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https://github.com/huggingface/datasets/issues/2170
Wikipedia historic dumps are deleted but hf/datasets hardcodes dump date
It seems that this can be fixed from user's end by including a `date` argument, like this: `dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')` You can get available dates from [here](https://dumps.wikimedia.org/enwiki/). This is not a proper fix however as all the files will still have '20200501' in their file names.
Wikimedia does not keep all historical dumps. For example, as of today https://dumps.wikimedia.org/kowiki/ only provides ``` 20201220/ 02-Feb-2021 01:36 - 20210101/ 21-Feb-2021 01:26 - 20210120/ 02-Mar-2021 01:25 - 20210201/ 21-Mar-2021 01:26 - 20210220/ 02-Apr-2021 01:26 - 20210301/ 03-Mar-2021 08:10 - 20210320/ 21-Mar-2021 18:13 - 20210401/ 03-Apr-2021 10:08 - latest/ 03-Apr-2021 10:08 - ``` However, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets: ``` ValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` The cached datasets: ``` % aws s3 --no-sign-request --endpoint-url https://storage.googleapis.com ls s3://huggingface-nlp/cache/datasets/wikipedia/ PRE 20200501.de/ PRE 20200501.en/ PRE 20200501.fr/ PRE 20200501.frr/ PRE 20200501.it/ PRE 20200501.simple/ ```
48
Wikipedia historic dumps are deleted but hf/datasets hardcodes dump date Wikimedia does not keep all historical dumps. For example, as of today https://dumps.wikimedia.org/kowiki/ only provides ``` 20201220/ 02-Feb-2021 01:36 - 20210101/ 21-Feb-2021 01:26 - 20210120/ 02-Mar-2021 01:25 - 20210201/ 21-Mar-2021 01:26 - 20210220/ 02-Apr-2021 01:26 - 20210301/ 03-Mar-2021 08:10 - 20210320/ 21-Mar-2021 18:13 - 20210401/ 03-Apr-2021 10:08 - latest/ 03-Apr-2021 10:08 - ``` However, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets: ``` ValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` The cached datasets: ``` % aws s3 --no-sign-request --endpoint-url https://storage.googleapis.com ls s3://huggingface-nlp/cache/datasets/wikipedia/ PRE 20200501.de/ PRE 20200501.en/ PRE 20200501.fr/ PRE 20200501.frr/ PRE 20200501.it/ PRE 20200501.simple/ ``` It seems that this can be fixed from user's end by including a `date` argument, like this: `dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')` You can get available dates from [here](https://dumps.wikimedia.org/enwiki/). This is not a proper fix however as all the files will still have '20200501' in their file names.
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0.201559037, 0.0890821069, -0.4260407686, -0.5251038671, -0.2948701084 ]
https://github.com/huggingface/datasets/issues/2166
Regarding Test Sets for the GEM datasets
Hi @vyraun ! The test references for CommonGen are not publicly available: you can reach out to the original dataset authors if you would like to ask for them, but we will not be releasing them as part of GEM (March 31st was the release date for the test set inputs, references are incidentally released for some of the test sets but shouldn't really be used for benchmark submissions) cc @sebastiangehrmann
@yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https://gem-benchmark.com/shared_task)? e.g. ``` from datasets import load_dataset DATASET_NAME="common_gen" data = load_dataset("gem", DATASET_NAME) ``` The test set doesn't have the target or references. ``` data['test'][0] {'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''} ```
71
Regarding Test Sets for the GEM datasets @yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https://gem-benchmark.com/shared_task)? e.g. ``` from datasets import load_dataset DATASET_NAME="common_gen" data = load_dataset("gem", DATASET_NAME) ``` The test set doesn't have the target or references. ``` data['test'][0] {'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''} ``` Hi @vyraun ! The test references for CommonGen are not publicly available: you can reach out to the original dataset authors if you would like to ask for them, but we will not be releasing them as part of GEM (March 31st was the release date for the test set inputs, references are incidentally released for some of the test sets but shouldn't really be used for benchmark submissions) cc @sebastiangehrmann
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https://github.com/huggingface/datasets/issues/2165
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
Hi, a HF dataset can be converted to a Torch Dataset with a simple wrapper as follows: ```python from torch.utils.data import Dataset class HFDataset(Dataset): def __init__(self, dset): self.dset = dset def __getitem__(self, idx): return self.dset[idx] def __len__(self): return len(self.dset) train_ds = HFDataset(train_ds) ``` @lhoestq Since the Arrow Dataset already provides `__getitem__` and `__len__`, I think we could use the [virtual subclass](https://docs.python.org/3/library/abc.html#abc.ABCMeta.register) mechanism from the `abc` module to elegantly solve this issue. This mechanism would allow the Arrow Dataset to be used in place of the Torch Dataset because the `isinstance(instance of Arrow Dataset, TorchDataset)` check would return True (DeepSpeed has this check [here](https://github.com/microsoft/DeepSpeed/blob/ab5534fc4c0f8ca21ada321f9730d723aa31288b/deepspeed/runtime/engine.py#L823)). And it requires a minimal change in the `arrow_dataset.py` file: ```python if config.TORCH_AVAILABLE: from torch.utils.data import Dataset as TorchDataset TorchDataset.register(Dataset) ```
Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
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How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset? Hi, a HF dataset can be converted to a Torch Dataset with a simple wrapper as follows: ```python from torch.utils.data import Dataset class HFDataset(Dataset): def __init__(self, dset): self.dset = dset def __getitem__(self, idx): return self.dset[idx] def __len__(self): return len(self.dset) train_ds = HFDataset(train_ds) ``` @lhoestq Since the Arrow Dataset already provides `__getitem__` and `__len__`, I think we could use the [virtual subclass](https://docs.python.org/3/library/abc.html#abc.ABCMeta.register) mechanism from the `abc` module to elegantly solve this issue. This mechanism would allow the Arrow Dataset to be used in place of the Torch Dataset because the `isinstance(instance of Arrow Dataset, TorchDataset)` check would return True (DeepSpeed has this check [here](https://github.com/microsoft/DeepSpeed/blob/ab5534fc4c0f8ca21ada321f9730d723aa31288b/deepspeed/runtime/engine.py#L823)). And it requires a minimal change in the `arrow_dataset.py` file: ```python if config.TORCH_AVAILABLE: from torch.utils.data import Dataset as TorchDataset TorchDataset.register(Dataset) ```
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https://github.com/huggingface/datasets/issues/2165
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
Interesting ! Thanks for sharing this @mariosasko . I like the idea This looks like something we should add IMO
Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
20
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset? Interesting ! Thanks for sharing this @mariosasko . I like the idea This looks like something we should add IMO
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https://github.com/huggingface/datasets/issues/2165
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
@mariosasko Thx for your code! It perfectly works with a small modification for HF NLP dataset: ``` original_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds = HFDataset(train_ds['train']) # needs splitting ```
Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
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How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset? @mariosasko Thx for your code! It perfectly works with a small modification for HF NLP dataset: ``` original_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds = HFDataset(train_ds['train']) # needs splitting ```
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https://github.com/huggingface/datasets/issues/2165
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
@lhoestq Sadly, from Python 3.7 onwards `torch.utils.data.Dataset` doesn't support the virtual subclass mechanism due to `typing.Generic` type no longer having `abc.ABCMeta` as its metaclass. With that in mind, another option is to remove a direct type check (`isinstance(dataset, torch.utils.data.Dataset)`) in `deepspeed.initalize` and to rewrite the checks in a manner similar to `torch.utils.data.DataLoader` ([link](https://github.com/pytorch/pytorch/blob/b80c6f863f2327c712c478f67c248b94d66b65ac/torch/utils/data/dataloader.py#L197-L239)). This is exactly why the `DataLoader` works with arbitrary objects that provide `__getitem__` and `__len__` (and in our case, the `ArrowDataset`). By doing so, their code wouldn't be any stricter in comparison to the `DataLoader`. So if you agree, I can open an issue in their repo and fix this if they like the idea.
Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
108
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset? @lhoestq Sadly, from Python 3.7 onwards `torch.utils.data.Dataset` doesn't support the virtual subclass mechanism due to `typing.Generic` type no longer having `abc.ABCMeta` as its metaclass. With that in mind, another option is to remove a direct type check (`isinstance(dataset, torch.utils.data.Dataset)`) in `deepspeed.initalize` and to rewrite the checks in a manner similar to `torch.utils.data.DataLoader` ([link](https://github.com/pytorch/pytorch/blob/b80c6f863f2327c712c478f67c248b94d66b65ac/torch/utils/data/dataloader.py#L197-L239)). This is exactly why the `DataLoader` works with arbitrary objects that provide `__getitem__` and `__len__` (and in our case, the `ArrowDataset`). By doing so, their code wouldn't be any stricter in comparison to the `DataLoader`. So if you agree, I can open an issue in their repo and fix this if they like the idea.
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https://github.com/huggingface/datasets/issues/2165
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
That makes sense ! Feel free to open an issue on their repo and discuss this idea
Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
17
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset? That makes sense ! Feel free to open an issue on their repo and discuss this idea
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https://github.com/huggingface/datasets/issues/2165
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
@y-rokutan Hi, now if you install `deepspeed` from master (this feature will be available in the next official release), the code should work without subclassing. Let us know if you still have any issues.
Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
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How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset? @y-rokutan Hi, now if you install `deepspeed` from master (this feature will be available in the next official release), the code should work without subclassing. Let us know if you still have any issues.
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https://github.com/huggingface/datasets/issues/2165
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
Worth mentioning that any function that expects a `torch..Dataset` (like `torch..DataLoader`) will fail a mypy-esque typecheck if a `datasets.Dataset` is passed, even though it implements the interface correctly (I think). The virtual subclass idea was a good one- I wonder if there's another workaround given the Generic issue. What we're really talking about is something similar to the structural subtyping semantics that `typing.Protocol` defines. If `torch..DataLoader` accepted anything that supports `__getitem__` and `__len__` methods this would be much easier. Not sure if there's a way to do this without the wrapper from the perspective of `datasets`.
Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
96
How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset? Worth mentioning that any function that expects a `torch..Dataset` (like `torch..DataLoader`) will fail a mypy-esque typecheck if a `datasets.Dataset` is passed, even though it implements the interface correctly (I think). The virtual subclass idea was a good one- I wonder if there's another workaround given the Generic issue. What we're really talking about is something similar to the structural subtyping semantics that `typing.Protocol` defines. If `torch..DataLoader` accepted anything that supports `__getitem__` and `__len__` methods this would be much easier. Not sure if there's a way to do this without the wrapper from the perspective of `datasets`.
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-0.4207517505, 0.447078377, 0.196485728, 0.0416077115, -0.1021854579, -0.3120924234 ]
https://github.com/huggingface/datasets/issues/2162
visualization for cc100 is broken
This looks like an issue with the cc100 dataset itself but not sure Did you try loading cc100 on your machine ?
Hi visualization through dataset viewer for cc100 is broken https://huggingface.co/datasets/viewer/ thanks a lot
22
visualization for cc100 is broken Hi visualization through dataset viewer for cc100 is broken https://huggingface.co/datasets/viewer/ thanks a lot This looks like an issue with the cc100 dataset itself but not sure Did you try loading cc100 on your machine ?
