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1,285,023,629
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https://api.github.com/repos/huggingface/datasets/issues/4573/labels{/name}
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This PR fixes the task in the evaluation metadata and removes the metrics info as we've decided this is not a great way to propagate this information downstream.
|
2023-09-24T09:35:07Z
| 4,573
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2022-06-26T20:29:32Z
|
https://api.github.com/repos/huggingface/datasets/issues/4573/comments
| null |
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Fix evaluation metadata for ncbi_disease
|
https://api.github.com/repos/huggingface/datasets/issues/4573/events
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"_The documentation is not available anymore as the PR was closed or merged._",
"As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets."
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### Link
https://huggingface.co/datasets/mlsum/viewer/de/train
### Description
There's seems to be a problem with the download / streaming of this dataset:
```
Server error
Status code: 400
Exception: BadZipFile
Message: File is not a zip file
```
### Owner
No
|
2022-07-21T12:40:01Z
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2022-06-26T20:24:17Z
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Dataset Viewer issue for mlsum
|
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2022-07-21T12:40:01Z
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|
[
"Thanks for reporting, @lewtun.\r\n\r\nAfter investigation, it seems that the server https://gitlab.lip6.fr does not allow HTTP Range requests.\r\n\r\nWe are trying to find a workaround..."
] |
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### Link
https://huggingface.co/datasets/gsarti/flores_101
### Description
It seems like streaming isn't supported for this dataset:
```
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol for TAR archives like 'https://dl.fbaipublicfiles.com/flores101/dataset/flores101_dataset.tar.gz' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead.
```
### Owner
No
|
2023-09-25T12:05:18Z
| 4,571
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https://api.github.com/repos/huggingface/datasets
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2022-06-26T11:19:09Z
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https://api.github.com/repos/huggingface/datasets/issues/4571/comments
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move under the facebook org?
|
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[
"Related to https://github.com/huggingface/datasets/issues/4562#issuecomment-1166911751\r\n\r\nI'll assign @albertvillanova ",
"I'm just wondering why we don't have this dataset under:\r\n- the `facebook` namespace\r\n- or the canonical dataset `flores`: why does this only have 2 languages?",
"fwiw: the dataset viewer is working. Renaming the issue"
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## Describe the bug
I'm not sure if this is a bug; more likely normal behavior but i wanted to double check.
Is it normal that `datasets.shard` does not produce chunks that, when concatenated produce the original ordering of the sharded dataset?
This might be related to this pull request (https://github.com/huggingface/datasets/pull/4466) but I have to admit I did not properly look into the changes made.
## Steps to reproduce the bug
```python
max_shard_size = convert_file_size_to_int('300MB')
dataset_nbytes = dataset.data.nbytes
num_shards = int(dataset_nbytes / max_shard_size) + 1
num_shards = max(num_shards, 1)
print(f"{num_shards=}")
for shard_index in range(num_shards):
shard = dataset.shard(num_shards=num_shards, index=shard_index)
shard.to_parquet(f"tokenized/tokenized-{shard_index:03d}.parquet")
os.listdir('tokenized/')
```
## Expected results
I expected the shards to match the order of the data of the original dataset; i.e. `dataset[10]` being the same as `shard_1[10]` for example
## Actual results
Only the first element is the same; i.e. `dataset[0]` is the same as `shard_1[0]`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.15.0-176-generic-x86_64-with-glibc2.31
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
|
2022-06-30T11:00:47Z
| 4,570
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2022-06-26T08:34:05Z
|
https://api.github.com/repos/huggingface/datasets/issues/4570/comments
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Dataset sharding non-contiguous?
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CONTRIBUTOR
|
2022-06-26T14:36:20Z
| null |
I_kwDODunzps5MlTJY
|
[
"This was silly; I was sure I'd looked for a `contiguous` argument, and was certain there wasn't one the first time I looked :smile:\r\n\r\nSorry about that.",
"Hi! You can pass `contiguous=True` to `.shard()` get contiguous shards. More info on this and the default behavior can be found in the [docs](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.shard).\r\n\r\nEDIT: Answered as you closed the thread 😄 ",
"Hahaha I'm sorry; my excuse is: it's Sunday. (Which makes me all the more grateful for your response :smiley: ",
"@mariosasko Sorry for reviving this, but I was curious as to why `contiguous=False` was the default. This might be a personal bias, but I feel that a user would expect the opposite to be the default. :thinking: ",
"This project started as a fork of TFDS, and `contiguous=False` is the default behavior [there](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard)."
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### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No
|
2022-06-27T06:37:48Z
| 4,569
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2022-06-26T07:32:54Z
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Dataset Viewer issue for sst2
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MEMBER
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2022-06-27T06:37:48Z
| null |
I_kwDODunzps5MlQGe
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[
"Hi @lewtun, thanks for reporting.\r\n\r\nI have checked locally and refreshed the preview and it seems working smooth now:\r\n```python\r\nIn [8]: ds\r\nOut[8]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'sentence', 'label'],\r\n num_rows: 67349\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'sentence', 'label'],\r\n num_rows: 872\r\n })\r\n test: Dataset({\r\n features: ['idx', 'sentence', 'label'],\r\n num_rows: 1821\r\n })\r\n})\r\n```\r\n\r\nCould you confirm? ",
"Thanks @albertvillanova - it is indeed working now (not sure what caused the error in the first place). Closing this :)"
] |
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### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
```
|
2022-07-04T14:29:40Z
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2022-06-25T16:43:56Z
|
https://api.github.com/repos/huggingface/datasets/issues/4568/comments
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XNLI cache reload is very slow
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|
CONTRIBUTOR
|
2022-07-04T14:29:40Z
| null |
I_kwDODunzps5MkkoI
|
[
"Hi,\r\nCould you tell us how you are running this code?\r\nI tested on my machine (M1 Mac). And it is running fine both on and off internet.\r\n\r\n<img width=\"1033\" alt=\"Screen Shot 2022-07-03 at 1 32 25 AM\" src=\"https://user-images.githubusercontent.com/8711912/177026364-4ad7cedb-e524-4513-97f7-7961bbb34c90.png\">\r\nTested on both stable and dev version. ",
"Sure, I was running it on a Linux machine.\r\nI found that if I turn the Internet off, it would still try to make a HTTPS call which would slow down the cache loading. If you can't reproduce then we can close the issue.",
"Hi @Muennighoff! You can set the env variable `HF_DATASETS_OFFLINE` to `1` to avoid this behavior in offline mode. More info is available [here](https://huggingface.co/docs/datasets/master/en/loading#offline)."
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2023-09-24T09:35:22Z
| 4,567
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2022-06-25T09:40:52Z
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https://api.github.com/repos/huggingface/datasets/issues/4567/comments
| null |
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Add evaluation data for amazon_reviews_multi
|
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MEMBER
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2022-09-23T09:37:23Z
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PR_kwDODunzps46Wh0-
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets."
] |
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## Describe the bug
A clear and concise description of what the bug is.
The [load_dataset_enhancing_performance](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#load_dataset_enhancing_performance) link [here](https://huggingface.co/docs/datasets/v2.3.2/en/package_reference/main_classes#datasets.Dataset.load_from_disk.keep_in_memory) points to nowhere, I guess it should point to https://huggingface.co/docs/datasets/v2.3.2/en/cache#improve-performance?
|
2023-01-24T16:33:40Z
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2022-06-25T01:18:19Z
|
https://api.github.com/repos/huggingface/datasets/issues/4566/comments
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Document link #load_dataset_enhancing_performance points to nowhere
|
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NONE
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2023-01-24T16:33:40Z
| null |
I_kwDODunzps5Mjloa
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[
"Hi! This is indeed the link the docstring should point to. Are you interested in submitting a PR to fix this?",
"https://github.com/huggingface/datasets/blame/master/docs/source/cache.mdx#L93\r\n\r\nThere seems already an anchor here. Somehow it doesn't work. I am not very familiar with how this online documentation works."
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## Adding a Dataset
- **Name:** UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset (v4)
- **Description:** The UFSC OCPap dataset comprises 9,797 labeled images of 1200x1600 pixels acquired from 5 slides of cancer diagnosed and 3 healthy of oral brush samples, from distinct patients.
- **Paper:** https://dx.doi.org/10.2139/ssrn.4119212
- **Data:** https://data.mendeley.com/datasets/dr7ydy9xbk/1
- **Motivation:** real data of pap stained oral cytology samples
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2022-07-06T19:03:02Z
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2022-06-24T20:07:54Z
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Add UFSC OCPap dataset
|
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2022-07-06T19:03:02Z
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Support streaming bookcorpus dataset.
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Support streaming allocine dataset.
Fix #4562.
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### Link
https://huggingface.co/datasets/allocine
### Description
Not sure if this is a problem with `bz2` compression, but I thought these datasets could be streamed:
```
Status code: 400
Exception: AttributeError
Message: 'TarContainedFile' object has no attribute 'readable'
```
### Owner
No
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2022-06-27T06:39:32Z
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"I removed my assignment as @huggingface/datasets should be able to answer better than me\r\n",
"Let me have a look...",
"Thanks for the quick fix @albertvillanova ",
"Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content *sequentially* (no random access).",
"> Note that the underlying issue is that datasets containing TAR files are not streamable out of the box: they need being iterated with `dl_manager.iter_archive` to avoid performance issues because they access their file content _sequentially_ (no random access).\r\n\r\nAh thanks for the clarification! I'll look out for this next time and implement the fix myself :)"
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"As discussed with @lewtun, we are closing this PR, because it requires first the task names to be aligned between AutoTrain and datasets."
