_disk` on smaller chunks: ```python from datasets import Dataset, concatenate_datasets MAX_SAMPLES_IN_MEMORY = 1000 samples_in_dset = 0 dset = Dataset.from_dict({"col1": [], "col2": []}) # empty dataset path_to_save_dir = "path/to/save/dir" num_chunks = 0 for example_dict in custom_example_dict_streamer("/path/to/raw/data"): dset = dset.add_item(example_dict) samples_in_dset += 1 if samples_in_dset == MAX_SAMPLES_IN_MEMORY: samples_in_dset = 0 dset.save_to_disk(f"{path_to_save_dir}{num_chunks}") num_chunks =+ 1 dset = Dataset.from_dict({"col1": [], "col2": []}) # empty dataset if samples_in_dset > 0: dset.save_to_disk(f"{path_to_save_dir}{num_chunks}") num_chunks =+ 1 loaded_dsets = [] # memory-mapped for chunk_num in range(num_chunks): dset = Dataset.load_from_disk(f"{path_to_save_dir}{chunk_num}") loaded_dsets.append(dset) final_dset = concatenate_datasets(dset) ``` If you still have issues with this approach, you can try to delete unused datasets with `gc.collect()` to free some memory.
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https://github.com/huggingface/datasets/issues/4086
Dataset viewer issue for McGill-NLP/feedbackQA
Hi @cslizc, thanks for reporting. I have just forced the refresh of the corresponding cache and the preview is working now.
## Dataset viewer issue for '*McGill-NLP/feedbackQA*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/McGill-NLP/feedbackQA)* *short description of the issue* The dataset can be loaded correctly with `load_dataset` but the preview doesn't work. Error message: ``` Status code: 400 Exception: Status400Error Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist. ``` Am I the one who added this dataset ? Yes
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Dataset viewer issue for McGill-NLP/feedbackQA ## Dataset viewer issue for '*McGill-NLP/feedbackQA*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/McGill-NLP/feedbackQA)* *short description of the issue* The dataset can be loaded correctly with `load_dataset` but the preview doesn't work. Error message: ``` Status code: 400 Exception: Status400Error Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist. ``` Am I the one who added this dataset ? Yes Hi @cslizc, thanks for reporting. I have just forced the refresh of the corresponding cache and the preview is working now.
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https://github.com/huggingface/datasets/issues/4085
datasets.set_progress_bar_enabled(False) not working in datasets v2
Now, I can't find any reference to set_progress_bar_enabled in the code. I think it have been deleted
## Describe the bug datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Steps to reproduce the bug ```python datasets.set_progress_bar_enabled(False) ``` ## Expected results datasets not using any progress bar ## Actual results AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled ## Environment info datasets version 2
17
datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Describe the bug datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Steps to reproduce the bug ```python datasets.set_progress_bar_enabled(False) ``` ## Expected results datasets not using any progress bar ## Actual results AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled ## Environment info datasets version 2 Now, I can't find any reference to set_progress_bar_enabled in the code. I think it have been deleted
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https://github.com/huggingface/datasets/issues/4085
datasets.set_progress_bar_enabled(False) not working in datasets v2
Hi @virilo, Please note that since `datasets` version 2.0.0, we have aligned with `transformers` the management of the progress bar (among other things): - #3897 Now, you should update your code to use `datasets.logging.disable_progress_bar`. You have more info in our docs: [Logging methods](https://huggingface.co/docs/datasets/package_reference/logging_methods)
## Describe the bug datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Steps to reproduce the bug ```python datasets.set_progress_bar_enabled(False) ``` ## Expected results datasets not using any progress bar ## Actual results AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled ## Environment info datasets version 2
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datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Describe the bug datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Steps to reproduce the bug ```python datasets.set_progress_bar_enabled(False) ``` ## Expected results datasets not using any progress bar ## Actual results AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled ## Environment info datasets version 2 Hi @virilo, Please note that since `datasets` version 2.0.0, we have aligned with `transformers` the management of the progress bar (among other things): - #3897 Now, you should update your code to use `datasets.logging.disable_progress_bar`. You have more info in our docs: [Logging methods](https://huggingface.co/docs/datasets/package_reference/logging_methods)
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https://github.com/huggingface/datasets/issues/4084
Errors in `Train with Datasets` Tensorflow code section on Huggingface.co
Hi @blackhat-coder, thanks for reporting. Please note that the `transformers` library updated their data collators API last year (version 4.10.0): - huggingface/transformers#13105 now requiring to pass `return_tensors` argument at Data Collator instantiation. And therefore, we also updated in the `datasets` library documentation all the examples using `transformers` data collators. If you would like to follow our examples, please update your installed `transformers` version: ``` pip install -U transformers ```
## Describe the bug Hi ### Error 1 Running the Tensforlow code on [Huggingface](https://huggingface.co/docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors' ### Error 2 `DataCollatorWithPadding` isn't imported ## Steps to reproduce the bug ```python import tensorflow as tf from datasets import load_dataset from transformers import AutoTokenizer dataset = load_dataset('glue', 'mrpc', split='train') tokenizer = AutoTokenizer.from_pretrained('bert-base-cased') dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") train_dataset = dataset["train"].to_tf_dataset( columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'], shuffle=True, batch_size=16, collate_fn=data_collator, ) ``` This is the same code on Huggingface.co ## Actual results TypeError: __init__() got an unexpected keyword argument 'return_tensors' ## Environment info - `datasets` version: 2.0.0 - Platform: Windows-10-10.0.19044-SP0 - Python version: 3.9.7 - PyArrow version: 6.0.0 - Pandas version: 1.4.1 >
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Errors in `Train with Datasets` Tensorflow code section on Huggingface.co ## Describe the bug Hi ### Error 1 Running the Tensforlow code on [Huggingface](https://huggingface.co/docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors' ### Error 2 `DataCollatorWithPadding` isn't imported ## Steps to reproduce the bug ```python import tensorflow as tf from datasets import load_dataset from transformers import AutoTokenizer dataset = load_dataset('glue', 'mrpc', split='train') tokenizer = AutoTokenizer.from_pretrained('bert-base-cased') dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") train_dataset = dataset["train"].to_tf_dataset( columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'], shuffle=True, batch_size=16, collate_fn=data_collator, ) ``` This is the same code on Huggingface.co ## Actual results TypeError: __init__() got an unexpected keyword argument 'return_tensors' ## Environment info - `datasets` version: 2.0.0 - Platform: Windows-10-10.0.19044-SP0 - Python version: 3.9.7 - PyArrow version: 6.0.0 - Pandas version: 1.4.1 > Hi @blackhat-coder, thanks for reporting. Please note that the `transformers` library updated their data collators API last year (version 4.10.0): - huggingface/transformers#13105 now requiring to pass `return_tensors` argument at Data Collator instantiation. And therefore, we also updated in the `datasets` library documentation all the examples using `transformers` data collators. If you would like to follow our examples, please update your installed `transformers` version: ``` pip install -U transformers ```
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https://github.com/huggingface/datasets/issues/4080
NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset
Hi @richarddwang, Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well): - #4002 That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by: - installing `datasets` from our GitHub repo: ```bash pip install git+https://github.com/huggingface/datasets#egg=datasets ``` - forcing the data files to be redownloaded ```python ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload") ``` Feel free to re-open this issue if the problem persists. Duplicate of: - #4031
## Steps to reproduce the bug ```python datasets.load_dataset("conll2012_ontonotesv5", "english_v12") ``` ## Actual results ``` Downloading builder script: 32.2kB [00:00, 9.72MB/s] Downloading metadata: 20.0kB [00:00, 10.4MB/s] Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size , total: 379.12 MiB) to ... Traceback (most recent call last): [315/390] File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module> train() File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train trainer.fit(model, datamodule=dm) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit self._call_and_handle_interrupt( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte rrupt return trainer_fn(*args, **kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run self._data_connector.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre pare_data self.trainer.datamodule.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn fn(*args, **kwargs) File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data raw_dsets = datasets.load_dataset(**load_dataset_kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset builder_instance.download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare verify_checksums( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0
83
NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset ## Steps to reproduce the bug ```python datasets.load_dataset("conll2012_ontonotesv5", "english_v12") ``` ## Actual results ``` Downloading builder script: 32.2kB [00:00, 9.72MB/s] Downloading metadata: 20.0kB [00:00, 10.4MB/s] Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size , total: 379.12 MiB) to ... Traceback (most recent call last): [315/390] File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module> train() File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train trainer.fit(model, datamodule=dm) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit self._call_and_handle_interrupt( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte rrupt return trainer_fn(*args, **kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run self._data_connector.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre pare_data self.trainer.datamodule.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn fn(*args, **kwargs) File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data raw_dsets = datasets.load_dataset(**load_dataset_kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset builder_instance.download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare verify_checksums( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 Hi @richarddwang, Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well): - #4002 That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by: - installing `datasets` from our GitHub repo: ```bash pip install git+https://github.com/huggingface/datasets#egg=datasets ``` - forcing the data files to be redownloaded ```python ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload") ``` Feel free to re-open this issue if the problem persists. Duplicate of: - #4031
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https://github.com/huggingface/datasets/issues/4075
Add CCAgT dataset
Awesome ! Let us know if you have questions or if we can help ;) I'm assigning you PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.
## Adding a Dataset - **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique - **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111ΞΌmΓ—0.111ΞΌm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR). - **Paper:** https://doi.org/10.1109/cbms49503.2020.00110 - **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0 - **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible
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Add CCAgT dataset ## Adding a Dataset - **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique - **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111ΞΌmΓ—0.111ΞΌm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR). - **Paper:** https://doi.org/10.1109/cbms49503.2020.00110 - **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0 - **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible Awesome ! Let us know if you have questions or if we can help ;) I'm assigning you PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.
