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https://github.com/huggingface/datasets/issues/6176
how to limit the size of memory mapped file?
Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
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how to limit the size of memory mapped file? ### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine) Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.
https://github.com/huggingface/datasets/issues/6176
how to limit the size of memory mapped file?
The trace of the error. Streaming works but is slower. ``` Root Cause (first observed failure): [0]: time : 2023-08-24_06:06:01 host : compute-126.cm.cluster rank : 0 (local_rank: 0) exitcode : 1 (pid: 48442) error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json traceback : Traceback (most recent call last): File "/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "Pretrain.py", line 214, in main pair_dataset, c4_dataset = create_dataset('pretrain', config) File "/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py", line 109, in create_dataset c4_dataset = load_dataset("c4", "en", split="train").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py", line 1810, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1145, in as_dataset datasets = map_nested( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 436, in map_nested return function(data_struct) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1175, in _build_single_dataset ds = self._as_dataset( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1246, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 65, in _memory_mapped_arrow_table_from_file opened_stream = _memory_mapped_record_batch_reader_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 50, in _memory_mapped_record_batch_reader_from_file memory_mapped_stream = pa.memory_map(filename) File "pyarrow/io.pxi", line 1009, in pyarrow.lib.memory_map File "pyarrow/io.pxi", line 956, in pyarrow.lib.MemoryMappedFile._open File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: Memory mapping file failed: Cannot allocate memory ```
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
229
how to limit the size of memory mapped file? ### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine) The trace of the error. Streaming works but is slower. ``` Root Cause (first observed failure): [0]: time : 2023-08-24_06:06:01 host : compute-126.cm.cluster rank : 0 (local_rank: 0) exitcode : 1 (pid: 48442) error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json traceback : Traceback (most recent call last): File "/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "Pretrain.py", line 214, in main pair_dataset, c4_dataset = create_dataset('pretrain', config) File "/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py", line 109, in create_dataset c4_dataset = load_dataset("c4", "en", split="train").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py", line 1810, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1145, in as_dataset datasets = map_nested( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 436, in map_nested return function(data_struct) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1175, in _build_single_dataset ds = self._as_dataset( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1246, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 65, in _memory_mapped_arrow_table_from_file opened_stream = _memory_mapped_record_batch_reader_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 50, in _memory_mapped_record_batch_reader_from_file memory_mapped_stream = pa.memory_map(filename) File "pyarrow/io.pxi", line 1009, in pyarrow.lib.memory_map File "pyarrow/io.pxi", line 956, in pyarrow.lib.MemoryMappedFile._open File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: Memory mapping file failed: Cannot allocate memory ```
https://github.com/huggingface/datasets/issues/6176
how to limit the size of memory mapped file?
This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping. PS: this is the API to reduce the size of the generated Arrow file: ```python from datasets import load_dataset builder = load_dataset_builder("c4", "en") builder.download_and_prepare(max_shard_size="5GB") dataset = builder.as_dataset() ``` If this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
64
how to limit the size of memory mapped file? ### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine) This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping. PS: this is the API to reduce the size of the generated Arrow file: ```python from datasets import load_dataset builder = load_dataset_builder("c4", "en") builder.download_and_prepare(max_shard_size="5GB") dataset = builder.as_dataset() ``` If this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.
https://github.com/huggingface/datasets/issues/6172
Make Dataset streaming queries retryable
Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries. > At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader A minor Hub outage that we experienced yesterday could be the cause.
### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing.
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Make Dataset streaming queries retryable ### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing. Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries. > At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader A minor Hub outage that we experienced yesterday could be the cause.
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
19
Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
29
Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed). I've tried uninstalling/reinstnalling to no avail
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
33
Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed). I've tried uninstalling/reinstnalling to no avail
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
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Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?
https://github.com/huggingface/datasets/issues/6162
load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file) `datasets` expects all the examples to have the same fields
### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields ### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file) `datasets` expects all the examples to have the same fields
https://github.com/huggingface/datasets/issues/6162
load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
@lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement?
### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields ### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement?
https://github.com/huggingface/datasets/issues/6162
load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
> @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Correct. Therefore any example that doesn't follow the inferred schema will make the code fail. > Maybe setting streaming=True can workaround this problem. Would you agree with my statement? You'll meet the same problem but later - when streaming and arriving at the problematic example
### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields ### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 > @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Correct. Therefore any example that doesn't follow the inferred schema will make the code fail. > Maybe setting streaming=True can workaround this problem. Would you agree with my statement? You'll meet the same problem but later - when streaming and arriving at the problematic example
https://github.com/huggingface/datasets/issues/6162
load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
@lhoestq I just run below test with streaming=True and is not failing at the problematic example ```python ds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True) count = 0 for i in ds['train']: count += 1 print(count) ``` and completes the 262241 samples successfully. It does error our when streaming is not used
### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields ### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 @lhoestq I just run below test with streaming=True and is not failing at the problematic example ```python ds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True) count = 0 for i in ds['train']: count += 1 print(count) ``` and completes the 262241 samples successfully. It does error our when streaming is not used
https://github.com/huggingface/datasets/issues/6157
DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.
### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ImagesConfig, self).__init__(**kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding' ### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ImagesConfig, self).__init__(**kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.
https://github.com/huggingface/datasets/issues/6157
DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
Does this error occur even if you change the cache directory (the `cache_dir` parameter in `load_dataset`)?
### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ImagesConfig, self).__init__(**kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding' ### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ImagesConfig, self).__init__(**kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 Does this error occur even if you change the cache directory (the `cache_dir` parameter in `load_dataset`)?
https://github.com/huggingface/datasets/issues/6156
Why not use self._epoch as seed to shuffle in distributed training with IterableDataset
`_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`). To fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`
### Describe the bug Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177 My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801 If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator? https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206 ### Steps to reproduce the bug As mentioned above. ### Expected behavior As mentioned above. ### Environment info Not related
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Why not use self._epoch as seed to shuffle in distributed training with IterableDataset ### Describe the bug Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177 My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801 If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator? https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206 ### Steps to reproduce the bug As mentioned above. ### Expected behavior As mentioned above. ### Environment info Not related `_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`). To fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`
https://github.com/huggingface/datasets/issues/6152
FolderBase Dataset automatically resolves under current directory when data_dir is not specified
Makes sense, I guess this can be fixed in the load_dataset_builder method. It concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)
### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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FolderBase Dataset automatically resolves under current directory when data_dir is not specified ### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 Makes sense, I guess this can be fixed in the load_dataset_builder method. It concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)
https://github.com/huggingface/datasets/issues/6152
FolderBase Dataset automatically resolves under current directory when data_dir is not specified
I think the behavior is related to these lines, which short circuited the error handling. https://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952 So should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.
### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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FolderBase Dataset automatically resolves under current directory when data_dir is not specified ### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 I think the behavior is related to these lines, which short circuited the error handling. https://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952 So should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.
https://github.com/huggingface/datasets/issues/6152
FolderBase Dataset automatically resolves under current directory when data_dir is not specified
This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed
### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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FolderBase Dataset automatically resolves under current directory when data_dir is not specified ### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed
https://github.com/huggingface/datasets/issues/6151
Faster sorting for single key items
`Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory) ```python sort_keys = [(col, "ascending") for col in column_names] indices = pc.sort_indices(self.data, sort_keys=sort_keys) return self.select(indices) ```
### Feature request A faster way to sort a dataset which contains a large number of rows. ### Motivation The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. **Code snippet:** ```python ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True) sorted_ds = ds.sort("pubDate", keep_in_memory=True) ``` However, once I switched to a different method which 1. unpacked to a list of tuples 2. sorted tuples by key 3. run `.select` with the sorted list of indices It was significantly faster (orders of magnitude, especially with M's of rows) ### Your contribution I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform; ```python # ds is a Dataset object # key_name is the sorting key class Dataset: ... def _sort(key_name: str) -> Dataset: index_keys = [(i,x) for i,x in enumerate(self[key_name])] sorted_rows = sorted(row_pubdate, key=lambda x: x[1]) sorted_indicies = [x[0] for x in sorted_rows] return self.select(sorted_indicies) ```
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Faster sorting for single key items ### Feature request A faster way to sort a dataset which contains a large number of rows. ### Motivation The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. **Code snippet:** ```python ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True) sorted_ds = ds.sort("pubDate", keep_in_memory=True) ``` However, once I switched to a different method which 1. unpacked to a list of tuples 2. sorted tuples by key 3. run `.select` with the sorted list of indices It was significantly faster (orders of magnitude, especially with M's of rows) ### Your contribution I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform; ```python # ds is a Dataset object # key_name is the sorting key class Dataset: ... def _sort(key_name: str) -> Dataset: index_keys = [(i,x) for i,x in enumerate(self[key_name])] sorted_rows = sorted(row_pubdate, key=lambda x: x[1]) sorted_indicies = [x[0] for x in sorted_rows] return self.select(sorted_indicies) ``` `Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory) ```python sort_keys = [(col, "ascending") for col in column_names] indices = pc.sort_indices(self.data, sort_keys=sort_keys) return self.select(indices) ```
https://github.com/huggingface/datasets/issues/6150
Allow dataset implement .take
``` dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode ```
### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)
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Allow dataset implement .take ### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False) ``` dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode ```
https://github.com/huggingface/datasets/issues/6150
Allow dataset implement .take
Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`.
### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)
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Allow dataset implement .take ### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False) Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`.
https://github.com/huggingface/datasets/issues/6149
Dataset.from_parquet cannot load subset of columns
Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns) This does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)
### Describe the bug When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following ``` ValueError: Couldn't cast a: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273 to {'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)} because column names don't match The above exception was the direct cause of the following exception: ``` Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286 ### Steps to reproduce the bug ``` import pandas as pd from datasets import Dataset pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq") Dataset.from_parquet("test.pq", columns=["a"]) ``` ### Expected behavior A subset of columns should be loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5 - Python version: 3.8.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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Dataset.from_parquet cannot load subset of columns ### Describe the bug When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following ``` ValueError: Couldn't cast a: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273 to {'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)} because column names don't match The above exception was the direct cause of the following exception: ``` Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286 ### Steps to reproduce the bug ``` import pandas as pd from datasets import Dataset pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq") Dataset.from_parquet("test.pq", columns=["a"]) ``` ### Expected behavior A subset of columns should be loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5 - Python version: 3.8.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns) This does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)
https://github.com/huggingface/datasets/issues/6147
ValueError when running BeamBasedBuilder with GCS path in cache_dir
The cause of the error seems to be that `datasets` adds "gcs://" as a schema, while `beam` checks only "gs://". datasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822 beam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101) ``` systems = [ fs for fs in FileSystem.get_all_subclasses() if fs.scheme() == path_scheme ] ```
### Describe the bug When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs: ``` ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] ``` Same error occurs after running `pip install apache-beam[gcp]` as instructed. ### Steps to reproduce the bug Put `my_beam_dataset.py`: ```python import datasets class MyBeamDataset(datasets.BeamBasedBuilder): def _info(self): features = datasets.Features({"value": datasets.Value("int64")}) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager, pipeline): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})] def _build_pcollection(self, pipeline): import apache_beam as beam return pipeline | beam.Create([{"value": i} for i in range(10)]) ``` Run: ```bash datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner" ``` ### Expected behavior Running the BeamBasedBuilder with a GCS cache path without any errors. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 2.0.3
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ValueError when running BeamBasedBuilder with GCS path in cache_dir ### Describe the bug When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs: ``` ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] ``` Same error occurs after running `pip install apache-beam[gcp]` as instructed. ### Steps to reproduce the bug Put `my_beam_dataset.py`: ```python import datasets class MyBeamDataset(datasets.BeamBasedBuilder): def _info(self): features = datasets.Features({"value": datasets.Value("int64")}) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager, pipeline): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})] def _build_pcollection(self, pipeline): import apache_beam as beam return pipeline | beam.Create([{"value": i} for i in range(10)]) ``` Run: ```bash datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner" ``` ### Expected behavior Running the BeamBasedBuilder with a GCS cache path without any errors. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 2.0.3 The cause of the error seems to be that `datasets` adds "gcs://" as a schema, while `beam` checks only "gs://". datasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822 beam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101) ``` systems = [ fs for fs in FileSystem.get_all_subclasses() if fs.scheme() == path_scheme ] ```
https://github.com/huggingface/datasets/issues/6146
DatasetGenerationError when load glue benchmark datasets from `load_dataset`
This issue can happen if there is a directory named "glue" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?
### Describe the bug Package version: datasets-2.14.4 When I run the codes: ``` from datasets import load_dataset dataset = load_dataset("glue", "ax") ``` I got the following errors: --------------------------------------------------------------------------- SchemaInferenceError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1948 num_shards = shard_id + 1 -> 1949 num_examples, num_bytes = writer.finalize() 1950 writer.close() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream) 597 self.stream.close() --> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data") 599 logger.debug( 600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}." 601 ) SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[5], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("glue", "ax") File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2135 # Download and prepare data -> 2136 builder_instance.download_and_prepare( 2137 download_config=download_config, 2138 download_mode=download_mode, 2139 verification_mode=verification_mode, 2140 try_from_hf_gcs=try_from_hf_gcs, 2141 num_proc=num_proc, 2142 storage_options=storage_options, 2143 ) 2145 # Build dataset for splits 2146 keep_in_memory = ( 2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2148 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("glue", "ax") ### Expected behavior When generating the train split: Generating train split: 0/0 [00:00<?, ? examples/s] It raise the error: DatasetGenerationError: An error occurred while generating the dataset ### Environment info datasets-2.14.4. Python 3.10
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DatasetGenerationError when load glue benchmark datasets from `load_dataset` ### Describe the bug Package version: datasets-2.14.4 When I run the codes: ``` from datasets import load_dataset dataset = load_dataset("glue", "ax") ``` I got the following errors: --------------------------------------------------------------------------- SchemaInferenceError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1948 num_shards = shard_id + 1 -> 1949 num_examples, num_bytes = writer.finalize() 1950 writer.close() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream) 597 self.stream.close() --> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data") 599 logger.debug( 600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}." 601 ) SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[5], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("glue", "ax") File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2135 # Download and prepare data -> 2136 builder_instance.download_and_prepare( 2137 download_config=download_config, 2138 download_mode=download_mode, 2139 verification_mode=verification_mode, 2140 try_from_hf_gcs=try_from_hf_gcs, 2141 num_proc=num_proc, 2142 storage_options=storage_options, 2143 ) 2145 # Build dataset for splits 2146 keep_in_memory = ( 2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2148 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("glue", "ax") ### Expected behavior When generating the train split: Generating train split: 0/0 [00:00<?, ? examples/s] It raise the error: DatasetGenerationError: An error occurred while generating the dataset ### Environment info datasets-2.14.4. Python 3.10 This issue can happen if there is a directory named "glue" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?