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Just to confirm: we should add this metadata via GitHub and not Hub PRs for canonical datasets right?
|
2023-09-24T09:35:49Z
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2022-06-24T09:04:23Z
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Add evaluation metadata to wmt16
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"> Just to confirm: we should add this metadata via GitHub and not Hub PRs for canonical datasets right?\r\n\r\nyes :)",
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### Link
https://huggingface.co/datasets/conll2003/viewer/conll2003/test
### Description
Seems like a cache problem with this config / split:
```
Server error
Status code: 400
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/conll2003/__init__.py'
```
### Owner
No
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2022-06-24T09:50:39Z
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Dataset Viewer issue for conll2003
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### Link
https://huggingface.co/datasets/xtreme/viewer/PAN-X.de/test
### Description
There seems to be a problem with the cache of this config / split:
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Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/cache/modules/datasets_modules/datasets/xtreme/349258adc25bb45e47de193222f95e68a44f7a7ab53c4283b3f007208a11bf7e/xtreme.py'
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### Owner
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Dataset Viewer issue for xtreme
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This PR is a fix for #4354
Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.
Let me know, if any additional changes are required.
Thanks
|
2022-07-08T15:39:20Z
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Fix WMT dataset loading issue and docs update (Re-opened)
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CONTRIBUTOR
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2022-07-08T15:27:44Z
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`to_tf_dataset()` dropped unnecessary columns before loading batches from the dataset, but this is causing problems when using a transform, because the dropped columns might be needed to compute the transform. Since there's no real way to check which columns the transform might need, we skip dropping columns and instead drop keys from the batch after we load it.
cc @amyeroberts and https://github.com/huggingface/notebooks/pull/202
|
2022-07-04T19:00:13Z
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2022-06-23T18:21:05Z
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Stop dropping columns in to_tf_dataset() before we load batches
|
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2022-07-04T18:49:01Z
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq Rebasing fixed the test failures, so this should be ready to review now! There's still a failure on Win but it seems unrelated.",
"Gentle ping @lhoestq ! This is a simple fix (dropping columns after loading a batch from the dataset rather than with `.remove_columns()` to make sure we don't break transforms), and tests are green so we're ready for review!",
"@lhoestq Test is in!"
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As noted in https://github.com/huggingface/datasets/pull/4534, it is still not clear that it is recommended to add datasets on the Hugging Face Hub directly instead of GitHub, so I updated some docs.
Moreover since users won't be able to get reviews from us on the Hub, I added a paragraph to tell users that they can open a discussion and tag `datasets` maintainers for reviews.
Finally I removed the _previous good reasons_ to add a dataset on GitHub to only keep this one:
> In some rare cases it makes more sense to open a PR on GitHub. For example when you are not the author of the dataset and there is no clear organization / namespace that you can put the dataset under.
Does it sound good to you @albertvillanova @julien-c ?
|
2022-06-26T15:49:46Z
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https://api.github.com/repos/huggingface/datasets/issues/4552/comments
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Tell users to upload on the hub directly
|
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2022-06-26T15:39:11Z
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks ! I updated the two remaining files"
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Fix #4549
|
2022-06-30T14:49:20Z
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2022-06-23T14:49:11Z
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Perform hidden file check on relative data file path
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2022-06-30T14:38:18Z
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"I'm aware of this behavior, which is tricky to solve due to fsspec's hidden file handling (see https://github.com/huggingface/datasets/issues/4115#issuecomment-1108819538). I've tested some regex patterns to address this, and they seem to work (will push them on Monday; btw they don't break any of fsspec's tests, so maybe we can contribute this as an enhancement to them). Also, perhaps we should include the files starting with `__` in the results again (we hadn't had issues with this pattern before). WDYT?",
"I see. Feel free to merge this one if it's good for you btw :)\r\n\r\n> Also, perhaps we should include the files starting with __ in the results again (we hadn't had issues with this pattern before)\r\n\r\nThe point was mainly to ignore `__pycache__` directories for example. Also also for consistency with the iter_files/iter_archive which are already ignoring them",
"Very elegant solution! Feel free to merge if the CI is green after adding the tests.",
"CI failure is unrelated to this PR"
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## Describe the bug
imdb dataset not loading
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("imdb")
```
## Expected results
## Actual results
```bash
06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source
06/23/2022 14:46:34 - INFO - datasets.utils.file_utils - HEAD request to http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz timed out, retrying... [1.0]
.....
ConnectionError: Couldn't reach http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz (ConnectTimeout(MaxRetryError("HTTPConnectionPool(host='ai.stanford.edu', port=80): Max retries exceeded with url: /~amaas/data/sentiment/aclImdb_v1.tar.gz (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7f2d750cf690>, 'Connection to ai.stanford.edu timed out. (connect timeout=100)'))")))
```
## Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
|
2022-06-23T13:47:05Z
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2022-06-23T13:02:52Z
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https://api.github.com/repos/huggingface/datasets/issues/4550/comments
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imdb source error
|
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NONE
|
2022-06-23T13:47:04Z
| null |
I_kwDODunzps5Mb3sp
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[
"Thanks for reporting, @Muhtasham.\r\n\r\nIndeed IMDB dataset is not accessible from yesterday, because the data is hosted on the data owners servers at Stanford (http://ai.stanford.edu/) and these are down due to a power outage originated by a fire: https://twitter.com/StanfordAILab/status/1539472302399623170?s=20&t=1HU1hrtaXprtn14U61P55w\r\n\r\nAs a temporary workaroud, you can load the IMDB dataset with this tweak:\r\n```python\r\nds = load_dataset(\"imdb\", revision=\"tmp-fix-imdb\")\r\n```\r\n"
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Bug experienced in the `accelerate` CI: https://github.com/huggingface/accelerate/runs/7016055148?check_suite_focus=true
This is related to https://github.com/huggingface/datasets/pull/4505 and the changes from https://github.com/huggingface/datasets/pull/4412
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2022-06-30T14:38:18Z
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FileNotFoundError when passing a data_file inside a directory starting with double underscores
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MEMBER
|
2022-06-30T14:38:18Z
| null |
I_kwDODunzps5MbosP
|
[
"I have consistently experienced this bug on GitHub actions when bumping to `2.3.2`",
"We're working on a fix ;)"
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If data contains a single `metadata.jsonl` file for several splits, it won't be included in a dataset's `data_files` and therefore ignored.
This happens when a directory is structured like as follows:
```
train/
file_1.jpg
file_2.jpg
test/
file_3.jpg
file_4.jpg
metadata.jsonl
```
or like as follows:
```
train_file_1.jpg
train_file_2.jpg
test_file_3.jpg
test_file_4.jpg
metadata.jsonl
```
The same for HF repos.
because it's ignored by the patterns [here](https://github.com/huggingface/datasets/blob/master/src/datasets/data_files.py#L29)
@lhoestq @mariosasko Do you think it's better to add this functionality in `data_files.py` or just specifically in imagefolder/audiofolder code? In `data_files.py` would me more general but I don't know if there are any other cases when that might be needed.
|
2022-06-30T10:15:32Z
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Metadata.jsonl for Imagefolder is ignored if it's in a parent directory to the splits directories/do not have "{split}_" prefix
|
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CONTRIBUTOR
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2022-06-30T10:15:32Z
| null |
I_kwDODunzps5MbRhw
|
[
"I agree it would be nice to support this. It doesn't fit really well in the current data_files.py, where files of each splits are separated in different folder though, maybe we have to modify a bit the logic here. \r\n\r\nOne idea would be to extend `get_patterns_in_dataset_repository` and `get_patterns_locally` to additionally check for `metadata.json`, but feel free to comment if you have better ideas (I feel like we're reaching the limits of what the current implementation IMO, so we could think of a different way of resolving the data files if necessary)"
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There are some warnings in the CI that are annoying, I tried to remove most of them
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2022-06-28T14:10:57Z
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[CI] Fix some warnings
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"There is a CI failure only related to the missing content of the universal_dependencies dataset card, we can ignore this failure in this PR",
"good catch, I thought I resolved them all sorry",
"Alright it should be good now"
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The latest setuptools-scm version supported on 3.6 is 6.4.2. However for some reason circleci has version 7, which doesn't work.
I fixed this by pinning the version of setuptools-scm in the circleci job
Fix https://github.com/huggingface/datasets/issues/4544
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[CI] fixing seqeval install in ci by pinning setuptools-scm
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# What does this PR do?
## Summary
*DuplicateKeysError error does not provide any information regarding the examples which have the same the key.*
*This information is very helpful for debugging the dataset generator script.*
## Additions
-
## Changes
- Changed `DuplicateKeysError Class` in `src/datasets/keyhash.py` to add current index and duplicate_key_indices to error message.
- Changed `check_duplicate_keys` function in `src/datasets/arrow_writer.py` to find indices of examples with duplicate hash if duplicate keys are found.
## Deletions
-
## To do :
- [x] Find way to find and print path `<Path to Dataset>` in Error message
## Issues Addressed :
Fixes #2556
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Make DuplicateKeysError more user friendly [For Issue #2556]
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"> Nice thanks !\r\n> \r\n> After your changes feel free to mark this PR as \"ready for review\" ;)\r\n\r\nMarking PR ready for review.\r\n\r\n@lhoestq Let me know if there is anything else required or if we are good to go ahead and merge.",
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The CI sometimes fails to install seqeval, which cause the `seqeval` metric tests to fail.