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https://github.com/huggingface/datasets/issues/4074
Error in google/xtreme_s dataset card
Hi @wranai, thanks for reporting. Please note that the information about language families and groups is taken form the original paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752). If that information is wrong, feel free to contact the paper's authors to suggest that correction. Just note that Hungarian language (contrary to their geographically surrounding neighbor languages) belongs to the Uralic (languages) family, together with (among others) Finnish, Estonian, some other languages in northern regions of Scandinavia...
**Link:** https://huggingface.co/datasets/google/xtreme_s Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way).
74
Error in google/xtreme_s dataset card **Link:** https://huggingface.co/datasets/google/xtreme_s Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way). Hi @wranai, thanks for reporting. Please note that the information about language families and groups is taken form the original paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752). If that information is wrong, feel free to contact the paper's authors to suggest that correction. Just note that Hungarian language (contrary to their geographically surrounding neighbor languages) belongs to the Uralic (languages) family, together with (among others) Finnish, Estonian, some other languages in northern regions of Scandinavia...
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https://github.com/huggingface/datasets/issues/4071
Loading issue for xuyeliu/notebookCDG dataset
Hi @Jun-jie-Huang, As the error message says, ".pkl" data files are not supported. If you would like to share your dataset on the Hub, you would need: - either to create a Python loading script, that loads the data in any format - or to transform your data files to one of the supported formats (listed in the error message above: CSV, JSON, Parquet, TXT,...) You can find the details in our docs: - How to share a dataset: https://huggingface.co/docs/datasets/share - How to create a dataset loading script: https://huggingface.co/docs/datasets/dataset_script Feel free to re-open this issue and ping us if you need further assistance.
## Dataset viewer issue for '*xuyeliu/notebookCDG*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/xuyeliu/notebookCDG)* *Couldn't load the xuyeliu/notebookCDG with provided scripts: * ``` from datasets import load_dataset dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl") ``` I get an error message as follows: FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] Am I the one who added this dataset ? No
103
Loading issue for xuyeliu/notebookCDG dataset ## Dataset viewer issue for '*xuyeliu/notebookCDG*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/xuyeliu/notebookCDG)* *Couldn't load the xuyeliu/notebookCDG with provided scripts: * ``` from datasets import load_dataset dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl") ``` I get an error message as follows: FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'] Am I the one who added this dataset ? No Hi @Jun-jie-Huang, As the error message says, ".pkl" data files are not supported. If you would like to share your dataset on the Hub, you would need: - either to create a Python loading script, that loads the data in any format - or to transform your data files to one of the supported formats (listed in the error message above: CSV, JSON, Parquet, TXT,...) You can find the details in our docs: - How to share a dataset: https://huggingface.co/docs/datasets/share - How to create a dataset loading script: https://huggingface.co/docs/datasets/dataset_script Feel free to re-open this issue and ping us if you need further assistance.
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https://github.com/huggingface/datasets/issues/4062
Loading mozilla-foundation/common_voice_7_0 dataset failed
Hi @aapot, thanks for reporting. We are investigating the cause of this issue. We will keep you informed.
## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5
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Loading mozilla-foundation/common_voice_7_0 dataset failed ## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5 Hi @aapot, thanks for reporting. We are investigating the cause of this issue. We will keep you informed.
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https://github.com/huggingface/datasets/issues/4062
Loading mozilla-foundation/common_voice_7_0 dataset failed
When making HTTP request from code line: ``` response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() ``` it cannot be decoded to JSON because it raises a 404 Not Found error. The request is fixed if removing the `/{use_cdn}` from the URL. Maybe there was a change in the Common Voice API? CC: @anton-l @patrickvonplaten @polinaeterna
## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5
52
Loading mozilla-foundation/common_voice_7_0 dataset failed ## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5 When making HTTP request from code line: ``` response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() ``` it cannot be decoded to JSON because it raises a 404 Not Found error. The request is fixed if removing the `/{use_cdn}` from the URL. Maybe there was a change in the Common Voice API? CC: @anton-l @patrickvonplaten @polinaeterna
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https://github.com/huggingface/datasets/issues/4062
Loading mozilla-foundation/common_voice_7_0 dataset failed
I have also made the hotfix for all the rest of Common Voice script versions: 8.0, 6.1, 6.0,..., 1.0
## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5
19
Loading mozilla-foundation/common_voice_7_0 dataset failed ## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5 I have also made the hotfix for all the rest of Common Voice script versions: 8.0, 6.1, 6.0,..., 1.0
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https://github.com/huggingface/datasets/issues/4061
Loading cnn_dailymail dataset failed
Hi @Arij-Aladel, thanks for reporting. This issue was already reported - #3784 and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it in our 2.0.0 release. See: - #3787 Please, update your `datasets` version: ``` pip install -U datasets ``` and retry loading the dataset by forcing its redownload: ```python dataset = load_dataset("cnn_dailymail", "3.0.0", download_mode="force_redownload") ```
## Describe the bug I wanted to load cnn_dailymail dataset from huggingface datasets on jupyter lab, but I am getting an error ` NotADirectoryError:[Errno20] Not a directory ` while loading it. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0') ``` ## Expected results load `cnn_dailymail` dataset succesfully ## Actual results failed to load and get error > NotADirectoryError: [Errno 20] Not a directory ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` 1.8.0: - Platform: Ubuntu-20.04 - Python version: 3.9.10 - PyArrow version: 3.0.0
66
Loading cnn_dailymail dataset failed ## Describe the bug I wanted to load cnn_dailymail dataset from huggingface datasets on jupyter lab, but I am getting an error ` NotADirectoryError:[Errno20] Not a directory ` while loading it. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0') ``` ## Expected results load `cnn_dailymail` dataset succesfully ## Actual results failed to load and get error > NotADirectoryError: [Errno 20] Not a directory ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` 1.8.0: - Platform: Ubuntu-20.04 - Python version: 3.9.10 - PyArrow version: 3.0.0 Hi @Arij-Aladel, thanks for reporting. This issue was already reported - #3784 and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it in our 2.0.0 release. See: - #3787 Please, update your `datasets` version: ``` pip install -U datasets ``` and retry loading the dataset by forcing its redownload: ```python dataset = load_dataset("cnn_dailymail", "3.0.0", download_mode="force_redownload") ```
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https://github.com/huggingface/datasets/issues/4057
`load_dataset` consumes too much memory
Hi ! Could it be because you need to free the memory used by `tarfile` by emptying the tar `members` by any chance ? ```python yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} audio_tarfile.members = [] # free memory key += 1 ``` and then you can set `DEFAULT_WRITER_BATCH_SIZE` to whatever value makes more sense for your dataset. Let me know if the issue persists (which could happen, given that you managed to run your generator without RAM issues and using os.walk didn't solve the issue)
## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
85
`load_dataset` consumes too much memory ## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 Hi ! Could it be because you need to free the memory used by `tarfile` by emptying the tar `members` by any chance ? ```python yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} audio_tarfile.members = [] # free memory key += 1 ``` and then you can set `DEFAULT_WRITER_BATCH_SIZE` to whatever value makes more sense for your dataset. Let me know if the issue persists (which could happen, given that you managed to run your generator without RAM issues and using os.walk didn't solve the issue)
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https://github.com/huggingface/datasets/issues/4057
`load_dataset` consumes too much memory
I also run out of memory when loading `mozilla-foundation/common_voice_8_0` that also uses `tarfile` via `dl_manager.iter_archive`. There seems to be some data files that stay in memory somewhere I don't have the issue with other compression formats like gzipped files
## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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`load_dataset` consumes too much memory ## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 I also run out of memory when loading `mozilla-foundation/common_voice_8_0` that also uses `tarfile` via `dl_manager.iter_archive`. There seems to be some data files that stay in memory somewhere I don't have the issue with other compression formats like gzipped files
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https://github.com/huggingface/datasets/issues/4057
`load_dataset` consumes too much memory
I'm facing a similar memory leak issue when loading cv8. As you said @lhoestq `load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True, writer_batch_size=1)` This issue is happening on a 32GB RAM machine. Any updates on how to fix this?
## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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`load_dataset` consumes too much memory ## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 I'm facing a similar memory leak issue when loading cv8. As you said @lhoestq `load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True, writer_batch_size=1)` This issue is happening on a 32GB RAM machine. Any updates on how to fix this?
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https://github.com/huggingface/datasets/issues/4057
`load_dataset` consumes too much memory
I've run a memory profiler to see where's the leak comes from: ![image](https://user-images.githubusercontent.com/5097052/165101712-e7060ae5-77b2-4f6a-92bd-2996dbd60b36.png) ... it seems that it's related to the tarfile lib buffer reader. But I don't know why it's only happening on the huggingface script
## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5
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`load_dataset` consumes too much memory ## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5 I've run a memory profiler to see where's the leak comes from: ![image](https://user-images.githubusercontent.com/5097052/165101712-e7060ae5-77b2-4f6a-92bd-2996dbd60b36.png) ... it seems that it's related to the tarfile lib buffer reader. But I don't know why it's only happening on the huggingface script
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https://github.com/huggingface/datasets/issues/4056
Unexpected behavior of _TempDirWithCustomCleanup
Hi ! Would setting TMPDIR at the beginning of your python script/session work ? I mean, even before importing transformers, datasets, etc. and using them ? I think this would be the most robust solution given any library that uses `tempfile`. I don't think we aim to support environment variables to be changed at run time
## Describe the bug This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side. When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect. ## Steps to reproduce the bug `_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected. For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`. ## Suggestion: I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust: Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.
56
Unexpected behavior of _TempDirWithCustomCleanup ## Describe the bug This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side. When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect. ## Steps to reproduce the bug `_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected. For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`. ## Suggestion: I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust: Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`. Hi ! Would setting TMPDIR at the beginning of your python script/session work ? I mean, even before importing transformers, datasets, etc. and using them ? I think this would be the most robust solution given any library that uses `tempfile`. I don't think we aim to support environment variables to be changed at run time
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https://github.com/huggingface/datasets/issues/4056
Unexpected behavior of _TempDirWithCustomCleanup
Hi, yeah setting the environment variable before the imports / as environment variable outside is another way to fix this. I am just arguing that `datasets` already uses its own global variable to track temporary files: `_TEMP_DIR_FOR_TEMP_CACHE_FILES`, and the creation of this global variable should respect TMPDIR instead of relying on tempfile to do so.