https://github.com/huggingface/datasets/issues/6153
custom load dataset to hub
> This is an issue for the [Datasets repo](https://github.com/huggingface/datasets). Thanks @sgugger , I guess I will wait for them to address the issue . Looking forward to hearing from them
### System Info kaggle notebook i transformed dataset: ``` dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt") ``` to formatted_dataset: ``` Dataset({ features: ['message_tree_id', 'message_tree_text'], num_rows: 33143 }) ``` but would like to know how to upload to hub ### Who can help? @ArthurZucker @younesbelkada ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction shared above ### Expected behavior load dataset to hub
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custom load dataset to hub ### System Info kaggle notebook i transformed dataset: ``` dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt") ``` to formatted_dataset: ``` Dataset({ features: ['message_tree_id', 'message_tree_text'], num_rows: 33143 }) ``` but would like to know how to upload to hub ### Who can help? @ArthurZucker @younesbelkada ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction shared above ### Expected behavior load dataset to hub > This is an issue for the [Datasets repo](https://github.com/huggingface/datasets). Thanks @sgugger , I guess I will wait for them to address the issue . Looking forward to hearing from them
https://github.com/huggingface/datasets/issues/6144
NIH exporter file not found
another file not found: ``` Traceback (most recent call last): File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 417, in _info await _file_info( File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 837, in _file_info r.raise_for_status() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/aiohttp/client_reqrep.py", line 1005, in raise_for_status raise ClientResponseError( aiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module> cli.main() File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main run() File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file runpy.run_path(target, run_name="__main__") File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path return _run_module_code(code, init_globals, run_name, File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code _run_code(code, mod_globals, init_globals, File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code exec(code, run_globals) File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 526, in <module> experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 475, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights column_names = next(iter(dataset)).keys() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ for key, example in ex_iterable: File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 257, in _generate_examples for path, file in files[subset]: File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 840, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 891, in _iter_from_urlpath with xopen(urlpath, "rb", download_config=download_config) as f: File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open return self.__enter__() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ f = self.fs.open(self.path, mode=mode) File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open f = self._open( File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open size = size or self.info(path, **kwargs)["size"] File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper return sync(self.loop, func, *args, **kwargs) File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync raise return_result File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner result[0] = await coro File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info raise FileNotFoundError(url) from exc FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar ```
### Describe the bug can't use or download the nih exporter pile data. ``` 15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() 16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights 17 column_names = next(iter(dataset)).keys() 18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ 19 for key, example in ex_iterable: 20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ 21 yield from self.generate_examples_fn(**self.kwargs) 22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples 23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f: 24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper 25 return function(*args, download_config=download_config, **kwargs) 26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen 27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open 29 return self.__enter__() 30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ 31 f = self.fs.open(self.path, mode=mode) 32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open 33 f = self._open( 34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open 35 size = size or self.info(path, **kwargs)["size"] 36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper 37 return sync(self.loop, func, *args, **kwargs) 38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync 39 raise return_result 40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner 41 result[0] = await coro 42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info 43 raise FileNotFoundError(url) from exc 44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst ``` ### Steps to reproduce the bug run this: ``` from datasets import load_dataset path, name = 'EleutherAI/pile', 'nih_exporter' # -- Get data set dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch") batch = dataset.take(512) print(f'{batch=}') ``` ### Expected behavior print the batch ### Environment info ``` (beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```
283
NIH exporter file not found ### Describe the bug can't use or download the nih exporter pile data. ``` 15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() 16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights 17 column_names = next(iter(dataset)).keys() 18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ 19 for key, example in ex_iterable: 20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ 21 yield from self.generate_examples_fn(**self.kwargs) 22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples 23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f: 24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper 25 return function(*args, download_config=download_config, **kwargs) 26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen 27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open 29 return self.__enter__() 30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ 31 f = self.fs.open(self.path, mode=mode) 32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open 33 f = self._open( 34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open 35 size = size or self.info(path, **kwargs)["size"] 36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper 37 return sync(self.loop, func, *args, **kwargs) 38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync 39 raise return_result 40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner 41 result[0] = await coro 42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info 43 raise FileNotFoundError(url) from exc 44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst ``` ### Steps to reproduce the bug run this: ``` from datasets import load_dataset path, name = 'EleutherAI/pile', 'nih_exporter' # -- Get data set dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch") batch = dataset.take(512) print(f'{batch=}') ``` ### Expected behavior print the batch ### Environment info ``` (beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ``` another file not found: ``` Traceback (most recent call last): File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 417, in _info await _file_info( File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 837, in _file_info r.raise_for_status() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/aiohttp/client_reqrep.py", line 1005, in raise_for_status raise ClientResponseError( aiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module> cli.main() File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main run() File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file runpy.run_path(target, run_name="__main__") File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path return _run_module_code(code, init_globals, run_name, File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code _run_code(code, mod_globals, init_globals, File "/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code exec(code, run_globals) File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 526, in <module> experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 475, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights column_names = next(iter(dataset)).keys() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ for key, example in ex_iterable: File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 257, in _generate_examples for path, file in files[subset]: File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 840, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 891, in _iter_from_urlpath with xopen(urlpath, "rb", download_config=download_config) as f: File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open return self.__enter__() File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ f = self.fs.open(self.path, mode=mode) File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open f = self._open( File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open size = size or self.info(path, **kwargs)["size"] File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper return sync(self.loop, func, *args, **kwargs) File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync raise return_result File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner result[0] = await coro File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info raise FileNotFoundError(url) from exc FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar ```
https://github.com/huggingface/datasets/issues/6144
NIH exporter file not found
this seems to work but it's rather annoying. Summary of how to make it work: 1. get urls to parquet files into a list 2. load list to load_dataset via `load_dataset('parquet', data_files=urls)` (note api names to hf are really confusing sometimes) 3. then it should work, print a batch of text. presudo code ```python urls_hacker_news = [ "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00000-of-00004.parquet", "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00001-of-00004.parquet", "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00002-of-00004.parquet", "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00003-of-00004.parquet" ] ... # streaming = False from diversity.pile_subset_urls import urls_hacker_news path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news # not changing batch_size = 512 today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss') run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})' print(f'{run_name=}') # - Init wandb debug: bool = mode == 'dryrun' run = wandb.init(mode=mode, project="beyond-scale", name=run_name, save_code=True) wandb.config.update({"num_batches": num_batches, "path": path, "name": name, "today": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files}) # run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891 print(f'{debug=}') print(f'{wandb.config=}') # -- Get probe network from datasets import load_dataset import torch from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained("gpt2") if tokenizer.pad_token_id is None: tokenizer.pad_token = tokenizer.eos_token probe_network = GPT2LMHeadModel.from_pretrained("gpt2") device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu") probe_network = probe_network.to(device) # -- Get data set def my_load_dataset(path, name): print(f'{path=} {name=} {streaming=}') if path == 'json' or path == 'bin' or path == 'csv': print(f'{data_files_prefix+name=}') return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split="train").with_format("torch") elif path == 'parquet': print(f'{data_files=}') return load_dataset(path, data_files=data_files, streaming=streaming, split="train").with_format("torch") else: return load_dataset(path, name, streaming=streaming, split="train").with_format("torch") # - get data set for real now if isinstance(path, str): dataset = my_load_dataset(path, name) else: print('-- interleaving datasets') datasets = [my_load_dataset(path, name).with_format("torch") for path, name in zip(path, name)] [print(f'{dataset.description=}') for dataset in datasets] dataset = interleave_datasets(datasets, probabilities) print(f'{dataset=}') batch = dataset.take(batch_size) print(f'{next(iter(batch))=}') column_names = next(iter(batch)).keys() print(f'{column_names=}') # - Prepare functions to tokenize batch def preprocess(examples): return tokenizer(examples["text"], padding="max_length", max_length=128, truncation=True, return_tensors="pt") remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader def map(batch): return batch.map(preprocess, batched=True, remove_columns=remove_columns) tokenized_batch = map(batch) print(f'{next(iter(tokenized_batch))=}') ``` https://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th/76902681#76902681 https://discuss.huggingface.co/t/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-the-files-are-not-available/50555/5?u=severo
### Describe the bug can't use or download the nih exporter pile data. ``` 15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() 16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights 17 column_names = next(iter(dataset)).keys() 18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ 19 for key, example in ex_iterable: 20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ 21 yield from self.generate_examples_fn(**self.kwargs) 22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples 23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f: 24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper 25 return function(*args, download_config=download_config, **kwargs) 26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen 27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open 29 return self.__enter__() 30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ 31 f = self.fs.open(self.path, mode=mode) 32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open 33 f = self._open( 34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open 35 size = size or self.info(path, **kwargs)["size"] 36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper 37 return sync(self.loop, func, *args, **kwargs) 38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync 39 raise return_result 40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner 41 result[0] = await coro 42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info 43 raise FileNotFoundError(url) from exc 44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst ``` ### Steps to reproduce the bug run this: ``` from datasets import load_dataset path, name = 'EleutherAI/pile', 'nih_exporter' # -- Get data set dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch") batch = dataset.take(512) print(f'{batch=}') ``` ### Expected behavior print the batch ### Environment info ``` (beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```
319
NIH exporter file not found ### Describe the bug can't use or download the nih exporter pile data. ``` 15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() 16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights 17 column_names = next(iter(dataset)).keys() 18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ 19 for key, example in ex_iterable: 20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ 21 yield from self.generate_examples_fn(**self.kwargs) 22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples 23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f: 24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper 25 return function(*args, download_config=download_config, **kwargs) 26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen 27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open 29 return self.__enter__() 30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ 31 f = self.fs.open(self.path, mode=mode) 32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open 33 f = self._open( 34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open 35 size = size or self.info(path, **kwargs)["size"] 36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper 37 return sync(self.loop, func, *args, **kwargs) 38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync 39 raise return_result 40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner 41 result[0] = await coro 42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info 43 raise FileNotFoundError(url) from exc 44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst ``` ### Steps to reproduce the bug run this: ``` from datasets import load_dataset path, name = 'EleutherAI/pile', 'nih_exporter' # -- Get data set dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch") batch = dataset.take(512) print(f'{batch=}') ``` ### Expected behavior print the batch ### Environment info ``` (beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ``` this seems to work but it's rather annoying. Summary of how to make it work: 1. get urls to parquet files into a list 2. load list to load_dataset via `load_dataset('parquet', data_files=urls)` (note api names to hf are really confusing sometimes) 3. then it should work, print a batch of text. presudo code ```python urls_hacker_news = [ "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00000-of-00004.parquet", "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00001-of-00004.parquet", "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00002-of-00004.parquet", "https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00003-of-00004.parquet" ] ... # streaming = False from diversity.pile_subset_urls import urls_hacker_news path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news # not changing batch_size = 512 today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss') run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})' print(f'{run_name=}') # - Init wandb debug: bool = mode == 'dryrun' run = wandb.init(mode=mode, project="beyond-scale", name=run_name, save_code=True) wandb.config.update({"num_batches": num_batches, "path": path, "name": name, "today": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files}) # run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891 print(f'{debug=}') print(f'{wandb.config=}') # -- Get probe network from datasets import load_dataset import torch from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained("gpt2") if tokenizer.pad_token_id is None: tokenizer.pad_token = tokenizer.eos_token probe_network = GPT2LMHeadModel.from_pretrained("gpt2") device = torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu") probe_network = probe_network.to(device) # -- Get data set def my_load_dataset(path, name): print(f'{path=} {name=} {streaming=}') if path == 'json' or path == 'bin' or path == 'csv': print(f'{data_files_prefix+name=}') return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split="train").with_format("torch") elif path == 'parquet': print(f'{data_files=}') return load_dataset(path, data_files=data_files, streaming=streaming, split="train").with_format("torch") else: return load_dataset(path, name, streaming=streaming, split="train").with_format("torch") # - get data set for real now if isinstance(path, str): dataset = my_load_dataset(path, name) else: print('-- interleaving datasets') datasets = [my_load_dataset(path, name).with_format("torch") for path, name in zip(path, name)] [print(f'{dataset.description=}') for dataset in datasets] dataset = interleave_datasets(datasets, probabilities) print(f'{dataset=}') batch = dataset.take(batch_size) print(f'{next(iter(batch))=}') column_names = next(iter(batch)).keys() print(f'{column_names=}') # - Prepare functions to tokenize batch def preprocess(examples): return tokenizer(examples["text"], padding="max_length", max_length=128, truncation=True, return_tensors="pt") remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader def map(batch): return batch.map(preprocess, batched=True, remove_columns=remove_columns) tokenized_batch = map(batch) print(f'{next(iter(tokenized_batch))=}') ``` https://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th/76902681#76902681 https://discuss.huggingface.co/t/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-the-files-are-not-available/50555/5?u=severo
https://github.com/huggingface/datasets/issues/6142
the-stack-dedup fails to generate
It seems that some parquet files have additional columns. I ran a scan and found that two files have the additional `__id__` column: 1. `hf://datasets/bigcode/the-stack-dedup/data/numpy/data-00000-of-00001.parquet` 2. `hf://datasets/bigcode/the-stack-dedup/data/omgrofl/data-00000-of-00001.parquet` We should open a PR to fix those two files
### Describe the bug I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens. ### Steps to reproduce the bug My code: ``` import os import datasets as ds MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local" MY_TOKEN="my-token" the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64) ``` The exception: ``` Generating train split: 233248251 examples [54:31, 57280.00 examples/s] multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1879, in _prepare_split_single for _, table in generator: File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 82, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 61, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2324, in table_cast return cast_table_to_schema(table, schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2282, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb ecause column names don't match") ValueError: Couldn't cast hexsha: string size: int64 ext: string lang: string max_stars_repo_path: string max_stars_repo_name: string max_stars_repo_head_hexsha: string max_stars_repo_licenses: list<item: string> child 0, item: string max_stars_count: int64 max_stars_repo_stars_event_min_datetime: string max_stars_repo_stars_event_max_datetime: string max_issues_repo_path: string max_issues_repo_name: string max_issues_repo_head_hexsha: string max_issues_repo_licenses: list<item: string> child 0, item: string max_issues_count: int64 max_issues_repo_issues_event_min_datetime: string max_issues_repo_issues_event_max_datetime: string max_forks_repo_path: string max_forks_repo_name: string max_forks_repo_head_hexsha: string max_forks_repo_licenses: list<item: string> child 0, item: string max_forks_count: int64 max_forks_repo_forks_event_min_datetime: string max_forks_repo_forks_event_max_datetime: string content: string avg_line_length: double max_line_length: int64 alphanum_fraction: double __id__: int64 -- schema metadata -- huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979 to {'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating th e dataset") from e datasets.builder.DatasetGenerationError: An error occurred while genera ting the dataset """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/download_the_stack.py", line 7, in <module> the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us e_auth_token=MY_TOKEN, num_proc=64) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load. py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1796, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 774, in get raise self._value datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior The dataset downloads properly. @lhoestq @loub ### Environment info Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04
37
the-stack-dedup fails to generate ### Describe the bug I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens. ### Steps to reproduce the bug My code: ``` import os import datasets as ds MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local" MY_TOKEN="my-token" the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64) ``` The exception: ``` Generating train split: 233248251 examples [54:31, 57280.00 examples/s] multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1879, in _prepare_split_single for _, table in generator: File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 82, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 61, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2324, in table_cast return cast_table_to_schema(table, schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2282, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb ecause column names don't match") ValueError: Couldn't cast hexsha: string size: int64 ext: string lang: string max_stars_repo_path: string max_stars_repo_name: string max_stars_repo_head_hexsha: string max_stars_repo_licenses: list<item: string> child 0, item: string max_stars_count: int64 max_stars_repo_stars_event_min_datetime: string max_stars_repo_stars_event_max_datetime: string max_issues_repo_path: string max_issues_repo_name: string max_issues_repo_head_hexsha: string max_issues_repo_licenses: list<item: string> child 0, item: string max_issues_count: int64 max_issues_repo_issues_event_min_datetime: string max_issues_repo_issues_event_max_datetime: string max_forks_repo_path: string max_forks_repo_name: string max_forks_repo_head_hexsha: string max_forks_repo_licenses: list<item: string> child 0, item: string max_forks_count: int64 max_forks_repo_forks_event_min_datetime: string max_forks_repo_forks_event_max_datetime: string content: string avg_line_length: double max_line_length: int64 alphanum_fraction: double __id__: int64 -- schema metadata -- huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979 to {'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating th e dataset") from e datasets.builder.DatasetGenerationError: An error occurred while genera ting the dataset """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/download_the_stack.py", line 7, in <module> the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us e_auth_token=MY_TOKEN, num_proc=64) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load. py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1796, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 774, in get raise self._value datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior The dataset downloads properly. @lhoestq @loub ### Environment info Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04 It seems that some parquet files have additional columns. I ran a scan and found that two files have the additional `__id__` column: 1. `hf://datasets/bigcode/the-stack-dedup/data/numpy/data-00000-of-00001.parquet` 2. `hf://datasets/bigcode/the-stack-dedup/data/omgrofl/data-00000-of-00001.parquet` We should open a PR to fix those two files
https://github.com/huggingface/datasets/issues/6142
the-stack-dedup fails to generate
The files have been fixed ! I'm closing this one but feel free to re-open if you still have the issue
### Describe the bug I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens. ### Steps to reproduce the bug My code: ``` import os import datasets as ds MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local" MY_TOKEN="my-token" the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64) ``` The exception: ``` Generating train split: 233248251 examples [54:31, 57280.00 examples/s] multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1879, in _prepare_split_single for _, table in generator: File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 82, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 61, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2324, in table_cast return cast_table_to_schema(table, schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2282, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb ecause column names don't match") ValueError: Couldn't cast hexsha: string size: int64 ext: string lang: string max_stars_repo_path: string max_stars_repo_name: string max_stars_repo_head_hexsha: string max_stars_repo_licenses: list<item: string> child 0, item: string max_stars_count: int64 max_stars_repo_stars_event_min_datetime: string max_stars_repo_stars_event_max_datetime: string max_issues_repo_path: string max_issues_repo_name: string max_issues_repo_head_hexsha: string max_issues_repo_licenses: list<item: string> child 0, item: string max_issues_count: int64 max_issues_repo_issues_event_min_datetime: string max_issues_repo_issues_event_max_datetime: string max_forks_repo_path: string max_forks_repo_name: string max_forks_repo_head_hexsha: string max_forks_repo_licenses: list<item: string> child 0, item: string max_forks_count: int64 max_forks_repo_forks_event_min_datetime: string max_forks_repo_forks_event_max_datetime: string content: string avg_line_length: double max_line_length: int64 alphanum_fraction: double __id__: int64 -- schema metadata -- huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979 to {'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating th e dataset") from e datasets.builder.DatasetGenerationError: An error occurred while genera ting the dataset """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/download_the_stack.py", line 7, in <module> the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us e_auth_token=MY_TOKEN, num_proc=64) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load. py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1796, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 774, in get raise self._value datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior The dataset downloads properly. @lhoestq @loub ### Environment info Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04
21
the-stack-dedup fails to generate ### Describe the bug I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens. ### Steps to reproduce the bug My code: ``` import os import datasets as ds MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local" MY_TOKEN="my-token" the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64) ``` The exception: ``` Generating train split: 233248251 examples [54:31, 57280.00 examples/s] multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1879, in _prepare_split_single for _, table in generator: File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 82, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 61, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2324, in table_cast return cast_table_to_schema(table, schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2282, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb ecause column names don't match") ValueError: Couldn't cast hexsha: string size: int64 ext: string lang: string max_stars_repo_path: string max_stars_repo_name: string max_stars_repo_head_hexsha: string max_stars_repo_licenses: list<item: string> child 0, item: string max_stars_count: int64 max_stars_repo_stars_event_min_datetime: string max_stars_repo_stars_event_max_datetime: string max_issues_repo_path: string max_issues_repo_name: string max_issues_repo_head_hexsha: string max_issues_repo_licenses: list<item: string> child 0, item: string max_issues_count: int64 max_issues_repo_issues_event_min_datetime: string max_issues_repo_issues_event_max_datetime: string max_forks_repo_path: string max_forks_repo_name: string max_forks_repo_head_hexsha: string max_forks_repo_licenses: list<item: string> child 0, item: string max_forks_count: int64 max_forks_repo_forks_event_min_datetime: string max_forks_repo_forks_event_max_datetime: string content: string avg_line_length: double max_line_length: int64 alphanum_fraction: double __id__: int64 -- schema metadata -- huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979 to {'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(**kwargs)): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating th e dataset") from e datasets.builder.DatasetGenerationError: An error occurred while genera ting the dataset """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/download_the_stack.py", line 7, in <module> the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us e_auth_token=MY_TOKEN, num_proc=64) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load. py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1796, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 774, in get raise self._value datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior The dataset downloads properly. @lhoestq @loub ### Environment info Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04 The files have been fixed ! I'm closing this one but feel free to re-open if you still have the issue
https://github.com/huggingface/datasets/issues/6141
TypeError: ClientSession._request() got an unexpected keyword argument 'https'
Hi! I cannot reproduce this error on my machine or in Colab. Which version of `fsspec` do you have installed?