The installation fails because of this error:
```
Collecting seqeval
Downloading seqeval-1.2.2.tar.gz (43 kB)
|███████▌ | 10 kB 42.1 MB/s eta 0:00:01
|███████████████ | 20 kB 53.3 MB/s eta 0:00:01
|██████████████████████▌ | 30 kB 67.2 MB/s eta 0:00:01
|██████████████████████████████ | 40 kB 76.1 MB/s eta 0:00:01
|████████████████████████████████| 43 kB 10.0 MB/s
Preparing metadata (setup.py) ... - error
ERROR: Command errored out with exit status 1:
command: /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"'; __file__='"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-pf54_vqy
cwd: /tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/
Complete output (22 lines):
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py", line 56, in <module>
'Programming Language :: Python :: Implementation :: PyPy'
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/__init__.py", line 143, in setup
return distutils.core.setup(**attrs)
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/core.py", line 108, in setup
_setup_distribution = dist = klass(attrs)
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 442, in __init__
k: v for k, v in attrs.items()
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/dist.py", line 281, in __init__
self.finalize_options()
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 601, in finalize_options
ep.load()(self, ep.name, value)
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2346, in load
return self.resolve()
File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2352, in resolve
module = __import__(self.module_name, fromlist=['__name__'], level=0)
File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/.eggs/setuptools_scm-7.0.2-py3.6.egg/setuptools_scm/__init__.py", line 5
from __future__ import annotations
^
SyntaxError: future feature annotations is not defined
----------------------------------------
WARNING: Discarding https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz#sha256=f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f (from https://pypi.org/simple/seqeval/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
```
for example in https://app.circleci.com/pipelines/github/huggingface/datasets/12665/workflows/93878eb9-a923-4b35-b2e7-c5e9b22f10ad/jobs/75300
Here is a diff of the pip install logs until the error is reached: https://www.diffchecker.com/VkQDLeQT
This could be caused by the latest updates of setuptools-scm
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[CI] seqeval installation fails sometimes on python 3.6
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Some tests were still using moon-stagign instead of hub-ci.
I also updated the token to use one dedicated to `datasets`
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[CI] Fix upstream hub test url
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To have better performance in TensorFlow, it is important to provide lists of data files in supported formats. For example sharded TFRecords datasets are extremely performant. This is because tf.data can better leverage parallelism in this case, and load one file at a time in memory.
It seems that using `tensorflow_io` we could have something similar for `to_tf_dataset` if we provide sharded Feather files: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowFeatherDataset
Feather is a format almost equivalent to the Arrow IPC Stream format we're using in `datasets`: Feather V2 is equivalent to Arrow IPC File format, which is an extension of the stream format (it has an extra footer). Therefore we could store datasets as Feather instead of Arrow IPC Stream format without breaking the whole library.
Here are a few points to explore
- [ ] check the performance of ArrowFeatherDataset in tf.data
- [ ] check what would change if we were to switch to Feather if needed, in particular check that those are fine: memory mapping, typing, writing, reading to python objects, etc.
We would also need to implement sharding when loading a dataset (this will be done anyway for #546)
cc @Rocketknight1 @gante feel free to comment in case I missed anything !
I'll share some files and scripts, so that we can benchmark performance of Feather files with tf.data
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2022-10-11T08:45:45Z
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[to_tf_dataset] Use Feather for better compatibility with TensorFlow ?
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"This has so much potential to be great! Also I think you tagged some poor random dude on the internet whose name is also Joao, lol, edited that for you! ",
"cc @sayakpaul here too, since he was interested in our new approaches to converting datasets!",
"Noted and I will look into the thread in detail tomorrow once I log back in. ",
"@lhoestq I have used TFRecords with `tf.data` for both vision and text and I can say that they are quite performant. I haven't worked with Feather yet as similarly as I have with TFRecords. If you haven't started the benchmarking script yet, I can prepare a Colab notebook that loads Feather files, converts them into a `tf.data` pipeline, and does some basic preprocessing. \r\n\r\nBut in my limited understanding, Feather might be better suited for CSV files. Not yet sure if it's good for modalities like images. ",
"> Not yet sure if it's good for modalities like images.\r\n\r\nWe store images pretty much the same way as tensorflow_datasets (i.e. storing the encoded image bytes, or a path to the local image, so that the image can be decoded on-the-fly), so as long as we use something similar as TFDS for image decoding it should be ok",
"So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly? But it introduces an I/O redundancy of having to read the images every time.\r\n\r\nWith caching it could be somewhat mitigated but it's not a good solution for bigger image datasets. ",
"> So for image datasets, we could potentially store the paths in the feather format and decode and read them on the fly?\r\n\r\nhopefully yes :) \r\n\r\nI double-checked the TFDS source code and they always save the bytes actually, not the path. Anyway we'll see if we run into issues or not (as a first step we can require the bytes to be in the feather file)",
"Yes. For images, TFDS actually prepares TFRecords first for encoding and then reuses them for every subsequent call. ",
"@lhoestq @Rocketknight1 I worked on [this PoC](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59) that\r\n\r\n* Creates Feather files from a medium resolution dataset (`tf_flowers`).\r\n* Explores different options with TensorFlow IO to load the Feather files. \r\n\r\nI haven't benchmarked those different options yet. There's also a gotcha that I have noted in the PoC. I hope it gets us started but I'm sorry if this is redundant. ",
"Cool thanks ! If I understand correctly in your PoC you store the flattened array of pixels in the feather file. This will take a lot of disk space.\r\n\r\nMaybe we could just save the encoded bytes and let users apply a `map` to decode/transform them into the format they need for training ? Users can use tf.image to do so for example",
"@lhoestq this is what I tried:\r\n\r\n```py\r\ndef read_image(path):\r\n with open(path, \"rb\") as f:\r\n return f.read()\r\n\r\n\r\ntotal_images_written = 0\r\n\r\nfor step in tqdm.tnrange(int(math.ceil(len(image_paths) / batch_size))):\r\n batch_image_paths = image_paths[step * batch_size : (step + 1) * batch_size]\r\n batch_image_labels = all_integer_labels[step * batch_size : (step + 1) * batch_size]\r\n\r\n data = [read_image(path) for path in batch_image_paths]\r\n table = pa.Table.from_arrays([data, batch_image_labels], [\"data\", \"labels\"])\r\n write_feather(table, f\"/tmp/flowers_feather_{step}.feather\", chunksize=chunk_size)\r\n total_images_written += len(batch_image_paths)\r\n print(f\"Total images written: {total_images_written}.\")\r\n\r\n del data\r\n```\r\n\r\nI got the feather files done (no resizing required as you can see):\r\n\r\n```sh\r\nls -lh /tmp/*.feather\r\n\r\n-rw-r--r-- 1 sayakpaul wheel 64M Jun 24 09:28 /tmp/flowers_feather_0.feather\r\n-rw-r--r-- 1 sayakpaul wheel 59M Jun 24 09:28 /tmp/flowers_feather_1.feather\r\n-rw-r--r-- 1 sayakpaul wheel 51M Jun 24 09:28 /tmp/flowers_feather_2.feather\r\n-rw-r--r-- 1 sayakpaul wheel 45M Jun 24 09:28 /tmp/flowers_feather_3.feather\r\n```\r\n\r\nNow there seems to be a problem with `tfio.arrow`:\r\n\r\n```py\r\nimport tensorflow_io.arrow as arrow_io\r\n\r\n\r\ndataset = arrow_io.ArrowFeatherDataset(\r\n [\"/tmp/flowers_feather_0.feather\"],\r\n columns=(0, 1),\r\n output_types=(tf.string, tf.int64),\r\n output_shapes=([], []),\r\n batch_mode=\"auto\",\r\n)\r\n\r\nprint(dataset.element_spec) \r\n```\r\n\r\nPrints:\r\n\r\n```\r\n(TensorSpec(shape=(None,), dtype=tf.string, name=None),\r\n TensorSpec(shape=(None,), dtype=tf.int64, name=None))\r\n```\r\n\r\nBut when I do `sample = next(iter(dataset))` it goes into:\r\n\r\n```py\r\nInternalError Traceback (most recent call last)\r\nInput In [30], in <cell line: 1>()\r\n----> 1 sample = next(iter(dataset))\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:766, in OwnedIterator.__next__(self)\r\n 764 def __next__(self):\r\n 765 try:\r\n--> 766 return self._next_internal()\r\n 767 except errors.OutOfRangeError:\r\n 768 raise StopIteration\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/data/ops/iterator_ops.py:749, in OwnedIterator._next_internal(self)\r\n 746 # TODO(b/77291417): This runs in sync mode as iterators use an error status\r\n 747 # to communicate that there is no more data to iterate over.\r\n 748 with context.execution_mode(context.SYNC):\r\n--> 749 ret = gen_dataset_ops.iterator_get_next(\r\n 750 self._iterator_resource,\r\n 751 output_types=self._flat_output_types,\r\n 752 output_shapes=self._flat_output_shapes)\r\n 754 try:\r\n 755 # Fast path for the case `self._structure` is not a nested structure.\r\n 756 return self._element_spec._from_compatible_tensor_list(ret) # pylint: disable=protected-access\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3017, in iterator_get_next(iterator, output_types, output_shapes, name)\r\n 3015 return _result\r\n 3016 except _core._NotOkStatusException as e:\r\n-> 3017 _ops.raise_from_not_ok_status(e, name)\r\n 3018 except _core._FallbackException:\r\n 3019 pass\r\n\r\nFile ~/.local/bin/.virtualenvs/jax/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:7164, in raise_from_not_ok_status(e, name)\r\n 7162 def raise_from_not_ok_status(e, name):\r\n 7163 e.message += (\" name: \" + name if name is not None else \"\")\r\n-> 7164 raise core._status_to_exception(e) from None\r\n\r\nInternalError: Invalid: INVALID_ARGUMENT: arrow data type 0x7ff9899d8038 is not supported: Type error: Arrow data type is not supported [Op:IteratorGetNext]\r\n```\r\n\r\nSome additional notes:\r\n\r\n* I can actually decode an image encoded with `read_image()` (shown earlier):\r\n\r\n ```py\r\n sample_image_path = image_paths[0]\r\n encoded_image = read_image(sample_image_path)\r\n image = tf.image.decode_png(encoded_image, 3)\r\n print(image.shape)\r\n ```\r\n\r\n* If the above `tf.data.Dataset` object would have succeeded my plan was to just map the decoder like so:\r\n\r\n ```py\r\n autotune = tf.data.AUTOTUNE\r\n dataset = dataset.map(lambda x, y: (tf.image.decode_png(x, 3), y), num_parallel_calls=autotune)\r\n ```",