## Describe the bug This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side. When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect. ## Steps to reproduce the bug `_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected. For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`. ## Suggestion: I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust: Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.
55
Unexpected behavior of _TempDirWithCustomCleanup ## Describe the bug This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side. When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect. ## Steps to reproduce the bug `_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected. For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`. ## Suggestion: I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust: Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`. Hi, yeah setting the environment variable before the imports / as environment variable outside is another way to fix this. I am just arguing that `datasets` already uses its own global variable to track temporary files: `_TEMP_DIR_FOR_TEMP_CACHE_FILES`, and the creation of this global variable should respect TMPDIR instead of relying on tempfile to do so.
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0.1649650186, 0.1921326816, -0.042077031, 0.2639269233, 0.2824708223, 0.0811312199, 0.2210099846, -0.0825632364 ]
https://github.com/huggingface/datasets/issues/4052
metric = metric_cls( TypeError: 'NoneType' object is not callable
Hi @klyuhang9, I'm sorry but I can't reproduce your problem: ```python In [2]: metric = load_metric('glue', 'rte') Downloading builder script: 5.76kB [00:00, 2.40MB/s] ``` Could you please, retry to load the metric? Sometimes there are temporary connectivity issues. Feel free to re-open this issue of the problem persists.
Hi, friend. I meet a problem. When I run the code: `metric = load_metric('glue', 'rte')` There is a problem raising: `metric = metric_cls( TypeError: 'NoneType' object is not callable ` I don't know why. Thanks for your help!
48
metric = metric_cls( TypeError: 'NoneType' object is not callable Hi, friend. I meet a problem. When I run the code: `metric = load_metric('glue', 'rte')` There is a problem raising: `metric = metric_cls( TypeError: 'NoneType' object is not callable ` I don't know why. Thanks for your help! Hi @klyuhang9, I'm sorry but I can't reproduce your problem: ```python In [2]: metric = load_metric('glue', 'rte') Downloading builder script: 5.76kB [00:00, 2.40MB/s] ``` Could you please, retry to load the metric? Sometimes there are temporary connectivity issues. Feel free to re-open this issue of the problem persists.
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https://github.com/huggingface/datasets/issues/4051
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
Hi @klyuhang9, I'm sorry but I can't reproduce your problem: ```python In [4]: ds = load_dataset("glue", "sst2", download_mode="force_redownload") Downloading builder script: 28.8kB [00:00, 9.15MB/s] Downloading metadata: 28.7kB [00:00, 10.7MB/s] Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.78 MiB, post-processed: Unknown size, total: 11.88 MiB) to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad... Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 7.44M/7.44M [00:01<00:00, 4.12MB/s] Dataset glue downloaded and prepared to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data. 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:00<00:00, 1047.96it/s] In [5]: ds Out[5]: DatasetDict({ train: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 67349 }) validation: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 872 }) test: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 1821 }) }) ``` Please, note that sometimes GitHub has some temporary connectivity issues. Feel free to retry and re-open this issue if the problem persists.
Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help!
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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help! Hi @klyuhang9, I'm sorry but I can't reproduce your problem: ```python In [4]: ds = load_dataset("glue", "sst2", download_mode="force_redownload") Downloading builder script: 28.8kB [00:00, 9.15MB/s] Downloading metadata: 28.7kB [00:00, 10.7MB/s] Downloading and preparing dataset glue/sst2 (download: 7.09 MiB, generated: 4.78 MiB, post-processed: Unknown size, total: 11.88 MiB) to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad... Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 7.44M/7.44M [00:01<00:00, 4.12MB/s] Dataset glue downloaded and prepared to .../.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data. 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:00<00:00, 1047.96it/s] In [5]: ds Out[5]: DatasetDict({ train: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 67349 }) validation: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 872 }) test: Dataset({ features: ['sentence', 'label', 'idx'], num_rows: 1821 }) }) ``` Please, note that sometimes GitHub has some temporary connectivity issues. Feel free to retry and re-open this issue if the problem persists.
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0.1743283123, 0.1223950237, -0.0286574736, 0.0292613301, -0.3014473617, 0.1271896362, -0.1846919358 ]
https://github.com/huggingface/datasets/issues/4051
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
> Are you able to access the URL in your web browser? Yes, with or without a VPN, we (people in China) can access the URL. And we can even use wget to download these files. We can download the pretrained language model automatically with the code. However, we CANNOT access glue.py & metric.py automatically. Every time, it will raise ConnectionError, and we have to download datasets manually (SQuAD is extremely hard to preprocess) and replace metric.py with scipy.metrics. If this problem is solved, many Chinese will save a lot of time.
Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help!
92
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help! > Are you able to access the URL in your web browser? Yes, with or without a VPN, we (people in China) can access the URL. And we can even use wget to download these files. We can download the pretrained language model automatically with the code. However, we CANNOT access glue.py & metric.py automatically. Every time, it will raise ConnectionError, and we have to download datasets manually (SQuAD is extremely hard to preprocess) and replace metric.py with scipy.metrics. If this problem is solved, many Chinese will save a lot of time.
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https://github.com/huggingface/datasets/issues/4051
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py > > I don't know why; it is ok when I use If you would query the question `ConnectionError: Couldn't reach` in www.baidu.com (Chinese Google, Google is banned and some people cannot access it), you will find that there are so many questions about accessing `https://raw.githubusercontent.com`. There are some solutions like adding `185.199.108.133 raw.githubusercontent.com` to `C:/windows/systen32/drives/etc/hosts`, but it is time-consuming, hard for green-hand, and invalid sometimes.
Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help!
71
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help! > ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py > > I don't know why; it is ok when I use If you would query the question `ConnectionError: Couldn't reach` in www.baidu.com (Chinese Google, Google is banned and some people cannot access it), you will find that there are so many questions about accessing `https://raw.githubusercontent.com`. There are some solutions like adding `185.199.108.133 raw.githubusercontent.com` to `C:/windows/systen32/drives/etc/hosts`, but it is time-consuming, hard for green-hand, and invalid sometimes.
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https://github.com/huggingface/datasets/issues/4048
Split size error on `amazon_us_reviews` / `PC_v1_00` dataset
Follow-up: I have confirmed there are no duplicate lines via `sort amazon_reviews_us_PC_v1_00.tsv | uniq -cd` after extracting the raw file.
## Describe the bug When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m. Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata. Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first. ## Steps to reproduce the bug ```python load_dataset('amazon_us_reviews', 'PC_v1_00') ``` ## Expected results Dataset is downloaded and extracted successfully. ## Actual results An split size exception is thrown. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
20
Split size error on `amazon_us_reviews` / `PC_v1_00` dataset ## Describe the bug When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m. Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata. Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first. ## Steps to reproduce the bug ```python load_dataset('amazon_us_reviews', 'PC_v1_00') ``` ## Expected results Dataset is downloaded and extracted successfully. ## Actual results An split size exception is thrown. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Follow-up: I have confirmed there are no duplicate lines via `sort amazon_reviews_us_PC_v1_00.tsv | uniq -cd` after extracting the raw file.
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https://github.com/huggingface/datasets/issues/4048
Split size error on `amazon_us_reviews` / `PC_v1_00` dataset
Hi @trentonstrong, thanks for reporting! I confirm that loading this dataset configuration throws a `NonMatchingSplitsSizesError`: ``` NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=350242049, num_examples=785730, dataset_name='amazon_us_reviews'), 'recorded': SplitInfo(name='train', num_bytes=3982712078, num_examples=6908554, dataset_name='amazon_us_reviews')}] ``` Also thank you for your offer to fix this. You can find information about how to update the metadata JSON file here: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata ```shell datasets-cli test datasets/amazon_us_reviews --save_infos --all_configs ``` Please, feel free to open a PR with this fix. And do not hesitate to ping me if you need any help.
## Describe the bug When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m. Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata. Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first. ## Steps to reproduce the bug ```python load_dataset('amazon_us_reviews', 'PC_v1_00') ``` ## Expected results Dataset is downloaded and extracted successfully. ## Actual results An split size exception is thrown. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1
80
Split size error on `amazon_us_reviews` / `PC_v1_00` dataset ## Describe the bug When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m. Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata. Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first. ## Steps to reproduce the bug ```python load_dataset('amazon_us_reviews', 'PC_v1_00') ``` ## Expected results Dataset is downloaded and extracted successfully. ## Actual results An split size exception is thrown. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1 Hi @trentonstrong, thanks for reporting! I confirm that loading this dataset configuration throws a `NonMatchingSplitsSizesError`: ``` NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=350242049, num_examples=785730, dataset_name='amazon_us_reviews'), 'recorded': SplitInfo(name='train', num_bytes=3982712078, num_examples=6908554, dataset_name='amazon_us_reviews')}] ``` Also thank you for your offer to fix this. You can find information about how to update the metadata JSON file here: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata ```shell datasets-cli test datasets/amazon_us_reviews --save_infos --all_configs ``` Please, feel free to open a PR with this fix. And do not hesitate to ping me if you need any help.
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https://github.com/huggingface/datasets/issues/4047
Dataset.unique(column: str) -> ArrowNotImplementedError
Hi @orkenstein, thanks for reporting. Please note that for this case, our `datasets` library uses under the hood the Apache Arrow `unique` function: https://arrow.apache.org/docs/python/generated/pyarrow.compute.unique.html#pyarrow.compute.unique And currently the Apache Arrow `unique` function is only implemented for these input types (see info in their [docs](https://arrow.apache.org/docs/cpp/compute.html#array-wise-vector-functions)): Boolean, Null, Numeric, Temporal, Binary- and String-like. However, the data types of the `wikiann` dataset are all `list<item: string>` (see its [dataset card](https://huggingface.co/datasets/wikiann#data-fields)), and thus, not yet supported by the Apache Arrow `unique` function.