### Describe the bug Hello, when I ran the [code snippet](https://huggingface.co/docs/datasets/v2.14.4/en/loading#json) on the document, I encountered the following problem: ``` Python 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" >>> dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset builder_instance = load_dataset_builder( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1413, in dataset_module_factory ).get_module() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 949, in get_module data_files = DataFilesDict.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 672, in from_patterns DataFilesList.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 578, in from_patterns resolve_pattern( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern for filepath, info in fs.glob(pattern, detail=True).items() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 113, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 98, in sync raise return_result File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 53, in _runner result[0] = await coro File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 449, in _glob elif await self._exists(path): File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 306, in _exists r = await session.get(self.encode_url(path), **kw) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/aiohttp/client.py", line 922, in get self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs) TypeError: ClientSession._request() got an unexpected keyword argument 'https' ``` ### Steps to reproduce the bug ``` from datasets import load_dataset base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") ``` ### Expected behavior able to load normally ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.54-2-x86_64-with-glibc2.27 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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TypeError: ClientSession._request() got an unexpected keyword argument 'https' ### Describe the bug Hello, when I ran the [code snippet](https://huggingface.co/docs/datasets/v2.14.4/en/loading#json) on the document, I encountered the following problem: ``` Python 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" >>> dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset builder_instance = load_dataset_builder( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1413, in dataset_module_factory ).get_module() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 949, in get_module data_files = DataFilesDict.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 672, in from_patterns DataFilesList.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 578, in from_patterns resolve_pattern( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern for filepath, info in fs.glob(pattern, detail=True).items() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 113, in wrapper return sync(self.loop, func, *args, **kwargs) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 98, in sync raise return_result File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 53, in _runner result[0] = await coro File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 449, in _glob elif await self._exists(path): File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 306, in _exists r = await session.get(self.encode_url(path), **kw) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/aiohttp/client.py", line 922, in get self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs) TypeError: ClientSession._request() got an unexpected keyword argument 'https' ``` ### Steps to reproduce the bug ``` from datasets import load_dataset base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") ``` ### Expected behavior able to load normally ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.54-2-x86_64-with-glibc2.27 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 Hi! I cannot reproduce this error on my machine or in Colab. Which version of `fsspec` do you have installed?
https://github.com/huggingface/datasets/issues/6139
Offline dataset viewer
Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc. If you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39.
### Feature request The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset. ### Motivation I want to easily view my dataset even when it is hosted locally. ### Your contribution N.A.
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Offline dataset viewer ### Feature request The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset. ### Motivation I want to easily view my dataset even when it is hosted locally. ### Your contribution N.A. Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc. If you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39.
https://github.com/huggingface/datasets/issues/6134
`datasets` cannot be installed alongside `apache-beam`
I noticed that this is actually covered by issue #5613, which for some reason I didn't see when I searched the issues in this repo the first time.
### Describe the bug If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to do something such as importing the `load_dataset` method from `datasets` results in a crashing error. I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions. ### Steps to reproduce the bug See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug. In some environments, I have been able to reproduce the bug by running the following in Bash: ```bash $ pip install datasets apache-beam ``` then the following in a Python shell: ```python from datasets import load_dataset ``` Here is my stacktrace from running on Google Colab: <details> <summary>stacktrace</summary> ``` [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.14.4" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 64 65 from . import config ---> 66 from .arrow_reader import ArrowReader 67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 68 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 28 import pyarrow.parquet as pq 29 ---> 30 from .download.download_config import DownloadConfig 31 from .naming import _split_re, filenames_for_dataset_split 32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables [/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module> 7 8 from .download_config import DownloadConfig ----> 9 from .download_manager import DownloadManager, DownloadMode 10 from .streaming_download_manager import StreamingDownloadManager [/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module> 33 from ..utils.info_utils import get_size_checksum_dict 34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm ---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str 36 from .download_config import DownloadConfig 37 [/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module> 38 import dill 39 import multiprocess ---> 40 import multiprocess.pool 41 import numpy as np 42 from packaging import version [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module> 607 # 608 --> 609 class ThreadPool(Pool): 610 611 from .dummy import Process [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool() 609 class ThreadPool(Pool): 610 --> 611 from .dummy import Process 612 613 def __init__(self, processes=None, initializer=None, initargs=()): [/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module> 85 # 86 ---> 87 class Condition(threading._Condition): 88 # XXX 89 if sys.version_info < (3, 0): AttributeError: module 'threading' has no attribute '_Condition' ``` </details> I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes pip to hang indefinitely. ### Expected behavior I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`. ### Environment info Google Colab
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`datasets` cannot be installed alongside `apache-beam` ### Describe the bug If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to do something such as importing the `load_dataset` method from `datasets` results in a crashing error. I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions. ### Steps to reproduce the bug See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug. In some environments, I have been able to reproduce the bug by running the following in Bash: ```bash $ pip install datasets apache-beam ``` then the following in a Python shell: ```python from datasets import load_dataset ``` Here is my stacktrace from running on Google Colab: <details> <summary>stacktrace</summary> ``` [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.14.4" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 64 65 from . import config ---> 66 from .arrow_reader import ArrowReader 67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 68 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 28 import pyarrow.parquet as pq 29 ---> 30 from .download.download_config import DownloadConfig 31 from .naming import _split_re, filenames_for_dataset_split 32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables [/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module> 7 8 from .download_config import DownloadConfig ----> 9 from .download_manager import DownloadManager, DownloadMode 10 from .streaming_download_manager import StreamingDownloadManager [/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module> 33 from ..utils.info_utils import get_size_checksum_dict 34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm ---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str 36 from .download_config import DownloadConfig 37 [/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module> 38 import dill 39 import multiprocess ---> 40 import multiprocess.pool 41 import numpy as np 42 from packaging import version [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module> 607 # 608 --> 609 class ThreadPool(Pool): 610 611 from .dummy import Process [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool() 609 class ThreadPool(Pool): 610 --> 611 from .dummy import Process 612 613 def __init__(self, processes=None, initializer=None, initargs=()): [/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module> 85 # 86 ---> 87 class Condition(threading._Condition): 88 # XXX 89 if sys.version_info < (3, 0): AttributeError: module 'threading' has no attribute '_Condition' ``` </details> I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes pip to hang indefinitely. ### Expected behavior I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`. ### Environment info Google Colab I noticed that this is actually covered by issue #5613, which for some reason I didn't see when I searched the issues in this repo the first time.
https://github.com/huggingface/datasets/issues/6133
Dataset is slower after calling `to_iterable_dataset`
It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?
### Describe the bug Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset` ### Steps to reproduce the bug Any dataset after converting to `IterableDataset` ### Expected behavior Maybe it should be faster on big dataset? I only test on small dataset ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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Dataset is slower after calling `to_iterable_dataset` ### Describe the bug Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset` ### Steps to reproduce the bug Any dataset after converting to `IterableDataset` ### Expected behavior Maybe it should be faster on big dataset? I only test on small dataset ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
What should be the behavior in this case ? Should it override the default config with the added parameter ?
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
20
default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 What should be the behavior in this case ? Should it override the default config with the added parameter ?
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
I know why it should be treated as a new config if overriding parameters are passed. But in some case, I just pass in some common fields like `data_dir`. For example, I want to extend the FolderBasedBuilder as a multi-config version, the `data_dir` or `data_files` are always passed by user and should not be considered as overriding the default config. In current state, I cannot leverage the feature of default config since passing `data_dir` will disable the default config.
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
79
default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 I know why it should be treated as a new config if overriding parameters are passed. But in some case, I just pass in some common fields like `data_dir`. For example, I want to extend the FolderBasedBuilder as a multi-config version, the `data_dir` or `data_files` are always passed by user and should not be considered as overriding the default config. In current state, I cannot leverage the feature of default config since passing `data_dir` will disable the default config.
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
Thinking more about it I think the current behavior is the right one. Provided parameters should be passed to instantiate a new BuilderConfig. What's the error you're getting ?
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
29
default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 Thinking more about it I think the current behavior is the right one. Provided parameters should be passed to instantiate a new BuilderConfig. What's the error you're getting ?
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
For example, this works to use default config with name '_all_': ```python datasets.load_dataset("indonesian-nlp/librivox-indonesia", split="train") ``` while this failed to use default config ```python datasets.load_dataset("indonesian-nlp/librivox-indonesia", split="train", data_dir='.') ``` After manually specifying it, it works again. ```python datasets.load_dataset("indonesian-nlp/librivox-indonesia", "_all_", split="train", data_dir='.') ```
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 For example, this works to use default config with name '_all_': ```python datasets.load_dataset("indonesian-nlp/librivox-indonesia", split="train") ``` while this failed to use default config ```python datasets.load_dataset("indonesian-nlp/librivox-indonesia", split="train", data_dir='.') ``` After manually specifying it, it works again. ```python datasets.load_dataset("indonesian-nlp/librivox-indonesia", "_all_", split="train", data_dir='.') ```
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
It should work if you explicitly ask for the config you want to override ```python load_dataset('/dataset/with/multiple/config', 'name_of_the_default_config', some_field_in_config='some') ``` Alternatively you can have a BuilderConfig class that when instantiated returns a config with the right default values. In this case this code would instantiate this config with the default values except for the parameter to override: ```python load_dataset('/dataset/with/multiple/config', some_field_in_config='some') ```
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 It should work if you explicitly ask for the config you want to override ```python load_dataset('/dataset/with/multiple/config', 'name_of_the_default_config', some_field_in_config='some') ``` Alternatively you can have a BuilderConfig class that when instantiated returns a config with the right default values. In this case this code would instantiate this config with the default values except for the parameter to override: ```python load_dataset('/dataset/with/multiple/config', some_field_in_config='some') ```
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
@lhoestq Yes. But it doesn't work for me. Here's my dataset for example. ``` lass MyDatasetConfig(datasets.BuilderConfig): def __init__(self, name: str, version: str, **kwargs): self.option1 = kwargs.pop("option1", False) self.option2 = kwargs.pop("option2", 5) super().__init__( name=name, version=datasets.Version(version), **kwargs) class MyDataset(datasets.GeneratorBasedBuilder): DEFAULT_CONFIG_NAME = "v1" BUILDER_CONFIGS = [ UnifiedTtsDatasetConfig( name="v1", version="1.0.0", description="Initial version of the dataset" ), ] def _info(self) -> DatasetInfo: _ = self.option1 .... ``` Here it's okay to use `load_dataset('my_dataset.py')` for loading the default config `v1`. But if I want to override the default values in config with `load_dataset('my_dataset.py', option2=3)`, it failed to find my default config `v1. Unless I use `load_dataset('my_dataset.py', 'v1', option2=3)` So according to your advice, how can I modify my dataset to be able to override default config without manually specifying it.
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 @lhoestq Yes. But it doesn't work for me. Here's my dataset for example. ``` lass MyDatasetConfig(datasets.BuilderConfig): def __init__(self, name: str, version: str, **kwargs): self.option1 = kwargs.pop("option1", False) self.option2 = kwargs.pop("option2", 5) super().__init__( name=name, version=datasets.Version(version), **kwargs) class MyDataset(datasets.GeneratorBasedBuilder): DEFAULT_CONFIG_NAME = "v1" BUILDER_CONFIGS = [ UnifiedTtsDatasetConfig( name="v1", version="1.0.0", description="Initial version of the dataset" ), ] def _info(self) -> DatasetInfo: _ = self.option1 .... ``` Here it's okay to use `load_dataset('my_dataset.py')` for loading the default config `v1`. But if I want to override the default values in config with `load_dataset('my_dataset.py', option2=3)`, it failed to find my default config `v1. Unless I use `load_dataset('my_dataset.py', 'v1', option2=3)` So according to your advice, how can I modify my dataset to be able to override default config without manually specifying it.
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
@lhoestq The error is ``` def _info(self) -> DatasetInfo: _ = self.option1 <- .... AttributeError: 'BuilderConfig' object has no attribute 'option1' ``` which seems to find another unknown config. You can try this line `datasets.load_dataset("indonesian-nlp/librivox-indonesia", split="train", data_dir='.')`, it's a multi-config dataset on HF hub and the error is the same. My insights: https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518 if `config_kwargs` is provided here, the if branch is skipped.
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
63
default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 @lhoestq The error is ``` def _info(self) -> DatasetInfo: _ = self.option1 <- .... AttributeError: 'BuilderConfig' object has no attribute 'option1' ``` which seems to find another unknown config. You can try this line `datasets.load_dataset("indonesian-nlp/librivox-indonesia", split="train", data_dir='.')`, it's a multi-config dataset on HF hub and the error is the same. My insights: https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518 if `config_kwargs` is provided here, the if branch is skipped.
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
I see, you just have to set this class attribute to your builder class :) ```python BUILDER_CONFIG_CLASS = MyDatasetConfig ```
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 I see, you just have to set this class attribute to your builder class :) ```python BUILDER_CONFIG_CLASS = MyDatasetConfig ```
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
So what does this attribute do? In most cases it's not used and the [documents for multi-config dataset](https://huggingface.co/docs/datasets/main/en/image_dataset#multiple-configurations) never mentioned that.
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 So what does this attribute do? In most cases it's not used and the [documents for multi-config dataset](https://huggingface.co/docs/datasets/main/en/image_dataset#multiple-configurations) never mentioned that.