"@lhoestq I think I was able to make it work in the way you were envisioning. Here's the PoC:\r\nhttps://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb\r\n\r\nSome details:\r\n\r\n* I am currently serializing the images as strings with `base64`). In comparison to the flattened arrays as before, the size of the individual feather files has reduced (144 MB -> 85 MB, largest).\r\n* When decoding, I am first decoding the base64 string and then decoding that string (with `tf.io.decode_base64`) as an image with `tf.image.decode_png()`. \r\n* The entire workflow (from generating the Feather files to loading them and preparing the batched `tf.data` pipeline) involves the following libraries: `pyarraow`, `tensorflow-io`, and `tensorflow`. \r\n\r\nCc: @Rocketknight1 @gante ",
"Cool thanks ! Too bad the Arrow binary type doesn't seem to be supported in `arrow_io.ArrowFeatherDataset` :/ We would also need it to support Arrow struct type. Indeed images in `datasets` are represented using an Arrow type\r\n```python\r\npa.struct({\"path\": pa.string(), \"bytes\": pa.binary()})\r\n```\r\nnot sure yet how hard it is to support this though.\r\n\r\nChanging the typing on our side would create concerning breaking changes, that's why it would be awesome if it could work using these types",
"If the ArrowFeatherDataset doesn't yet support it, I guess our hands are a bit tied at the moment. \r\n\r\nIIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?\r\n\r\n```\r\npa.struct({\"path\": pa.string(), \"bytes\": pa.binary()})\r\n``` \r\n\r\nIn that case, `pa.binary()` isn't yet supported.",
"> IIUC, in my [latest PoC notebook](https://gist.github.com/sayakpaul/f7d5cc312cd01cb31098fad3fd9c6b59#file-feather-tf-poc-bytes-ipynb), you wanted to see each entry in the feather file to be represented like so?\r\n> \r\n> pa.struct({\"path\": pa.string(), \"bytes\": pa.binary()})\r\n\r\nYea because that's the data format we're using. If we were to use base64, then we would have to process the full dataset to convert it, which can take some time. Converting to TFRecords would be simpler than converting to base64 in Feather files.\r\n\r\nMaybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset. What do you think ? Any other alternative in mind ?",
"> Maybe it would take too much time to be worth exploring, but according to https://github.com/tensorflow/io/issues/1361#issuecomment-819029002 it's possible to add support for binary type in ArrowFeatherDataset.\r\n\r\nShould be possible as per the comment but there hasn't been any progress and it's been more than a year. \r\n\r\n> If we were to use base64, then we would have to process the full dataset to convert it, which can take some time.\r\n\r\nI don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from. \r\n\r\n> What do you think ? Any other alternative in mind ?\r\n\r\nTFRecords since the TensorFlow ecosystem has developed good support for it over the years. ",
"> I don't understand this. I would think TFRecords would also need something similar but I need the context you're coming from.\r\n\r\nUsers already have a copy of the dataset in Arrow format (we can change this to Feather). So to load the Arrow/feather files to a TF dataset we need TF IO or something like that. Otherwise the user has to convert all the files from Arrow to TFRecords to use TF data efficiently. But the conversion needs resources: CPU, disk, time. Converting the images to base64 require the same sort of resources.\r\n\r\nSo the issue we're trying to tackle is how to load the Arrow data in TF without having to convert anything ^^",
"Yeah, it looks like in its current state the tfio support for `Feather` is incomplete, so we'd end up having to write a lot of it, or do a conversion that defeats the whole point (because if we're going to convert the whole dataset we might as well convert to `TFRecord`).",
"Understood @lhoestq. Thanks for explaining!\r\n\r\nAgreed with @Rocketknight1. ",
"@lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there?",
"> @lhoestq Although I think this is a dead-end for now unfortunately, because of the limitations at TF's end, we could still explore automatic conversion to TFRecord, or I could dive into refining `to_tf_dataset()` to yield unbatched samples and/or load samples with multiprocessing to improve throughput. Do you have any preferences there?\r\n\r\nHappy to take part there @Rocketknight1.",
"If `to_tf_dataset` can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?",
"@lhoestq why one would convert to TFRecords after unbatching? ",
"> If to_tf_dataset can be unbatched, then it should be fairly easy for users to convert the TF dataset to TFRecords right ?\r\n\r\nSort of! A `tf.data.Dataset` is more like an iterator, and does not support sample indexing. `to_tf_dataset()` creates an iterator, but to convert that to `TFRecord`, the user would have to iterate over the whole thing and manually save the stream of samples to files. ",
"Someone would like to try to dive into tfio to fix this ? Sounds like a good opportunity to learn what are the best ways to load a dataset for TF, and also the connections between Arrow and TF.\r\n\r\nIf we can at least have the Arrow `binary` type working for TF that would be awesome already (issue https://github.com/tensorflow/io/issues/1361)\r\n\r\nalso cc @nateraw in case you'd be interested ;)",
"> Sounds like a good opportunity to learn what are the best ways to load a dataset for TF\r\n\r\nThe recommended way would likely be a combination of TFRecords and `tf.data`. \r\n\r\nExploring the connection between Arrow and TensorFlow is definitely worth pursuing though. But I am not sure about the implications of storing images in a format supported by Arrow. I guess we'll know more once we have at least figured out the support for `binary` type for TFIO. I will spend some time on it and keep this thread updated. ",
"I am currently working on a fine-tuning notebook for the TFSegFormer model (Semantic Segmentation). The resolution is high for both the input images and the labels - (512, 512, 3). Here's the [Colab Notebook](https://colab.research.google.com/drive/1jAtR7Z0lYX6m6JsDI5VByh5vFaNhHIbP?usp=sharing) (it's a WIP so please bear that in mind).\r\n\r\nI think the current implementation of `to_tf_dataset()` does create a bottleneck here since the GPU utilization is quite low. ",
"Here's a notebook showing the performance difference: https://colab.research.google.com/gist/sayakpaul/d7ca67c90beb47e354942c9d8c0bd8ef/scratchpad.ipynb. \r\n\r\nNote that I acknowledge that it's not an apples-to-apples comparison in many aspects (the dataset isn't the same, data serialization format isn't the same, etc.) but this is the best I could do. ",
"Thanks ! I think the speed difference can be partly explained: you use ds.shuffle in your dataset, which is an exact shuffling (compared to TFDS which does buffer shuffling): it slows down query time by 2x to 10x since it has to play with data that are not contiguous.\r\n\r\nThe rest of the speed difference seems to be caused by image decoding (from 330µs/image to 30ms/image)",
"Fair enough. Can do one without shuffling too. But it's an important one to consider I guess. "
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Arrow accepts both pd.Timestamp and datetime.datetime objects to create timestamp arrays.
However a timestamp array is always converted to datetime.datetime objects.
This created an inconsistency between streaming in non-streaming. e.g. the `ett` dataset outputs datetime.datetime objects in non-streaming but pd.timestamp in streaming.
I fixed this by always converting pd.Timestamp to datetime.datetime during the example encoding step.
I fixed the same issue for pd.Timedelta as well. Finally I added an extra step of conversion for Series and DataFrame to take this into account in case such data are passed as Series or DataFrame.
Fix https://github.com/huggingface/datasets/issues/4533
Related to https://github.com/huggingface/datasets-server/issues/397
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2022-06-22T16:39:27Z
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Fix timestamp conversion from Pandas to Python datetime in streaming mode
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"_The documentation is not available anymore as the PR was closed or merged._",
"CI failures are unrelated to this PR, merging"
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Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.
Step to reproduce.
- If you have a home folder which ends with `.py`
- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line
` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .
Let me what are your thought on this and I can try to fix it by A PR.
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2022-07-07T13:17:44Z
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Avoid splitting by` .py` for the file.
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2022-07-07T13:17:44Z
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[
"Hi @espoirMur, thanks for reporting.\r\n\r\nYou are right: that code line could be improved and made more generically valid.\r\n\r\nOn the other hand, I would suggest using `os.path.splitext` instead.\r\n\r\nAre you willing to open a PR? :)",
"I will have a look.. \r\n\r\nThis weekend .. ",
"@albertvillanova , Can you have a look at #4590. \r\n\r\nThanks ",
"#self-assign"
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Replace `logging.warn` (deprecated in [Python 2.7, 2011](https://github.com/python/cpython/commit/04d5bc00a219860c69ea17eaa633d3ab9917409f)) with `logging.warning` (added in [Python 2.3, 2003](https://github.com/python/cpython/commit/6fa635df7aa88ae9fd8b41ae42743341316c90f7)).
* https://docs.python.org/3/library/logging.html#logging.Logger.warning
* https://github.com/python/cpython/issues/57444
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2022-06-22T13:43:23Z
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### Link
https://huggingface.co/datasets/pile-of-law/pile-of-law
### Description
Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?
Thanks so much!
### Owner
Yes
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Dataset Viewer issue for Pile of Law
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2022-06-26T22:26:22Z
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[
"Hi @Breakend, yes – we'll propose a solution today",
"Thanks so much, I appreciate it!",
"Thanks so much for adding the docs. I was able to successfully hide the viewer using the \r\n```\r\nviewer: false\r\n```\r\nflag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again!",
"Awesome! Thanks for confirming. cc @severo ",
"Just for the record:\r\n\r\n- the doc\r\n \r\n<img width=\"1430\" alt=\"Capture d’écran 2022-06-27 à 09 29 27\" src=\"https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png\">\r\n\r\n- the dataset main page\r\n\r\n<img width=\"1134\" alt=\"Capture d’écran 2022-06-27 à 09 29 05\" src=\"https://user-images.githubusercontent.com/1676121/175884152-5f285bf0-3471-45de-927a-e141b00ebb33.png\">\r\n\r\n- the dataset viewer page\r\n\r\n<img width=\"567\" alt=\"Capture d’écran 2022-06-27 à 09 29 16\" src=\"https://user-images.githubusercontent.com/1676121/175884191-ab6a297b-1c11-417e-bbde-0b7623278a79.png\">\r\n"
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This PR is a fix for #4354
Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.