## Describe the bug I'm trying to use `unique()` function, but it fails ## Steps to reproduce the bug 1. Get dataset 2. Call `unique` 3. Error # Sample code to reproduce the bug ```python !pip show datasets from datasets import load_dataset dataset = load_dataset('wikiann', 'en') dataset['train'].column_names dataset['train'].unique(dataset['train'].column_names[0]) ``` ## Expected results It would be nice to actually see unique items ## Actual results Error: ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) [<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>() 6 7 dataset['train'].column_names ----> 8 dataset['train'].unique(dataset['train'].column_names[0]) 5 frames /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>]) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Google Collab - Python version: 3.7.13 - PyArrow version: 6.0.1
77
Dataset.unique(column: str) -> ArrowNotImplementedError ## Describe the bug I'm trying to use `unique()` function, but it fails ## Steps to reproduce the bug 1. Get dataset 2. Call `unique` 3. Error # Sample code to reproduce the bug ```python !pip show datasets from datasets import load_dataset dataset = load_dataset('wikiann', 'en') dataset['train'].column_names dataset['train'].unique(dataset['train'].column_names[0]) ``` ## Expected results It would be nice to actually see unique items ## Actual results Error: ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) [<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>() 6 7 dataset['train'].column_names ----> 8 dataset['train'].unique(dataset['train'].column_names[0]) 5 frames /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>]) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Google Collab - Python version: 3.7.13 - PyArrow version: 6.0.1 Hi @orkenstein, thanks for reporting. Please note that for this case, our `datasets` library uses under the hood the Apache Arrow `unique` function: https://arrow.apache.org/docs/python/generated/pyarrow.compute.unique.html#pyarrow.compute.unique And currently the Apache Arrow `unique` function is only implemented for these input types (see info in their [docs](https://arrow.apache.org/docs/cpp/compute.html#array-wise-vector-functions)): Boolean, Null, Numeric, Temporal, Binary- and String-like. However, the data types of the `wikiann` dataset are all `list<item: string>` (see its [dataset card](https://huggingface.co/datasets/wikiann#data-fields)), and thus, not yet supported by the Apache Arrow `unique` function.
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https://github.com/huggingface/datasets/issues/4047
Dataset.unique(column: str) -> ArrowNotImplementedError
As a workaround solution you can use pandas: ```python from datasets import load_dataset dataset = load_dataset('wikiann', 'en', split='train') df = dataset.to_pandas() unique_df = df[~df.tokens.apply(tuple).duplicated()] # from https://stackoverflow.com/a/46958336/17517845 ``` Note that pandas loads the dataset in memory (this one is small so it's fine).
## Describe the bug I'm trying to use `unique()` function, but it fails ## Steps to reproduce the bug 1. Get dataset 2. Call `unique` 3. Error # Sample code to reproduce the bug ```python !pip show datasets from datasets import load_dataset dataset = load_dataset('wikiann', 'en') dataset['train'].column_names dataset['train'].unique(dataset['train'].column_names[0]) ``` ## Expected results It would be nice to actually see unique items ## Actual results Error: ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) [<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>() 6 7 dataset['train'].column_names ----> 8 dataset['train'].unique(dataset['train'].column_names[0]) 5 frames /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>]) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Google Collab - Python version: 3.7.13 - PyArrow version: 6.0.1
43
Dataset.unique(column: str) -> ArrowNotImplementedError ## Describe the bug I'm trying to use `unique()` function, but it fails ## Steps to reproduce the bug 1. Get dataset 2. Call `unique` 3. Error # Sample code to reproduce the bug ```python !pip show datasets from datasets import load_dataset dataset = load_dataset('wikiann', 'en') dataset['train'].column_names dataset['train'].unique(dataset['train'].column_names[0]) ``` ## Expected results It would be nice to actually see unique items ## Actual results Error: ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) [<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>() 6 7 dataset['train'].column_names ----> 8 dataset['train'].unique(dataset['train'].column_names[0]) 5 frames /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>]) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Google Collab - Python version: 3.7.13 - PyArrow version: 6.0.1 As a workaround solution you can use pandas: ```python from datasets import load_dataset dataset = load_dataset('wikiann', 'en', split='train') df = dataset.to_pandas() unique_df = df[~df.tokens.apply(tuple).duplicated()] # from https://stackoverflow.com/a/46958336/17517845 ``` Note that pandas loads the dataset in memory (this one is small so it's fine).
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https://github.com/huggingface/datasets/issues/4041
Add support for IIIF in datasets
Hi! Thanks for the detailed analysis of adding IIIF support. I like the idea of "using IIIF through datasets scripts" due to its ease of use. Another approach that I like is yielding image ids and using the `piffle` library (which offers a bit more flexibility) + `map` to download + cache images. We can handle bad URLs in `map` by returning `None`. Plus, we can add a `Dataset Preprocessing` section with the code that explains this approach to the card of such datasets. WDYT? > currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models. This is why (currently) adding a new feature type would be overkill, IMO.
This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred. ## What is [IIIF](https://iiif.io/)? IIIF (International Image Interoperability Framework) > is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions. The tl;dr is that IIIF provides various specifications for implementing useful functionality for: - Institutions to make available images for various use cases - Users to have a consistent way of interacting/requesting these images - For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF). Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/ ## IIIF APIs IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/) ### IIIF Image API The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL: ```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}``` A concrete example of this: ```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg``` As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return: ![](https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg) We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg` ![](https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg) We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg` ![](https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg) A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size ## Why would/could this be useful for datasets? There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows: - images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller - can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use. - options for quality, rotation, the format can all be encoded in the URL request. These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact. ## What could this look like in datasets? I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach. ### Use through datasets scripts Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script: ```python ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg") ``` This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script. ### Support through dataset scripts (with some datasets support) This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like: ```python features = {"label": ClassLabel(names=['dog','cat']), "url": datasets.IIIFURL()} ``` inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset. ### Other possible integrations Some other possible pseudocode ways that a user could interact with IIIF URLs: The ability to cast to an `IIIFImage` feature type: ``` ds.cast_column('url', IIIFImage, download=False) ``` The ability to specify some options associated with IIIF urls. ``` ds = ds.set_iiif_options(column='url', size="250,250") ``` I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways. ## prerequisite requirements There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support: ### support for handling failed images loaded via a URL (or a specific IIIFImage feature). Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs. ```python from datasets import Dataset import datasets urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3 urls.append("badurl.com/image.jpg") data = {"url":urls} ds = Dataset.from_dict(data) ds = ds.cast_column('url', datasets.Image()) ds[3]['url'] ``` returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this. ### Caching support Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs. ### Support for Parsing IIIF URLs This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share. ## Why it might not be worthwhile/suitable for datasets There are some reasons that this might not be worth implementing: - currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models. - It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble. - The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from. Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets. ## Suggested next steps: I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.
145
Add support for IIIF in datasets This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred. ## What is [IIIF](https://iiif.io/)? IIIF (International Image Interoperability Framework) > is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions. The tl;dr is that IIIF provides various specifications for implementing useful functionality for: - Institutions to make available images for various use cases - Users to have a consistent way of interacting/requesting these images - For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF). Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/ ## IIIF APIs IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/) ### IIIF Image API The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL: ```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}``` A concrete example of this: ```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg``` As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return: ![](https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg) We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg` ![](https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg) We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg` ![](https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg) A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size ## Why would/could this be useful for datasets? There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows: - images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller - can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use. - options for quality, rotation, the format can all be encoded in the URL request. These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact. ## What could this look like in datasets? I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach. ### Use through datasets scripts Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script: ```python ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg") ``` This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script. ### Support through dataset scripts (with some datasets support) This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like: ```python features = {"label": ClassLabel(names=['dog','cat']), "url": datasets.IIIFURL()} ``` inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset. ### Other possible integrations Some other possible pseudocode ways that a user could interact with IIIF URLs: The ability to cast to an `IIIFImage` feature type: ``` ds.cast_column('url', IIIFImage, download=False) ``` The ability to specify some options associated with IIIF urls. ``` ds = ds.set_iiif_options(column='url', size="250,250") ``` I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways. ## prerequisite requirements There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support: ### support for handling failed images loaded via a URL (or a specific IIIFImage feature). Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs. ```python from datasets import Dataset import datasets urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3 urls.append("badurl.com/image.jpg") data = {"url":urls} ds = Dataset.from_dict(data) ds = ds.cast_column('url', datasets.Image()) ds[3]['url'] ``` returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this. ### Caching support Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs. ### Support for Parsing IIIF URLs This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share. ## Why it might not be worthwhile/suitable for datasets There are some reasons that this might not be worth implementing: - currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models. - It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble. - The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from. Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets. ## Suggested next steps: I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those. Hi! Thanks for the detailed analysis of adding IIIF support. I like the idea of "using IIIF through datasets scripts" due to its ease of use. Another approach that I like is yielding image ids and using the `piffle` library (which offers a bit more flexibility) + `map` to download + cache images. We can handle bad URLs in `map` by returning `None`. Plus, we can add a `Dataset Preprocessing` section with the code that explains this approach to the card of such datasets. WDYT? > currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models. This is why (currently) adding a new feature type would be overkill, IMO.
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https://github.com/huggingface/datasets/issues/4040
Calling existing metrics from other metrics
That's definitely the way to go to avoid implementation bugs and making sure we can fix issues globally when we detect them in a metric. Thanks for reporting!
There are several cases of metrics calling other metrics, e.g. [Wiki Split](https://huggingface.co/metrics/wiki_split) which calls [BLEU](https://huggingface.co/metrics/bleu) and [SARI](https://huggingface.co/metrics/sari). These are all currently re-implemented each time (often with external code). A potentially more efficient and centralized way of doing things would maybe to have a single implementation and calling that implementation. E.g. @lhoestq 's proposal: ``` def _compute(...): bleu = load_metric("bleu", cache_dir=self.cache_dir, seed=self.seed) output = bleu._compute(...) ``` Something to keep in mind for the big metric reorg!