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
It tells which builder config class to instantiate if additional config parameters are passed to load_dataset
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
16
default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 It tells which builder config class to instantiate if additional config parameters are passed to load_dataset
https://github.com/huggingface/datasets/issues/6130
default config name doesn't work when config kwargs are specified.
@lhoestq maybe we can enhance the document to say something about the common attributes of `DatasetBuilder`
### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
16
default config name doesn't work when config kwargs are specified. ### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 @lhoestq maybe we can enhance the document to say something about the common attributes of `DatasetBuilder`
https://github.com/huggingface/datasets/issues/6128
IndexError: Invalid key: 88 is out of bounds for size 0
> I tried this and got the following error: ``` Traceback (most recent call last): File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 324, in _compile out_code = transform_code_object(code, transform) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 445, in transform_code_object transformations(instructions, code_options) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 311, in transform tracer.run() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1726, in run super().run() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 576, in run and self.step() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 540, in step getattr(self, inst.opname)(inst) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1030, in LOAD_ATTR result = BuiltinVariable(getattr).call_function( File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 566, in call_function result = handler(tx, *args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 931, in call_getattr return obj.var_getattr(tx, name).add_options(options) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py", line 124, in var_getattr subobj = inspect.getattr_static(base, name) File "/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py", line 1777, in getattr_static raise AttributeError(attr) AttributeError: config from user code: File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py", line 909, in forward if self.base_model.config.model_type == "mpt": Set torch._dynamo.config.verbose=True for more information You can suppress this exception and fall back to eager by setting: torch._dynamo.config.suppress_errors = True The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py", line 228, in <module> main() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py", line 221, in main trainer.train() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 1809, in _inner_training_loop tr_loss_step = self.training_step(model, inputs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 2654, in training_step loss = self.compute_loss(model, inputs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 2679, in compute_loss outputs = model(**inputs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 82, in forward return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 209, in _fn return fn(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py", line 581, in forward return model_forward(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py", line 569, in __call__ return convert_to_fp32(self.model_forward(*args, **kwargs)) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast return func(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 337, in catch_errors return callback(frame, cache_size, hooks) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 404, in _convert_frame result = inner_convert(frame, cache_size, hooks) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 104, in _fn return fn(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 262, in _convert_frame_assert return _compile( File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 163, in time_wrapper r = func(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 394, in _compile raise InternalTorchDynamoError() from e torch._dynamo.exc.InternalTorchDynamoError ```
### Describe the bug This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib. ### Steps to reproduce the bug I use the following code to fine-tune Falcon on my private dataset. ```python import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, AutoConfig, DataCollatorForSeq2Seq, Trainer, Seq2SeqTrainer, HfArgumentParser, Seq2SeqTrainingArguments, BitsAndBytesConfig, ) from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) import torch import os import evaluate import functools from datasets import load_dataset import bitsandbytes as bnb import logging import json import copy from typing import Dict, Optional, Sequence from dataclasses import dataclass, field # Lora settings LORA_R = 8 LORA_ALPHA = 16 LORA_DROPOUT= 0.05 LORA_TARGET_MODULES = ["query_key_value"] @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B") @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."}) num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."}) @dataclass class TrainingArguments(transformers.TrainingArguments): # cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."}) def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True): result = tokenizer( text, truncation=True, max_length=max_seq_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < max_seq_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) if add_eos_token and len(result["input_ids"]) >= max_seq_len: result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id result["attention_mask"][max_seq_len - 1] = 1 result["labels"] = result["input_ids"].copy() return result def main(): parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, trust_remote_code=True, ) if training_args.is_lora: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, torch_dtype=torch.float16, trust_remote_code=True, load_in_8bit=True, quantization_config=BitsAndBytesConfig( load_in_8bit=True, llm_int8_threshold=6.0 ), ) model = prepare_model_for_int8_training(model) config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=LORA_TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) else: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, torch_dtype=torch.float16, cache_dir=data_args.cache_path, trust_remote_code=True, ) model.config.use_cache = False def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) print_trainable_parameters(model) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, model_max_length=training_args.model_max_length, padding_side="left", use_fast=True, trust_remote_code=True, ) tokenizer.pad_token = tokenizer.eos_token # Load dataset def generate_and_tokenize_prompt(sample): input_text = sample["input"] target_text = sample["output"] + tokenizer.eos_token full_text = input_text + target_text tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512) tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512) input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:] return tokenized_full_text data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.eval_file is not None: data_files["eval"] = data_args.eval_file dataset = load_dataset(data_args.data_path, data_files=data_files) train_dataset = dataset["train"] eval_dataset = dataset["eval"] train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True) # Evaluation metrics def compute_metrics(eval_preds, tokenizer): metric = evaluate.load('exact_match') preds, labels = eval_preds # In case the model returns more than the prediction logits if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Replace -100s in the labels as we can't decode them labels[labels == -100] = tokenizer.pad_token_id decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] decoded_labels = [label.strip() for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels) return {'exact_match': result['exact_match']} compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer) model = torch.compile(model) # Training trainer = Trainer( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics_fn, ) trainer.train() trainer.save_state() trainer.save_model(output_dir=training_args.output_dir) tokenizer.save_pretrained(save_directory=training_args.output_dir) if __name__ == "__main__": main() ``` When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error: ``` Traceback (most recent call last): File "falcon_sft.py", line 230, in <module> main() File "falcon_sft.py", line 223, in main trainer.train() File "python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop for step, inputs in enumerate(epoch_iterator): File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__ current_batch = next(dataloader_iter) File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 88 is out of bounds for size 0 ``` So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`? ### Expected behavior I want to use `torch.compile` in my code. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
326
IndexError: Invalid key: 88 is out of bounds for size 0 ### Describe the bug This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib. ### Steps to reproduce the bug I use the following code to fine-tune Falcon on my private dataset. ```python import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, AutoConfig, DataCollatorForSeq2Seq, Trainer, Seq2SeqTrainer, HfArgumentParser, Seq2SeqTrainingArguments, BitsAndBytesConfig, ) from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) import torch import os import evaluate import functools from datasets import load_dataset import bitsandbytes as bnb import logging import json import copy from typing import Dict, Optional, Sequence from dataclasses import dataclass, field # Lora settings LORA_R = 8 LORA_ALPHA = 16 LORA_DROPOUT= 0.05 LORA_TARGET_MODULES = ["query_key_value"] @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B") @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."}) num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."}) @dataclass class TrainingArguments(transformers.TrainingArguments): # cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."}) def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True): result = tokenizer( text, truncation=True, max_length=max_seq_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < max_seq_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) if add_eos_token and len(result["input_ids"]) >= max_seq_len: result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id result["attention_mask"][max_seq_len - 1] = 1 result["labels"] = result["input_ids"].copy() return result def main(): parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, trust_remote_code=True, ) if training_args.is_lora: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, torch_dtype=torch.float16, trust_remote_code=True, load_in_8bit=True, quantization_config=BitsAndBytesConfig( load_in_8bit=True, llm_int8_threshold=6.0 ), ) model = prepare_model_for_int8_training(model) config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=LORA_TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) else: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, torch_dtype=torch.float16, cache_dir=data_args.cache_path, trust_remote_code=True, ) model.config.use_cache = False def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) print_trainable_parameters(model) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, model_max_length=training_args.model_max_length, padding_side="left", use_fast=True, trust_remote_code=True, ) tokenizer.pad_token = tokenizer.eos_token # Load dataset def generate_and_tokenize_prompt(sample): input_text = sample["input"] target_text = sample["output"] + tokenizer.eos_token full_text = input_text + target_text tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512) tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512) input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:] return tokenized_full_text data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.eval_file is not None: data_files["eval"] = data_args.eval_file dataset = load_dataset(data_args.data_path, data_files=data_files) train_dataset = dataset["train"] eval_dataset = dataset["eval"] train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True) # Evaluation metrics def compute_metrics(eval_preds, tokenizer): metric = evaluate.load('exact_match') preds, labels = eval_preds # In case the model returns more than the prediction logits if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Replace -100s in the labels as we can't decode them labels[labels == -100] = tokenizer.pad_token_id decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] decoded_labels = [label.strip() for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels) return {'exact_match': result['exact_match']} compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer) model = torch.compile(model) # Training trainer = Trainer( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics_fn, ) trainer.train() trainer.save_state() trainer.save_model(output_dir=training_args.output_dir) tokenizer.save_pretrained(save_directory=training_args.output_dir) if __name__ == "__main__": main() ``` When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error: ``` Traceback (most recent call last): File "falcon_sft.py", line 230, in <module> main() File "falcon_sft.py", line 223, in main trainer.train() File "python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop for step, inputs in enumerate(epoch_iterator): File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__ current_batch = next(dataloader_iter) File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 88 is out of bounds for size 0 ``` So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`? ### Expected behavior I want to use `torch.compile` in my code. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 > I tried this and got the following error: ``` Traceback (most recent call last): File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 324, in _compile out_code = transform_code_object(code, transform) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 445, in transform_code_object transformations(instructions, code_options) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 311, in transform tracer.run() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1726, in run super().run() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 576, in run and self.step() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 540, in step getattr(self, inst.opname)(inst) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1030, in LOAD_ATTR result = BuiltinVariable(getattr).call_function( File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 566, in call_function result = handler(tx, *args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 931, in call_getattr return obj.var_getattr(tx, name).add_options(options) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py", line 124, in var_getattr subobj = inspect.getattr_static(base, name) File "/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py", line 1777, in getattr_static raise AttributeError(attr) AttributeError: config from user code: File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py", line 909, in forward if self.base_model.config.model_type == "mpt": Set torch._dynamo.config.verbose=True for more information You can suppress this exception and fall back to eager by setting: torch._dynamo.config.suppress_errors = True The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py", line 228, in <module> main() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py", line 221, in main trainer.train() File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 1809, in _inner_training_loop tr_loss_step = self.training_step(model, inputs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 2654, in training_step loss = self.compute_loss(model, inputs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py", line 2679, in compute_loss outputs = model(**inputs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 82, in forward return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 209, in _fn return fn(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py", line 581, in forward return model_forward(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py", line 569, in __call__ return convert_to_fp32(self.model_forward(*args, **kwargs)) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast return func(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 337, in catch_errors return callback(frame, cache_size, hooks) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 404, in _convert_frame result = inner_convert(frame, cache_size, hooks) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 104, in _fn return fn(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 262, in _convert_frame_assert return _compile( File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 163, in time_wrapper r = func(*args, **kwargs) File "/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 394, in _compile raise InternalTorchDynamoError() from e torch._dynamo.exc.InternalTorchDynamoError ```
https://github.com/huggingface/datasets/issues/6128
IndexError: Invalid key: 88 is out of bounds for size 0
Hi @TomasAndersonFang, I guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo.
### Describe the bug This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib. ### Steps to reproduce the bug I use the following code to fine-tune Falcon on my private dataset. ```python import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, AutoConfig, DataCollatorForSeq2Seq, Trainer, Seq2SeqTrainer, HfArgumentParser, Seq2SeqTrainingArguments, BitsAndBytesConfig, ) from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) import torch import os import evaluate import functools from datasets import load_dataset import bitsandbytes as bnb import logging import json import copy from typing import Dict, Optional, Sequence from dataclasses import dataclass, field # Lora settings LORA_R = 8 LORA_ALPHA = 16 LORA_DROPOUT= 0.05 LORA_TARGET_MODULES = ["query_key_value"] @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B") @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."}) num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."}) @dataclass class TrainingArguments(transformers.TrainingArguments): # cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."}) def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True): result = tokenizer( text, truncation=True, max_length=max_seq_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < max_seq_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) if add_eos_token and len(result["input_ids"]) >= max_seq_len: result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id result["attention_mask"][max_seq_len - 1] = 1 result["labels"] = result["input_ids"].copy() return result def main(): parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, trust_remote_code=True, ) if training_args.is_lora: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, torch_dtype=torch.float16, trust_remote_code=True, load_in_8bit=True, quantization_config=BitsAndBytesConfig( load_in_8bit=True, llm_int8_threshold=6.0 ), ) model = prepare_model_for_int8_training(model) config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=LORA_TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) else: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, torch_dtype=torch.float16, cache_dir=data_args.cache_path, trust_remote_code=True, ) model.config.use_cache = False def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) print_trainable_parameters(model) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, model_max_length=training_args.model_max_length, padding_side="left", use_fast=True, trust_remote_code=True, ) tokenizer.pad_token = tokenizer.eos_token # Load dataset def generate_and_tokenize_prompt(sample): input_text = sample["input"] target_text = sample["output"] + tokenizer.eos_token full_text = input_text + target_text tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512) tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512) input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:] return tokenized_full_text data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.eval_file is not None: data_files["eval"] = data_args.eval_file dataset = load_dataset(data_args.data_path, data_files=data_files) train_dataset = dataset["train"] eval_dataset = dataset["eval"] train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True) # Evaluation metrics def compute_metrics(eval_preds, tokenizer): metric = evaluate.load('exact_match') preds, labels = eval_preds # In case the model returns more than the prediction logits if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Replace -100s in the labels as we can't decode them labels[labels == -100] = tokenizer.pad_token_id decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] decoded_labels = [label.strip() for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels) return {'exact_match': result['exact_match']} compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer) model = torch.compile(model) # Training trainer = Trainer( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics_fn, ) trainer.train() trainer.save_state() trainer.save_model(output_dir=training_args.output_dir) tokenizer.save_pretrained(save_directory=training_args.output_dir) if __name__ == "__main__": main() ``` When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error: ``` Traceback (most recent call last): File "falcon_sft.py", line 230, in <module> main() File "falcon_sft.py", line 223, in main trainer.train() File "python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop for step, inputs in enumerate(epoch_iterator): File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__ current_batch = next(dataloader_iter) File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 88 is out of bounds for size 0 ``` So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`? ### Expected behavior I want to use `torch.compile` in my code. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
26
IndexError: Invalid key: 88 is out of bounds for size 0 ### Describe the bug This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib. ### Steps to reproduce the bug I use the following code to fine-tune Falcon on my private dataset. ```python import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, AutoConfig, DataCollatorForSeq2Seq, Trainer, Seq2SeqTrainer, HfArgumentParser, Seq2SeqTrainingArguments, BitsAndBytesConfig, ) from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) import torch import os import evaluate import functools from datasets import load_dataset import bitsandbytes as bnb import logging import json import copy from typing import Dict, Optional, Sequence from dataclasses import dataclass, field # Lora settings LORA_R = 8 LORA_ALPHA = 16 LORA_DROPOUT= 0.05 LORA_TARGET_MODULES = ["query_key_value"] @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B") @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."}) num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."}) @dataclass class TrainingArguments(transformers.TrainingArguments): # cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."}) def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True): result = tokenizer( text, truncation=True, max_length=max_seq_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < max_seq_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) if add_eos_token and len(result["input_ids"]) >= max_seq_len: result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id result["attention_mask"][max_seq_len - 1] = 1 result["labels"] = result["input_ids"].copy() return result def main(): parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, trust_remote_code=True, ) if training_args.is_lora: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, torch_dtype=torch.float16, trust_remote_code=True, load_in_8bit=True, quantization_config=BitsAndBytesConfig( load_in_8bit=True, llm_int8_threshold=6.0 ), ) model = prepare_model_for_int8_training(model) config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=LORA_TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) else: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, torch_dtype=torch.float16, cache_dir=data_args.cache_path, trust_remote_code=True, ) model.config.use_cache = False def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) print_trainable_parameters(model) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, model_max_length=training_args.model_max_length, padding_side="left", use_fast=True, trust_remote_code=True, ) tokenizer.pad_token = tokenizer.eos_token # Load dataset def generate_and_tokenize_prompt(sample): input_text = sample["input"] target_text = sample["output"] + tokenizer.eos_token full_text = input_text + target_text tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512) tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512) input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:] return tokenized_full_text data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.eval_file is not None: data_files["eval"] = data_args.eval_file dataset = load_dataset(data_args.data_path, data_files=data_files) train_dataset = dataset["train"] eval_dataset = dataset["eval"] train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True) # Evaluation metrics def compute_metrics(eval_preds, tokenizer): metric = evaluate.load('exact_match') preds, labels = eval_preds # In case the model returns more than the prediction logits if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Replace -100s in the labels as we can't decode them labels[labels == -100] = tokenizer.pad_token_id decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] decoded_labels = [label.strip() for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels) return {'exact_match': result['exact_match']} compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer) model = torch.compile(model) # Training trainer = Trainer( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics_fn, ) trainer.train() trainer.save_state() trainer.save_model(output_dir=training_args.output_dir) tokenizer.save_pretrained(save_directory=training_args.output_dir) if __name__ == "__main__": main() ``` When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error: ``` Traceback (most recent call last): File "falcon_sft.py", line 230, in <module> main() File "falcon_sft.py", line 223, in main trainer.train() File "python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop for step, inputs in enumerate(epoch_iterator): File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__ current_batch = next(dataloader_iter) File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 88 is out of bounds for size 0 ``` So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`? ### Expected behavior I want to use `torch.compile` in my code. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 Hi @TomasAndersonFang, I guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo.
https://github.com/huggingface/datasets/issues/6126
Private datasets do not load when passing token
Our CI did not catch this issue because with current implementation, stored token in `HfFolder` (which always exists) is used by default.
### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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Private datasets do not load when passing token ### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 Our CI did not catch this issue because with current implementation, stored token in `HfFolder` (which always exists) is used by default.
https://github.com/huggingface/datasets/issues/6126
Private datasets do not load when passing token
I can confirm this and have the same problem (and just went almost crazy because I couldn't figure out the source of this problem because on another computer everything worked well even with `DownloadMode.FORCE_REDOWNLOAD`).
### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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Private datasets do not load when passing token ### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 I can confirm this and have the same problem (and just went almost crazy because I couldn't figure out the source of this problem because on another computer everything worked well even with `DownloadMode.FORCE_REDOWNLOAD`).
https://github.com/huggingface/datasets/issues/6126
Private datasets do not load when passing token
We are planning to do a patch release today, after the merge of the fix: - #6127 In the meantime, the problem can be circumvented by passing `download_config` instead: ```python from datasets import DownloadConfig, load_dataset load_dataset("<DATASET-NAME>", split="train", download_config=DownloadConfig(token="<TOKEN>")) ```
### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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Private datasets do not load when passing token ### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 We are planning to do a patch release today, after the merge of the fix: - #6127 In the meantime, the problem can be circumvented by passing `download_config` instead: ```python from datasets import DownloadConfig, load_dataset load_dataset("<DATASET-NAME>", split="train", download_config=DownloadConfig(token="<TOKEN>")) ```
https://github.com/huggingface/datasets/issues/6126
Private datasets do not load when passing token
> We are planning to do a patch release today, after the merge of the fix: > > * [Fix authentication issues #6127](https://github.com/huggingface/datasets/pull/6127) > > > In the meantime, the problem can be circumvented by passing `download_config` instead: > > ```python > from datasets import DownloadConfig, load_dataset > > load_dataset("<DATASET-NAME>", split="train", download_config=DownloadConfig(token="<TOKEN>")) > ``` This did not work for me (there was some other error with the split being an unexpected size 0). Downgrading to 2.13 fixed it....
### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3
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Private datasets do not load when passing token ### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 > We are planning to do a patch release today, after the merge of the fix: > > * [Fix authentication issues #6127](https://github.com/huggingface/datasets/pull/6127) > > > In the meantime, the problem can be circumvented by passing `download_config` instead: > > ```python > from datasets import DownloadConfig, load_dataset > > load_dataset("<DATASET-NAME>", split="train", download_config=DownloadConfig(token="<TOKEN>")) > ``` This did not work for me (there was some other error with the split being an unexpected size 0). Downgrading to 2.13 fixed it....
https://github.com/huggingface/datasets/issues/6124
Datasets crashing runs due to KeyError
i once had the same error and I could fix that by pushing a fake or a dummy commit on my hugging face dataset repo
### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11
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Datasets crashing runs due to KeyError ### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11 i once had the same error and I could fix that by pushing a fake or a dummy commit on my hugging face dataset repo
https://github.com/huggingface/datasets/issues/6124
Datasets crashing runs due to KeyError
Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?
### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11
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Datasets crashing runs due to KeyError ### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11 Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?
https://github.com/huggingface/datasets/issues/6124
Datasets crashing runs due to KeyError
> Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)? Hi Mario, Unfortunately, the dataset in question is currently private until the model is trained and released. This is not happening with one dataset but numerous hosted private datasets. I am only loading the dataset and doing nothing else currently. It seems to happen completely sporadically. Thank you, Enrico
### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11
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Datasets crashing runs due to KeyError ### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11 > Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)? Hi Mario, Unfortunately, the dataset in question is currently private until the model is trained and released. This is not happening with one dataset but numerous hosted private datasets. I am only loading the dataset and doing nothing else currently. It seems to happen completely sporadically. Thank you, Enrico
https://github.com/huggingface/datasets/issues/6123
Inaccurate Bounding Boxes in "wildreceipt" Dataset
Hi! Thanks for the investigation, but we are not the authors of these datasets, so please report this on the Hub instead so that the actual authors can fix it.
### Describe the bug I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset. To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face: **Example 1:** ![image](https://github.com/huggingface/datasets/assets/50714796/7a6604d2-899d-4102-a008-1a28c90698f1) ![image](https://github.com/huggingface/datasets/assets/50714796/eba458c7-d3af-4868-a520-8b683aa96f66) ![image](https://github.com/huggingface/datasets/assets/50714796/9f394891-5f5b-46f7-8e52-071b724aedab) **Example 2:** ![image](https://github.com/huggingface/datasets/assets/50714796/a2b2a8d3-124e-4990-b64a-5133cf4be2fe) ![image](https://github.com/huggingface/datasets/assets/50714796/6ee25642-35aa-40ad-ac1e-899d33be90df) ![image](https://github.com/huggingface/datasets/assets/50714796/5e42ff91-9fc4-4520-8803-0e225656f96c) It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar). This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated. ### Steps to reproduce the bug ```python import matplotlib.pyplot as plt from datasets import load_dataset # Define functions to convert bounding box formats def convert_format1(box): x, y, w, h = box x2, y2 = x + w, y + h return [x, y, x2, y2] def convert_format2(box): x1, y1, x2, y2 = box return [x1, y1, x2, y2] def plot_cropped_image(image, box, title): cropped_image = image.crop(box) plt.imshow(cropped_image) plt.title(title) plt.axis('off') plt.savefig(title+'.png') plt.show() doc_index = 1 word_index = 3 dataset = load_dataset("Theivaprakasham/wildreceipt")['train'] bbox_hugging_face = dataset[doc_index]['bboxes'][word_index] text_unit_face = dataset[doc_index]['words'][word_index] common_box_hugface_1 = convert_format1(bbox_hugging_face) common_box_hugface_2 = convert_format2(bbox_hugging_face) plot_cropped_image(image_hugging, common_box_hugface_1, f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}') plot_cropped_image(image_hugging, common_box_hugface_2, f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}') ``` ### Expected behavior The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset. ### Environment info - Python version: 3.8 - Hugging Face datasets version: 2.14.2 - Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar
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Inaccurate Bounding Boxes in "wildreceipt" Dataset ### Describe the bug I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset. To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face: **Example 1:** ![image](https://github.com/huggingface/datasets/assets/50714796/7a6604d2-899d-4102-a008-1a28c90698f1) ![image](https://github.com/huggingface/datasets/assets/50714796/eba458c7-d3af-4868-a520-8b683aa96f66) ![image](https://github.com/huggingface/datasets/assets/50714796/9f394891-5f5b-46f7-8e52-071b724aedab) **Example 2:** ![image](https://github.com/huggingface/datasets/assets/50714796/a2b2a8d3-124e-4990-b64a-5133cf4be2fe) ![image](https://github.com/huggingface/datasets/assets/50714796/6ee25642-35aa-40ad-ac1e-899d33be90df) ![image](https://github.com/huggingface/datasets/assets/50714796/5e42ff91-9fc4-4520-8803-0e225656f96c) It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar). This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated. ### Steps to reproduce the bug ```python import matplotlib.pyplot as plt from datasets import load_dataset # Define functions to convert bounding box formats def convert_format1(box): x, y, w, h = box x2, y2 = x + w, y + h return [x, y, x2, y2] def convert_format2(box): x1, y1, x2, y2 = box return [x1, y1, x2, y2] def plot_cropped_image(image, box, title): cropped_image = image.crop(box) plt.imshow(cropped_image) plt.title(title) plt.axis('off') plt.savefig(title+'.png') plt.show() doc_index = 1 word_index = 3 dataset = load_dataset("Theivaprakasham/wildreceipt")['train'] bbox_hugging_face = dataset[doc_index]['bboxes'][word_index] text_unit_face = dataset[doc_index]['words'][word_index] common_box_hugface_1 = convert_format1(bbox_hugging_face) common_box_hugface_2 = convert_format2(bbox_hugging_face) plot_cropped_image(image_hugging, common_box_hugface_1, f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}') plot_cropped_image(image_hugging, common_box_hugface_2, f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}') ``` ### Expected behavior The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset. ### Environment info - Python version: 3.8 - Hugging Face datasets version: 2.14.2 - Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar Hi! Thanks for the investigation, but we are not the authors of these datasets, so please report this on the Hub instead so that the actual authors can fix it.
https://github.com/huggingface/datasets/issues/6120
Lookahead streaming support?
In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading.
### Feature request From what I understand, streaming dataset currently pulls the data, and process the data as it is requested. This can introduce significant latency delays when data is loaded into the training process, needing to wait for each segment. While the delays might be dataset specific (or even mapping instruction/tokenizer specific) Is it possible to introduce a `streaming_lookahead` parameter, which is used for predictable workloads (even shuffled dataset with fixed seed). As we can predict in advance what the next few datasamples will be. And fetch them while the current set is being trained. With enough CPU & bandwidth to keep up with the training process, and a sufficiently large lookahead, this will reduce the various latency involved while waiting for the dataset to be ready between batches. ### Motivation Faster streaming performance, while training over extra large TB sized datasets ### Your contribution I currently use HF dataset, with pytorch lightning trainer for RWKV project, and would be able to help test this feature if supported.
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Lookahead streaming support? ### Feature request From what I understand, streaming dataset currently pulls the data, and process the data as it is requested. This can introduce significant latency delays when data is loaded into the training process, needing to wait for each segment. While the delays might be dataset specific (or even mapping instruction/tokenizer specific) Is it possible to introduce a `streaming_lookahead` parameter, which is used for predictable workloads (even shuffled dataset with fixed seed). As we can predict in advance what the next few datasamples will be. And fetch them while the current set is being trained. With enough CPU & bandwidth to keep up with the training process, and a sufficiently large lookahead, this will reduce the various latency involved while waiting for the dataset to be ready between batches. ### Motivation Faster streaming performance, while training over extra large TB sized datasets ### Your contribution I currently use HF dataset, with pytorch lightning trainer for RWKV project, and would be able to help test this feature if supported. In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading.
https://github.com/huggingface/datasets/issues/6118
IterableDataset.from_generator() fails with pickle error when provided a generator or iterator
Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`). You can fix the above snippet as follows: ```python train_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={"files": model_training_files}) ```
### Describe the bug **Description** Providing a generator in an instantiation of IterableDataset.from_generator() fails with `TypeError: cannot pickle 'generator' object` when the generator argument is supplied with a generator. **Code example** ``` def line_generator(files: List[Path]): if isinstance(files, str): files = [Path(files)] for file in files: if isinstance(file, str): file = Path(file) yield from open(file,'r').readlines() ... model_training_files = ['file1.txt', 'file2.txt', 'file3.txt'] train_dataset = IterableDataset.from_generator(generator=line_generator(model_training_files)) ``` **Traceback** Traceback (most recent call last): File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/contextlib.py", line 135, in __exit__ self.gen.throw(type, value, traceback) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 691, in _no_cache_fields yield File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 701, in dumps dump(obj, file) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 676, in dump Pickler(file, recurse=True).dump(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 394, in dump StockPickler.dump(self, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 487, in dump self.save(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 1186, in save_module_dict StockPickler.save_dict(pickler, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 971, in save_dict self._batch_setitems(obj.items()) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 997, in _batch_setitems save(v) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 578, in save rv = reduce(self.proto) TypeError: cannot pickle 'generator' object ### Steps to reproduce the bug 1. Create a set of text files to iterate over. 2. Create a generator that returns the lines in each file until all files are exhausted. 3. Instantiate the dataset over the generator by instantiating an IterableDataset.from_generator(). 4. Wait for the explosion. ### Expected behavior I would expect that since the function claims to accept a generator that there would be no crash. Instead, I would expect the dataset to return all the lines in the files as queued up in the `line_generator()` function. ### Environment info datasets.__version__ == '2.13.1' Python 3.9.6 Platform: Darwin WE35261 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:22 PDT 2023; root:xnu-8796.121.3~7/RELEASE_X86_64 x86_64
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IterableDataset.from_generator() fails with pickle error when provided a generator or iterator ### Describe the bug **Description** Providing a generator in an instantiation of IterableDataset.from_generator() fails with `TypeError: cannot pickle 'generator' object` when the generator argument is supplied with a generator. **Code example** ``` def line_generator(files: List[Path]): if isinstance(files, str): files = [Path(files)] for file in files: if isinstance(file, str): file = Path(file) yield from open(file,'r').readlines() ... model_training_files = ['file1.txt', 'file2.txt', 'file3.txt'] train_dataset = IterableDataset.from_generator(generator=line_generator(model_training_files)) ``` **Traceback** Traceback (most recent call last): File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/contextlib.py", line 135, in __exit__ self.gen.throw(type, value, traceback) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 691, in _no_cache_fields yield File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 701, in dumps dump(obj, file) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 676, in dump Pickler(file, recurse=True).dump(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 394, in dump StockPickler.dump(self, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 487, in dump self.save(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 1186, in save_module_dict StockPickler.save_dict(pickler, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 971, in save_dict self._batch_setitems(obj.items()) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 997, in _batch_setitems save(v) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 578, in save rv = reduce(self.proto) TypeError: cannot pickle 'generator' object ### Steps to reproduce the bug 1. Create a set of text files to iterate over. 2. Create a generator that returns the lines in each file until all files are exhausted. 3. Instantiate the dataset over the generator by instantiating an IterableDataset.from_generator(). 4. Wait for the explosion. ### Expected behavior I would expect that since the function claims to accept a generator that there would be no crash. Instead, I would expect the dataset to return all the lines in the files as queued up in the `line_generator()` function. ### Environment info datasets.__version__ == '2.13.1' Python 3.9.6 Platform: Darwin WE35261 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:22 PDT 2023; root:xnu-8796.121.3~7/RELEASE_X86_64 x86_64 Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`). You can fix the above snippet as follows: ```python train_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={"files": model_training_files}) ```
https://github.com/huggingface/datasets/issues/6114
Cache not being used when loading commonvoice 8.0.0
You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).
### Describe the bug I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially: ``` dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>") ``` it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` 2. dataset is updated by maintainers 3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` ### Expected behavior I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded? EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example: ``` load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en") > ... > File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str) 1937 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ... 1794 e = e.__context__ -> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux
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Cache not being used when loading commonvoice 8.0.0 ### Describe the bug I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially: ``` dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>") ``` it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` 2. dataset is updated by maintainers 3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` ### Expected behavior I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded? EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example: ``` load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en") > ... > File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str) 1937 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ... 1794 e = e.__context__ -> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).
https://github.com/huggingface/datasets/issues/6114
Cache not being used when loading commonvoice 8.0.0
Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. It is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else.
### Describe the bug I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially: ``` dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>") ``` it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` 2. dataset is updated by maintainers 3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` ### Expected behavior I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded? EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example: ``` load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en") > ... > File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str) 1937 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ... 1794 e = e.__context__ -> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux
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Cache not being used when loading commonvoice 8.0.0 ### Describe the bug I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially: ``` dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>") ``` it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` 2. dataset is updated by maintainers 3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` ### Expected behavior I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded? EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example: ``` load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en") > ... > File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str) 1937 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ... 1794 e = e.__context__ -> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. It is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else.
https://github.com/huggingface/datasets/issues/6113
load_dataset() fails with streamlit caching inside docker
Hi! This should be fixed in the latest (patch) release (run `pip install -U datasets` to install it). This behavior was due to a bug in our authentication logic.