As I am on a M1 Mac, I am not able to create a virtual `dev` environment using `pip install -e ".[dev]"`. Issue is with `tensorflow-text` not supported on M1s and there is no supporting repo by Apple or Google. So, if I was needed to perform local testing, I am not able to do that.
Let me know, if any additional changes are required.
Thanks
|
2022-06-24T07:05:43Z
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Fix WMT dataset loading issue and docs update
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2022-06-24T07:05:10Z
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[
"The PR branch now has some commits unrelated to the changes, probably due to rebasing. Can you please close this PR and open a new one from a new branch? You can use `git cherry-pick` to preserve the relevant changes:\r\n```bash\r\ngit checkout master\r\ngit remote add upstream git@github.com:huggingface/datasets.git\r\ngit pull --ff-only upstream master\r\ngit checkout -b wmt-datasets-fix2\r\ngit cherry-pick f2d6c995d5153131168f64fc60fe33a7813739a4 a9fdead5f435aeb88c237600be28eb8d4fde4c55\r\n```",
"Closing this PR due to unwanted commit changes. Will be opening new PR for the same issue."
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`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub.
Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"
|
2022-06-28T10:46:51Z
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Properly raise FileNotFound even if the dataset is private
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2022-06-28T10:36:10Z
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"_The documentation is not available anymore as the PR was closed or merged._"
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Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`.
This is useful so as to tweak the `batch_size` according to the VM specifications.
|
2022-06-27T16:25:09Z
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Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`
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2022-06-27T16:14:36Z
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"Also, I had a doubt while checking the code related to the indices... \r\n\r\n@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an `ArrowDataset` in disk, but since those indices are never stored AFAIK, is that actually required?\r\n\r\nhttps://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/config.py#L183\r\n\r\nhttps://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/arrow_dataset.py#L1079-L1092\r\n\r\nSo should I also remove that?\r\n\r\nP.S. I also edited the following code comment which I found misleading as it's not actually storing the indices.\r\n\r\nhttps://github.com/huggingface/datasets/blob/8ddc4bbeb1e2bd307b21f5d21f884649aa2bf640/src/datasets/arrow_dataset.py#L1122",
"_The documentation is not available anymore as the PR was closed or merged._",
"> @lhoestq, there's a value in config.py named DATASET_INDICES_FILENAME which has the arrow extension (which I assume it should be indices.faiss, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an ArrowDataset in disk, but since those indices are never stored AFAIK, is that actually required?\r\n\r\nThe arrow file is used to store an indices mapping (when you shuffle the dataset for example) - not for a faiss index ;)",
"Ok cool thanks a lot for the explanation @lhoestq I was not sure about that :+1: I'll also add it there as you suggested!",
"CI failures are unrelated to this PR and fixed on master, merging"
] |
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This PR aims at adding support for a news dataset: `tldr news`.
This dataset is based on the daily [tldr tech newsletter](https://tldr.tech/newsletter) and contains a `headline` as well as a `content` for every piece of news contained in a newsletter.
|
2022-06-23T14:33:54Z
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Add `tldr_news` dataset
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NONE
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2022-06-21T14:21:11Z
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[
"Hey @lhoestq, \r\nSorry for opening a PR, I was following the guide [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)! Thanks for the review anyway, I will follow the instructions you sent 😃 ",
"Thanks, we will update the guide ;)"
] |
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As reported in (internal) https://github.com/huggingface/datasets-server/issues/397
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("ett", name="h2", split="test", streaming=True)
>>> d = next(iter(dataset))
>>> d['start']
Timestamp('2016-07-01 00:00:00')
```
while loading in non-streaming mode it returns `datetime.datetime(2016, 7, 1, 0, 0)`
|
2022-06-22T16:29:09Z
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Timestamp not returned as datetime objects in streaming mode
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2022-06-22T16:29:09Z
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The following adds a `Video` feature for encoding/decoding videos on the fly from in memory bytes. It uses my own `encoded-video` library which is basically `pytorchvideo`'s encoded video but with all the `torch` specific stuff stripped out. Because of that, and because the tool I used under the hood is not very mature, I leave this as a draft idea that we can use to build off of.
|
2022-11-10T16:59:51Z
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2022-06-20T16:36:41Z
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https://api.github.com/repos/huggingface/datasets/issues/4532/comments
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Add Video feature
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CONTRIBUTOR
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2022-11-10T16:59:51Z
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PR_kwDODunzps459kB7
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4532). All of your documentation changes will be reflected on that endpoint.",
"@nateraw do you have any plans to continue this pr? Or should I write a custom loader script to use my video dataset in the hub?",
"@fcakyon I think we still want this feature in here, but my solution here isn't the right one, I'm afraid. Using my (very hacky) library is not the right move. Let's move to an issue to discuss the feature/workarounds for now. "
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### Link
https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin
### Description
I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well.
You can replicate the problem by simply uploading any CSV dataset.
### Owner
Yes
|
2022-06-21T08:28:46Z
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Dataset Viewer issue for CSV datasets
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CONTRIBUTOR
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2022-06-21T08:28:27Z
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[
"this should now be fixed",
"Confirmed, it's fixed now. Thanks for reporting, and thanks @coyotte508 for fixing it\r\n\r\n<img width=\"1123\" alt=\"Capture d’écran 2022-06-21 à 10 28 05\" src=\"https://user-images.githubusercontent.com/1676121/174753833-1b453a5a-6a90-4717-bca1-1b5fc6b75e4a.png\">\r\n"
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will close #3964
AudioFolder is almost identical to ImageFolder except for inferring labels is not the default behavior (`drop_labels` is set to True in config), the option of inferring them is preserved though.
The weird thing is happening with the `test_data_files_with_metadata_and_archives` when `streaming` is `True`. Here is the log from the CI:
```
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/datasets/features/audio.py:237: in _decode_non_mp3_path_like
array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/util/decorators.py:88: in inner_f
return f(*args, **kwargs)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:176: in load
raise (exc)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/librosa/core/audio.py:155: in load
context = sf.SoundFile(path)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:629: in __init__
self._file = self._open(file, mode_int, closefd)
../.pyenv/versions/3.6.15/lib/python3.6/site-packages/soundfile.py:1184: in _open
"Error opening {0!r}: ".format(self.name))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
err = 72
prefix = "Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: "
def _error_check(err, prefix=""):
"""Pretty-print a numerical error code if there is an error."""
if err != 0:
err_str = _snd.sf_error_number(err)
> raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
E RuntimeError: Error opening <zipfile.ZipExtFile name='audio_file.wav' mode='r' compress_type=deflate>: Error in WAV file. No 'data' chunk marker.
```
I hadn't been able to reproduce this locally until I created the same test environment (I mean with `pip install .[tests]`) with python3.6. The same env but with python3.8 passes the test! I didn't manage to figure out what's wrong, I also tried simply to replace the test wav file and still got the same error. Versions of `soundfile`, `librosa` and `libsndfile` are identical. Might it be something with zip compression? Sounds weird but I don't have any other ideas...
TODO:
- [x] align with #4622
- [x] documentation
- [x] tests for AutoFolder?
|
2022-08-22T14:36:49Z
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Add AudioFolder packaged loader
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CONTRIBUTOR
|
2022-08-22T14:20:40Z
|
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PR_kwDODunzps458n_S
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq @mariosasko I don't know what to do with the test, do you have any ideas? :)",
"also it's passed in `pyarrow_latest_WIN`",
"If the error only happens on 3.6, maybe #4460 can help ^^' It seems to work in 3.7 on the windows CI\r\n\r\n> inferring labels is not the default behavior (drop_labels is set to True in config)\r\n\r\nI think it a missed opportunity to have a consistent API between imagefolder and audiofolder, since they do everything the same way. Can you give more details why you think we should drop the labels by default ?",
"Considering audio classification in audio is not as common as image classification in image, I'm ok with having different config defaults as long as they are properly documented (check [Papers With Code](https://paperswithcode.com/datasets) for stats and compare the classification numbers to the other tasks, do this for both modalities)\r\n\r\nAlso, WDYT about creating a generic folder loader that ImageFolder and AudioFolder then subclass to avoid having to update both of them when there is something to update/fix?",
"@lhoestq I think it doesn't change the API itself, it just doesn't infer labels by default, but you can **still** set `drop_labels=False` to `load_dataset` and the labels will be inferred. \r\nSuppose that one has data structured as follows:\r\n```\r\ndata/\r\n train/\r\n audio/\r\n file1.wav\r\n file2.wav\r\n file3.wav\r\n metadata.jsonl\r\n test/\r\n audio/\r\n file1.wav\r\n file2.wav\r\n file3.wav\r\n metadata.jsonl\r\n```\r\nIf users load this dataset with `load_dataset(\"audiofolder\", data_dir=\"data\")` (the most native way), they will get a `label` feature that will always be equal to 0 (= \"audio\"). To mitigate this, they will have to always specify `load_dataset(\"audiofolder\", data_dir=\"data\", drop_labels=True)` explicitly and I believe it's not convenient. \r\n\r\nAt the same time, `label` column can be added just as easy as adding one argument:` load_dataset(\"audiofolder\", data_dir=\"data\", drop_labels=False)`. As classification task is not as common, I think it should require more symbols to be added to the code :D \r\n\r\nBut this is definitely should be explained in the docs, which I've forgotten to update... I'll add this section soon.\r\n\r\nAlso +to the generic loader, will work on it. \r\n\r\n",
"If a metadata.jsonl file is present, then it doesn't have to infer the labels I agree. Note that this is already the case for imagefolder ;) in your case `load_dataset(\"audiofolder\", data_dir=\"data\")` won't return labels !\r\n\r\nLabels are only inferred if there are no metadata.jsonl",
"Feel free to merge the `main` branch into yours after updating your fork of `datasets`: https://github.com/huggingface/datasets/issues/4629\r\n\r\nThis should fix some errors in the CI",
"@mariosasko could you please review this PR again? :)\r\n\r\nmost of the tests for AutoFolder (base class for AudioFolder and ImageFolder) are now basically copied from Image/AudioFolder (their tests are also almost identical too) and adapted to test other methods. it should be refactored but i think this is not that important for now and might be done in the future PR, wdyt?",
"@mariosasko thank you for the review! I'm sorry I accidentally asked for the review again, ignore it."