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0.1243475229, 0.5360301137, 0.3529992402, -0.188002333, 0.047364749, -0.0968635529 ]
https://github.com/huggingface/datasets/issues/2162
visualization for cc100 is broken
Hi loading works fine, but the viewer only is broken thanks On Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***> wrote: > This looks like an issue with the cc100 dataset itself but not sure > Did you try loading cc100 on your machine ? > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/2162#issuecomment-814793809>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AS37NMRUO33JSOYGT6RETWLTHQWNLANCNFSM42IUOR6Q> > . >
Hi visualization through dataset viewer for cc100 is broken https://huggingface.co/datasets/viewer/ thanks a lot
80
visualization for cc100 is broken Hi visualization through dataset viewer for cc100 is broken https://huggingface.co/datasets/viewer/ thanks a lot Hi loading works fine, but the viewer only is broken thanks On Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***> wrote: > This looks like an issue with the cc100 dataset itself but not sure > Did you try loading cc100 on your machine ? > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/2162#issuecomment-814793809>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AS37NMRUO33JSOYGT6RETWLTHQWNLANCNFSM42IUOR6Q> > . >
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https://github.com/huggingface/datasets/issues/2161
any possibility to download part of large datasets only?
oh, great, really awesome feature to have, thank you very much for the great, fabulous work
Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
16
any possibility to download part of large datasets only? Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks oh, great, really awesome feature to have, thank you very much for the great, fabulous work
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https://github.com/huggingface/datasets/issues/2161
any possibility to download part of large datasets only?
We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)
Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
18
any possibility to download part of large datasets only? Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)
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-0.1833289266, -0.1224539354, 0.1712696403, 0.2516165376, -0.440536797, -0.2349564433, -0.331454128 ]
https://github.com/huggingface/datasets/issues/2161
any possibility to download part of large datasets only?
thanks a lot Quentin, this would be really really a great feature to have On Wed, Apr 7, 2021 at 12:14 PM Quentin Lhoest ***@***.***> wrote: > We'll work on dataset streaming soon. This should allow you to only load > the examples you need ;) > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/2161#issuecomment-814791922>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AS37NMROD62QAKIJMAKWISTTHQWBVANCNFSM42IUI5JQ> > . >
Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
79
any possibility to download part of large datasets only? Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks thanks a lot Quentin, this would be really really a great feature to have On Wed, Apr 7, 2021 at 12:14 PM Quentin Lhoest ***@***.***> wrote: > We'll work on dataset streaming soon. This should allow you to only load > the examples you need ;) > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/2161#issuecomment-814791922>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AS37NMROD62QAKIJMAKWISTTHQWBVANCNFSM42IUI5JQ> > . >
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https://github.com/huggingface/datasets/issues/2161
any possibility to download part of large datasets only?
Is streaming completed? On the 1.8.0 docs it is mentioned (https://huggingface.co/docs/datasets/dataset_streaming.html), but when following the example I get the following error: ``` >>> dataset2 = load_dataset("amazon_us_reviews", "Pet_Products_v1_00", split='train', streaming=True) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-21-1eedab26cff1> in <module>() ----> 1 en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True) 3 frames /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs) 339 if value is not None: 340 if not hasattr(builder_config, key): --> 341 raise ValueError(f"BuilderConfig {builder_config} doesn't have a '{key}' key.") 342 setattr(builder_config, key, value) 343 ValueError: BuilderConfig OscarConfig(name='unshuffled_deduplicated_en', version=1.0.0, data_dir=None, data_files=None, description='Unshuffled and deduplicated, English OSCAR dataset') doesn't have a 'streaming' key. ``` UPDATE: Managed to get streaming working by building from source and installing the additional `datasets[streaming]` package: ``` !pip install git+https://github.com/huggingface/datasets.git !pip install datasets[streaming] ```
Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
123
any possibility to download part of large datasets only? Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks Is streaming completed? On the 1.8.0 docs it is mentioned (https://huggingface.co/docs/datasets/dataset_streaming.html), but when following the example I get the following error: ``` >>> dataset2 = load_dataset("amazon_us_reviews", "Pet_Products_v1_00", split='train', streaming=True) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-21-1eedab26cff1> in <module>() ----> 1 en_dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True) 3 frames /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs) 339 if value is not None: 340 if not hasattr(builder_config, key): --> 341 raise ValueError(f"BuilderConfig {builder_config} doesn't have a '{key}' key.") 342 setattr(builder_config, key, value) 343 ValueError: BuilderConfig OscarConfig(name='unshuffled_deduplicated_en', version=1.0.0, data_dir=None, data_files=None, description='Unshuffled and deduplicated, English OSCAR dataset') doesn't have a 'streaming' key. ``` UPDATE: Managed to get streaming working by building from source and installing the additional `datasets[streaming]` package: ``` !pip install git+https://github.com/huggingface/datasets.git !pip install datasets[streaming] ```
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https://github.com/huggingface/datasets/issues/2161
any possibility to download part of large datasets only?
Hi ! Streaming is available on `master` only right now. We'll make a new release 1.9.0 on Monday :)
Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
19
any possibility to download part of large datasets only? Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks Hi ! Streaming is available on `master` only right now. We'll make a new release 1.9.0 on Monday :)
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https://github.com/huggingface/datasets/issues/2160
data_args.preprocessing_num_workers almost freezes
Hi. I cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks ``` #3: 11%|███████████████▊ | 172/1583 [00:46<06:21, 3.70ba/s] #4: 9%|█████████████▏ | 143/1583 [00:46<07:46, 3.09ba/s] #7: 6%|█████████ | 98/1583 [00:45<11:34, 2.14ba/s] #5: 8%|███████████▍ | 124/1583 [00:46<09:03, 2.68ba/s] #6: 7%|██████████▏ ```
Hi @lhoestq I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up. thanks
71
data_args.preprocessing_num_workers almost freezes Hi @lhoestq I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up. thanks Hi. I cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks ``` #3: 11%|███████████████▊ | 172/1583 [00:46<06:21, 3.70ba/s] #4: 9%|█████████████▏ | 143/1583 [00:46<07:46, 3.09ba/s] #7: 6%|█████████ | 98/1583 [00:45<11:34, 2.14ba/s] #5: 8%|███████████▍ | 124/1583 [00:46<09:03, 2.68ba/s] #6: 7%|██████████▏ ```
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https://github.com/huggingface/datasets/issues/2158
viewer "fake_news_english" error
Thanks for reporting ! The viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly
When I visit the [Huggingface - viewer](https://huggingface.co/datasets/viewer/) web site, under the dataset "fake_news_english" I've got this error: > ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance' as well as the error Traceback.
26
viewer "fake_news_english" error When I visit the [Huggingface - viewer](https://huggingface.co/datasets/viewer/) web site, under the dataset "fake_news_english" I've got this error: > ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance' as well as the error Traceback. Thanks for reporting ! The viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly
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https://github.com/huggingface/datasets/issues/2153
load_dataset ignoring features
Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.
First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything. I'm using datasets 1.5.0 ![image](https://user-images.githubusercontent.com/37592763/113114369-8f376580-920b-11eb-900d-94365b59f04b.png) As you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work. Code to reproduce: ```python import datasets data_location = "/data/prueba_multiclase" features = datasets.Features( {"texto": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["false", "true"])} ) dataset = datasets.load_dataset( "csv", data_files=data_location, delimiter="\t", features=features ) ``` Dataset I used: [prueba_multiclase.zip](https://github.com/huggingface/datasets/files/6235022/prueba_multiclase.zip) (it has to be unzipped) Thank you! ❤️
32
load_dataset ignoring features First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything. I'm using datasets 1.5.0 ![image](https://user-images.githubusercontent.com/37592763/113114369-8f376580-920b-11eb-900d-94365b59f04b.png) As you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work. Code to reproduce: ```python import datasets data_location = "/data/prueba_multiclase" features = datasets.Features( {"texto": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["false", "true"])} ) dataset = datasets.load_dataset( "csv", data_files=data_location, delimiter="\t", features=features ) ``` Dataset I used: [prueba_multiclase.zip](https://github.com/huggingface/datasets/files/6235022/prueba_multiclase.zip) (it has to be unzipped) Thank you! ❤️ Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.
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https://github.com/huggingface/datasets/issues/2148
Add configurable options to `seqeval` metric
Hi @marrodion. Thanks for pointing this out. It would be great to incorporate this metric-specific enhancement. Another possibility would be to require the user to input the scheme as a string `mode="strict", scheme="IOB2"` and then dynamically import the corresponding module using Python `importlib`: ```python if scheme: scheme = importlib.import_module(f"seqeval.scheme.{scheme}") ``` Feel free to create a Pull Request to make this contribution.
Right now `load_metric("seqeval")` only works in the default mode of evaluation (equivalent to conll evaluation). However, seqeval library [supports](https://github.com/chakki-works/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute` https://github.com/huggingface/datasets/blob/85cf7ff920c90ca2e12bedca12b36d2a043c3da2/metrics/seqeval/seqeval.py#L109 Things that would be relevant are, for example, supporting `mode="strict", scheme=IOB2` to count only full entity match as a true positive and omit partial matches. The only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases. It can be solved naively with mapping like `{"IOB2": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation. If that makes sense, I am happy to implement the change.
61
Add configurable options to `seqeval` metric Right now `load_metric("seqeval")` only works in the default mode of evaluation (equivalent to conll evaluation). However, seqeval library [supports](https://github.com/chakki-works/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute` https://github.com/huggingface/datasets/blob/85cf7ff920c90ca2e12bedca12b36d2a043c3da2/metrics/seqeval/seqeval.py#L109 Things that would be relevant are, for example, supporting `mode="strict", scheme=IOB2` to count only full entity match as a true positive and omit partial matches. The only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases. It can be solved naively with mapping like `{"IOB2": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation. If that makes sense, I am happy to implement the change. Hi @marrodion. Thanks for pointing this out. It would be great to incorporate this metric-specific enhancement. Another possibility would be to require the user to input the scheme as a string `mode="strict", scheme="IOB2"` and then dynamically import the corresponding module using Python `importlib`: ```python if scheme: scheme = importlib.import_module(f"seqeval.scheme.{scheme}") ``` Feel free to create a Pull Request to make this contribution.
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https://github.com/huggingface/datasets/issues/2146
Dataset file size on disk is very large with 3D Array
Hi ! In the arrow file we store all the integers as uint8. So your arrow file should weigh around `height x width x n_channels x n_images` bytes. What feature type do your TFDS dataset have ? If it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for example). Since these encodings are made for compression, the resulting tfrecord is smaller that the arrow file. We are working on adding a similar feature in `datasets`: the ability to store the encoded data instead of the raw integers for images, but also for audio data. This way, arrow files will have similar sizes as tfrecords for images.
Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help.
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Dataset file size on disk is very large with 3D Array Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help. Hi ! In the arrow file we store all the integers as uint8. So your arrow file should weigh around `height x width x n_channels x n_images` bytes. What feature type do your TFDS dataset have ? If it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for example). Since these encodings are made for compression, the resulting tfrecord is smaller that the arrow file. We are working on adding a similar feature in `datasets`: the ability to store the encoded data instead of the raw integers for images, but also for audio data. This way, arrow files will have similar sizes as tfrecords for images.
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https://github.com/huggingface/datasets/issues/2146
Dataset file size on disk is very large with 3D Array
Thanks for the prompt response. You're right about the encoding, I have the `tfds.features.Image` feature type you mentioned. However, as described in the `dataset_info.json`, my dataset is made of 1479 (224x224x3) images. 1479 x 224 x 224 x 3 = 222630912 bytes which is far from the actual size 520803408 bytes. Anyway I look forward to the Image feature type in `datasets`.
Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help.
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Dataset file size on disk is very large with 3D Array Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help. Thanks for the prompt response. You're right about the encoding, I have the `tfds.features.Image` feature type you mentioned. However, as described in the `dataset_info.json`, my dataset is made of 1479 (224x224x3) images. 1479 x 224 x 224 x 3 = 222630912 bytes which is far from the actual size 520803408 bytes. Anyway I look forward to the Image feature type in `datasets`.