### Describe the bug When calling `load_dataset` in a streamlit application running within a docker container, get a failure with the error message: EmptyDatasetError: The directory at hf://datasets/fetch-rewards/inc-rings-2000@bea27cf60842b3641eae418f38864a2ec4cde684 doesn't contain any data files Traceback: File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script exec(code, module.__dict__) File "/home/user/app/app.py", line 62, in <module> dashboard() File "/home/user/app/app.py", line 47, in dashboard feat_dict, path_gml = load_data(hf_repo, model_gml_dict[selected_model], hf_token) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 211, in wrapper return cached_func(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 240, in __call__ return self._get_or_create_cached_value(args, kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 266, in _get_or_create_cached_value return self._handle_cache_miss(cache, value_key, func_args, func_kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 320, in _handle_cache_miss computed_value = self._info.func(*func_args, **func_kwargs) File "/home/user/app/hf_interface.py", line 16, in load_data hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2109, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1795, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1486, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1476, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1032, in get_module else get_data_patterns(base_path, download_config=self.download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 458, in get_data_patterns raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None ### Steps to reproduce the bug ```python @st.cache_resource def load_data(repo_id: str, hf_token=None): """Load data from HuggingFace Hub """ hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) hf_dataset = hf_dataset.map(lambda x: json.loads(x["ground_truth"]), remove_columns=["ground_truth"]) return hf_dataset ``` ### Expected behavior Expect to load. Note: works fine with datasets==2.13.1 ### Environment info datasets==2.14.2, Ubuntu bionic-based Docker container.
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load_dataset() fails with streamlit caching inside docker ### Describe the bug When calling `load_dataset` in a streamlit application running within a docker container, get a failure with the error message: EmptyDatasetError: The directory at hf://datasets/fetch-rewards/inc-rings-2000@bea27cf60842b3641eae418f38864a2ec4cde684 doesn't contain any data files Traceback: File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script exec(code, module.__dict__) File "/home/user/app/app.py", line 62, in <module> dashboard() File "/home/user/app/app.py", line 47, in dashboard feat_dict, path_gml = load_data(hf_repo, model_gml_dict[selected_model], hf_token) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 211, in wrapper return cached_func(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 240, in __call__ return self._get_or_create_cached_value(args, kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 266, in _get_or_create_cached_value return self._handle_cache_miss(cache, value_key, func_args, func_kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 320, in _handle_cache_miss computed_value = self._info.func(*func_args, **func_kwargs) File "/home/user/app/hf_interface.py", line 16, in load_data hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2109, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1795, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1486, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1476, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1032, in get_module else get_data_patterns(base_path, download_config=self.download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 458, in get_data_patterns raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None ### Steps to reproduce the bug ```python @st.cache_resource def load_data(repo_id: str, hf_token=None): """Load data from HuggingFace Hub """ hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) hf_dataset = hf_dataset.map(lambda x: json.loads(x["ground_truth"]), remove_columns=["ground_truth"]) return hf_dataset ``` ### Expected behavior Expect to load. Note: works fine with datasets==2.13.1 ### Environment info datasets==2.14.2, Ubuntu bionic-based Docker container. Hi! This should be fixed in the latest (patch) release (run `pip install -U datasets` to install it). This behavior was due to a bug in our authentication logic.
https://github.com/huggingface/datasets/issues/6112
yaml error using push_to_hub with generated README.md
Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon.
### Describe the bug When I construct a dataset with the following features: ``` features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) ``` and run `push_to_hub`, the individual `*.parquet` files are pushed, but when trying to upload the auto-generated README, I run into the following error: ``` Traceback (most recent call last): File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status response.raise_for_status() File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/looppayments/multitask_document_classification_dataset/commit/main The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 297, in <module> build_dataset() File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 290, in build_dataset push_to_hub(dataset, "multitask_document_classification_dataset") File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 135, in push_to_hub dataset.push_to_hub(f"looppayments/{dataset_name}", private=True) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5577, in push_to_hub HfApi(endpoint=config.HF_ENDPOINT).upload_file( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2728, in create_commit hf_raise_for_status(commit_resp, endpoint_name="commit") File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 299, in hf_raise_for_status raise BadRequestError(message, response=response) from e huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-64ca9c3d-2d2bbef354e102482a9a168e;bc00371c-8549-4859-9f41-43ff140ad36e) Bad request for commit endpoint: Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (10:9) 7 | - 3 8 | - 224 9 | - 224 10 | dtype: float64 --------------^ 11 | - name: input_ids 12 | sequence: int64 ``` My guess is that the auto-generated yaml is unable to be parsed for some reason. ### Steps to reproduce the bug The description contains most of what's needed to reproduce the issue, but I've added a shortened code snippet: ``` from datasets import Array2D, Array3D, ClassLabel, Dataset, Features, Sequence, Value from PIL import Image from transformers import AutoProcessor features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) def preprocess_dataset(rows): # Get images images = [ Image.open(png_filename).convert("RGB") for png_filename in rows["png_filename"] ] encoding = processor( images, rows["tokens"], boxes=rows["bbox"], truncation=True, padding="max_length", ) encoding["tokens"] = rows["tokens"] return encoding dataset = dataset.map( preprocess_dataset, batched=True, batch_size=5, features=features, ) ``` ### Expected behavior Using datasets==2.11.0, I'm able to succesfully push_to_hub, no issues, but with datasets==2.14.2, I run into the above error. ### Environment info - `datasets` version: 2.14.2 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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yaml error using push_to_hub with generated README.md ### Describe the bug When I construct a dataset with the following features: ``` features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) ``` and run `push_to_hub`, the individual `*.parquet` files are pushed, but when trying to upload the auto-generated README, I run into the following error: ``` Traceback (most recent call last): File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status response.raise_for_status() File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/looppayments/multitask_document_classification_dataset/commit/main The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 297, in <module> build_dataset() File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 290, in build_dataset push_to_hub(dataset, "multitask_document_classification_dataset") File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 135, in push_to_hub dataset.push_to_hub(f"looppayments/{dataset_name}", private=True) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5577, in push_to_hub HfApi(endpoint=config.HF_ENDPOINT).upload_file( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(*args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, *args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2728, in create_commit hf_raise_for_status(commit_resp, endpoint_name="commit") File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 299, in hf_raise_for_status raise BadRequestError(message, response=response) from e huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-64ca9c3d-2d2bbef354e102482a9a168e;bc00371c-8549-4859-9f41-43ff140ad36e) Bad request for commit endpoint: Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (10:9) 7 | - 3 8 | - 224 9 | - 224 10 | dtype: float64 --------------^ 11 | - name: input_ids 12 | sequence: int64 ``` My guess is that the auto-generated yaml is unable to be parsed for some reason. ### Steps to reproduce the bug The description contains most of what's needed to reproduce the issue, but I've added a shortened code snippet: ``` from datasets import Array2D, Array3D, ClassLabel, Dataset, Features, Sequence, Value from PIL import Image from transformers import AutoProcessor features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) def preprocess_dataset(rows): # Get images images = [ Image.open(png_filename).convert("RGB") for png_filename in rows["png_filename"] ] encoding = processor( images, rows["tokens"], boxes=rows["bbox"], truncation=True, padding="max_length", ) encoding["tokens"] = rows["tokens"] return encoding dataset = dataset.map( preprocess_dataset, batched=True, batch_size=5, features=features, ) ``` ### Expected behavior Using datasets==2.11.0, I'm able to succesfully push_to_hub, no issues, but with datasets==2.14.2, I run into the above error. ### Environment info - `datasets` version: 2.14.2 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3 Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon.
https://github.com/huggingface/datasets/issues/6111
raise FileNotFoundError("Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." )
This should work: `load_dataset("path/to/downloaded_repo")` `load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`
### Describe the bug For researchers in some countries or regions, it is usually the case that the download ability of `load_dataset` is disabled due to the complex network environment. People in these regions often prefer to use git clone or other programming tricks to manually download the files to the disk (for example, [How to elegantly download hf models, zhihu zhuanlan](https://zhuanlan.zhihu.com/p/475260268) proposed a crawlder based solution, and [Is there any mirror for hf_hub, zhihu answer](https://www.zhihu.com/question/371644077) provided some cloud based solutions, and [How to avoid pitfalls on Hugging face downloading, zhihu zhuanlan] gave some useful suggestions), and then use `load_from_disk` to get the dataset object. However, when one finally has the local files on the disk, it is still buggy when trying to load the files into objects. ### Steps to reproduce the bug Steps to reproduce the bug: 1. Found CIFAR dataset in hugging face: https://huggingface.co/datasets/cifar100/tree/main 2. Click ":" button to show "Clone repository" option, and then follow the prompts on the box: ```bash cd my_directory_absolute git lfs install git clone https://huggingface.co/datasets/cifar100 ls my_directory_absolute/cifar100 # confirm that the directory exists and it is OK. ``` 3. Write A python file to try to load the dataset ```python from datasets import load_dataset, load_from_disk dataset = load_from_disk("my_directory_absolute/cifar100") ``` Notice that according to issue #3700 , it is wrong to use load_dataset("my_directory_absolute/cifar100"), so we must use load_from_disk instead. 4. Then you will see the error reported: ```log --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[5], line 9 1 from datasets import load_dataset, load_from_disk ----> 9 dataset = load_from_disk("my_directory_absolute/cifar100") File [~/miniconda3/envs/ai/lib/python3.10/site-packages/datasets/load.py:2232), in load_from_disk(dataset_path, fs, keep_in_memory, storage_options) 2230 return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) 2231 else: -> 2232 raise FileNotFoundError( 2233 f"Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." 2234 ) FileNotFoundError: Directory my_directory_absolute/cifar100 is neither a `Dataset` directory nor a `DatasetDict` directory. ``` ### Expected behavior The dataset should be load successfully. ### Environment info ```bash datasets-cli env ``` -> results: ```txt Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.2 - Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```
17
raise FileNotFoundError("Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." ) ### Describe the bug For researchers in some countries or regions, it is usually the case that the download ability of `load_dataset` is disabled due to the complex network environment. People in these regions often prefer to use git clone or other programming tricks to manually download the files to the disk (for example, [How to elegantly download hf models, zhihu zhuanlan](https://zhuanlan.zhihu.com/p/475260268) proposed a crawlder based solution, and [Is there any mirror for hf_hub, zhihu answer](https://www.zhihu.com/question/371644077) provided some cloud based solutions, and [How to avoid pitfalls on Hugging face downloading, zhihu zhuanlan] gave some useful suggestions), and then use `load_from_disk` to get the dataset object. However, when one finally has the local files on the disk, it is still buggy when trying to load the files into objects. ### Steps to reproduce the bug Steps to reproduce the bug: 1. Found CIFAR dataset in hugging face: https://huggingface.co/datasets/cifar100/tree/main 2. Click ":" button to show "Clone repository" option, and then follow the prompts on the box: ```bash cd my_directory_absolute git lfs install git clone https://huggingface.co/datasets/cifar100 ls my_directory_absolute/cifar100 # confirm that the directory exists and it is OK. ``` 3. Write A python file to try to load the dataset ```python from datasets import load_dataset, load_from_disk dataset = load_from_disk("my_directory_absolute/cifar100") ``` Notice that according to issue #3700 , it is wrong to use load_dataset("my_directory_absolute/cifar100"), so we must use load_from_disk instead. 4. Then you will see the error reported: ```log --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[5], line 9 1 from datasets import load_dataset, load_from_disk ----> 9 dataset = load_from_disk("my_directory_absolute/cifar100") File [~/miniconda3/envs/ai/lib/python3.10/site-packages/datasets/load.py:2232), in load_from_disk(dataset_path, fs, keep_in_memory, storage_options) 2230 return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) 2231 else: -> 2232 raise FileNotFoundError( 2233 f"Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." 2234 ) FileNotFoundError: Directory my_directory_absolute/cifar100 is neither a `Dataset` directory nor a `DatasetDict` directory. ``` ### Expected behavior The dataset should be load successfully. ### Environment info ```bash datasets-cli env ``` -> results: ```txt Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.2 - Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ``` This should work: `load_dataset("path/to/downloaded_repo")` `load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`
https://github.com/huggingface/datasets/issues/6110
[BUG] Dataset initialized from in-memory data does not create cache.
This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached.
### Describe the bug `Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`. ### Steps to reproduce the bug ```python # below code was run the second time so the map function can be loaded from cache if exists from datasets import load_dataset, Dataset dataset = load_dataset("tatsu-lab/alpaca")['train'] dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map print(len(dataset.cache_files)) # 1 # copy the exact same data but initialize from a dictionary memory_dataset = Dataset.from_dict({ 'instruction': dataset['instruction'], 'input': dataset['input'], 'output': dataset['output'], 'text': dataset['text']}) memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map print(len(memory_dataset.cache_files)) # Map: 100%|██████████| 52002[/52002] # 0 ``` ### Expected behavior The `map` function should create cache regardless of the method the `Dataset` was created. ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
21
[BUG] Dataset initialized from in-memory data does not create cache. ### Describe the bug `Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`. ### Steps to reproduce the bug ```python # below code was run the second time so the map function can be loaded from cache if exists from datasets import load_dataset, Dataset dataset = load_dataset("tatsu-lab/alpaca")['train'] dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map print(len(dataset.cache_files)) # 1 # copy the exact same data but initialize from a dictionary memory_dataset = Dataset.from_dict({ 'instruction': dataset['instruction'], 'input': dataset['input'], 'output': dataset['output'], 'text': dataset['text']}) memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map print(len(memory_dataset.cache_files)) # Map: 100%|██████████| 52002[/52002] # 0 ``` ### Expected behavior The `map` function should create cache regardless of the method the `Dataset` was created. ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached.
https://github.com/huggingface/datasets/issues/6109
Problems in downloading Amazon reviews from HF
Thanks for reporting, @610v4nn1. Indeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset. We are adding a notification about this issue to the dataset card. See: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0
### Describe the bug I have a script downloading `amazon_reviews_multi`. When the download starts, I get ``` Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.43MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.54s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 842.40it/s] Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 928kB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.42s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 832.70it/s] Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.81MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.40s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 1294.14it/s] Generating train split: 0%| | 0/200000 [00:00<?, ? examples/s] ``` the file is clearly too small to contain the requested dataset, in fact it contains en error message: ``` <?xml version="1.0" encoding="UTF-8"?> <Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>AGJWSY3ZADT2QVWE</RequestId><HostId>Gx1O2KXnxtQFqvzDLxyVSTq3+TTJuTnuVFnJL3SP89Yp8UzvYLPTVwd1PpniE4EvQzT3tCaqEJw=</HostId></Error> ``` obviously the script fails: ``` > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug 1. load_dataset("amazon_reviews_multi", name="en", split="train", cache_dir="ADDYOURPATHHERE") ### Expected behavior I would expect the dataset to be downloaded and processed ### Environment info * The problem is present with both datasets 2.12.0 and 2.14.2 * python version 3.10.12
49
Problems in downloading Amazon reviews from HF ### Describe the bug I have a script downloading `amazon_reviews_multi`. When the download starts, I get ``` Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.43MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.54s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 842.40it/s] Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 928kB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.42s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 832.70it/s] Downloading data files: 0%| | 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.81MB/s] Downloading data files: 100%|██████████| 1/1 [00:01<00:00, 1.40s/it] Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 1294.14it/s] Generating train split: 0%| | 0/200000 [00:00<?, ? examples/s] ``` the file is clearly too small to contain the requested dataset, in fact it contains en error message: ``` <?xml version="1.0" encoding="UTF-8"?> <Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>AGJWSY3ZADT2QVWE</RequestId><HostId>Gx1O2KXnxtQFqvzDLxyVSTq3+TTJuTnuVFnJL3SP89Yp8UzvYLPTVwd1PpniE4EvQzT3tCaqEJw=</HostId></Error> ``` obviously the script fails: ``` > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug 1. load_dataset("amazon_reviews_multi", name="en", split="train", cache_dir="ADDYOURPATHHERE") ### Expected behavior I would expect the dataset to be downloaded and processed ### Environment info * The problem is present with both datasets 2.12.0 and 2.14.2 * python version 3.10.12 Thanks for reporting, @610v4nn1. Indeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset. We are adding a notification about this issue to the dataset card. See: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0
https://github.com/huggingface/datasets/issues/6108
Loading local datasets got strangely stuck
Yesterday I waited for more than 12 hours to make sure it was really **stuck** instead of proceeding too slow.
### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2
20
Loading local datasets got strangely stuck ### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2 Yesterday I waited for more than 12 hours to make sure it was really **stuck** instead of proceeding too slow.
https://github.com/huggingface/datasets/issues/6108
Loading local datasets got strangely stuck
I've had similar weird issues with `load_dataset` as well. Not multiple files, but dataset is quite big, about 50G.
### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2
19
Loading local datasets got strangely stuck ### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2 I've had similar weird issues with `load_dataset` as well. Not multiple files, but dataset is quite big, about 50G.
https://github.com/huggingface/datasets/issues/6108
Loading local datasets got strangely stuck
We use a generic multiprocessing code, so there is little we can do about this - unfortunately, turning off multiprocessing seems to be the only solution. Multithreading would make our code easier to maintain and (most likely) avoid issues such as this one, but we cannot use it until the GIL is dropped (no-GIL Python should be released in 2024, so we can start exploring this then)
### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2
67
Loading local datasets got strangely stuck ### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2 We use a generic multiprocessing code, so there is little we can do about this - unfortunately, turning off multiprocessing seems to be the only solution. Multithreading would make our code easier to maintain and (most likely) avoid issues such as this one, but we cannot use it until the GIL is dropped (no-GIL Python should be released in 2024, so we can start exploring this then)
https://github.com/huggingface/datasets/issues/6106
load local json_file as dataset
Hi! We use PyArrow to read JSON files, and PyArrow doesn't allow different value types in the same column. #5776 should address this. In the meantime, you can combine `Dataset.from_generator` with the above code to cast the values to the same type.
### Describe the bug I tried to load local json file as dataset but failed to parsing json file because some columns are 'float' type. ### Steps to reproduce the bug 1. load json file with certain columns are 'float' type. For example `data = load_data("json", data_files=JSON_PATH)` 2. Then, the error will be triggered like `ArrowInvalid: Could not convert '-0.2253' with type str: tried to convert to double ### Expected behavior Should allow some columns are 'float' type, at least it should convert those columns to str type. I tried to avoid the error by naively convert the float item to str: ```python # if col type is not str, we need to convert it to str mapping = {} for col in keys: if isinstance(dataset[0][col], str): mapping[col] = [row.get(col) for row in dataset] else: mapping[col] = [str(row.get(col)) for row in dataset] ``` ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.4.0-52-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.0 - Pandas version: 2.0.1
42
load local json_file as dataset ### Describe the bug I tried to load local json file as dataset but failed to parsing json file because some columns are 'float' type. ### Steps to reproduce the bug 1. load json file with certain columns are 'float' type. For example `data = load_data("json", data_files=JSON_PATH)` 2. Then, the error will be triggered like `ArrowInvalid: Could not convert '-0.2253' with type str: tried to convert to double ### Expected behavior Should allow some columns are 'float' type, at least it should convert those columns to str type. I tried to avoid the error by naively convert the float item to str: ```python # if col type is not str, we need to convert it to str mapping = {} for col in keys: if isinstance(dataset[0][col], str): mapping[col] = [row.get(col) for row in dataset] else: mapping[col] = [str(row.get(col)) for row in dataset] ``` ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.4.0-52-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 Hi! We use PyArrow to read JSON files, and PyArrow doesn't allow different value types in the same column. #5776 should address this. In the meantime, you can combine `Dataset.from_generator` with the above code to cast the values to the same type.
https://github.com/huggingface/datasets/issues/6099
How do i get "amazon_us_reviews
Seems like the problem isn't with the library, but the dataset itself hosted on AWS S3. Its [homepage](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) returns an `AccessDenied` XML response, which is the same thing you get if you try to log the `record` that triggers the exception ```python try: example = self.info.features.encode_example(record) if self.info.features is not None else record except Exception as e: print(record) ``` ⬇️ ``` {'<?xml version="1.0" encoding="UTF-8"?>': '<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>N2HFJ82ZV8SZW9BV</RequestId><HostId>Zw2DQ0V2GdRmvH5qWEpumK4uj5+W8YPcilQbN9fLBr3VqQOcKPHOhUZLG3LcM9X5fkOetxp48Os=</HostId></Error>'} ```
### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data
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How do i get "amazon_us_reviews ### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data Seems like the problem isn't with the library, but the dataset itself hosted on AWS S3. Its [homepage](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) returns an `AccessDenied` XML response, which is the same thing you get if you try to log the `record` that triggers the exception ```python try: example = self.info.features.encode_example(record) if self.info.features is not None else record except Exception as e: print(record) ``` ⬇️ ``` {'<?xml version="1.0" encoding="UTF-8"?>': '<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>N2HFJ82ZV8SZW9BV</RequestId><HostId>Zw2DQ0V2GdRmvH5qWEpumK4uj5+W8YPcilQbN9fLBr3VqQOcKPHOhUZLG3LcM9X5fkOetxp48Os=</HostId></Error>'} ```
https://github.com/huggingface/datasets/issues/6099
How do i get "amazon_us_reviews
I have figured it out. there was an option of **parquet formated files** i downloaded some from there.
### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data
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How do i get "amazon_us_reviews ### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data I have figured it out. there was an option of **parquet formated files** i downloaded some from there.
https://github.com/huggingface/datasets/issues/6099
How do i get "amazon_us_reviews
Thanks for reporting, @IqraBaluch. We contacted the authors and unfortunately they reported that Amazon has decided to stop distributing this dataset.
### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data
21
How do i get "amazon_us_reviews ### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data Thanks for reporting, @IqraBaluch. We contacted the authors and unfortunately they reported that Amazon has decided to stop distributing this dataset.
https://github.com/huggingface/datasets/issues/6099
How do i get "amazon_us_reviews
I noticed that some book data is missing, we can only get Books_v1_02 data. Is there any way we can get the Books_v1_00 and Books_v1_01? Really appreciate !!!
### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data
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How do i get "amazon_us_reviews ### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data I noticed that some book data is missing, we can only get Books_v1_02 data. Is there any way we can get the Books_v1_00 and Books_v1_01? Really appreciate !!!
https://github.com/huggingface/datasets/issues/6099
How do i get "amazon_us_reviews
@albertvillanova will this dataset be retired given the data are no longer hosted on S3? What is done in cases such as these?
### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data
23
How do i get "amazon_us_reviews ### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` **ERROR** `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data @albertvillanova will this dataset be retired given the data are no longer hosted on S3? What is done in cases such as these?
https://github.com/huggingface/datasets/issues/6089
AssertionError: daemonic processes are not allowed to have children
We could add a "threads" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).
### Describe the bug When I load_dataset with num_proc > 0 in a deamon process, I got an error: ```python File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract return self.extract(self.download(url_or_urls)) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map return _map_with_multiprocessing_pool( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool with Pool(num_proc, initargs=initargs, initializer=initializer) as pool: ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__ self._repopulate_pool() ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static w.start() File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start assert not _current_process._config.get('daemon'), ^^^^^^^^^^^^^^^^^ AssertionError: daemonic processes are not allowed to have children ``` The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process. ### Steps to reproduce the bug 1. start a deamon process 2. run load_dataset with num_proc > 0 ### Expected behavior No error. ### Environment info Python 3.11.4 datasets latest master
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AssertionError: daemonic processes are not allowed to have children ### Describe the bug When I load_dataset with num_proc > 0 in a deamon process, I got an error: ```python File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract return self.extract(self.download(url_or_urls)) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map return _map_with_multiprocessing_pool( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool with Pool(num_proc, initargs=initargs, initializer=initializer) as pool: ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__ self._repopulate_pool() ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static w.start() File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start assert not _current_process._config.get('daemon'), ^^^^^^^^^^^^^^^^^ AssertionError: daemonic processes are not allowed to have children ``` The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process. ### Steps to reproduce the bug 1. start a deamon process 2. run load_dataset with num_proc > 0 ### Expected behavior No error. ### Environment info Python 3.11.4 datasets latest master We could add a "threads" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).
https://github.com/huggingface/datasets/issues/6089
AssertionError: daemonic processes are not allowed to have children
> We could add a "threads" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks). Great! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot.
### Describe the bug When I load_dataset with num_proc > 0 in a deamon process, I got an error: ```python File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract return self.extract(self.download(url_or_urls)) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map return _map_with_multiprocessing_pool( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool with Pool(num_proc, initargs=initargs, initializer=initializer) as pool: ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__ self._repopulate_pool() ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static w.start() File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start assert not _current_process._config.get('daemon'), ^^^^^^^^^^^^^^^^^ AssertionError: daemonic processes are not allowed to have children ``` The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process. ### Steps to reproduce the bug 1. start a deamon process 2. run load_dataset with num_proc > 0 ### Expected behavior No error. ### Environment info Python 3.11.4 datasets latest master
58
AssertionError: daemonic processes are not allowed to have children ### Describe the bug When I load_dataset with num_proc > 0 in a deamon process, I got an error: ```python File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract return self.extract(self.download(url_or_urls)) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map return _map_with_multiprocessing_pool( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool with Pool(num_proc, initargs=initargs, initializer=initializer) as pool: ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__ self._repopulate_pool() ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static w.start() File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start assert not _current_process._config.get('daemon'), ^^^^^^^^^^^^^^^^^ AssertionError: daemonic processes are not allowed to have children ``` The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process. ### Steps to reproduce the bug 1. start a deamon process 2. run load_dataset with num_proc > 0 ### Expected behavior No error. ### Environment info Python 3.11.4 datasets latest master > We could add a "threads" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks). Great! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot.
https://github.com/huggingface/datasets/issues/6086
Support `fsspec` in `Dataset.to_<format>` methods
I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?
Supporting this should be fairly easy. Requested on the forum [here](https://discuss.huggingface.co/t/how-can-i-convert-a-loaded-dataset-in-to-a-parquet-file-and-save-it-to-the-s3/48353).
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Support `fsspec` in `Dataset.to_<format>` methods Supporting this should be fairly easy. Requested on the forum [here](https://discuss.huggingface.co/t/how-can-i-convert-a-loaded-dataset-in-to-a-parquet-file-and-save-it-to-the-s3/48353). I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here?
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here?
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
Thanks @mariosasko for your prompt response, here's the stack trace: ``` KeyboardInterrupt Traceback (most recent call last) Cell In[12], line 4 2 t = time.time() 3 iter_ = 0 ----> 4 for batch in train_dataloader: 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch) 6 iter_ += 1 8 if iter_ == 1: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self) 631 if self._sampler_iter is None: 632 # TODO(https://github.com/pytorch/pytorch/issues/76750) 633 self._reset() # type: ignore[call-arg] --> 634 data = self._next_data() 635 self._num_yielded += 1 636 if self._dataset_kind == _DatasetKind.Iterable and \ 637 self._IterableDataset_len_called is not None and \ 638 self._num_yielded > self._IterableDataset_len_called: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self) 676 def _next_data(self): 677 index = self._next_index() # may raise StopIteration --> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 679 if self._pin_memory: 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index) 30 for _ in possibly_batched_index: 31 try: ---> 32 data.append(next(self.dataset_iter)) 33 except StopIteration: 34 self.ended = True File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self) 954 # this is the shuffle buffer that we keep in memory 955 mem_buffer = [] --> 956 for x in self.ex_iterable: 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it 958 i = next(indices_iterator) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self) 294 for key, pa_table in self.generate_tables_fn(**kwargs_with_shuffled_shards): 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER): --> 296 formatted_batch = formatter.format_batch(pa_subtable) 297 for example in _batch_to_examples(formatted_batch): 298 yield key, example File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table) 446 if self.lazy: 447 return LazyBatch(pa_table, self) --> 448 batch = self.python_arrow_extractor().extract_batch(pa_table) 449 batch = self.python_features_decoder.decode_batch(batch) 450 return batch File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table) 149 def extract_batch(self, pa_table: pa.Table) -> dict: --> 150 return pa_table.to_pydict() KeyboardInterrupt: ```
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 Thanks @mariosasko for your prompt response, here's the stack trace: ``` KeyboardInterrupt Traceback (most recent call last) Cell In[12], line 4 2 t = time.time() 3 iter_ = 0 ----> 4 for batch in train_dataloader: 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch) 6 iter_ += 1 8 if iter_ == 1: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self) 631 if self._sampler_iter is None: 632 # TODO(https://github.com/pytorch/pytorch/issues/76750) 633 self._reset() # type: ignore[call-arg] --> 634 data = self._next_data() 635 self._num_yielded += 1 636 if self._dataset_kind == _DatasetKind.Iterable and \ 637 self._IterableDataset_len_called is not None and \ 638 self._num_yielded > self._IterableDataset_len_called: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self) 676 def _next_data(self): 677 index = self._next_index() # may raise StopIteration --> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 679 if self._pin_memory: 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index) 30 for _ in possibly_batched_index: 31 try: ---> 32 data.append(next(self.dataset_iter)) 33 except StopIteration: 34 self.ended = True File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self) 1350 yield formatter.format_row(pa_table) 1351 return -> 1353 for key, example in ex_iterable: 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self) 954 # this is the shuffle buffer that we keep in memory 955 mem_buffer = [] --> 956 for x in self.ex_iterable: 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it 958 i = next(indices_iterator) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self) 294 for key, pa_table in self.generate_tables_fn(**kwargs_with_shuffled_shards): 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER): --> 296 formatted_batch = formatter.format_batch(pa_subtable) 297 for example in _batch_to_examples(formatted_batch): 298 yield key, example File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table) 446 if self.lazy: 447 return LazyBatch(pa_table, self) --> 448 batch = self.python_arrow_extractor().extract_batch(pa_table) 449 batch = self.python_features_decoder.decode_batch(batch) 450 return batch File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table) 149 def extract_batch(self, pa_table: pa.Table) -> dict: --> 150 return pa_table.to_pydict() KeyboardInterrupt: ```
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
Update: If i let it run, it eventually fails with: ``` RuntimeError Traceback (most recent call last) Cell In[16], line 4 2 t = time.time() 3 iter_ = 0 ----> 4 for batch in train_dataloader: 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch) 6 iter_ += 1 8 if iter_ == 1: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self) 631 if self._sampler_iter is None: 632 # TODO(https://github.com/pytorch/pytorch/issues/76750) 633 self._reset() # type: ignore[call-arg] --> 634 data = self._next_data() 635 self._num_yielded += 1 636 if self._dataset_kind == _DatasetKind.Iterable and \ 637 self._IterableDataset_len_called is not None and \ 638 self._num_yielded > self._IterableDataset_len_called: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self) 676 def _next_data(self): 677 index = self._next_index() # may raise StopIteration --> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 679 if self._pin_memory: 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index) 30 for _ in possibly_batched_index: 31 try: ---> 32 data.append(next(self.dataset_iter)) 33 except StopIteration: 34 self.ended = True File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self) 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) -> 1360 yield format_dict(example) if format_dict else example File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct) 84 def recursive_tensorize(self, data_struct: dict): ---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc) 461 num_proc = 1 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: --> 463 mapped = [ 464 _single_map_nested((function, obj, types, None, True, None)) 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 466 ] 467 else: 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0) 461 num_proc = 1 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: 463 mapped = [ --> 464 _single_map_nested((function, obj, types, None, True, None)) 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 466 ] 467 else: 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args) 364 # Singleton first to spare some computation 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 366 return function(data_struct) 368 # Reduce logging to keep things readable in multiprocessing with tqdm 369 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct) 80 elif isinstance(data_struct, (list, tuple)): 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct]) ---> 82 return self._tensorize(data_struct) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value) 66 if isinstance(value, PIL.Image.Image): 67 value = np.asarray(value) ---> 68 return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs}) RuntimeError: Could not infer dtype of decimal.Decimal ```
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 Update: If i let it run, it eventually fails with: ``` RuntimeError Traceback (most recent call last) Cell In[16], line 4 2 t = time.time() 3 iter_ = 0 ----> 4 for batch in train_dataloader: 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch) 6 iter_ += 1 8 if iter_ == 1: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self) 631 if self._sampler_iter is None: 632 # TODO(https://github.com/pytorch/pytorch/issues/76750) 633 self._reset() # type: ignore[call-arg] --> 634 data = self._next_data() 635 self._num_yielded += 1 636 if self._dataset_kind == _DatasetKind.Iterable and \ 637 self._IterableDataset_len_called is not None and \ 638 self._num_yielded > self._IterableDataset_len_called: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self) 676 def _next_data(self): 677 index = self._next_index() # may raise StopIteration --> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 679 if self._pin_memory: 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index) 30 for _ in possibly_batched_index: 31 try: ---> 32 data.append(next(self.dataset_iter)) 33 except StopIteration: 34 self.ended = True File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self) 1354 if self.features: 1355 # `IterableDataset` automatically fills missing columns with None. 1356 # This is done with `_apply_feature_types_on_example`. 1357 example = _apply_feature_types_on_example( 1358 example, self.features, token_per_repo_id=self._token_per_repo_id 1359 ) -> 1360 yield format_dict(example) if format_dict else example File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct) 84 def recursive_tensorize(self, data_struct: dict): ---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc) 461 num_proc = 1 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: --> 463 mapped = [ 464 _single_map_nested((function, obj, types, None, True, None)) 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 466 ] 467 else: 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0) 461 num_proc = 1 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: 463 mapped = [ --> 464 _single_map_nested((function, obj, types, None, True, None)) 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc) 466 ] 467 else: 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args) 364 # Singleton first to spare some computation 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 366 return function(data_struct) 368 # Reduce logging to keep things readable in multiprocessing with tqdm 369 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct) 80 elif isinstance(data_struct, (list, tuple)): 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct]) ---> 82 return self._tensorize(data_struct) File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value) 66 if isinstance(value, PIL.Image.Image): 67 value = np.asarray(value) ---> 68 return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs}) RuntimeError: Could not infer dtype of decimal.Decimal ```
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
17
Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
I already have cast in collate_fn, in which I perform .astype(float) for each numerical field. On the same instance, I installed a conda env with python 3.6, and this works well. Sample: ``` def streaming_data_collate_fn(batch): df = pd.DataFrame.from_dict(batch) feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float)))) ```
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 I already have cast in collate_fn, in which I perform .astype(float) for each numerical field. On the same instance, I installed a conda env with python 3.6, and this works well. Sample: ``` def streaming_data_collate_fn(batch): df = pd.DataFrame.from_dict(batch) feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float)))) ```
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
`collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format("numpy")`, which should make this conversion faster.