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## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**
**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.
It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.
I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).
|
2023-10-26T09:12:32Z
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2022-06-20T10:39:34Z
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https://api.github.com/repos/huggingface/datasets/issues/4529/comments
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Ecoset
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completed
|
NONE
|
2023-10-04T18:19:52Z
| null |
I_kwDODunzps5MGVfX
|
[
"Hi! Very cool dataset! I answered your questions on the forum. Also, feel free to comment `#self-assign` on this issue to self-assign it.",
"The dataset lives on the Hub [here](https://huggingface.co/datasets/kietzmannlab/ecoset), so I'm closing this issue.",
"Hey There, thanks for closing 🤗 \r\n\r\nForgot the issue existed, so I didn't close it after implementing the downloader :)"
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e## Describe the bug
It seems that memory never gets freed after iterating a `Dataset` (using `.map()` or a simple `for` loop)
## Steps to reproduce the bug
```python
import gc
import logging
import time
import pyarrow
from datasets import load_dataset
from tqdm import trange
import os, psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
process = psutil.Process(os.getpid())
print(process.memory_info().rss) # output: 633507840 bytes
corpus = load_dataset("BeIR/msmarco", 'corpus', keep_in_memory=False, streaming=False)['corpus'] # or "BeIR/trec-covid" for a smaller dataset
print(process.memory_info().rss) # output: 698601472 bytes
logger.info("Applying method to all examples in all splits")
for i in trange(0, len(corpus), 1000):
batch = corpus[i:i+1000]
data = pyarrow.total_allocated_bytes()
if data > 0:
logger.info(f"{i}/{len(corpus)}: {data}")
print(process.memory_info().rss) # output: 3788247040 bytes
del batch
gc.collect()
print(process.memory_info().rss) # output: 3788247040 bytes
logger.info("Done...")
time.sleep(100)
```
## Expected results
Limited memory usage, and memory to be freed after processing
## Actual results
Memory leak
You can see how the memory allocation keeps increasing until it reaches a steady state when we hit the `time.sleep(100)`, which showcases that even the garbage collector couldn't free the allocated memory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.0-90-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
|
2022-09-12T08:51:39Z
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https://api.github.com/repos/huggingface/datasets/issues/4528/comments
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Memory leak when iterating a Dataset
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MEMBER
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2022-09-12T08:51:39Z
| null |
I_kwDODunzps5MGJPz
|
[
"Is someone assigned to this issue?",
"The same issue is being debugged here: https://github.com/huggingface/datasets/issues/4883\r\n",
"Here is a modified repro example that makes it easier to see the leak:\r\n\r\n```\r\n$ cat ds2.py\r\nimport gc, sys\r\nimport time\r\nfrom datasets import load_dataset\r\nimport os, psutil\r\n\r\nprocess = psutil.Process(os.getpid())\r\n\r\nprint(process.memory_info().rss/2**20)\r\n\r\ncorpus = load_dataset(\"BeIR/msmarco\", 'corpus', keep_in_memory=False, streaming=False)['corpus']\r\ncorpus = corpus.select(range(200000))\r\n\r\nprint(process.memory_info().rss/2**20)\r\n\r\nbatch = None\r\n\r\nmem_before_start = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n\r\nstep = 20000\r\nfor i in range(0, 10*step, step):\r\n mem_before = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n batch = corpus[i:i+step]\r\n import objgraph\r\n #objgraph.show_refs([batch])\r\n #objgraph.show_refs([corpus])\r\n #sys.exit()\r\n gc.collect()\r\n\r\n mem_after = psutil.Process(os.getpid()).memory_info().rss / 2**20\r\n print(f\"{i:6d} {mem_after - mem_before:12.4f} {mem_after - mem_before_start:12.4f}\")\r\n\r\n```\r\n\r\nLet's run:\r\n\r\n```\r\n$ python ds2.py\r\n 0 36.5391 36.5391\r\n 20000 10.4609 47.0000\r\n 40000 5.9766 52.9766\r\n 60000 7.8906 60.8672\r\n 80000 6.0586 66.9258\r\n100000 8.4453 75.3711\r\n120000 6.7422 82.1133\r\n140000 8.5664 90.6797\r\n160000 5.7344 96.4141\r\n180000 8.3398 104.7539\r\n```\r\n\r\nYou can see the last column of total RSS memory keeps on growing in MBs. The mid column is by how much it was grown during a single iteration of the repro script (20000 items)",
"@NouamaneTazi, please check my analysis here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242599722 so if you agree with my research this Issue can be closed as well.\r\n\r\nI also made a suggestion at how to proceed to hunt for a real leak here https://github.com/huggingface/datasets/issues/4883#issuecomment-1242600626\r\n\r\nyou may find this one to be useful as well https://github.com/huggingface/datasets/issues/4883#issuecomment-1242597966",
"Amazing job! Thanks for taking time to debug this 🤗\r\n\r\nFor my side, I tried to do some more research as well, but to no avail. https://github.com/huggingface/datasets/issues/4883#issuecomment-1243415957"
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### Link
https://huggingface.co/datasets/vadis/sv-ident
### Description
The dataset preview does not work:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
However, the dataset is streamable and works locally:
```python
In [1]: from datasets import load_dataset; ds = load_dataset("sv-ident.py", split="train", streaming=True); item = next(iter(ds)); item
Using custom data configuration default
Out[1]:
{'sentence': 'Our point, however, is that so long as downward (favorable) comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.',
'is_variable': 1,
'variable': ['exploredata-ZA5400_VarV66', 'exploredata-ZA5400_VarV53'],
'research_data': ['ZA5400'],
'doc_id': '73106',
'uuid': 'b9fbb80f-3492-4b42-b9d5-0254cc33ac10',
'lang': 'en'}
```
CC: @e-tornike
### Owner
No
|
2022-06-21T16:42:46Z
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Dataset Viewer issue for vadis/sv-ident
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MEMBER
|
2022-06-21T16:42:45Z
| null |
I_kwDODunzps5MFx5w
|
[
"Fixed, thanks!\r\n![Uploading Capture d’écran 2022-06-21 à 18.42.40.png…]()\r\n\r\n"
] |
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## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through
```
class myDataModule:
def train_dataloader(self):
ds = load_dataset('squad', split='train')
ds = ds.map(some_function)
return [ds]
def val_dataloader(self):
ds = load_dataset('squad', split="validation")
ds = ds.map(some_function)
return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.
If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!
|
2022-06-28T14:04:58Z
| 4,526
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2022-06-20T08:44:58Z
|
https://api.github.com/repos/huggingface/datasets/issues/4526/comments
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split cache used when processing different split
|
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[] | null | null |
CONTRIBUTOR
| null | null |
I_kwDODunzps5MFxFZ
|
[
"I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).\r\n\r\nIf you can provide a MWE that would be perfect ! :)",
"Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.\r\n\r\nOtherwise I will try to provide a MWE"
] |
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## Describe the bug
While running the preprocessing of the natural_question dataset (see PR #4368), there is an issue for the "default" config (train+dev files).
Previously we ran the preprocessing for the "dev" config (only dev files) with success.
Train data files are larger than dev ones and apparently workers run out of memory while processing them.
Any help/hint is welcome!
Error message:
```
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
Info from the Diagnostics tab:
```
Out of memory: Killed process 1882 (python) total-vm:6041764kB, anon-rss:3290928kB, file-rss:0kB, shmem-rss:0kB, UID:0 pgtables:9520kB oom_score_adj:900
The worker VM had to shut down one or more processes due to lack of memory.
```
## Additional information
### Stack trace
```
Traceback (most recent call last):
File "/home/albert_huggingface_co/natural_questions/venv/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/commands/run_beam.py", line 127, in run
builder.download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/datasets/builder.py", line 1389, in _download_and_prepare
pipeline_results.wait_until_finish()
File "/home/albert_huggingface_co/natural_questions/venv/lib/python3.9/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 1667, in wait_until_finish
raise DataflowRuntimeException(
apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Data channel closed, unable to receive additional data from SDK sdk-0-0
```
### Logs
```
Error message from worker: Data channel closed, unable to receive additional data from SDK sdk-0-0
Workflow failed. Causes: S30:train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/Read+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/GroupByKey/GroupByWindow+train/ReadAllFromText/ReadAllFiles/Reshard/ReshufflePerKey/FlatMap(restore_timestamps)+train/ReadAllFromText/ReadAllFiles/Reshard/RemoveRandomKeys+train/ReadAllFromText/ReadAllFiles/ReadRange+train/Map(_parse_example)+train/Encode+train/Count N. Examples+train/Get values/Values+train/Save to parquet/Write/WriteImpl/WindowInto(WindowIntoFn)+train/Save to parquet/Write/WriteImpl/WriteBundles+train/Save to parquet/Write/WriteImpl/Pair+train/Save to parquet/Write/WriteImpl/GroupByKey/Write failed., The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers: beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: Data channel closed, unable to receive additional data from SDK sdk-0-0, beamapp-alberthuggingface-06170554-5p23-harness-t4v9 Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-bwsj Root cause: The worker lost contact with the service., beamapp-alberthuggingface-06170554-5p23-harness-5052 Root cause: The worker lost contact with the service.