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https://github.com/huggingface/datasets/issues/2146
Dataset file size on disk is very large with 3D Array
@lhoestq I changed the data structure so I have a 2D Array feature type instead of a 3D Array by grouping the two last dimensions ( a 224x672 2D Array instead of a 224x224x3 3D Array). The file size is now 223973964 bytes, nearly half the previous size! Which is around of what I would expect. I found similar behavior in existing `datasets` collection, when comparing black and white vs color image, for example MNIST vs CIFAR.
Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help.
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Dataset file size on disk is very large with 3D Array Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help. @lhoestq I changed the data structure so I have a 2D Array feature type instead of a 3D Array by grouping the two last dimensions ( a 224x672 2D Array instead of a 224x224x3 3D Array). The file size is now 223973964 bytes, nearly half the previous size! Which is around of what I would expect. I found similar behavior in existing `datasets` collection, when comparing black and white vs color image, for example MNIST vs CIFAR.
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https://github.com/huggingface/datasets/issues/2146
Dataset file size on disk is very large with 3D Array
Interesting ! This may be because of the offsets that are stored with the array data. Currently the offsets are stored even if the `shape` of the arrays is fixed. This was needed because of some issues with pyarrow a few months ago. I think these issues have been addressed now, so we can probably try to remove them to make the file lighter. Ideally in your case the floats data should be 220 MB for both Array2D and Array3D
Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help.
80
Dataset file size on disk is very large with 3D Array Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help. Interesting ! This may be because of the offsets that are stored with the array data. Currently the offsets are stored even if the `shape` of the arrays is fixed. This was needed because of some issues with pyarrow a few months ago. I think these issues have been addressed now, so we can probably try to remove them to make the file lighter. Ideally in your case the floats data should be 220 MB for both Array2D and Array3D
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https://github.com/huggingface/datasets/issues/2146
Dataset file size on disk is very large with 3D Array
Yeah for sure, can you be a bit more specific about where the offset is stored in the code base ? And any reference to pyarrow issues if you have some. I would be very interested in contributing to `datasets` by trying to fix this issue.
Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help.
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Dataset file size on disk is very large with 3D Array Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help. Yeah for sure, can you be a bit more specific about where the offset is stored in the code base ? And any reference to pyarrow issues if you have some. I would be very interested in contributing to `datasets` by trying to fix this issue.
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https://github.com/huggingface/datasets/issues/2146
Dataset file size on disk is very large with 3D Array
Pyarrow has two types of lists: variable length lists and fixed size lists. Currently we store the ArrayXD data as variable length lists. They take more disk space because they must store both actual data and offsets. In the `datasets` code this is done here: https://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L346-L352 To use a fixed length list, one should use the `list_size` argument of `pyarrow.list_()`. I believe this would work directly modulo some changes in the numpy conversion here: https://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L381-L395
Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help.
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Dataset file size on disk is very large with 3D Array Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help. Pyarrow has two types of lists: variable length lists and fixed size lists. Currently we store the ArrayXD data as variable length lists. They take more disk space because they must store both actual data and offsets. In the `datasets` code this is done here: https://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L346-L352 To use a fixed length list, one should use the `list_size` argument of `pyarrow.list_()`. I believe this would work directly modulo some changes in the numpy conversion here: https://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L381-L395
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https://github.com/huggingface/datasets/issues/2144
Loading wikipedia 20200501.en throws pyarrow related error
That's how I loaded the dataset ```python from datasets import load_dataset ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') ```
**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
17
Loading wikipedia 20200501.en throws pyarrow related error **Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0] That's how I loaded the dataset ```python from datasets import load_dataset ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') ```
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https://github.com/huggingface/datasets/issues/2144
Loading wikipedia 20200501.en throws pyarrow related error
Hi ! It looks like the arrow file in the folder `/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted. Can you take a look and check that it's 18.3GB ? If not, then maybe you need to redownload it: ```python from datasets import load_dataset ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode="force_redownload") ```
**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
46
Loading wikipedia 20200501.en throws pyarrow related error **Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0] Hi ! It looks like the arrow file in the folder `/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted. Can you take a look and check that it's 18.3GB ? If not, then maybe you need to redownload it: ```python from datasets import load_dataset ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode="force_redownload") ```
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https://github.com/huggingface/datasets/issues/2144
Loading wikipedia 20200501.en throws pyarrow related error
> Hi ! It looks like the arrow file in the folder > `/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted. > > Can you take a look and check that it's 18.3GB ? > > If not, then maybe you need to redownload it: > > ```python > from datasets import load_dataset > ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode="force_redownload") > ``` Hi Ihoestq, thanks for the reply! Actually i think my issue is i couldn't download the dataset beyond 10.7G. It feels like the whole dataset is split into different volumes and after the first one was downloaded it crashed before proceeding to the next one. I did try 'force_redownload' mode but still got the same issue.
**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
113
Loading wikipedia 20200501.en throws pyarrow related error **Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0] > Hi ! It looks like the arrow file in the folder > `/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted. > > Can you take a look and check that it's 18.3GB ? > > If not, then maybe you need to redownload it: > > ```python > from datasets import load_dataset > ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode="force_redownload") > ``` Hi Ihoestq, thanks for the reply! Actually i think my issue is i couldn't download the dataset beyond 10.7G. It feels like the whole dataset is split into different volumes and after the first one was downloaded it crashed before proceeding to the next one. I did try 'force_redownload' mode but still got the same issue.
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https://github.com/huggingface/datasets/issues/2144
Loading wikipedia 20200501.en throws pyarrow related error
I just tried on my side and got no issues. When downloading the dataset again, did it crash at 10.7GB as well ?
**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
23
Loading wikipedia 20200501.en throws pyarrow related error **Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0] I just tried on my side and got no issues. When downloading the dataset again, did it crash at 10.7GB as well ?
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https://github.com/huggingface/datasets/issues/2144
Loading wikipedia 20200501.en throws pyarrow related error
> I just tried on my side and got no issues. > When downloading the dataset again, did it crash at 10.7GB as well ? Yes i have tried it multiple times on different machines. I am wondering if you could share the screenshot of your dependency versions and i will try to make them the same as yours?
**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
59
Loading wikipedia 20200501.en throws pyarrow related error **Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0] > I just tried on my side and got no issues. > When downloading the dataset again, did it crash at 10.7GB as well ? Yes i have tried it multiple times on different machines. I am wondering if you could share the screenshot of your dependency versions and i will try to make them the same as yours?
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https://github.com/huggingface/datasets/issues/2144
Loading wikipedia 20200501.en throws pyarrow related error
I tried using `datasets` from `master` on macos with python 3.7.2 I also have `requests==2.23.0` and `tqdm==4.45.0`.
**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
17
Loading wikipedia 20200501.en throws pyarrow related error **Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0] I tried using `datasets` from `master` on macos with python 3.7.2 I also have `requests==2.23.0` and `tqdm==4.45.0`.
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https://github.com/huggingface/datasets/issues/2139
TypeError when using save_to_disk in a dataset loaded with ReadInstruction split
Hi ! I think this has been fixed recently on `master`. Can you try again by installing `datasets` from `master` ? ``` pip install git+https://github.com/huggingface/datasets.git ```
Hi, Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`. Here is the minimal reproducible example: ```python from datasets import load_dataset from datasets import ReadInstruction data_1 = load_dataset( "wikiann", "en", split="validation", ) data_1.save_to_disk("temporary_path_1") print("Save with regular split works.") data_2 = load_dataset( "wikiann", "en", split=ReadInstruction("validation", to=50, unit="%"), ) data_2.save_to_disk("temporary_path_2") ``` and the corresponding output: ``` Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Save with regular split works. Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Traceback (most recent call last): File "bug.py", line 20, in <module> data_2.save_to_disk("temporary_path_2") File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk json.dump(state, state_file, indent=2, sort_keys=True) File "/usr/lib/python3.7/json/__init__.py", line 179, in dump for chunk in iterable: File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode yield from _iterencode_dict(o, _current_indent_level) File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict yield from chunks File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode o = _default(o) File "/usr/lib/python3.7/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.__class__.__name__} ' TypeError: Object of type ReadInstruction is not JSON serializable ``` Let me know if there is some misuse from my end. Thanks in advance.
26
TypeError when using save_to_disk in a dataset loaded with ReadInstruction split Hi, Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`. Here is the minimal reproducible example: ```python from datasets import load_dataset from datasets import ReadInstruction data_1 = load_dataset( "wikiann", "en", split="validation", ) data_1.save_to_disk("temporary_path_1") print("Save with regular split works.") data_2 = load_dataset( "wikiann", "en", split=ReadInstruction("validation", to=50, unit="%"), ) data_2.save_to_disk("temporary_path_2") ``` and the corresponding output: ``` Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Save with regular split works. Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Traceback (most recent call last): File "bug.py", line 20, in <module> data_2.save_to_disk("temporary_path_2") File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk json.dump(state, state_file, indent=2, sort_keys=True) File "/usr/lib/python3.7/json/__init__.py", line 179, in dump for chunk in iterable: File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode yield from _iterencode_dict(o, _current_indent_level) File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict yield from chunks File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode o = _default(o) File "/usr/lib/python3.7/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.__class__.__name__} ' TypeError: Object of type ReadInstruction is not JSON serializable ``` Let me know if there is some misuse from my end. Thanks in advance. Hi ! I think this has been fixed recently on `master`. Can you try again by installing `datasets` from `master` ? ``` pip install git+https://github.com/huggingface/datasets.git ```
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https://github.com/huggingface/datasets/issues/2135
en language data from MLQA dataset is missing
Hi ! Indeed only the languages of the `translate-train` data are included... I can't find a link to download the english train set on https://github.com/facebookresearch/MLQA though, do you know where we can download it ?
Hi I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue.
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en language data from MLQA dataset is missing Hi I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue. Hi ! Indeed only the languages of the `translate-train` data are included... I can't find a link to download the english train set on https://github.com/facebookresearch/MLQA though, do you know where we can download it ?
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https://github.com/huggingface/datasets/issues/2135
en language data from MLQA dataset is missing
Hi @lhoestq thank you very much for coming back to me, now I see, you are right, in the link you sent I see split of {split}-context-{context_language}-question-{question_language}.json with context_language=question_language=en, TFDS most probably has extracted english ones from these files as en language files, but translate-train/test do not have en indeed. thanks a lot for the great explanations
Hi I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue.
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en language data from MLQA dataset is missing Hi I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue. Hi @lhoestq thank you very much for coming back to me, now I see, you are right, in the link you sent I see split of {split}-context-{context_language}-question-{question_language}.json with context_language=question_language=en, TFDS most probably has extracted english ones from these files as en language files, but translate-train/test do not have en indeed. thanks a lot for the great explanations
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https://github.com/huggingface/datasets/issues/2135
en language data from MLQA dataset is missing
I close the ticket, since I do not see any en existing, they have trained on "SQuAD V1.1" instead. Thanks.
Hi I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue.
20
en language data from MLQA dataset is missing Hi I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue. I close the ticket, since I do not see any en existing, they have trained on "SQuAD V1.1" instead. Thanks.