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 `collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format("numpy")`, which should make this conversion faster.
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
Thanks! Python 3.10 conda-env: After replacing with_format("torch") with with_format("numpy"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds. Python 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format("torch") is the only thing that works, and executes in 1.6 seconds. I feel something else is also amiss here.
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 Thanks! Python 3.10 conda-env: After replacing with_format("torch") with with_format("numpy"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds. Python 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format("torch") is the only thing that works, and executes in 1.6 seconds. I feel something else is also amiss here.
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
Can you share the `datasets` and `torch` versions installed in these conda envs? > Once I removed with_format call altogether, it is finishing in 11 seconds. Hmm, that's surprising. What are your dataset's `.features`?
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
34
Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 Can you share the `datasets` and `torch` versions installed in these conda envs? > Once I removed with_format call altogether, it is finishing in 11 seconds. Hmm, that's surprising. What are your dataset's `.features`?
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
Python 3.6: datasets.__version__ 2.4.0 torch.__version__ 1.10.1+cu102 Python 3.10: datasets.__version__ 2.14.0 torch.__version__ 2.0.0 Anonymized features are of the form (subset shown here): { 'string_feature_i': Value(dtype='string', id=None), 'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None), 'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), } There is no output from .features in python 3.6 kernel BTW.
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 Python 3.6: datasets.__version__ 2.4.0 torch.__version__ 1.10.1+cu102 Python 3.10: datasets.__version__ 2.14.0 torch.__version__ 2.0.0 Anonymized features are of the form (subset shown here): { 'string_feature_i': Value(dtype='string', id=None), 'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None), 'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), } There is no output from .features in python 3.6 kernel BTW.
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6. 512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
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Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6. 512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!
https://github.com/huggingface/datasets/issues/6079
Iterating over DataLoader based on HF datasets is stuck forever
**Update**: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads: 1. https://github.com/pytorch/pytorch/issues/102494 2. https://github.com/pytorch/pytorch/issues/102269 3. https://github.com/pytorch/pytorch/issues/99625 Out of the suggested solutions, the one that worked in my case was: ``` os.environ['KMP_AFFINITY'] = "disabled" ``` It is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.
### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64
87
Iterating over DataLoader based on HF datasets is stuck forever ### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64 **Update**: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads: 1. https://github.com/pytorch/pytorch/issues/102494 2. https://github.com/pytorch/pytorch/issues/102269 3. https://github.com/pytorch/pytorch/issues/99625 Out of the suggested solutions, the one that worked in my case was: ``` os.environ['KMP_AFFINITY'] = "disabled" ``` It is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.
https://github.com/huggingface/datasets/issues/6078
resume_download with streaming=True
Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380).
### Describe the bug I used: ``` dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, split="train" ) ``` Unfortunately, the server had a problem during the training process. I saved the step my training stopped at. But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset? `download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True. ### Steps to reproduce the bug ``` from datasets import load_dataset, DownloadConfig dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, # optional split="train", download_config=DownloadConfig(resume_download=True) ) # interupt the run and try to relaunch it => this restart from scratch ``` ### Expected behavior I would expect a parameter to start streaming from a given index in the dataset. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.0
21
resume_download with streaming=True ### Describe the bug I used: ``` dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, split="train" ) ``` Unfortunately, the server had a problem during the training process. I saved the step my training stopped at. But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset? `download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True. ### Steps to reproduce the bug ``` from datasets import load_dataset, DownloadConfig dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, # optional split="train", download_config=DownloadConfig(resume_download=True) ) # interupt the run and try to relaunch it => this restart from scratch ``` ### Expected behavior I would expect a parameter to start streaming from a given index in the dataset. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.0 Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380).
https://github.com/huggingface/datasets/issues/6077
Mapping gets stuck at 99%
The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass). Also, these arrays are big, so it makes sense to reduce `batch_size`/`writer_batch_size` to avoid RAM issues and slow IO.
### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2
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Mapping gets stuck at 99% ### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2 The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass). Also, these arrays are big, so it makes sense to reduce `batch_size`/`writer_batch_size` to avoid RAM issues and slow IO.
https://github.com/huggingface/datasets/issues/6077
Mapping gets stuck at 99%
Hi @mariosasko ! I agree, it's an ugly hack, but it was convenient since the resulting `mean_std` could be cached by the library. For my large dataset (which doesn't fit in RAM), I'm actually using something similar to what you suggested. I got rid of the first mapping in the above scripts and replaced it with an iterator, but the issue with the second mapping still persists.
### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2
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Mapping gets stuck at 99% ### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2 Hi @mariosasko ! I agree, it's an ugly hack, but it was convenient since the resulting `mean_std` could be cached by the library. For my large dataset (which doesn't fit in RAM), I'm actually using something similar to what you suggested. I got rid of the first mapping in the above scripts and replaced it with an iterator, but the issue with the second mapping still persists.
https://github.com/huggingface/datasets/issues/6077
Mapping gets stuck at 99%
Have you tried to reduce `batch_size`/`writer_batch_size` in the 2nd `.map`? Also, can you interrupt the process when it gets stuck and share the error stack trace?
### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2
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Mapping gets stuck at 99% ### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2 Have you tried to reduce `batch_size`/`writer_batch_size` in the 2nd `.map`? Also, can you interrupt the process when it gets stuck and share the error stack trace?
https://github.com/huggingface/datasets/issues/6077
Mapping gets stuck at 99%
I think `batch_size/writer_batch_size` is already at its lowest in the 2nd `.map` since `batched=False` implies `batch_size=1` and `len(ds) = 1000 = writer_batch_size`. Here is also a bunch of stack traces when I interrupted the process: <details> <summary>stack trace 1</summary> ```python (pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py Found cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066) Applying mean/std: 97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 967/1000 [00:01<00:00, 534.87 examples/s]Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3449, in _map_single writer.write(example) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 263, in _cast_to_python_objects def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]: KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py", line 62, in <module> ds_normalized = ds.map( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3492, in _map_single writer.finalize() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 584, in finalize self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in <listcomp> [ KeyboardInterrupt ``` </details> <details> <summary>stack trace 2</summary> ```python (pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py Found cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066) Applying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 988/1000 [00:20<00:00, 526.19 examples/s]Applying mean/std: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:21<00:00, 9.66 examples/s]Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3449, in _map_single writer.write(example) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 263, in _cast_to_python_objects def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]: KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py", line 62, in <module> ds_normalized = ds.map( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3492, in _map_single writer.finalize() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 584, in finalize self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 291, in _cast_to_python_objects if config.JAX_AVAILABLE and "jax" in sys.modules: KeyboardInterrupt ``` </details> <details> <summary>stack trace 3</summary> ```python (pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py Found cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066) Applying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 989/1000 [00:01<00:00, 504.80 examples/s]Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3449, in _map_single writer.write(example) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py", line 62, in <module> ds_normalized = ds.map( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3492, in _map_single writer.finalize() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 584, in finalize self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 298, in _cast_to_python_objects if obj.ndim == 0: KeyboardInterrupt ``` </details>
### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2
1,454
Mapping gets stuck at 99% ### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2 I think `batch_size/writer_batch_size` is already at its lowest in the 2nd `.map` since `batched=False` implies `batch_size=1` and `len(ds) = 1000 = writer_batch_size`. Here is also a bunch of stack traces when I interrupted the process: <details> <summary>stack trace 1</summary> ```python (pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py Found cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066) Applying mean/std: 97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 967/1000 [00:01<00:00, 534.87 examples/s]Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3449, in _map_single writer.write(example) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 263, in _cast_to_python_objects def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]: KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py", line 62, in <module> ds_normalized = ds.map( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3492, in _map_single writer.finalize() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 584, in finalize self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in <listcomp> [ KeyboardInterrupt ``` </details> <details> <summary>stack trace 2</summary> ```python (pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py Found cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066) Applying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 988/1000 [00:20<00:00, 526.19 examples/s]Applying mean/std: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:21<00:00, 9.66 examples/s]Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3449, in _map_single writer.write(example) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 263, in _cast_to_python_objects def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]: KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py", line 62, in <module> ds_normalized = ds.map( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3492, in _map_single writer.finalize() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 584, in finalize self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 291, in _cast_to_python_objects if config.JAX_AVAILABLE and "jax" in sys.modules: KeyboardInterrupt ``` </details> <details> <summary>stack trace 3</summary> ```python (pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py Found cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066) Applying mean/std: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 989/1000 [00:01<00:00, 504.80 examples/s]Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3449, in _map_single writer.write(example) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 179, in __arrow_array__ storage = to_pyarrow_listarray(data, pa_type) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 1466, in to_pyarrow_listarray return pa.array(data, pa_type.storage_dtype) File "pyarrow/array.pxi", line 320, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860], [-0.5034, -1.2685, -0.0558], [-1.0908, -1.1820, -0.3178], ..., [-0.8171, 0.1781, -0.5903], [ 0.4370, 1.9305, 0.5899], [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py", line 62, in <module> ds_normalized = ds.map( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3492, in _map_single writer.finalize() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 584, in finalize self.write_examples_on_file() File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 553, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py", line 223, in __arrow_array__ return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 446, in cast_to_python_objects return _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 407, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 408, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 319, in _cast_to_python_objects [ File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 320, in <listcomp> _cast_to_python_objects( File "/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py", line 298, in _cast_to_python_objects if obj.ndim == 0: KeyboardInterrupt ``` </details>
https://github.com/huggingface/datasets/issues/6075
Error loading music files using `load_dataset`
This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.
### Describe the bug I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test I got the following error - ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem formatted_output = format_table( File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table return formatter(pa_table, query_type=query_type) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__ return self.format_column(pa_table) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column return self.features.decode_column(column, column_name) if self.features else column File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp> [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example array, sampling_rate = sf.read(f) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read with SoundFile(file, 'r', samplerate, channels, File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__ self._file = self._open(file, mode_int, closefd) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open _error_check(_snd.sf_error(file_ptr), File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised. ``` ### Steps to reproduce the bug Code to reproduce the error - ```python from datasets import load_dataset ds = load_dataset("susnato/pop2piano_real_music_test", split="test") print(ds[0]) ``` ### Expected behavior I should be able to read the music file without any error. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3
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Error loading music files using `load_dataset` ### Describe the bug I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test I got the following error - ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem formatted_output = format_table( File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table return formatter(pa_table, query_type=query_type) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__ return self.format_column(pa_table) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column return self.features.decode_column(column, column_name) if self.features else column File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp> [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example array, sampling_rate = sf.read(f) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read with SoundFile(file, 'r', samplerate, channels, File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__ self._file = self._open(file, mode_int, closefd) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open _error_check(_snd.sf_error(file_ptr), File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised. ``` ### Steps to reproduce the bug Code to reproduce the error - ```python from datasets import load_dataset ds = load_dataset("susnato/pop2piano_real_music_test", split="test") print(ds[0]) ``` ### Expected behavior I should be able to read the music file without any error. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.
https://github.com/huggingface/datasets/issues/6176
how to limit the size of memory mapped file?
Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
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how to limit the size of memory mapped file? ### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine) Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.
https://github.com/huggingface/datasets/issues/6176
how to limit the size of memory mapped file?
The trace of the error. Streaming works but is slower. ``` Root Cause (first observed failure): [0]: time : 2023-08-24_06:06:01 host : compute-126.cm.cluster rank : 0 (local_rank: 0) exitcode : 1 (pid: 48442) error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json traceback : Traceback (most recent call last): File "/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "Pretrain.py", line 214, in main pair_dataset, c4_dataset = create_dataset('pretrain', config) File "/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py", line 109, in create_dataset c4_dataset = load_dataset("c4", "en", split="train").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py", line 1810, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1145, in as_dataset datasets = map_nested( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 436, in map_nested return function(data_struct) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1175, in _build_single_dataset ds = self._as_dataset( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1246, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 65, in _memory_mapped_arrow_table_from_file opened_stream = _memory_mapped_record_batch_reader_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 50, in _memory_mapped_record_batch_reader_from_file memory_mapped_stream = pa.memory_map(filename) File "pyarrow/io.pxi", line 1009, in pyarrow.lib.memory_map File "pyarrow/io.pxi", line 956, in pyarrow.lib.MemoryMappedFile._open File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: Memory mapping file failed: Cannot allocate memory ```
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
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how to limit the size of memory mapped file? ### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine) The trace of the error. Streaming works but is slower. ``` Root Cause (first observed failure): [0]: time : 2023-08-24_06:06:01 host : compute-126.cm.cluster rank : 0 (local_rank: 0) exitcode : 1 (pid: 48442) error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json traceback : Traceback (most recent call last): File "/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper return f(*args, **kwargs) File "Pretrain.py", line 214, in main pair_dataset, c4_dataset = create_dataset('pretrain', config) File "/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py", line 109, in create_dataset c4_dataset = load_dataset("c4", "en", split="train").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py", line 1810, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1145, in as_dataset datasets = map_nested( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 436, in map_nested return function(data_struct) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1175, in _build_single_dataset ds = self._as_dataset( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py", line 1246, in _as_dataset dataset_kwargs = ArrowReader(cache_dir, self.info).read( File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 244, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 265, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 200, in _read_files pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 336, in _get_table_from_filename table = ArrowReader.read_table(filename, in_memory=in_memory) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py", line 357, in read_table return table_cls.from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 1059, in from_file table = _memory_mapped_arrow_table_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 65, in _memory_mapped_arrow_table_from_file opened_stream = _memory_mapped_record_batch_reader_from_file(filename) File "/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py", line 50, in _memory_mapped_record_batch_reader_from_file memory_mapped_stream = pa.memory_map(filename) File "pyarrow/io.pxi", line 1009, in pyarrow.lib.memory_map File "pyarrow/io.pxi", line 956, in pyarrow.lib.MemoryMappedFile._open File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: Memory mapping file failed: Cannot allocate memory ```
https://github.com/huggingface/datasets/issues/6176
how to limit the size of memory mapped file?
This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping. PS: this is the API to reduce the size of the generated Arrow file: ```python from datasets import load_dataset builder = load_dataset_builder("c4", "en") builder.download_and_prepare(max_shard_size="5GB") dataset = builder.as_dataset() ``` If this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine)
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how to limit the size of memory mapped file? ### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE(Sun Grid Engine) This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping. PS: this is the API to reduce the size of the generated Arrow file: ```python from datasets import load_dataset builder = load_dataset_builder("c4", "en") builder.download_and_prepare(max_shard_size="5GB") dataset = builder.as_dataset() ``` If this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.
https://github.com/huggingface/datasets/issues/6172
Make Dataset streaming queries retryable
Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries. > At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader A minor Hub outage that we experienced yesterday could be the cause.
### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing.
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Make Dataset streaming queries retryable ### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing. Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries. > At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader A minor Hub outage that we experienced yesterday could be the cause.
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
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Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
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Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed). I've tried uninstalling/reinstnalling to no avail
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
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Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed). I've tried uninstalling/reinstnalling to no avail
https://github.com/huggingface/datasets/issues/6169
Configurations in yaml not working
hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?
### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
19
Configurations in yaml not working ### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1 hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?