28
Calling existing metrics from other metrics There are several cases of metrics calling other metrics, e.g. [Wiki Split](https://huggingface.co/metrics/wiki_split) which calls [BLEU](https://huggingface.co/metrics/bleu) and [SARI](https://huggingface.co/metrics/sari). These are all currently re-implemented each time (often with external code). A potentially more efficient and centralized way of doing things would maybe to have a single implementation and calling that implementation. E.g. @lhoestq 's proposal: ``` def _compute(...): bleu = load_metric("bleu", cache_dir=self.cache_dir, seed=self.seed) output = bleu._compute(...) ``` Something to keep in mind for the big metric reorg! That's definitely the way to go to avoid implementation bugs and making sure we can fix issues globally when we detect them in a metric. Thanks for reporting!
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https://github.com/huggingface/datasets/issues/4037
Error while building documentation
After some investigation, maybe the bug is in `doc-builder`. I've opened an issue there: - huggingface/doc-builder#160
## Describe the bug Documentation building is failing: - https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true ``` ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format. Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct. ```
16
Error while building documentation ## Describe the bug Documentation building is failing: - https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true ``` ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format. Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct. ``` After some investigation, maybe the bug is in `doc-builder`. I've opened an issue there: - huggingface/doc-builder#160
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https://github.com/huggingface/datasets/issues/4031
Cannot load the dataset conll2012_ontonotesv5
Hi @cathyxl, thanks for reporting. Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well): - #4002 That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by: - installing `datasets` from our GitHub repo: ```bash pip install git+https://github.com/huggingface/datasets#egg=datasets ``` - forcing the data files to be redownloaded ```python ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload") ``` Feel free to re-open this issue if the problem persists.
## Describe the bug Cannot load the dataset conll2012_ontonotesv5 ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset dataset = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test") print(dataset) ``` ## Expected results The datasets should be downloaded successfully ## Actual results raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.4.0-88-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 7.0.0
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Cannot load the dataset conll2012_ontonotesv5 ## Describe the bug Cannot load the dataset conll2012_ontonotesv5 ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset dataset = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test") print(dataset) ``` ## Expected results The datasets should be downloaded successfully ## Actual results raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.4.0-88-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 7.0.0 Hi @cathyxl, thanks for reporting. Indeed, we have recently updated the loading script of that dataset (and fixed that bug as well): - #4002 That fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by: - installing `datasets` from our GitHub repo: ```bash pip install git+https://github.com/huggingface/datasets#egg=datasets ``` - forcing the data files to be redownloaded ```python ds = load_dataset('conll2012_ontonotesv5', 'english_v4', split="test", download_mode="force_redownload") ``` Feel free to re-open this issue if the problem persists.
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https://github.com/huggingface/datasets/issues/4029
Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold
Hi ! You can access the faiss index with ```python faiss_index = my_dataset.get_index("my_index_name").faiss_index ``` and then do whatever you want with it, e.g. query it using range_search: ```python threshold = 0.95 limits, distances, indices = faiss_index.range_search(x=xq, thresh=threshold) texts = dataset[indices] ```
**Is your feature request related to a problem? Please describe.** I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly. **Describe the solution you'd like** dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold. It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold. see details here: https://github.com/facebookresearch/faiss/issues/1273 **Describe alternatives you've considered** I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets **Additional context** The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
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Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold **Is your feature request related to a problem? Please describe.** I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly. **Describe the solution you'd like** dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold. It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold. see details here: https://github.com/facebookresearch/faiss/issues/1273 **Describe alternatives you've considered** I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets **Additional context** The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts. Hi ! You can access the faiss index with ```python faiss_index = my_dataset.get_index("my_index_name").faiss_index ``` and then do whatever you want with it, e.g. query it using range_search: ```python threshold = 0.95 limits, distances, indices = faiss_index.range_search(x=xq, thresh=threshold) texts = dataset[indices] ```
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https://github.com/huggingface/datasets/issues/4029
Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold
wow, that's great, thank you for the explanation. (if that's not already in the documentation, could be worth adding it) which type of faiss index is Datasets using? I looked into faiss recently and I understand that there are several different types of indexes and the choice is important, e.g. regarding which distance metric you use (euclidian vs. cosine/dot product), the size of my dataset etc. can I chose the type of index somehow as well?
**Is your feature request related to a problem? Please describe.** I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly. **Describe the solution you'd like** dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold. It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold. see details here: https://github.com/facebookresearch/faiss/issues/1273 **Describe alternatives you've considered** I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets **Additional context** The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
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Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold **Is your feature request related to a problem? Please describe.** I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly. **Describe the solution you'd like** dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold. It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold. see details here: https://github.com/facebookresearch/faiss/issues/1273 **Describe alternatives you've considered** I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets **Additional context** The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts. wow, that's great, thank you for the explanation. (if that's not already in the documentation, could be worth adding it) which type of faiss index is Datasets using? I looked into faiss recently and I understand that there are several different types of indexes and the choice is important, e.g. regarding which distance metric you use (euclidian vs. cosine/dot product), the size of my dataset etc. can I chose the type of index somehow as well?
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https://github.com/huggingface/datasets/issues/4029
Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold
`Dataset.add_faiss_index` has a `string_factory` parameter, used to set the type of index (see the faiss documentation about [index factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)). Alternatively, you can pass an index you've defined yourself using faiss with the `custom_index` parameter of `Dataset.add_faiss_index` Here is the full documentation of `Dataset.add_faiss_index`: https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index
**Is your feature request related to a problem? Please describe.** I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly. **Describe the solution you'd like** dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold. It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold. see details here: https://github.com/facebookresearch/faiss/issues/1273 **Describe alternatives you've considered** I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets **Additional context** The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
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Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold **Is your feature request related to a problem? Please describe.** I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly. **Describe the solution you'd like** dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold. It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold. see details here: https://github.com/facebookresearch/faiss/issues/1273 **Describe alternatives you've considered** I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets **Additional context** The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts. `Dataset.add_faiss_index` has a `string_factory` parameter, used to set the type of index (see the faiss documentation about [index factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)). Alternatively, you can pass an index you've defined yourself using faiss with the `custom_index` parameter of `Dataset.add_faiss_index` Here is the full documentation of `Dataset.add_faiss_index`: https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index
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https://github.com/huggingface/datasets/issues/4027
ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme'
Hi, @MoritzLaurer, thanks for reporting. Normally this is due to a mismatch between the versions of your Elasticsearch client and server: - your ES client is passing only keyword arguments to your ES server - whereas your ES server expects a positional argument called 'scheme' In order to fix this, you should align the major versions of both Elasticsearch client and server. You can have more info: - on this other issue page: https://github.com/huggingface/datasets/issues/3956#issuecomment-1072115173 - Elasticsearch client docs: https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/overview.html#_compatibility Feel free to re-open this issue if the problem persists. Duplicate of: - #3956
## Describe the bug I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch ``` from datasets import load_dataset squad = load_dataset('crime_and_punish', split='train[:1000]') ``` When I run the line: `squad.add_elasticsearch_index("context", host="localhost", port="9200")` I get the error: `TypeError: __init__() missing 1 required positional argument: 'scheme'` ## Expected results No error message ## Actual results ``` TypeError Traceback (most recent call last) [<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>() 1 import elasticsearch ----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200") 6 frames [/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host) 209 options["path_prefix"] = options.pop("url_prefix") 210 --> 211 return NodeConfig(**options) # type: ignore 212 213 TypeError: __init__() missing 1 required positional argument: 'scheme' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux, Google Colab - Python version: Google Colab (probably 3.7) - PyArrow version: ?
93
ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme' ## Describe the bug I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch ``` from datasets import load_dataset squad = load_dataset('crime_and_punish', split='train[:1000]') ``` When I run the line: `squad.add_elasticsearch_index("context", host="localhost", port="9200")` I get the error: `TypeError: __init__() missing 1 required positional argument: 'scheme'` ## Expected results No error message ## Actual results ``` TypeError Traceback (most recent call last) [<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>() 1 import elasticsearch ----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200") 6 frames [/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host) 209 options["path_prefix"] = options.pop("url_prefix") 210 --> 211 return NodeConfig(**options) # type: ignore 212 213 TypeError: __init__() missing 1 required positional argument: 'scheme' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux, Google Colab - Python version: Google Colab (probably 3.7) - PyArrow version: ? Hi, @MoritzLaurer, thanks for reporting. Normally this is due to a mismatch between the versions of your Elasticsearch client and server: - your ES client is passing only keyword arguments to your ES server - whereas your ES server expects a positional argument called 'scheme' In order to fix this, you should align the major versions of both Elasticsearch client and server. You can have more info: - on this other issue page: https://github.com/huggingface/datasets/issues/3956#issuecomment-1072115173 - Elasticsearch client docs: https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/overview.html#_compatibility Feel free to re-open this issue if the problem persists. Duplicate of: - #3956
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https://github.com/huggingface/datasets/issues/4027
ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme'
1. Check elasticsearch version ``` import elasticsearch print(elasticsearch.__version__) ``` Ex: 7.9.1 2. Uninstall current elasticsearch package `pip uninstall elasticsearch` 3. Install elasticsearch 7.9.1 package `pip install elasticsearch==7.9.1`
## Describe the bug I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch ``` from datasets import load_dataset squad = load_dataset('crime_and_punish', split='train[:1000]') ``` When I run the line: `squad.add_elasticsearch_index("context", host="localhost", port="9200")` I get the error: `TypeError: __init__() missing 1 required positional argument: 'scheme'` ## Expected results No error message ## Actual results ``` TypeError Traceback (most recent call last) [<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>() 1 import elasticsearch ----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200") 6 frames [/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host) 209 options["path_prefix"] = options.pop("url_prefix") 210 --> 211 return NodeConfig(**options) # type: ignore 212 213 TypeError: __init__() missing 1 required positional argument: 'scheme' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux, Google Colab - Python version: Google Colab (probably 3.7) - PyArrow version: ?