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https://github.com/huggingface/datasets/issues/2134
Saving large in-memory datasets with save_to_disk crashes because of pickling
Hi ! Indeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example: ```python import pyarrow as pa import pickle arr = pa.array([0] * ((4 * 8 << 30) // 64)) table = pa.Table.from_arrays([a], names=["foo"]) pickle.dumps(table) # fails with an OverflowError pickle.dumps(table, 4) # works ! ``` We'll do the change to use `protocol=4`. Moreover I've also seen other users complain about this error ``` struct.error: 'I' format requires 0 <= number <= 4294967295 ``` It looks like something related to the 4GB limit as well but I'm not able to reproduce on my side. Do you think you can provide a script that reproduces the issue ? How big is your dataset ? (number of bytes, number of rows)
Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ```
134
Saving large in-memory datasets with save_to_disk crashes because of pickling Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` Hi ! Indeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example: ```python import pyarrow as pa import pickle arr = pa.array([0] * ((4 * 8 << 30) // 64)) table = pa.Table.from_arrays([a], names=["foo"]) pickle.dumps(table) # fails with an OverflowError pickle.dumps(table, 4) # works ! ``` We'll do the change to use `protocol=4`. Moreover I've also seen other users complain about this error ``` struct.error: 'I' format requires 0 <= number <= 4294967295 ``` It looks like something related to the 4GB limit as well but I'm not able to reproduce on my side. Do you think you can provide a script that reproduces the issue ? How big is your dataset ? (number of bytes, number of rows)
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https://github.com/huggingface/datasets/issues/2134
Saving large in-memory datasets with save_to_disk crashes because of pickling
Hi! So I've managed to created a minimum working (well technically crashing) example for the multiprocessing case, I create a huge list of zeros, like in your example, and then I try to .map(None, num_proc=2) over it, which then crashes, here's the code: ```python from datasets import Dataset if __name__ == '__main__': ton_of_zeroes = [0] * ((12 * 8 << 30) // 64) large_dataset = Dataset.from_dict({'col': ton_of_zeroes}) print("Start") large_dataset.map(function=None, num_proc=2) print("Done - should not print") ``` The amount of zeros could probably be reduced, I haven't tried to minimize it to find the breaking point, I just increased it from your code (which by quick glance I assumed tried to allocate over 4 GiB) Running this results in the following traceback: ``` Parameter 'indices'=[ 0 1 2 ... 805306365 805306366 805306367] of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. Traceback (most recent call last): File "./crash_multiproc_pickle.py", line 7, in <module> large_dataset.map(function=None, num_proc=2) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` My datasets usually have hundreds of thousands to low millions of rows, with each row containing a list of 10 strings and list of vectors of different length (the strings tokenized), which in the worst case have 10\*512\*8 = 40960 bytes (but usually it is much smaller, as the vectors tend to be shorter. I need these groups of text lines to create training data for the Inverse Cloze Task. Anyway I don't think my particular dataset is relevant, as the tiny script I created also manages to crash. But I think the issue is the same as the save_to_disk, from the traceback it seems that in multiprocessing, it tries to use dill to return the result of the map workers, which tries to pickle the data and can't do it, probably because it's again using the older pickle protocol. That's my guess anyway.
Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ```
832
Saving large in-memory datasets with save_to_disk crashes because of pickling Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` Hi! So I've managed to created a minimum working (well technically crashing) example for the multiprocessing case, I create a huge list of zeros, like in your example, and then I try to .map(None, num_proc=2) over it, which then crashes, here's the code: ```python from datasets import Dataset if __name__ == '__main__': ton_of_zeroes = [0] * ((12 * 8 << 30) // 64) large_dataset = Dataset.from_dict({'col': ton_of_zeroes}) print("Start") large_dataset.map(function=None, num_proc=2) print("Done - should not print") ``` The amount of zeros could probably be reduced, I haven't tried to minimize it to find the breaking point, I just increased it from your code (which by quick glance I assumed tried to allocate over 4 GiB) Running this results in the following traceback: ``` Parameter 'indices'=[ 0 1 2 ... 805306365 805306366 805306367] of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. Traceback (most recent call last): File "./crash_multiproc_pickle.py", line 7, in <module> large_dataset.map(function=None, num_proc=2) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` My datasets usually have hundreds of thousands to low millions of rows, with each row containing a list of 10 strings and list of vectors of different length (the strings tokenized), which in the worst case have 10\*512\*8 = 40960 bytes (but usually it is much smaller, as the vectors tend to be shorter. I need these groups of text lines to create training data for the Inverse Cloze Task. Anyway I don't think my particular dataset is relevant, as the tiny script I created also manages to crash. But I think the issue is the same as the save_to_disk, from the traceback it seems that in multiprocessing, it tries to use dill to return the result of the map workers, which tries to pickle the data and can't do it, probably because it's again using the older pickle protocol. That's my guess anyway.
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https://github.com/huggingface/datasets/issues/2134
Saving large in-memory datasets with save_to_disk crashes because of pickling
I just merged a fix #2150 that allows to pickle tables bigger than 4GiB Feel free to try it on the `master` branch !
Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ```
24
Saving large in-memory datasets with save_to_disk crashes because of pickling Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` I just merged a fix #2150 that allows to pickle tables bigger than 4GiB Feel free to try it on the `master` branch !
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https://github.com/huggingface/datasets/issues/2134
Saving large in-memory datasets with save_to_disk crashes because of pickling
awesome! I started getting this error as well when I tried to tokenize with a longer sequence length
Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ```
18
Saving large in-memory datasets with save_to_disk crashes because of pickling Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` awesome! I started getting this error as well when I tried to tokenize with a longer sequence length
[ -0.3168885708, 0.0995955095, 0.1197799817, 0.3708882928, 0.2521230876, 0.0021296495, -0.283498019, 0.4639685452, 0.190556705, 0.0964655131, 0.0808834657, 0.5146648884, -0.3989092708, 0.2905988395, -0.157480225, -0.083372362, 0.2200171649, -0.1045618355, -0.2615798116, 0.171037212, -0.2519758046, -0.1009906158, 0.0205407199, -0.1659641266, -0.1712377965, -0.3141447306, -0.0271553937, 0.3956104219, -0.3193681538, -0.3452962935, -0.14097628, -0.2231155485, 0.2281499952, 0.3274106979, -0.0001218503, 0.0387925431, 0.1374745369, -0.1475668848, -0.4962762892, 0.004615189, 0.0253550597, -0.5504592657, -0.1945044547, -0.2504110932, 0.3338276446, -0.0910537541, 0.1185292602, -0.3753314316, 0.2385224998, 0.2061813027, 0.1406993419, 0.3183521628, 0.2884151042, -0.0236259121, 0.0810791999, 0.2532387376, -0.2371630967, 0.2104444802, 0.2143961191, 0.0940102488, -0.0697051063, -0.1063090786, -0.0489086621, -0.2752485275, 0.0091197062, -0.0715227276, -0.3575344682, -0.2352927625, 0.1131329164, 0.1359724104, 0.2058555186, -0.4716518819, -0.6115642786, -0.4440332353, -0.0978168547, -0.2725955844, 0.0673025399, 0.2560086846, -0.0856660977, -0.0091383485, -0.148708865, -0.5428616405, 0.065051727, 0.0935428068, 0.3128340542, -0.2640571892, -0.2599280775, 0.2104805112, 0.4827491045, -0.2832580805, -0.2492697537, -0.1989862174, 0.0249467045, -0.0148135442, -0.0100061819, -0.1636582166, -0.0667289346, -0.0531579703, 0.2876334488, 0.1288197786, -0.0264492873, 0.1699508876, -0.3184285164, -0.0102002434, 0.3466799855, -0.0542852394, -0.2104746699, 0.3277972341, 0.3184901774, 0.0511619858, 0.115170382, 0.0862567723, -0.0244318079, -0.1946863234, 0.0468378402, -0.139344871, 0.1426373273, -0.122171402, -0.0298000202, 0.1717314422, -0.1409706324, 0.2041667402, -0.0327623449, 0.2195822895, -0.0259778742, 0.0620770678, -0.2864434421, 0.0794295594, -0.1583690941, 0.0751292482, -0.1170246825, 0.1987027973, 0.0240921006, 0.2353448719, 0.1321487576, 0.0247919876, 0.2072652727, -0.0151788229, -0.256757021, 0.1097205505, 0.0652008131, -0.4047948718, 0.1266947836, 0.2791752517, -0.0430322699, 0.1116224527, 0.0867824405, 0.0422737561, -0.240793407, 0.1412680894, 0.0091915457, -0.210431084, -0.045482371, 0.0081624072, -0.1934385002, -0.125914827, -0.5878512263, 0.0739090815, 0.3553924859, -0.1766406298, -0.1262134463, -0.2640078366, -0.1701365858, -0.3441281021, 0.0026653875, 0.3127560616, -0.3485506773, 0.1430541277, -0.0181173813, 0.1768347621, 0.504804194, 0.4281788766, 0.017092498, -0.09400931, -0.2584462464, 0.4258444607, 0.2039239705, -0.0345926955, -0.4821709991, 0.1800987571, 0.0055723181, -0.0328228511, -0.128440246, 0.1374110579, 0.0278898664, -0.0201429594, -0.0864939541, 0.1751219779, 0.0206467472, 0.188828364, -0.3232868016, -0.4281207621, -0.0569949709, 0.0229821689, -0.0687386617, -0.0665231571, 0.043806646, -0.2526403666, 0.1586154848, -0.1642065048, 0.2267968506, 0.3692829907, 0.310709089, -0.1413412392, -0.2410430759, -0.2540669143, -0.3209949732, 0.2904057503, 0.1346237659, -0.2128847837, -0.2942925096, 0.0248155445, -0.0160081163, 0.0344790854, -0.0461660288, 0.3929960132, -0.0561229922, -0.0432412699, 0.2098882049, 0.1974906027, -0.0971101373, -0.1669192165, -0.2190025896, 0.1228189915, -0.247102797, 0.1366460323, -0.0582225099, -0.4469140172, 0.0568947122, -0.0252150446, 0.011301361, -0.1903937459, -0.056796927, 0.1131045744, 0.2439712435, -0.036153309, -0.0844452456, 0.1641012579, 0.0477743261, 0.0527438596, 0.1824995577, -0.017229585, 0.2800436616, 0.0196368862, -0.1887226701, 0.3480972052, 0.0926225036, 0.1415982693, 0.3416218758, -0.1152978614, 0.0515541658, 0.0729830787, 0.2104121, -0.1721232086, -0.1512311697, 0.1688515991, 0.355353117, -0.0358188227, -0.3565801382, 0.0416286699, 0.5232878327, 0.0071559078, 0.4139182866, 0.288895607, -0.0300788507, -0.1796032041, 0.0495373309, 0.1489825547, 0.4825510383, 0.0719782263, 0.203198418, -0.049734205, 0.0984952748, -0.0927230492, 0.0490240939, 0.1893745214, 0.2143325806, 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https://github.com/huggingface/datasets/issues/2134
Saving large in-memory datasets with save_to_disk crashes because of pickling
@prokopCerny does this fix work for you? I found that with the latest master, my container with 500GB RAM starts crashing when I try to map a large dataset using `num_proc`. @lhoestq would it be possible to implement some logic to keep the individual cache files small (say below 100mb)? I find this helps with loading large datasets, but the "hack" I was using (increasing `num_proc` to a large number) doesn't work anymore with the latest master; my container crashes even with `num_proc=200` now
Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ```
84
Saving large in-memory datasets with save_to_disk crashes because of pickling Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` @prokopCerny does this fix work for you? I found that with the latest master, my container with 500GB RAM starts crashing when I try to map a large dataset using `num_proc`. @lhoestq would it be possible to implement some logic to keep the individual cache files small (say below 100mb)? I find this helps with loading large datasets, but the "hack" I was using (increasing `num_proc` to a large number) doesn't work anymore with the latest master; my container crashes even with `num_proc=200` now
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https://github.com/huggingface/datasets/issues/2134
Saving large in-memory datasets with save_to_disk crashes because of pickling
Closing since the original issue was fixed in #2150 Feel free to reopen if you are still experiencing it. For the other problems, please open separate issues
Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ```
27
Saving large in-memory datasets with save_to_disk crashes because of pickling Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ``` Closing since the original issue was fixed in #2150 Feel free to reopen if you are still experiencing it. For the other problems, please open separate issues
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-0.0191372745, -0.1319442242, -0.0674830601, 1.1606018543, -0.264290452, -0.225251466, 0.2253786772, -0.0351090953 ]
https://github.com/huggingface/datasets/issues/2133
bug in mlqa dataset
If you print those questions, you get readable texts: ```python >>> questions = [ ... "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", ... "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", ... "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", ... "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", ... "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ... ] >>> print(questions) ['متى بدات المجلة المدرسية في نوتردام بالنشر?', 'كم مرة يتم نشرها في نوتردام?', 'ما هي الورقة اليومية للطلاب في نوتردام?', 'كم عدد الاوراق الاخبارية للطلاب التي وجدت في نوتردام?', 'في اي سنة بدات ورقة الطالب الحس السليم بالنشر في نوتردام?'] ``` I don't think we can change this