```
|
2024-03-26T12:54:45Z
| 4,525
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2022-06-20T07:28:12Z
|
https://api.github.com/repos/huggingface/datasets/issues/4525/comments
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Out of memory error on workers while running Beam+Dataflow
|
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[] | null | null |
MEMBER
| null | null |
I_kwDODunzps5MFbZ6
|
[
"Some naive ideas to cope with this:\r\n- enable more RAM on each worker\r\n- force the spanning of more workers\r\n- others?",
"@albertvillanova We were finally able to process the full NQ dataset on our machines using 600 gb with 5 workers. Maybe these numbers will work for you as well.",
"Thanks a lot for the hint, @seirasto.\r\n\r\nI have one question: what runner did you use? Direct, Apache Flink/Nemo/Samza/Spark, Google Dataflow...? Thank you.",
"I asked my colleague who ran the code and he said apache beam.",
"@albertvillanova Since we have already processed the NQ dataset on our machines can we upload it to datasets so the NQ PR can be merged?",
"Maybe @lhoestq can give a more accurate answer as I am not sure about the authentication requirements to upload those files to our cloud bucket.\r\n\r\nAnyway I propose to continue this discussion on the dedicated PR for Natural questions dataset:\r\n- #4368",
"> I asked my colleague who ran the code and he said apache beam.\r\n\r\nHe looked into it further and he just used DirectRunner. @albertvillanova ",
"OK, thank you @seirasto for your hint.\r\n\r\nThat explains why you did not encounter the out of memory error: this only appears when the processing is distributed (on workers memory) and DirectRunner does not distribute the processing (all is done in a single machine). ",
"@albertvillanova Doesn't DirectRunner offer distributed processing through?\r\n\r\nhttps://beam.apache.org/documentation/runners/direct/\r\n\r\n```\r\nSetting parallelism\r\n\r\nNumber of threads or subprocesses is defined by setting the direct_num_workers pipeline option. From 2.22.0, direct_num_workers = 0 is supported. When direct_num_workers is set to 0, it will set the number of threads/subprocess to the number of cores of the machine where the pipeline is running.\r\n\r\nSetting running mode\r\n\r\nIn Beam 2.19.0 and newer, you can use the direct_running_mode pipeline option to set the running mode. direct_running_mode can be one of ['in_memory', 'multi_threading', 'multi_processing'].\r\n\r\nin_memory: Runner and workers’ communication happens in memory (not through gRPC). This is a default mode.\r\n\r\nmulti_threading: Runner and workers communicate through gRPC and each worker runs in a thread.\r\n\r\nmulti_processing: Runner and workers communicate through gRPC and each worker runs in a subprocess.\r\n```",
"Unrelated to the OOM issue, but we deprecated datasets with Beam scripts in #6474. I think we can close this issue"
] |
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## Describe the bug
When downloading some `wikipedia` languages (in particular, I'm having a hard time with Spanish, Cebuano, and Russian) via FlinkRunner, I encounter the exception in the title. I have been playing with package versions a lot, because unfortunately, the different dependencies required by these packages seem to be incompatible in terms of versions (dill and requests, for instance). It should be noted that the following code runs for several hours without issue, executing the `load_dataset()` function, before the exception occurs.
## Steps to reproduce the bug
```python
# bash commands
!pip install datasets
!pip install apache-beam[interactive]
!pip install mwparserfromhell
!pip install dill==0.3.5.1
!pip install requests==2.23.0
# imports
import os
from datasets import load_dataset
import apache_beam as beam
import mwparserfromhell
from google.colab import drive
import dill
import requests
# mount drive
drive_dir = os.path.join(os.getcwd(), 'drive')
drive.mount(drive_dir)
# confirming the versions of these two packages are the ones that are suggested by the outputs from the bash commands
print(dill.__version__)
print(requests.__version__)
lang = 'es' # or 'ru' or 'ceb' - these are the ones causing the issue
lang_dir = os.path.join(drive_dir, 'path/to/my/folder', lang)
if not os.path.exists(lang_dir):
x = None
x = load_dataset('wikipedia', '20220301.' + lang, beam_runner='Flink',
split='train')
x.save_to_disk(lang_dir)
```
## Expected results
Although some warnings are generally produced by this code (run in Colab Notebook), most languages I've tried have been successfully downloaded. It should simply go through without issue, but for these languages, I am continually encountering this error.
## Actual results
Traceback below:
```
Exception in thread run_worker_3-1:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 234, in run
for work_request in self._control_stub.Control(get_responses()):
File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__
return self._next()
File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = "Socket closed"
debug_error_string = "{"created":"@1655593643.871830638","description":"Error received from peer ipv4:127.0.0.1:44441","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Socket closed","grpc_status":14}"
>
Traceback (most recent call last):
File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
self._cache[target_window] = self._side_input_data.view_fn(raw_view)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
lambda iterable: from_runtime_iterable(iterable, view_options))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
head = list(itertools.islice(it, 2))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
self._underlying.get_raw(state_key, continuation_token))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
continuation_token=continuation_token)))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 267, in _execute
response = task()
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 340, in <lambda>
lambda: self.create_worker().do_instruction(request), request)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 581, in do_instruction
getattr(request, request_type), request.instruction_id)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 618, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 996, in process_bundle
element.data)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 221, in process_encoded
self.output(decoded_value)
File "apache_beam/runners/worker/operations.py", line 346, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam/runners/worker/operations.py", line 348, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam/runners/worker/operations.py", line 215, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive
File "apache_beam/runners/worker/operations.py", line 707, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/worker/operations.py", line 708, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/common.py", line 1200, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 1281, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
self._cache[target_window] = self._side_input_data.view_fn(raw_view)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
lambda iterable: from_runtime_iterable(iterable, view_options))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
head = list(itertools.islice(it, 2))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
self._underlying.get_raw(state_key, continuation_token))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
continuation_token=continuation_token)))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
ERROR:apache_beam.runners.worker.sdk_worker:Error processing instruction 26. Original traceback is
Traceback (most recent call last):
File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
self._cache[target_window] = self._side_input_data.view_fn(raw_view)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
lambda iterable: from_runtime_iterable(iterable, view_options))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
head = list(itertools.islice(it, 2))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
self._underlying.get_raw(state_key, continuation_token))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
continuation_token=continuation_token)))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 267, in _execute
response = task()
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 340, in <lambda>
lambda: self.create_worker().do_instruction(request), request)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 581, in do_instruction
getattr(request, request_type), request.instruction_id)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 618, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 996, in process_bundle
element.data)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 221, in process_encoded
self.output(decoded_value)
File "apache_beam/runners/worker/operations.py", line 346, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam/runners/worker/operations.py", line 348, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam/runners/worker/operations.py", line 215, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive
File "apache_beam/runners/worker/operations.py", line 707, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/worker/operations.py", line 708, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/common.py", line 1200, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 1281, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam/runners/common.py", line 1198, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 718, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 426, in __getitem__
self._cache[target_window] = self._side_input_data.view_fn(raw_view)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 391, in <lambda>
lambda iterable: from_runtime_iterable(iterable, view_options))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/pvalue.py", line 512, in _from_runtime_iterable
head = list(itertools.islice(it, 2))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1228, in _lazy_iterator
self._underlying.get_raw(state_key, continuation_token))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1019, in get_raw
continuation_token=continuation_token)))
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 1060, in _blocking_request
raise RuntimeError(response.error)
RuntimeError: Unknown process bundle instruction id '26' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
ERROR:root:org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException: CANCELLED: client cancelled
ERROR:apache_beam.runners.worker.data_plane:Failed to read inputs in the data plane.
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 634, in _read_inputs
for elements in elements_iterator:
File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__
return self._next()
File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.CANCELLED
details = "Multiplexer hanging up"
debug_error_string = "{"created":"@1655593654.436885887","description":"Error received from peer ipv4:127.0.0.1:43263","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Multiplexer hanging up","grpc_status":1}"
>
Exception in thread read_grpc_client_inputs:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 651, in <lambda>
target=lambda: self._read_inputs(elements_iterator),
File "/usr/local/lib/python3.7/dist-packages/apache_beam/runners/worker/data_plane.py", line 634, in _read_inputs
for elements in elements_iterator:
File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 426, in __next__
return self._next()
File "/usr/local/lib/python3.7/dist-packages/grpc/_channel.py", line 826, in _next
raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
status = StatusCode.CANCELLED
details = "Multiplexer hanging up"
debug_error_string = "{"created":"@1655593654.436885887","description":"Error received from peer ipv4:127.0.0.1:43263","file":"src/core/lib/surface/call.cc","file_line":952,"grpc_message":"Multiplexer hanging up","grpc_status":1}"
>
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[/tmp/ipykernel_219/3869142325.py](https://localhost:8080/#) in <module>
18 x = None
19 x = load_dataset('wikipedia', '20220301.' + lang, beam_runner='Flink',
---> 20 split='train')
21 x.save_to_disk(lang_dir)
3 frames
[/usr/local/lib/python3.7/dist-packages/apache_beam/runners/portability/portable_runner.py](https://localhost:8080/#) in wait_until_finish(self, duration)
604
605 if self._runtime_exception:
--> 606 raise self._runtime_exception
607
608 return self._state
RuntimeError: Pipeline BeamApp-root-0618220708-b3b59a0e_d8efcf67-9119-4f76-b013-70de7b29b54d failed in state FAILED: org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException: CANCELLED: client cancelled
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
|
2022-06-21T00:38:20Z
| 4,524
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https://api.github.com/repos/huggingface/datasets
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2022-06-18T23:36:45Z
|
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Downloading via Apache Pipeline, client cancelled (org.apache.beam.vendor.grpc.v1p43p2.io.grpc.StatusRuntimeException)
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NONE
| null | null |
I_kwDODunzps5MDNRC
|
[
"Hi @dan-the-meme-man, thanks for reporting.\r\n\r\nWe are investigating a similar issue but with Beam+Dataflow (instead of Beam+Flink): \r\n- #4525\r\n\r\nIn order to go deeper into the root cause, we need as much information as possible: logs from the main process + logs from the workers are very informative.\r\n\r\nIn the case of the issue with Beam+Dataflow, the logs from the workers report an out of memory issue.",
"As I continued working on this today, I came to suspect that it is in fact an out of memory issue - I have a few more notebooks that I've left running, and if they produce the same error, I will try to get the logs. In the meantime, if there's any chance that there is a repo out there with those three languages already as .arrow files, or if you know about how much memory would be needed to actually download those sets, please let me know!"