27
ElasticSearch Indexing example: TypeError: __init__() missing 1 required positional argument: 'scheme' ## Describe the bug I am following the example in the documentation for elastic search step by step (on google colab): https://huggingface.co/docs/datasets/faiss_es#elasticsearch ``` from datasets import load_dataset squad = load_dataset('crime_and_punish', split='train[:1000]') ``` When I run the line: `squad.add_elasticsearch_index("context", host="localhost", port="9200")` I get the error: `TypeError: __init__() missing 1 required positional argument: 'scheme'` ## Expected results No error message ## Actual results ``` TypeError Traceback (most recent call last) [<ipython-input-23-9205593edef3>](https://localhost:8080/#) in <module>() 1 import elasticsearch ----> 2 squad.add_elasticsearch_index("text", host="localhost", port="9200") 6 frames [/usr/local/lib/python3.7/dist-packages/elasticsearch/_sync/client/utils.py](https://localhost:8080/#) in host_mapping_to_node_config(host) 209 options["path_prefix"] = options.pop("url_prefix") 210 --> 211 return NodeConfig(**options) # type: ignore 212 213 TypeError: __init__() missing 1 required positional argument: 'scheme' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.2.0 - Platform: Linux, Google Colab - Python version: Google Colab (probably 3.7) - PyArrow version: ? 1. Check elasticsearch version ``` import elasticsearch print(elasticsearch.__version__) ``` Ex: 7.9.1 2. Uninstall current elasticsearch package `pip uninstall elasticsearch` 3. Install elasticsearch 7.9.1 package `pip install elasticsearch==7.9.1`
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https://github.com/huggingface/datasets/issues/4015
Can not correctly parse the classes with imagefolder
I found that the problem arises because the image files in my folder are actually symbolic links (for my own reasons). After modifications, the classes can now be correctly parsed. Therefore, I close this issue.
## Describe the bug I try to load my own image dataset with imagefolder, but the parsing of classes is incorrect. ## Steps to reproduce the bug I organized my dataset (ImageNet) in the following structure: ``` - imagenet/ - train/ - n01440764/ - ILSVRC2012_val_00000293.jpg - ...... - n01695060/ - ...... - val/ - n01440764/ - n01695060/ - ...... ``` At first, I followed the instructions from the Huggingface [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification#using-your-own-data) to load my data as: ``` from datasets import load_dataset data_files = {'train': 'imagenet/train', 'val': 'imagenet/val'} ds = load_dataset("nateraw/image-folder", data_files=data_files, task="image-classification") ``` but it resulted following error (I mask my personal path as <PERSONAL_PATH>): ``` FileNotFoundError: Unable to find 'https://huggingface.co/datasets/nateraw/image-folder/resolve/main/imagenet/train' at <PERSONAL_PATH>/ImageNet/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main ``` Next, I followed a recent issue #3960 to load data as: ``` from datasets import load_dataset data_files = {'train': ['imagenet/train/**'], 'val': ['imagenet/val/**']} ds = load_dataset("imagefolder", data_files=data_files, task="image-classification") ``` and the data can be loaded without error as: (I copy val folder to train folder for illustration) ``` >>> ds DatasetDict({ train: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) val: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) }) ``` However, the parsed classes is wrong (should be 1000 classes): ``` >>> ds["train"].features {'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=1, names=['val'], id=None)} ``` ## Expected results I expect that the "labels" in ds["train"].features should contain 1000 classes. ## Actual results The "labels" in ds["train"].features contains only 1 wrong class. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Ubuntu 18.04 - Python version: Python 3.7.12 - PyArrow version: 7.0.0
35
Can not correctly parse the classes with imagefolder ## Describe the bug I try to load my own image dataset with imagefolder, but the parsing of classes is incorrect. ## Steps to reproduce the bug I organized my dataset (ImageNet) in the following structure: ``` - imagenet/ - train/ - n01440764/ - ILSVRC2012_val_00000293.jpg - ...... - n01695060/ - ...... - val/ - n01440764/ - n01695060/ - ...... ``` At first, I followed the instructions from the Huggingface [example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification#using-your-own-data) to load my data as: ``` from datasets import load_dataset data_files = {'train': 'imagenet/train', 'val': 'imagenet/val'} ds = load_dataset("nateraw/image-folder", data_files=data_files, task="image-classification") ``` but it resulted following error (I mask my personal path as <PERSONAL_PATH>): ``` FileNotFoundError: Unable to find 'https://huggingface.co/datasets/nateraw/image-folder/resolve/main/imagenet/train' at <PERSONAL_PATH>/ImageNet/https:/huggingface.co/datasets/nateraw/image-folder/resolve/main ``` Next, I followed a recent issue #3960 to load data as: ``` from datasets import load_dataset data_files = {'train': ['imagenet/train/**'], 'val': ['imagenet/val/**']} ds = load_dataset("imagefolder", data_files=data_files, task="image-classification") ``` and the data can be loaded without error as: (I copy val folder to train folder for illustration) ``` >>> ds DatasetDict({ train: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) val: Dataset({ features: ['image', 'labels'], num_rows: 50000 }) }) ``` However, the parsed classes is wrong (should be 1000 classes): ``` >>> ds["train"].features {'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=1, names=['val'], id=None)} ``` ## Expected results I expect that the "labels" in ds["train"].features should contain 1000 classes. ## Actual results The "labels" in ds["train"].features contains only 1 wrong class. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Ubuntu 18.04 - Python version: Python 3.7.12 - PyArrow version: 7.0.0 I found that the problem arises because the image files in my folder are actually symbolic links (for my own reasons). After modifications, the classes can now be correctly parsed. Therefore, I close this issue.
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https://github.com/huggingface/datasets/issues/4013
Cannot preview "hazal/Turkish-Biomedical-corpus-trM"
Hi @hazalturkmen, thanks for reporting. Note that your dataset repository does not contain any loading script; it only contains a data file named `tr_article_2`. When there is no loading script but only data files, the `datasets` library tries to infer how to load the data by looking at the data file extensions. However, your data file does not have any extension. Note that current supported data file extensions are: 'csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'. You have more info on our docs: [How to share a dataset](https://huggingface.co/docs/datasets/share).
## Dataset viewer issue for '*hazal/Turkish-Biomedical-corpus-trM' **Link:** *https://huggingface.co/datasets/hazal/Turkish-Biomedical-corpus-trM* *I cannot see the dataset preview.* ``` Server Error Status code: 400 Exception: HTTPError Message: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/hazal/Turkish-Biomedical-corpus-trM?full=true ``` Am I the one who added this dataset ? Yes
151
Cannot preview "hazal/Turkish-Biomedical-corpus-trM" ## Dataset viewer issue for '*hazal/Turkish-Biomedical-corpus-trM' **Link:** *https://huggingface.co/datasets/hazal/Turkish-Biomedical-corpus-trM* *I cannot see the dataset preview.* ``` Server Error Status code: 400 Exception: HTTPError Message: 403 Client Error: Forbidden for url: https://huggingface.co/api/datasets/hazal/Turkish-Biomedical-corpus-trM?full=true ``` Am I the one who added this dataset ? Yes Hi @hazalturkmen, thanks for reporting. Note that your dataset repository does not contain any loading script; it only contains a data file named `tr_article_2`. When there is no loading script but only data files, the `datasets` library tries to infer how to load the data by looking at the data file extensions. However, your data file does not have any extension. Note that current supported data file extensions are: 'csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip'. You have more info on our docs: [How to share a dataset](https://huggingface.co/docs/datasets/share).
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https://github.com/huggingface/datasets/issues/4007
set_format does not work with multi dimension tensor
Hi! Use the `ArrayXD` feature type (where X is the number of dimensions) to get correctly formated tensors. So in your case, define the dataset as follows : ```python ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}, features=Features({"A": Array2D(shape=(2, 2), dtype="float32")})) ```
## Describe the bug set_format only transforms the last dimension of a multi-dimension list to tensor ## Steps to reproduce the bug ```python import torch from datasets import Dataset ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}) # ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result ds = ds.with_format("torch") print(ds[0]) ``` ## Expected results ``` {'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]} ``` ## Actual results ``` {'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - datasets version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0
39
set_format does not work with multi dimension tensor ## Describe the bug set_format only transforms the last dimension of a multi-dimension list to tensor ## Steps to reproduce the bug ```python import torch from datasets import Dataset ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}) # ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result ds = ds.with_format("torch") print(ds[0]) ``` ## Expected results ``` {'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]} ``` ## Actual results ``` {'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - datasets version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0 Hi! Use the `ArrayXD` feature type (where X is the number of dimensions) to get correctly formated tensors. So in your case, define the dataset as follows : ```python ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}, features=Features({"A": Array2D(shape=(2, 2), dtype="float32")})) ```
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https://github.com/huggingface/datasets/issues/4007
set_format does not work with multi dimension tensor
Hi @mariosasko I'm facing the same issue and the only work around I've found so far is to convert my `DatasetDict` to a dictionary and then create new objects with `Dataset.from_dict`. ``` dataset = load_dataset("my_dataset.py") dataset = dataset.map(lambda example: blabla(example)) dict_dataset_test = dataset["test"].to_dict() ... dataset_test = Dataset.from_dict(dict_dataset_test, features=Features(features)) ``` However, converting a `Dataset` object to a dict takes quite a lot of time and memory... Is there a way to directly create an `Array2D` without having to transform the original `Dataset` to a dict?