Hi Looking into MLQA dataset for langauge "ar": ``` "question": [ "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ] ``` the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq
111
bug in mlqa dataset Hi Looking into MLQA dataset for langauge "ar": ``` "question": [ "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ] ``` the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq If you print those questions, you get readable texts: ```python >>> questions = [ ... "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", ... "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", ... "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", ... "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", ... "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ... ] >>> print(questions) ['متى بدات المجلة المدرسية في نوتردام بالنشر?', 'كم مرة يتم نشرها في نوتردام?', 'ما هي الورقة اليومية للطلاب في نوتردام?', 'كم عدد الاوراق الاخبارية للطلاب التي وجدت في نوتردام?', 'في اي سنة بدات ورقة الطالب الحس السليم بالنشر في نوتردام?'] ``` I don't think we can change this
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https://github.com/huggingface/datasets/issues/2133
bug in mlqa dataset
Hi @dorost1234. In Python 3, strings are sequences of Unicode _code points_. Unicode is a specification that maps all characters (and emoji symbols) with its unique representation in terms of code points. That is what you see: Unicode code points (represented by a \u escaped sequence of 16-bit hex values). Characters are usually represented (on screen and papers) with a graphical element called _glyph_. That is what you would like to see: glyphs. But Python does not care about glyphs: that is the job of the GUI or the terminal; glyphs are what you get with the `print` function (if your terminal is properly configured to display those glyphs). You have more detailed information about Unicode in the Python documentation: https://docs.python.org/3/howto/unicode.html
Hi Looking into MLQA dataset for langauge "ar": ``` "question": [ "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ] ``` the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq
121
bug in mlqa dataset Hi Looking into MLQA dataset for langauge "ar": ``` "question": [ "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ] ``` the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq Hi @dorost1234. In Python 3, strings are sequences of Unicode _code points_. Unicode is a specification that maps all characters (and emoji symbols) with its unique representation in terms of code points. That is what you see: Unicode code points (represented by a \u escaped sequence of 16-bit hex values). Characters are usually represented (on screen and papers) with a graphical element called _glyph_. That is what you would like to see: glyphs. But Python does not care about glyphs: that is the job of the GUI or the terminal; glyphs are what you get with the `print` function (if your terminal is properly configured to display those glyphs). You have more detailed information about Unicode in the Python documentation: https://docs.python.org/3/howto/unicode.html
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https://github.com/huggingface/datasets/issues/2132
TydiQA dataset is mixed and is not split per language
You can filter the languages this way: ```python tydiqa_en = tydiqa_dataset.filter(lambda x: x["language"] == "english") ``` Otherwise maybe we can have one configuration per language ? What do you think of this for example ? ```python load_dataset("tydiqa", "primary_task.en") ```
Hi @lhoestq Currently TydiQA is mixed and user can only access the whole training set of all languages: https://www.tensorflow.org/datasets/catalog/tydi_qa for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have them split and I appreciate your help on this. Meanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot
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TydiQA dataset is mixed and is not split per language Hi @lhoestq Currently TydiQA is mixed and user can only access the whole training set of all languages: https://www.tensorflow.org/datasets/catalog/tydi_qa for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have them split and I appreciate your help on this. Meanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot You can filter the languages this way: ```python tydiqa_en = tydiqa_dataset.filter(lambda x: x["language"] == "english") ``` Otherwise maybe we can have one configuration per language ? What do you think of this for example ? ```python load_dataset("tydiqa", "primary_task.en") ```
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https://github.com/huggingface/datasets/issues/2132
TydiQA dataset is mixed and is not split per language
Hi thank you very much for the great response, this will be really wonderful to have one configuration per language, as one need the dataset in majority of case per language for cross-lingual evaluations. This becomes also then more close to TFDS format, which is separated per language https://www.tensorflow.org/datasets/catalog/tydi_qa which will be really awesome to have. thanks On Mon, Mar 29, 2021 at 6:17 PM Quentin Lhoest ***@***.***> wrote: > You can filter the languages this way: > > tydiqa_en = tydiqa_dataset.filter(lambda x: x["language"] == "english") > > Otherwise maybe we can have one configuration per language ? > What do you think of this for example ? > > load_dataset("tydiqa", "primary_task.en") > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/2132#issuecomment-809516799>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AS37NMXPW2PWSQ2RHG73O7TTGCY4LANCNFSM4Z7ER7IA> > . >
Hi @lhoestq Currently TydiQA is mixed and user can only access the whole training set of all languages: https://www.tensorflow.org/datasets/catalog/tydi_qa for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have them split and I appreciate your help on this. Meanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot
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TydiQA dataset is mixed and is not split per language Hi @lhoestq Currently TydiQA is mixed and user can only access the whole training set of all languages: https://www.tensorflow.org/datasets/catalog/tydi_qa for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have them split and I appreciate your help on this. Meanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot Hi thank you very much for the great response, this will be really wonderful to have one configuration per language, as one need the dataset in majority of case per language for cross-lingual evaluations. This becomes also then more close to TFDS format, which is separated per language https://www.tensorflow.org/datasets/catalog/tydi_qa which will be really awesome to have. thanks On Mon, Mar 29, 2021 at 6:17 PM Quentin Lhoest ***@***.***> wrote: > You can filter the languages this way: > > tydiqa_en = tydiqa_dataset.filter(lambda x: x["language"] == "english") > > Otherwise maybe we can have one configuration per language ? > What do you think of this for example ? > > load_dataset("tydiqa", "primary_task.en") > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/2132#issuecomment-809516799>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AS37NMXPW2PWSQ2RHG73O7TTGCY4LANCNFSM4Z7ER7IA> > . >
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https://github.com/huggingface/datasets/issues/2131
When training with Multi-Node Multi-GPU the worker 2 has TypeError: 'NoneType' object
Hi ! Thanks for reporting I was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker. I just opened https://github.com/huggingface/datasets/pull/2137 to fix this issue
version: 1.5.0 met a very strange error, I am training large scale language model, and need train on 2 machines(workers). And sometimes I will get this error `TypeError: 'NoneType' object is not iterable` This is traceback ``` 71 |   | Traceback (most recent call last): -- | -- | -- 72 |   | File "run_gpt.py", line 316, in <module> 73 |   | main() 74 |   | File "run_gpt.py", line 222, in main 75 |   | delimiter="\t", column_names=["input_ids", "attention_mask", "chinese_ref"]) 76 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/load.py", line 747, in load_dataset 77 |   | use_auth_token=use_auth_token, 78 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 513, in download_and_prepare 79 |   | self.download_post_processing_resources(dl_manager) 80 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 673, in download_post_processing_resources 81 |   | for split in self.info.splits: 82 |   | TypeError: 'NoneType' object is not iterable 83 |   | WARNING:datasets.builder:Reusing dataset csv (/usr/local/app/.cache/huggingface/datasets/csv/default-1c257ebd48e225e7/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2) 84 |   | Traceback (most recent call last): 85 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main 86 |   | "__main__", mod_spec) 87 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 85, in _run_code 88 |   | exec(code, run_globals) 89 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module> 90 |   | main() 91 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main 92 |   | sigkill_handler(signal.SIGTERM, None) # not coming back 93 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler 94 |   | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd) ``` On worker 1 it loads the dataset well, however on worker 2 will get this error. And I will meet this error from time to time, sometimes it just goes well.
37
When training with Multi-Node Multi-GPU the worker 2 has TypeError: 'NoneType' object version: 1.5.0 met a very strange error, I am training large scale language model, and need train on 2 machines(workers). And sometimes I will get this error `TypeError: 'NoneType' object is not iterable` This is traceback ``` 71 |   | Traceback (most recent call last): -- | -- | -- 72 |   | File "run_gpt.py", line 316, in <module> 73 |   | main() 74 |   | File "run_gpt.py", line 222, in main 75 |   | delimiter="\t", column_names=["input_ids", "attention_mask", "chinese_ref"]) 76 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/load.py", line 747, in load_dataset 77 |   | use_auth_token=use_auth_token, 78 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 513, in download_and_prepare 79 |   | self.download_post_processing_resources(dl_manager) 80 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 673, in download_post_processing_resources 81 |   | for split in self.info.splits: 82 |   | TypeError: 'NoneType' object is not iterable 83 |   | WARNING:datasets.builder:Reusing dataset csv (/usr/local/app/.cache/huggingface/datasets/csv/default-1c257ebd48e225e7/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2) 84 |   | Traceback (most recent call last): 85 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main 86 |   | "__main__", mod_spec) 87 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 85, in _run_code 88 |   | exec(code, run_globals) 89 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module> 90 |   | main() 91 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main 92 |   | sigkill_handler(signal.SIGTERM, None) # not coming back 93 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler 94 |   | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd) ``` On worker 1 it loads the dataset well, however on worker 2 will get this error. And I will meet this error from time to time, sometimes it just goes well. Hi ! Thanks for reporting I was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker. I just opened https://github.com/huggingface/datasets/pull/2137 to fix this issue
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https://github.com/huggingface/datasets/issues/2130
wikiann dataset is missing columns
Here please find TFDS format of this dataset: https://www.tensorflow.org/datasets/catalog/wikiann where there is a span column, this is really necessary to be able to use the data, and I appreciate your help @lhoestq
Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq
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wikiann dataset is missing columns Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq Here please find TFDS format of this dataset: https://www.tensorflow.org/datasets/catalog/wikiann where there is a span column, this is really necessary to be able to use the data, and I appreciate your help @lhoestq
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https://github.com/huggingface/datasets/issues/2130
wikiann dataset is missing columns
Hi ! Apparently you can get the spans from the NER tags using `tags_to_spans` defined here: https://github.com/tensorflow/datasets/blob/c7096bd38e86ed240b8b2c11ecab9893715a7d55/tensorflow_datasets/text/wikiann/wikiann.py#L81-L126 It would be nice to include the `spans` field in this dataset as in TFDS. This could be a good first issue for new contributors ! The objective is to use `tags_to_spans` in the `_generate_examples` method [here](https://github.com/huggingface/nlp/blob/c98e4b8f23e3770c401c6d9326e243e1ffd599ec/datasets/wikiann/wikiann.py#L292-L316) to create he `spans` for each example.
Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq
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wikiann dataset is missing columns Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq Hi ! Apparently you can get the spans from the NER tags using `tags_to_spans` defined here: https://github.com/tensorflow/datasets/blob/c7096bd38e86ed240b8b2c11ecab9893715a7d55/tensorflow_datasets/text/wikiann/wikiann.py#L81-L126 It would be nice to include the `spans` field in this dataset as in TFDS. This could be a good first issue for new contributors ! The objective is to use `tags_to_spans` in the `_generate_examples` method [here](https://github.com/huggingface/nlp/blob/c98e4b8f23e3770c401c6d9326e243e1ffd599ec/datasets/wikiann/wikiann.py#L292-L316) to create he `spans` for each example.
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https://github.com/huggingface/datasets/issues/2130
wikiann dataset is missing columns
Hi @lhoestq thank you very much for the help, it would be very nice to have it included, here is the full code, one need to also convert tags to string first: ``` import datasets from datasets import load_dataset def tags_to_spans(tags): """Convert tags to spans.""" spans = set() span_start = 0 span_end = 0 active_conll_tag = None for index, string_tag in enumerate(tags): # Actual BIO tag. bio_tag = string_tag[0] assert bio_tag in ["B", "I", "O"], "Invalid Tag" conll_tag = string_tag[2:] if bio_tag == "O": # The span has ended. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) active_conll_tag = None # We don't care about tags we are # told to ignore, so we do nothing. continue elif bio_tag == "B": # We are entering a new span; reset indices and active tag to new span. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) active_conll_tag = conll_tag span_start = index span_end = index elif bio_tag == "I" and conll_tag == active_conll_tag: # We're inside a span. span_end += 1 else: # This is the case the bio label is an "I", but either: # 1) the span hasn't started - i.e. an ill formed span. # 2) We have IOB1 tagging scheme. # We'll process the previous span if it exists, but also include this # span. This is important, because otherwise, a model may get a perfect # F1 score whilst still including false positive ill-formed spans. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) active_conll_tag = conll_tag span_start = index span_end = index # Last token might have been a part of a valid span. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) # Return sorted list of spans return sorted(list(spans), key=lambda x: x[1][0]) dataset = load_dataset('wikiann', 'en', split="train") ner_tags = { 0:"O", 1:"B-PER", 2:"I-PER", 3:"B-ORG", 4:"I-ORG", 5:"B-LOC", 6:"I-LOC" } def get_spans(tokens, tags): """Convert tags to textspans.""" spans = tags_to_spans(tags) text_spans = [ x[0] + ": " + " ".join([tokens[i] for i in range(x[1][0], x[1][1] + 1)]) for x in spans ] if not text_spans: text_spans = ["None"] return text_spans for i, d in enumerate(dataset): tokens = d['tokens'] tags = d['ner_tags'] tags = [ner_tags[i] for i in tags] spans = get_spans(tokens, tags) print("spans ", spans) print(d) if i > 10: break; ``` I am not sure how to contribute to the repository and how things work, could you let me know how one can access the datasets to be able to contribute to the repository? Maybe I could do it then thanks
Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq
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wikiann dataset is missing columns Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq Hi @lhoestq thank you very much for the help, it would be very nice to have it included, here is the full code, one need to also convert tags to string first: ``` import datasets from datasets import load_dataset def tags_to_spans(tags): """Convert tags to spans.""" spans = set() span_start = 0 span_end = 0 active_conll_tag = None for index, string_tag in enumerate(tags): # Actual BIO tag. bio_tag = string_tag[0] assert bio_tag in ["B", "I", "O"], "Invalid Tag" conll_tag = string_tag[2:] if bio_tag == "O": # The span has ended. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) active_conll_tag = None # We don't care about tags we are # told to ignore, so we do nothing. continue elif bio_tag == "B": # We are entering a new span; reset indices and active tag to new span. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) active_conll_tag = conll_tag span_start = index span_end = index elif bio_tag == "I" and conll_tag == active_conll_tag: # We're inside a span. span_end += 1 else: # This is the case the bio label is an "I", but either: # 1) the span hasn't started - i.e. an ill formed span. # 2) We have IOB1 tagging scheme. # We'll process the previous span if it exists, but also include this # span. This is important, because otherwise, a model may get a perfect # F1 score whilst still including false positive ill-formed spans. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) active_conll_tag = conll_tag span_start = index span_end = index # Last token might have been a part of a valid span. if active_conll_tag: spans.add((active_conll_tag, (span_start, span_end))) # Return sorted list of spans return sorted(list(spans), key=lambda x: x[1][0]) dataset = load_dataset('wikiann', 'en', split="train") ner_tags = { 0:"O", 1:"B-PER", 2:"I-PER", 3:"B-ORG", 4:"I-ORG", 5:"B-LOC", 6:"I-LOC" } def get_spans(tokens, tags): """Convert tags to textspans.""" spans = tags_to_spans(tags) text_spans = [ x[0] + ": " + " ".join([tokens[i] for i in range(x[1][0], x[1][1] + 1)]) for x in spans ] if not text_spans: text_spans = ["None"] return text_spans for i, d in enumerate(dataset): tokens = d['tokens'] tags = d['ner_tags'] tags = [ner_tags[i] for i in tags] spans = get_spans(tokens, tags) print("spans ", spans) print(d) if i > 10: break; ``` I am not sure how to contribute to the repository and how things work, could you let me know how one can access the datasets to be able to contribute to the repository? Maybe I could do it then thanks
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https://github.com/huggingface/datasets/issues/2130
wikiann dataset is missing columns
Cool ! Let me give you some context: #### Contribution guide You can find the contribution guide here: https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md It explains how to set up your dev environment in a few steps. #### Dataset loading Each Dataset is defined by a Table that have many rows (one row = one example) and columns (one column = one feature). To change how a dataset is constructed, you have to modify its dataset script that you can find here: https://github.com/huggingface/datasets/blob/master/datasets/wikiann/wikiann.py It includes everything needed to load the WikiANN dataset. You can load locally a modified version of `wikiann.py` with `load_dataset("path/to/wikiann.py")`. #### Define a new column Each column has a name and a type. You can see how the features of WikiANN are defined here: https://github.com/huggingface/datasets/blob/c98e4b8f23e3770c401c6d9326e243e1ffd599ec/datasets/wikiann/wikiann.py#L245-L263 Ideally we would have one additional feature "spans": ```python "spans": datasets.Sequence(datasets.Value("string")), ``` #### Compute the content of each row To build the WikiANN rows, the _generate_examples method from [here](https://github.com/huggingface/nlp/blob/c98e4b8f23e3770c401c6d9326e243e1ffd599ec/datasets/wikiann/wikiann.py#L292-L316) is used. This function `yield` one python dictionary for each example: ```python yield guid_index, {"tokens": tokens, "ner_tags": ner_tags, "langs": langs} ``` The objective would be to return instead something like ```python spans = spans = get_spans(tokens, tags) yield guid_index, {"tokens": tokens, "ner_tags": ner_tags, "langs": langs, "spans": spans} ``` Let me know if you have questions !
Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq
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wikiann dataset is missing columns Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq Cool ! Let me give you some context: #### Contribution guide You can find the contribution guide here: https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md It explains how to set up your dev environment in a few steps. #### Dataset loading Each Dataset is defined by a Table that have many rows (one row = one example) and columns (one column = one feature). To change how a dataset is constructed, you have to modify its dataset script that you can find here: https://github.com/huggingface/datasets/blob/master/datasets/wikiann/wikiann.py It includes everything needed to load the WikiANN dataset. You can load locally a modified version of `wikiann.py` with `load_dataset("path/to/wikiann.py")`. #### Define a new column Each column has a name and a type. You can see how the features of WikiANN are defined here: https://github.com/huggingface/datasets/blob/c98e4b8f23e3770c401c6d9326e243e1ffd599ec/datasets/wikiann/wikiann.py#L245-L263 Ideally we would have one additional feature "spans": ```python "spans": datasets.Sequence(datasets.Value("string")), ``` #### Compute the content of each row To build the WikiANN rows, the _generate_examples method from [here](https://github.com/huggingface/nlp/blob/c98e4b8f23e3770c401c6d9326e243e1ffd599ec/datasets/wikiann/wikiann.py#L292-L316) is used. This function `yield` one python dictionary for each example: ```python yield guid_index, {"tokens": tokens, "ner_tags": ner_tags, "langs": langs} ``` The objective would be to return instead something like ```python spans = spans = get_spans(tokens, tags) yield guid_index, {"tokens": tokens, "ner_tags": ner_tags, "langs": langs, "spans": spans} ``` Let me know if you have questions !
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https://github.com/huggingface/datasets/issues/2129
How to train BERT model with next sentence prediction?
Hi ! We're not using `TextDatasetForNextSentencePrediction` in `datasets`. Although you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.
Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
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How to train BERT model with next sentence prediction? Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ? Hi ! We're not using `TextDatasetForNextSentencePrediction` in `datasets`. Although you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.
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-0.3854332864, 0.5604228973, 0.342128247, 0.0988505408, -0.3323817551, -0.2206932455 ]
https://github.com/huggingface/datasets/issues/2129
How to train BERT model with next sentence prediction?
Thanks. Do you mean that `TextDatasetForNextSentencePrediction.create_exapmles_from_document` can be applied to dataset object other than `TextDatasetForNextSentencePrediction` e.g. a `Dataset` object which is loaded by `datasets.load_dataset`?
Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
24
How to train BERT model with next sentence prediction? Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ? Thanks. Do you mean that `TextDatasetForNextSentencePrediction.create_exapmles_from_document` can be applied to dataset object other than `TextDatasetForNextSentencePrediction` e.g. a `Dataset` object which is loaded by `datasets.load_dataset`?
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0.1255244613, -0.3565335572, 0.5560403466, 0.2431779355, 0.1063826829, -0.3350412846, -0.2991438508 ]
https://github.com/huggingface/datasets/issues/2129
How to train BERT model with next sentence prediction?
It would probably require a bit of tweaking, but you can apply it to a dataset, yes. This should give you a new dataset with sentence pairs you can train a model on. You can find the documentation about dataset processing here: https://huggingface.co/docs/datasets/processing.html#processing-data-with-map
Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
43
How to train BERT model with next sentence prediction? Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ? It would probably require a bit of tweaking, but you can apply it to a dataset, yes. This should give you a new dataset with sentence pairs you can train a model on. You can find the documentation about dataset processing here: https://huggingface.co/docs/datasets/processing.html#processing-data-with-map
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https://github.com/huggingface/datasets/issues/2128
Dialogue action slot name and value are reversed in MultiWoZ 2.2
Hi Good catch ! Thanks for reporting If you are interested in contributing, feel free to open a PR to fix this :)
Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial! I spot an error that the order of Dialogue action slot names and values are reversed. https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262
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Dialogue action slot name and value are reversed in MultiWoZ 2.2 Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial! I spot an error that the order of Dialogue action slot names and values are reversed. https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262 Hi Good catch ! Thanks for reporting If you are interested in contributing, feel free to open a PR to fix this :)
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-0.1231330931, -0.0099010998, 0.5217806697, -0.3492457569, 0.2818294168, 0.1111742631 ]
https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Hi, sadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with: ```bash pip install git+https://github.com/huggingface/datasets ```
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
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Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Hi, sadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with: ```bash pip install git+https://github.com/huggingface/datasets ```
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https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Is there an error message ? What stacktrace do you get if you interrupt the execution of the program while downloading ?
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
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Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Is there an error message ? What stacktrace do you get if you interrupt the execution of the program while downloading ?
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-0.0223945528, 0.2566051483, -0.0247100592, -0.2528835237, 0.2719272673, -0.0967397913 ]
https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Sorry for the long time since my last comment, I tried again and don't seem to have the problem anymore, thanks for your support!
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
24
Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Sorry for the long time since my last comment, I tried again and don't seem to have the problem anymore, thanks for your support!