## Describe the bug set_format only transforms the last dimension of a multi-dimension list to tensor ## Steps to reproduce the bug ```python import torch from datasets import Dataset ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}) # ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result ds = ds.with_format("torch") print(ds[0]) ``` ## Expected results ``` {'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]} ``` ## Actual results ``` {'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - datasets version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0
84
set_format does not work with multi dimension tensor ## Describe the bug set_format only transforms the last dimension of a multi-dimension list to tensor ## Steps to reproduce the bug ```python import torch from datasets import Dataset ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}) # ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result ds = ds.with_format("torch") print(ds[0]) ``` ## Expected results ``` {'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]} ``` ## Actual results ``` {'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - datasets version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0 Hi @mariosasko I'm facing the same issue and the only work around I've found so far is to convert my `DatasetDict` to a dictionary and then create new objects with `Dataset.from_dict`. ``` dataset = load_dataset("my_dataset.py") dataset = dataset.map(lambda example: blabla(example)) dict_dataset_test = dataset["test"].to_dict() ... dataset_test = Dataset.from_dict(dict_dataset_test, features=Features(features)) ``` However, converting a `Dataset` object to a dict takes quite a lot of time and memory... Is there a way to directly create an `Array2D` without having to transform the original `Dataset` to a dict?
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https://github.com/huggingface/datasets/issues/4007
set_format does not work with multi dimension tensor
Hi! Yes, you can directly pass the `Features` dictionary as `features` in `map` to cast the column to `Array2D`: ```python dataset = dataset.map(lambda example: blabla(example), features=Features(features)) ``` Or you can use `cast` after `map` to do that: ```python dataset = dataset.map(lambda example: blabla(example)) dataset = dataset.cast(Features(features)) ```
## Describe the bug set_format only transforms the last dimension of a multi-dimension list to tensor ## Steps to reproduce the bug ```python import torch from datasets import Dataset ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}) # ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result ds = ds.with_format("torch") print(ds[0]) ``` ## Expected results ``` {'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]} ``` ## Actual results ``` {'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - datasets version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0
47
set_format does not work with multi dimension tensor ## Describe the bug set_format only transforms the last dimension of a multi-dimension list to tensor ## Steps to reproduce the bug ```python import torch from datasets import Dataset ds = Dataset.from_dict({"A": [torch.rand((2, 2))]}) # ds = Dataset.from_dict({"A": [np.random.rand(2, 2)]}) # => same result ds = ds.with_format("torch") print(ds[0]) ``` ## Expected results ``` {'A': [tensor([[0.6689, 0.1516], [0.1403, 0.5567]])]} ``` ## Actual results ``` {'A': [tensor([0.6689, 0.1516]), tensor([0.1403, 0.5567])]} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - datasets version: 2.0.0 - Platform: Mac OSX - Python version: 3.8.12 - PyArrow version: 7.0.0 Hi! Yes, you can directly pass the `Features` dictionary as `features` in `map` to cast the column to `Array2D`: ```python dataset = dataset.map(lambda example: blabla(example), features=Features(features)) ``` Or you can use `cast` after `map` to do that: ```python dataset = dataset.map(lambda example: blabla(example)) dataset = dataset.cast(Features(features)) ```
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https://github.com/huggingface/datasets/issues/4005
Yelp not working
I don't think it's an issue with the dataset-viewer. Maybe @lhoestq or @albertvillanova could confirm. ```python >>> from datasets import load_dataset, DownloadMode >>> import itertools >>> # without streaming >>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD) Downloading builder script: 4.39kB [00:00, 5.97MB/s] Downloading metadata: 2.13kB [00:00, 3.14MB/s] Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /home/slesage/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43... Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.10k/1.10k [00:00<00:00, 1.39MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets/src/datasets/load.py", line 1687, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/slesage/hf/datasets/src/datasets/builder.py", line 676, in _download_and_prepare verify_checksums( File "/home/slesage/hf/datasets/src/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0'] >>> # with streaming >>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD, streaming=True) Downloading builder script: 4.39kB [00:00, 5.53MB/s] Downloading metadata: 2.13kB [00:00, 3.14MB/s] Traceback (most recent call last): File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 375, in _info await _file_info( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 736, in _file_info r.raise_for_status() File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/aiohttp/client_reqrep.py", line 1000, in raise_for_status raise ClientResponseError( aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://doc-0g-bs-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/gklhpdq1arj8v15qrg7ces34a8c3413d/1648144575000/07511006523564980941/*/0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0?e=download') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets/src/datasets/load.py", line 1677, in load_dataset return builder_instance.as_streaming_dataset( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 906, in as_streaming_dataset splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)} File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/yelp_review_full/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43/yelp_review_full.py", line 102, in _split_generators data_dir = dl_manager.download_and_extract(my_urls) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 800, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 778, in extract urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True) File "/home/slesage/hf/datasets/src/datasets/utils/py_utils.py", line 306, in map_nested return function(data_struct) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 783, in _extract protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 372, in _get_extraction_protocol with fsspec.open(urlpath, **kwargs) as f: File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/core.py", line 102, in __enter__ f = self.fs.open(self.path, mode=mode) File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/spec.py", line 978, in open f = self._open( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 335, in _open size = size or self.info(path, **kwargs)["size"] File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 88, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 69, in sync raise result[0] File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 25, in _runner result[0] = await coro File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 388, in _info raise FileNotFoundError(url) from exc FileNotFoundError: https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0&confirm=t ``` And this is before even trying to access the rows with ```python >>> rows = list(itertools.islice(dataset, 100)) >>> rows = list(dataset.take(100)) ```
## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
383
Yelp not working ## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads. I don't think it's an issue with the dataset-viewer. Maybe @lhoestq or @albertvillanova could confirm. ```python >>> from datasets import load_dataset, DownloadMode >>> import itertools >>> # without streaming >>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD) Downloading builder script: 4.39kB [00:00, 5.97MB/s] Downloading metadata: 2.13kB [00:00, 3.14MB/s] Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /home/slesage/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43... Downloading data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.10k/1.10k [00:00<00:00, 1.39MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets/src/datasets/load.py", line 1687, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/slesage/hf/datasets/src/datasets/builder.py", line 676, in _download_and_prepare verify_checksums( File "/home/slesage/hf/datasets/src/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0'] >>> # with streaming >>> dataset = load_dataset("yelp_review_full", name="yelp_review_full", split="train", download_mode=DownloadMode.FORCE_REDOWNLOAD, streaming=True) Downloading builder script: 4.39kB [00:00, 5.53MB/s] Downloading metadata: 2.13kB [00:00, 3.14MB/s] Traceback (most recent call last): File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 375, in _info await _file_info( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 736, in _file_info r.raise_for_status() File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/aiohttp/client_reqrep.py", line 1000, in raise_for_status raise ClientResponseError( aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://doc-0g-bs-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/gklhpdq1arj8v15qrg7ces34a8c3413d/1648144575000/07511006523564980941/*/0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0?e=download') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets/src/datasets/load.py", line 1677, in load_dataset return builder_instance.as_streaming_dataset( File "/home/slesage/hf/datasets/src/datasets/builder.py", line 906, in as_streaming_dataset splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)} File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/yelp_review_full/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43/yelp_review_full.py", line 102, in _split_generators data_dir = dl_manager.download_and_extract(my_urls) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 800, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 778, in extract urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True) File "/home/slesage/hf/datasets/src/datasets/utils/py_utils.py", line 306, in map_nested return function(data_struct) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 783, in _extract protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token) File "/home/slesage/hf/datasets/src/datasets/utils/streaming_download_manager.py", line 372, in _get_extraction_protocol with fsspec.open(urlpath, **kwargs) as f: File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/core.py", line 102, in __enter__ f = self.fs.open(self.path, mode=mode) File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/spec.py", line 978, in open f = self._open( File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 335, in _open size = size or self.info(path, **kwargs)["size"] File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 88, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 69, in sync raise result[0] File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/asyn.py", line 25, in _runner result[0] = await coro File "/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/fsspec/implementations/http.py", line 388, in _info raise FileNotFoundError(url) from exc FileNotFoundError: https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0&confirm=t ``` And this is before even trying to access the rows with ```python >>> rows = list(itertools.islice(dataset, 100)) >>> rows = list(dataset.take(100)) ```
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0.2827530205, 0.1280816942, -0.1550481468, 0.259942174, -0.0272895172 ]
https://github.com/huggingface/datasets/issues/4005
Yelp not working
Yet another issue related to google drive not being nice. Most likely your IP has been banned from using their API programmatically. Do you know if we are allowed to host and redistribute the data ourselves ?
## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
37
Yelp not working ## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads. Yet another issue related to google drive not being nice. Most likely your IP has been banned from using their API programmatically. Do you know if we are allowed to host and redistribute the data ourselves ?
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-0.055208236, 0.2374397367, 0.1661210209, -0.1491921395, 0.2630926669, 0.0586083084 ]
https://github.com/huggingface/datasets/issues/4005
Yelp not working
Hi, Facing the same issue while loading the dataset: `Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files` Thanks
## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
18
Yelp not working ## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads. Hi, Facing the same issue while loading the dataset: `Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files` Thanks
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0.0433981344, -0.062931478, 0.2252497524, 0.1289425939, -0.180690065, 0.1817030013, -0.0476325899 ]
https://github.com/huggingface/datasets/issues/4005
Yelp not working
> Facing the same issue while loading the dataset: > > Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files Thanks for reporting. I think this is the same issue. Feel free to try again later, once Google Drive stopped blocking you. You can retry by passing `download_mode="force_redownload"` to `load_dataset`
## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
49
Yelp not working ## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads. > Facing the same issue while loading the dataset: > > Error: {NonMatchingChecksumError}Checksums didn't match for dataset source files Thanks for reporting. I think this is the same issue. Feel free to try again later, once Google Drive stopped blocking you. You can retry by passing `download_mode="force_redownload"` to `load_dataset`
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https://github.com/huggingface/datasets/issues/4005
Yelp not working
I noticed that FastAI hosts the Yelp dataset at https://s3.amazonaws.com/fast-ai-nlp/yelp_review_full_csv.tgz (from their catalog [here](https://course.fast.ai/datasets)) Let's update the yelp dataset script to download from there instead of Google Drive
## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
28
Yelp not working ## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads. I noticed that FastAI hosts the Yelp dataset at https://s3.amazonaws.com/fast-ai-nlp/yelp_review_full_csv.tgz (from their catalog [here](https://course.fast.ai/datasets)) Let's update the yelp dataset script to download from there instead of Google Drive
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0.1416539401, -0.2759971917, 0.1177629307, -0.1141982526 ]
https://github.com/huggingface/datasets/issues/4005
Yelp not working
I updated the link to not use Google Drive anymore, we will do a release early next week with the updated download url of the dataset :)
## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads.