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https://github.com/huggingface/datasets/issues/2123
Problem downloading GEM wiki_auto_asset_turk dataset
Great ! I'm closing the issue then. Feel free to re-open if you experience this issue again
@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family
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Problem downloading GEM wiki_auto_asset_turk dataset @yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family Great ! I'm closing the issue then. Feel free to re-open if you experience this issue again
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https://github.com/huggingface/datasets/issues/2116
Creating custom dataset results in error while calling the map() function
Hi, the `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the "association over inheritance" approach with a simple wrapper class that delegates calls to a wrapped `Dataset` (map, etc.). Btw, the library offers the `datasets.Dataset.from_pandas` class method to directly create a `datasets.Dataset` from the dataframe.
calling `map()` of `datasets` library results into an error while defining a Custom dataset. Reproducible example: ``` import datasets class MyDataset(datasets.Dataset): def __init__(self, sentences): "Initialization" self.samples = sentences def __len__(self): "Denotes the total number of samples" return len(self.samples) def __getitem__(self, index): "Generates one sample of data" # Select sample # Load data and get label samples = self.samples[index] return samples def preprocess_function_train(examples): inputs = examples labels = [example+tokenizer.eos_token for example in examples ] inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True) labels = tokenizer(labels, max_length=30, padding=True, truncation=True) model_inputs = inputs model_inputs["labels"] = labels["input_ids"] print("about to return") return model_inputs ##train["sentence"] is dataframe column train_dataset = MyDataset(train['sentence'].values.tolist()) train_dataset = train_dataset.map( preprocess_function, batched = True, batch_size=32 ) ``` Stack trace of error: ``` Traceback (most recent call last): File "dir/train_generate.py", line 362, in <module> main() File "dir/train_generate.py", line 245, in main train_dataset = train_dataset.map( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1244, in map return self._map_single( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 149, in wrapper unformatted_columns = set(self.column_names) - set(self._format_columns or []) File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 526, in column_names return self._data.column_names AttributeError: 'MyDataset' object has no attribute '_data' ```
75
Creating custom dataset results in error while calling the map() function calling `map()` of `datasets` library results into an error while defining a Custom dataset. Reproducible example: ``` import datasets class MyDataset(datasets.Dataset): def __init__(self, sentences): "Initialization" self.samples = sentences def __len__(self): "Denotes the total number of samples" return len(self.samples) def __getitem__(self, index): "Generates one sample of data" # Select sample # Load data and get label samples = self.samples[index] return samples def preprocess_function_train(examples): inputs = examples labels = [example+tokenizer.eos_token for example in examples ] inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True) labels = tokenizer(labels, max_length=30, padding=True, truncation=True) model_inputs = inputs model_inputs["labels"] = labels["input_ids"] print("about to return") return model_inputs ##train["sentence"] is dataframe column train_dataset = MyDataset(train['sentence'].values.tolist()) train_dataset = train_dataset.map( preprocess_function, batched = True, batch_size=32 ) ``` Stack trace of error: ``` Traceback (most recent call last): File "dir/train_generate.py", line 362, in <module> main() File "dir/train_generate.py", line 245, in main train_dataset = train_dataset.map( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1244, in map return self._map_single( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 149, in wrapper unformatted_columns = set(self.column_names) - set(self._format_columns or []) File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 526, in column_names return self._data.column_names AttributeError: 'MyDataset' object has no attribute '_data' ``` Hi, the `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the "association over inheritance" approach with a simple wrapper class that delegates calls to a wrapped `Dataset` (map, etc.). Btw, the library offers the `datasets.Dataset.from_pandas` class method to directly create a `datasets.Dataset` from the dataframe.
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-0.0276849624, 0.2933388054, -0.2039104402, -0.1067740992, 0.214243114, -0.2125303298 ]
https://github.com/huggingface/datasets/issues/2106
WMT19 Dataset for Kazakh-English is not formatted correctly
Hi ! Thanks for reporting By looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue. Moreover these issues are not always the same: - L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line - L2897 is only `kk` text and must be appended at the end of the `kk` text of the **previous** line - L1247 and L1248 are only `kk` texts and must be inserted at the **beginning** of the `kk` text of the next line - (and there are many others) It would be nice to have a corrected version of this file ! The file is available in the `wmt/news-commentary` repository on the Datasets Hub here: https://huggingface.co/datasets/wmt/news-commentary/tree/main/v14/training Then maybe we can notify the WMT authors and host the corrected version somewhere
In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error. The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here: > Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc’s dramatic appreciation over the past few years. Швейцарияның Ұлттық банкі өз тарапынан, соңғы бірнеше жыл ішінде франк құнының қатты өсуінің дефляциялық әсерімен күресіп келеді. > > Line 95. Дефляциялық күштер 2008 жылы терең және ұзаққа созылған жаһандық дағдарысқа байланысты орын алған ірі экономикалық және қаржылық орын алмасулардың арқасында босатылды. Жеке қарыз қаражаты үлесінің қысқаруы орталық банктің рефляцияға жұмсалған күш-жігеріне тұрақты соққан қарсы желдей болды. > > Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. As you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code ``` import datasets from datasets import load_dataset dataset = load_dataset('wmt19', 'kk-en') for key in dataset['train']['translation']: if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']: print(key['en']) print(key['kk']) break ``` we get: > 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. > The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. which shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one. Please let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface.
144
WMT19 Dataset for Kazakh-English is not formatted correctly In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error. The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here: > Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc’s dramatic appreciation over the past few years. Швейцарияның Ұлттық банкі өз тарапынан, соңғы бірнеше жыл ішінде франк құнының қатты өсуінің дефляциялық әсерімен күресіп келеді. > > Line 95. Дефляциялық күштер 2008 жылы терең және ұзаққа созылған жаһандық дағдарысқа байланысты орын алған ірі экономикалық және қаржылық орын алмасулардың арқасында босатылды. Жеке қарыз қаражаты үлесінің қысқаруы орталық банктің рефляцияға жұмсалған күш-жігеріне тұрақты соққан қарсы желдей болды. > > Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. As you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code ``` import datasets from datasets import load_dataset dataset = load_dataset('wmt19', 'kk-en') for key in dataset['train']['translation']: if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']: print(key['en']) print(key['kk']) break ``` we get: > 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. > The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. which shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one. Please let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface. Hi ! Thanks for reporting By looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue. Moreover these issues are not always the same: - L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line - L2897 is only `kk` text and must be appended at the end of the `kk` text of the **previous** line - L1247 and L1248 are only `kk` texts and must be inserted at the **beginning** of the `kk` text of the next line - (and there are many others) It would be nice to have a corrected version of this file ! The file is available in the `wmt/news-commentary` repository on the Datasets Hub here: https://huggingface.co/datasets/wmt/news-commentary/tree/main/v14/training Then maybe we can notify the WMT authors and host the corrected version somewhere
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https://github.com/huggingface/datasets/issues/2105
Request to remove S2ORC dataset
Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) Until you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?
Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!
54
Request to remove S2ORC dataset Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) Until you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?
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0.0004404406, 0.1347308755, 0.3854473531, 0.1125350744, -0.1136493832, 0.2185109407, -0.0870213509 ]
https://github.com/huggingface/datasets/issues/2105
Request to remove S2ORC dataset
Hi @kyleclo, as of today, you have not removed your bucket data yet, and therefore HuggingFace can download it from there. Is it OK? Are you planning to eventually delete it? Thank you.
Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!
33
Request to remove S2ORC dataset Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! Hi @kyleclo, as of today, you have not removed your bucket data yet, and therefore HuggingFace can download it from there. Is it OK? Are you planning to eventually delete it? Thank you.
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https://github.com/huggingface/datasets/issues/2105
Request to remove S2ORC dataset
Hi! Sorry I missed @yjernite 's previous message, thanks for responding! Is there an option where we can keep our data in our bucket, but the HF script no longer pulls data from it?
Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!
34
Request to remove S2ORC dataset Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! Hi! Sorry I missed @yjernite 's previous message, thanks for responding! Is there an option where we can keep our data in our bucket, but the HF script no longer pulls data from it?
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https://github.com/huggingface/datasets/issues/2104
Trouble loading wiki_movies
Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`. To use `wiki_movies`, please update `datasets` with ``` pip install --upgrade datasets ```
Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you!
27
Trouble loading wiki_movies Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you! Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`. To use `wiki_movies`, please update `datasets` with ``` pip install --upgrade datasets ```
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https://github.com/huggingface/datasets/issues/2104
Trouble loading wiki_movies
Thanks a lot! That solved it and I was able to upload a model trained on it as well :)
Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you!
20
Trouble loading wiki_movies Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you! Thanks a lot! That solved it and I was able to upload a model trained on it as well :)
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https://github.com/huggingface/datasets/issues/2103
citation, homepage, and license fields of `dataset_info.json` are duplicated many times
Thanks for reporting :) Maybe we can concatenate fields only if they are different. Currently this is done here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196 This can be a good first contribution to the library. Please comment if you'd like to improve this and open a PR :)
This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation. Example result: ``` "citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n ``` @lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times.
43
citation, homepage, and license fields of `dataset_info.json` are duplicated many times This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation. Example result: ``` "citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n ``` @lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times. Thanks for reporting :) Maybe we can concatenate fields only if they are different. Currently this is done here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196 This can be a good first contribution to the library. Please comment if you'd like to improve this and open a PR :)
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Hi ! Can you share more information about the features of your dataset ? You can get them by printing `my_dataset.features` Can you also share the code of your `map` function ?
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
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load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Hi ! Can you share more information about the features of your dataset ? You can get them by printing `my_dataset.features` Can you also share the code of your `map` function ?
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a list of integers. The `text` column is removed during tokenization. ``` def add_len_and_seq(example): end_idx = example['input_ids'].index(SEP) example['actual_len'] = end_idx-1 seq_len = len(example['input_ids']) example['seq'] = [PAD_ID] + [np.uint8(example['some_integer'])]*(end_idx-1) + [PAD_ID]*(seq_len-end_idx) return example ```
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
51
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a list of integers. The `text` column is removed during tokenization. ``` def add_len_and_seq(example): end_idx = example['input_ids'].index(SEP) example['actual_len'] = end_idx-1 seq_len = len(example['input_ids']) example['seq'] = [PAD_ID] + [np.uint8(example['some_integer'])]*(end_idx-1) + [PAD_ID]*(seq_len-end_idx) return example ```
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Is `PAD_ID` a python integer ? You need all the integers in `example['seq']` to have the same type. Does this work if you remove the `np.uint8` and use python integers instead ?
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
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load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Is `PAD_ID` a python integer ? You need all the integers in `example['seq']` to have the same type. Does this work if you remove the `np.uint8` and use python integers instead ?
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
yup I casted it to `np.uint8` outside the function where it was defined. It was originally using python integers.
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
19
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) yup I casted it to `np.uint8` outside the function where it was defined. It was originally using python integers.
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Strangely, even when I manually created `np.arrays` of specific `dtypes`, the types in the final `dataset_info.json` that gets written are still `int64`. Update: I tried creating lists of `int8`s and got the same result.
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
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load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Strangely, even when I manually created `np.arrays` of specific `dtypes`, the types in the final `dataset_info.json` that gets written are still `int64`. Update: I tried creating lists of `int8`s and got the same result.
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Yes this is a known issue: #625 We're working on making the precision kept for numpy :) To specify the precision of the integers, currently one needs to specify the output features with `.map(..., features=output_features)`
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
35
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Yes this is a known issue: #625 We're working on making the precision kept for numpy :) To specify the precision of the integers, currently one needs to specify the output features with `.map(..., features=output_features)`
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
Do you know what step is taking forever in the code ? What happens if you interrupt the execution of the dataset loading ?
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
24
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) Do you know what step is taking forever in the code ? What happens if you interrupt the execution of the dataset loading ?
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https://github.com/huggingface/datasets/issues/2099
load_from_disk takes a long time to load local dataset
After a synchronous discussion, we found that the cache file sizes have an enormous effect on the loading speed: smaller cache files result in faster load times. `num_proc` controls the number of cache files that are being written and is inversely proportional to the individual file size. In other words, increase `num_proc` for smaller cache files :) Maybe this can be highlighted somewhere in the docs.
I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)
66
load_from_disk takes a long time to load local dataset I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :) After a synchronous discussion, we found that the cache file sizes have an enormous effect on the loading speed: smaller cache files result in faster load times. `num_proc` controls the number of cache files that are being written and is inversely proportional to the individual file size. In other words, increase `num_proc` for smaller cache files :) Maybe this can be highlighted somewhere in the docs.