27
Yelp not working ## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/yelp_review_full/viewer/yelp_review_full/train Doesn't work: ``` Server error Status code: 400 Exception: Error Message: line contains NULL ``` Am I the one who added this dataset ? No A seamingly copy of the dataset: https://huggingface.co/datasets/SetFit/yelp_review_full works . The original one: https://huggingface.co/datasets/yelp_review_full has > 20K downloads. I updated the link to not use Google Drive anymore, we will do a release early next week with the updated download url of the dataset :)
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https://github.com/huggingface/datasets/issues/4003
ASSIN2 dataset checksum bug
Using latest code, I am still facing the issue. ```python (base) vimos@vimosmu ➜ ~ ipython Python 3.6.7 | packaged by conda-forge | (default, Nov 6 2019, 16:19:42) Type 'copyright', 'credits' or 'license' for more information IPython 7.11.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: from datasets import load_dataset In [2]: load_dataset("assin2") Downloading builder script: 4.24kB [00:00, 244kB/s] Downloading metadata: 2.58kB [00:00, 2.19MB/s] Using custom data configuration default Downloading and preparing dataset assin2/default (download: 2.02 MiB, generated: 1.21 MiB, post-processed: Unknown size, total: 3.23 MiB) to /home/vimos/.cache/huggingface/datasets/assin2/default/1.0.0/8467f7acbda82f62ab960ca869dc1e96350e0e103a1ef7eaa43bbee530b80061... Downloading data: 1.51MB [00:00, 102MB/s] Downloading data: 116kB [00:00, 63.6MB/s] Downloading data: 493kB [00:00, 95.8MB/s] Downloading data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:00<00:00, 8.27it/s] --------------------------------------------------------------------------- ExpectedMoreDownloadedFiles Traceback (most recent call last) <ipython-input-2-b367d1ffd68e> in <module> ----> 1 load_dataset("assin2") ~/anaconda3/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1694 ignore_verifications=ignore_verifications, 1695 try_from_hf_gcs=try_from_hf_gcs, -> 1696 use_auth_token=use_auth_token, 1697 ) 1698 ~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info ~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: ~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 675 if verify_infos: 676 verify_checksums( --> 677 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 678 ) 679 ~/anaconda3/lib/python3.6/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'https://drive.google.com/u/0/uc?id=1kb7xq6Mb3eaqe9cOAo70BaG9ypwkIqEU&export=download', 'https://drive.google.com/u/0/uc?id=1J3FpQaHxpM-FDfBUyooh-sZF-B-bM_lU&export=download', 'https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'} ```
## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1
264
ASSIN2 dataset checksum bug ## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1 Using latest code, I am still facing the issue. ```python (base) vimos@vimosmu ➜ ~ ipython Python 3.6.7 | packaged by conda-forge | (default, Nov 6 2019, 16:19:42) Type 'copyright', 'credits' or 'license' for more information IPython 7.11.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: from datasets import load_dataset In [2]: load_dataset("assin2") Downloading builder script: 4.24kB [00:00, 244kB/s] Downloading metadata: 2.58kB [00:00, 2.19MB/s] Using custom data configuration default Downloading and preparing dataset assin2/default (download: 2.02 MiB, generated: 1.21 MiB, post-processed: Unknown size, total: 3.23 MiB) to /home/vimos/.cache/huggingface/datasets/assin2/default/1.0.0/8467f7acbda82f62ab960ca869dc1e96350e0e103a1ef7eaa43bbee530b80061... Downloading data: 1.51MB [00:00, 102MB/s] Downloading data: 116kB [00:00, 63.6MB/s] Downloading data: 493kB [00:00, 95.8MB/s] Downloading data files: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3/3 [00:00<00:00, 8.27it/s] --------------------------------------------------------------------------- ExpectedMoreDownloadedFiles Traceback (most recent call last) <ipython-input-2-b367d1ffd68e> in <module> ----> 1 load_dataset("assin2") ~/anaconda3/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1694 ignore_verifications=ignore_verifications, 1695 try_from_hf_gcs=try_from_hf_gcs, -> 1696 use_auth_token=use_auth_token, 1697 ) 1698 ~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info ~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: ~/anaconda3/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 675 if verify_infos: 676 verify_checksums( --> 677 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" 678 ) 679 ~/anaconda3/lib/python3.6/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 31 return 32 if len(set(expected_checksums) - set(recorded_checksums)) > 0: ---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) 34 if len(set(recorded_checksums) - set(expected_checksums)) > 0: 35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) ExpectedMoreDownloadedFiles: {'https://drive.google.com/u/0/uc?id=1kb7xq6Mb3eaqe9cOAo70BaG9ypwkIqEU&export=download', 'https://drive.google.com/u/0/uc?id=1J3FpQaHxpM-FDfBUyooh-sZF-B-bM_lU&export=download', 'https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'} ```
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-0.1593693942, 0.3337382376, 0.003726986, 0.2636365592, 0.0279542301, -0.2065555304, 0.0435521044, -0.0215669461 ]
https://github.com/huggingface/datasets/issues/4003
ASSIN2 dataset checksum bug
That's true. Steps to reproduce the bug on Google Colab: ``` git clone https://github.com/huggingface/datasets.git cd datasets pip install -e . python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])" ``` However the dataset will load without any problems if you just install version 2.0.0: ``` pip install datasets python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])" ``` Any thoughts @lhoestq ?
## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1
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ASSIN2 dataset checksum bug ## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1 That's true. Steps to reproduce the bug on Google Colab: ``` git clone https://github.com/huggingface/datasets.git cd datasets pip install -e . python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])" ``` However the dataset will load without any problems if you just install version 2.0.0: ``` pip install datasets python -c "from datasets import load_dataset; print(load_dataset('assin2')['train'][0])" ``` Any thoughts @lhoestq ?
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https://github.com/huggingface/datasets/issues/4003
ASSIN2 dataset checksum bug
Right indeed ! Let me open a PR to fix this. The dataset_infos.json file that stores some metadata about the dataset to download (and is used to verify it was correctly downloaded) hasn't been updated correctly
## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1
36
ASSIN2 dataset checksum bug ## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1 Right indeed ! Let me open a PR to fix this. The dataset_infos.json file that stores some metadata about the dataset to download (and is used to verify it was correctly downloaded) hasn't been updated correctly
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-0.1593693942, 0.3337382376, 0.003726986, 0.2636365592, 0.0279542301, -0.2065555304, 0.0435521044, -0.0215669461 ]
https://github.com/huggingface/datasets/issues/4003
ASSIN2 dataset checksum bug
Not sure what the status of this is, but personally I am still getting this error, with glue.
## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1
18
ASSIN2 dataset checksum bug ## Describe the bug Checksum error after trying to load the [ASSIN 2 dataset](https://huggingface.co/datasets/assin2). `NonMatchingChecksumError` triggered by calling `load_dataset("assin2")`. Similar to #3952 , #3942 , #3941 , etc. ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) [<ipython-input-13-c664a92ad5e7>](https://localhost:8080/#) in <module>() ----> 1 load_dataset('assin2') 4 frames [/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=1Q9j1a83CuKzsHCGaNulSkNxBm7Dkn7Ln&export=download'] ``` ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("assin2") ``` ## Expected results Load the dataset. ## Actual results The dataset won't load. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Google Colab - Python version: 3.7.12 - PyArrow version: 6.0.1 Not sure what the status of this is, but personally I am still getting this error, with glue.
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https://github.com/huggingface/datasets/issues/4001
How to use generate this multitask dataset for SQUAD? I am getting a value error.
Hi! Replacing `nlp.<obj>` with `datasets.<obj>` in the script should fix the problem. `nlp` has been renamed to `datasets` more than a year ago, so please use `datasets` instead to avoid weird issues.
## Dataset viewer issue for 'squad_multitask*' **Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask *short description of the issue* I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine. I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format') Error: Status code: 400 Exception: TypeError Message: argument of type 'Value' is not iterable Kindly advice.
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How to use generate this multitask dataset for SQUAD? I am getting a value error. ## Dataset viewer issue for 'squad_multitask*' **Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask *short description of the issue* I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine. I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format') Error: Status code: 400 Exception: TypeError Message: argument of type 'Value' is not iterable Kindly advice. Hi! Replacing `nlp.<obj>` with `datasets.<obj>` in the script should fix the problem. `nlp` has been renamed to `datasets` more than a year ago, so please use `datasets` instead to avoid weird issues.
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https://github.com/huggingface/datasets/issues/4001
How to use generate this multitask dataset for SQUAD? I am getting a value error.
But I request you to please fix the same in the dataset hub explorer as well...
## Dataset viewer issue for 'squad_multitask*' **Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask *short description of the issue* I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine. I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format') Error: Status code: 400 Exception: TypeError Message: argument of type 'Value' is not iterable Kindly advice.
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How to use generate this multitask dataset for SQUAD? I am getting a value error. ## Dataset viewer issue for 'squad_multitask*' **Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask *short description of the issue* I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine. I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format') Error: Status code: 400 Exception: TypeError Message: argument of type 'Value' is not iterable Kindly advice. But I request you to please fix the same in the dataset hub explorer as well...
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