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
Hi ! We plan to add streaming features in the future. This should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead. What do you think about this ? If you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us !
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
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How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? Hi ! We plan to add streaming features in the future. This should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead. What do you think about this ? If you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us !
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ?
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
49
How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ?
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
@lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
31
How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? @lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
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How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
@lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
23
How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? @lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?
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https://github.com/huggingface/datasets/issues/2092
How to disable making arrow tables in load_dataset ?
We're still working on this :) This will be available soon Users will be able to put their processed arrow files on the Hub
Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?
24
How to disable making arrow tables in load_dataset ? Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? We're still working on this :) This will be available soon Users will be able to put their processed arrow files on the Hub
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add. We are also adding the full list of tags in #2107 This covers multilinguality, language_creators, licenses, size_categories and task_categories. In general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name. edit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the "official" list :)
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
94
Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add. We are also adding the full list of tags in #2107 This covers multilinguality, language_creators, licenses, size_categories and task_categories. In general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name. edit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the "official" list :)
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
@lhoestq hmm - ok thanks for the answer. To be honest I am not sure if this issue can be closed now. I just wanted to point out that this should either be documented or linked in the documentation. If you feel like it is (will be) please just close this.
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
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Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip @lhoestq hmm - ok thanks for the answer. To be honest I am not sure if this issue can be closed now. I just wanted to point out that this should either be documented or linked in the documentation. If you feel like it is (will be) please just close this.
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
We're still working on the validation+documentation in this. Feel free to keep this issue open till we've added them
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
19
Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip We're still working on the validation+documentation in this. Feel free to keep this issue open till we've added them
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0.1313603669, 0.0618942082, -0.1494856626, -0.2070049345, -0.1194759235 ]
https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
Hi ! There's the tagging app at https://huggingface.co/datasets/tagging/ that you can use. It shows the list of all the tags you can use. It is based on all the tag sets defined in this folder: https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
36
Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip Hi ! There's the tagging app at https://huggingface.co/datasets/tagging/ that you can use. It shows the list of all the tags you can use. It is based on all the tag sets defined in this folder: https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources
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https://github.com/huggingface/datasets/issues/2089
Add documentaton for dataset README.md files
I don't think so. Feel free to take a look at the tags of other models (example [here](https://huggingface.co/bert-base-uncased/blob/main/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this
Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip
52
Add documentaton for dataset README.md files Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip I don't think so. Feel free to take a look at the tags of other models (example [here](https://huggingface.co/bert-base-uncased/blob/main/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this
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https://github.com/huggingface/datasets/issues/2083
`concatenate_datasets` throws error when changing the order of datasets to concatenate
Hi, this bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit: ```python common_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment']) ``` The order is important because the resulting dataset inherits the schema metadata of the first dataset passed to the `concatenate_datasets(...)` function (`pa.concat_tables` [docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html)). I'll try to fix this ASAP.
Hey, I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO. Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing
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`concatenate_datasets` throws error when changing the order of datasets to concatenate Hey, I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO. Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing Hi, this bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit: ```python common_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment']) ``` The order is important because the resulting dataset inherits the schema metadata of the first dataset passed to the `concatenate_datasets(...)` function (`pa.concat_tables` [docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html)). I'll try to fix this ASAP.
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https://github.com/huggingface/datasets/issues/2080
Multidimensional arrays in a Dataset
Hi ! This is actually supported ! but not yet in `from_pandas`. You can use `from_dict` for now instead: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset) ``` This will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset, features=Features({ "bbox": Array2D(shape=(3, 4), dtype="int64"), "input_ids": Value("int64") })) dataset.set_format("torch") print(dataset[0]['bbox']) # tensor([[1, 2, 3, 4], # [1, 2, 3, 4], # [1, 2, 3, 4]]) ``` If you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value("int64"))) and therefore the torch formatter will return list of tensors
Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263).
165
Multidimensional arrays in a Dataset Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263). Hi ! This is actually supported ! but not yet in `from_pandas`. You can use `from_dict` for now instead: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset) ``` This will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape: ```python from datasets import Dataset, Array2D, Features, Value import pandas as pd import numpy as np dataset = { 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] } dataset = Dataset.from_dict(dataset, features=Features({ "bbox": Array2D(shape=(3, 4), dtype="int64"), "input_ids": Value("int64") })) dataset.set_format("torch") print(dataset[0]['bbox']) # tensor([[1, 2, 3, 4], # [1, 2, 3, 4], # [1, 2, 3, 4]]) ``` If you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value("int64"))) and therefore the torch formatter will return list of tensors
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https://github.com/huggingface/datasets/issues/2080
Multidimensional arrays in a Dataset
Thanks for the explanation. With my original DataFrame, I did ``` dataset = dataset.to_dict("list") ``` and then the rest of the transformation from dictionary works just fine.
Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263).
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Multidimensional arrays in a Dataset Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263). Thanks for the explanation. With my original DataFrame, I did ``` dataset = dataset.to_dict("list") ``` and then the rest of the transformation from dictionary works just fine.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi ! Thanks for reporting. We're indeed using `jiwer` to compute the WER. Maybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible. Currently the code to compute the WER is defined here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/metrics/wer/wer.py#L93-L94
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
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MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi ! Thanks for reporting. We're indeed using `jiwer` to compute the WER. Maybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible. Currently the code to compute the WER is defined here: https://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/metrics/wer/wer.py#L93-L94
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi, I've just pushed a pull request that is related to this issue https://github.com/huggingface/datasets/pull/2169. It's not iterative, but it should avoid memory errors. It's based on the editdistance python library. An iterative implementation should be as easy as storing scores and words stepwise and dividing at the end.
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
48
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi, I've just pushed a pull request that is related to this issue https://github.com/huggingface/datasets/pull/2169. It's not iterative, but it should avoid memory errors. It's based on the editdistance python library. An iterative implementation should be as easy as storing scores and words stepwise and dividing at the end.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
I see, this was solved by other thread. Ok, let me know if you want to switch the implementation for any reason :)
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
23
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. I see, this was solved by other thread. Ok, let me know if you want to switch the implementation for any reason :)
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Thanks for diving into this anyway ^^' As you said this actually got solved a few days ago
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
18
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Thanks for diving into this anyway ^^' As you said this actually got solved a few days ago
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Someone created an issue https://github.com/jitsi/jiwer/issues/40 at jiwer which shows that this is still a problem in the current version. Would be curious to figure out how this can be fixed by jiwer... :) I assume that it runs of out memory because it's trying to compute the WER over (too many) test samples?
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
53
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Someone created an issue https://github.com/jitsi/jiwer/issues/40 at jiwer which shows that this is still a problem in the current version. Would be curious to figure out how this can be fixed by jiwer... :) I assume that it runs of out memory because it's trying to compute the WER over (too many) test samples?
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi ! It's computed iteratively so not sure what could go wrong https://github.com/huggingface/datasets/blob/8afd0ba8c27800a55ea69d9fcd702dc97d9c16d8/metrics/wer/wer.py#L100-L106 @NiklasHoltmeyer what version of `datasets` are you running ?
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
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MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi ! It's computed iteratively so not sure what could go wrong https://github.com/huggingface/datasets/blob/8afd0ba8c27800a55ea69d9fcd702dc97d9c16d8/metrics/wer/wer.py#L100-L106 @NiklasHoltmeyer what version of `datasets` are you running ?
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
One possible explanation might be that it is the user who is passing all the sentences in a single element to `wer.compute`? As current implementation iterates over the elements of `predictions` and `references`, this can be problematic if `predictions` and `references` contain a single huge element each. This could be the case, for example, with a single string with all sentences: ```python result["predicted"] = "One sentence. Other sentence." ``` or with a __double__ nested list of sentence lists ```python result["predicted"] = [[ ["One sentence."], ["Other sentence"] ]] ``` The user should check the dimensions of the data structure passed to `predictions` and `references`.
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
103
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. One possible explanation might be that it is the user who is passing all the sentences in a single element to `wer.compute`? As current implementation iterates over the elements of `predictions` and `references`, this can be problematic if `predictions` and `references` contain a single huge element each. This could be the case, for example, with a single string with all sentences: ```python result["predicted"] = "One sentence. Other sentence." ``` or with a __double__ nested list of sentence lists ```python result["predicted"] = [[ ["One sentence."], ["Other sentence"] ]] ``` The user should check the dimensions of the data structure passed to `predictions` and `references`.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
Hi all, in my case I was using and older version of datasets and, as @albertvillanova points out, passing the full list of sentences for the metric calculation. The problem was in the way jiwer implements WER, as it tries to compute WER for the full list at once instead of doing it element-wise. I think that with the latest implementation of datasets, or by using the alternative WER function that I've contributed on this [pull request](https://github.com/huggingface/datasets/pull/2169) there shouldn't be memory errors.
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
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MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. Hi all, in my case I was using and older version of datasets and, as @albertvillanova points out, passing the full list of sentences for the metric calculation. The problem was in the way jiwer implements WER, as it tries to compute WER for the full list at once instead of doing it element-wise. I think that with the latest implementation of datasets, or by using the alternative WER function that I've contributed on this [pull request](https://github.com/huggingface/datasets/pull/2169) there shouldn't be memory errors.
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https://github.com/huggingface/datasets/issues/2078
MemoryError when computing WER metric
@lhoestq i was using Datasets==1.5.0 with 1.6.1 it worked (atleast the first run) but 1.5.0 is not compatible with my preprocessing. i cant save my dataset to a parquet file while using the latest datasets version -> ``` File "../preprocess_dataset.py", line 132, in <module> pq.write_table(train_dataset.data, f'{resampled_data_dir}/{data_args.dataset_config_name}.train.parquet') File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 1674, in write_table writer.write_table(table, row_group_size=row_group_size) File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 588, in write_table self.writer.write_table(table, row_group_size=row_group_size) TypeError: Argument 'table' has incorrect type (expected pyarrow.lib.Table, got ConcatenationTable) ``` if i do ``` import pyarrow.parquet as pq ... ... pq.write_table(train_dataset.data, 'train.parquet') pq.write_table(eval_dataset.data, 'eval.parquet') ``` while using 1.6.1. and its working with 1.5.0
Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
96
MemoryError when computing WER metric Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function. @lhoestq i was using Datasets==1.5.0 with 1.6.1 it worked (atleast the first run) but 1.5.0 is not compatible with my preprocessing. i cant save my dataset to a parquet file while using the latest datasets version -> ``` File "../preprocess_dataset.py", line 132, in <module> pq.write_table(train_dataset.data, f'{resampled_data_dir}/{data_args.dataset_config_name}.train.parquet') File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 1674, in write_table writer.write_table(table, row_group_size=row_group_size) File "/usr/local/lib/python3.8/dist-packages/pyarrow/parquet.py", line 588, in write_table self.writer.write_table(table, row_group_size=row_group_size) TypeError: Argument 'table' has incorrect type (expected pyarrow.lib.Table, got ConcatenationTable) ``` if i do ``` import pyarrow.parquet as pq ... ... pq.write_table(train_dataset.data, 'train.parquet') pq.write_table(eval_dataset.data, 'eval.parquet') ``` while using 1.6.1. and its working with 1.5.0
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
Hi @XuhuiZhou, thanks for reporting this issue. Indeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
32
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? Hi @XuhuiZhou, thanks for reporting this issue. Indeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.
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https://github.com/huggingface/datasets/issues/2076
Issue: Dataset download error
It would be nice to update the urls indeed ! To do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with ``` datasets-cli test ./datasets/iwslt2017 --all_configs --save_infos --ignore_verifications ```
The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?
37
Issue: Dataset download error The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link? It would be nice to update the urls indeed ! To do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with ``` datasets-cli test ./datasets/iwslt2017 --all_configs --save_infos --ignore_verifications ```
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