] |
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|
Improve the download URL (reported here: https://huggingface.co/datasets/cats_vs_dogs/discussions/1), remove the `image_file_path` column (not used in Transformers, so it should be safe) and add more info to the card.
|
2022-06-21T14:23:26Z
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https://api.github.com/repos/huggingface/datasets
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2022-06-17T12:59:44Z
|
https://api.github.com/repos/huggingface/datasets/issues/4523/comments
| null |
https://api.github.com/repos/huggingface/datasets/issues/4523/timeline
|
Update download url and improve card of `cats_vs_dogs` dataset
|
https://api.github.com/repos/huggingface/datasets/issues/4523/events
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[] | null | null |
CONTRIBUTOR
|
2022-06-21T14:13:08Z
|
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PR_kwDODunzps452hgh
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] |
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1,274,929,328
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> Currently, 41 canonical datasets require manual download. I checked their scripts and I'm pretty sure this number can be reduced to ≈ 30 by not relying on bash scripts to download data, hosting data directly on the Hub when the license permits, etc. Then, we will mostly be left with datasets with restricted access, which we can ignore
from https://github.com/huggingface/datasets-server/issues/12#issuecomment-1026920432
|
2022-06-17T11:52:48Z
| 4,522
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https://api.github.com/repos/huggingface/datasets
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2022-06-17T11:42:03Z
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https://api.github.com/repos/huggingface/datasets/issues/4522/comments
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Try to reduce the number of datasets that require manual download
|
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I_kwDODunzps5L_eCw
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## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function
## Steps to reproduce the bug
```python
from datasets import load_dataset
# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
def prepare_dataset(batch):
return(batch)
ds = ds.map(
prepare_dataset,
num_proc=1,
desc="preprocess train dataset",
)
```
## Expected results
Hashed and cached dataset preprocessing
## Actual results
Does not hash properly:
```
Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single 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.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
cc @lhoestq
|
2022-08-04T12:08:16Z
| 4,521
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https://api.github.com/repos/huggingface/datasets
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2022-06-17T11:31:10Z
|
https://api.github.com/repos/huggingface/datasets/issues/4521/comments
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Datasets method `.map` not hashing
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completed
|
CONTRIBUTOR
|
2022-06-28T13:23:05Z
| null |
I_kwDODunzps5L_boN
|
[
"Fix posted: https://github.com/huggingface/datasets/issues/4506#issuecomment-1157417219",
"Didn't realize it's a bug when I asked the question yesterday! Free free to post an answer if you are sure the cause has been addressed.\r\n\r\nhttps://stackoverflow.com/questions/72664827/can-pickle-dill-foo-but-not-lambda-x-foox",
"Thank @nalzok . That works for me:\r\n\r\n`pip install \"dill<0.3.5\"`"
] |
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Dataclasses cannot be hashed. As a result, they cannot be hashed or cached if used in the `.map` method. Dataclasses are used extensively in Transformers examples scripts: (c.f. [CTC example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)). Since dataclasses cannot be hashed, one has to define separate variables prior to passing dataclass attributes to the `.map` method:
```python
phoneme_language = data_args.phoneme_language
```
in the example https://github.com/huggingface/transformers/blob/3c7e56fbb11f401de2528c1dcf0e282febc031cd/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L603-L630
## Steps to reproduce the bug
```python
from dataclasses import dataclass, field
from datasets.fingerprint import Hasher
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
phoneme_language: str = field(
default=None, metadata={"help": "The name of the phoneme language to use."}
)
data_args = DataTrainingArguments(phoneme_language ="foo")
Hasher.hash(data_args)
phoneme_language = data_args.phoneme_language
Hasher.hash(phoneme_language)
```
## Expected results
A hash.
## Actual results
<details>
<summary> Traceback </summary>
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Input In [1], in <cell line: 16>()
10 phoneme_language: str = field(
11 default=None, metadata={"help": "The name of the phoneme language to use."}
12 )
14 data_args = DataTrainingArguments(phoneme_language ="foo")
---> 16 Hasher.hash(data_args)
18 phoneme_language = data_args. phoneme_language
20 Hasher.hash(phoneme_language)
File ~/datasets/src/datasets/fingerprint.py:237, in Hasher.hash(cls, value)
235 return cls.dispatch[type(value)](cls, value)
236 else:
--> 237 return cls.hash_default(value)
File ~/datasets/src/datasets/fingerprint.py:230, in Hasher.hash_default(cls, value)
228 @classmethod
229 def hash_default(cls, value: Any) -> str:
--> 230 return cls.hash_bytes(dumps(value))
File ~/datasets/src/datasets/utils/py_utils.py:564, in dumps(obj)
562 file = StringIO()
563 with _no_cache_fields(obj):
--> 564 dump(obj, file)
565 return file.getvalue()
File ~/datasets/src/datasets/utils/py_utils.py:539, in dump(obj, file)
537 def dump(obj, file):
538 """pickle an object to a file"""
--> 539 Pickler(file, recurse=True).dump(obj)
540 return
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:620, in Pickler.dump(self, obj)
618 raise PicklingError(msg)
619 else:
--> 620 StockPickler.dump(self, obj)
621 return
File /usr/lib/python3.8/pickle.py:487, in _Pickler.dump(self, obj)
485 if self.proto >= 4:
486 self.framer.start_framing()
--> 487 self.save(obj)
488 self.write(STOP)
489 self.framer.end_framing()
File /usr/lib/python3.8/pickle.py:603, in _Pickler.save(self, obj, save_persistent_id)
599 raise PicklingError("Tuple returned by %s must have "
600 "two to six elements" % reduce)
602 # Save the reduce() output and finally memoize the object
--> 603 self.save_reduce(obj=obj, *rv)
File /usr/lib/python3.8/pickle.py:687, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
684 raise PicklingError(
685 "args[0] from __newobj__ args has the wrong class")
686 args = args[1:]
--> 687 save(cls)
688 save(args)
689 write(NEWOBJ)
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1838, in save_type(pickler, obj, postproc_list)
1836 postproc_list = []
1837 postproc_list.append((setattr, (obj, '__qualname__', obj_name)))
-> 1838 _save_with_postproc(pickler, (_create_type, (
1839 type(obj), obj.__name__, obj.__bases__, _dict
1840 )), obj=obj, postproc_list=postproc_list)
1841 log.info("# %s" % _t)
1842 else:
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1140, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
1137 pickler._postproc[id(obj)] = postproc_list
1139 # TODO: Use state_setter in Python 3.8 to allow for faster cPickle implementations
-> 1140 pickler.save_reduce(*reduction, obj=obj)
1142 if is_pickler_dill:
1143 # pickler.x -= 1
1144 # print(pickler.x*' ', 'pop', obj, id(obj))
1145 postproc = pickler._postproc.pop(id(obj))
File /usr/lib/python3.8/pickle.py:692, in _Pickler.save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj)
690 else:
691 save(func)
--> 692 save(args)
693 write(REDUCE)
695 if obj is not None:
696 # If the object is already in the memo, this means it is
697 # recursive. In this case, throw away everything we put on the
698 # stack, and fetch the object back from the memo.
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File /usr/lib/python3.8/pickle.py:901, in _Pickler.save_tuple(self, obj)
899 write(MARK)
900 for element in obj:
--> 901 save(element)
903 if id(obj) in memo:
904 # Subtle. d was not in memo when we entered save_tuple(), so
905 # the process of saving the tuple's elements must have saved
(...)
909 # could have been done in the "for element" loop instead, but
910 # recursive tuples are a rare thing.
911 get = self.get(memo[id(obj)][0])
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1251, in save_module_dict(pickler, obj)
1248 if is_dill(pickler, child=False) and pickler._session:
1249 # we only care about session the first pass thru
1250 pickler._first_pass = False
-> 1251 StockPickler.save_dict(pickler, obj)
1252 log.info("# D2")
1253 return
File /usr/lib/python3.8/pickle.py:971, in _Pickler.save_dict(self, obj)
968 self.write(MARK + DICT)
970 self.memoize(obj)
--> 971 self._batch_setitems(obj.items())
File /usr/lib/python3.8/pickle.py:997, in _Pickler._batch_setitems(self, items)
995 for k, v in tmp:
996 save(k)
--> 997 save(v)
998 write(SETITEMS)
999 elif n:
File /usr/lib/python3.8/pickle.py:560, in _Pickler.save(self, obj, save_persistent_id)
558 f = self.dispatch.get(t)
559 if f is not None:
--> 560 f(self, obj) # Call unbound method with explicit self
561 return
563 # Check private dispatch table if any, or else
564 # copyreg.dispatch_table
File ~/datasets/src/datasets/utils/py_utils.py:862, in save_function(pickler, obj)
859 if state_dict:
860 state = state, state_dict
--> 862 dill._dill._save_with_postproc(
863 pickler,
864 (
865 dill._dill._create_function,
866 (obj.__code__, globs, obj.__name__, obj.__defaults__, closure),
867 state,
868 ),
869 obj=obj,
870 postproc_list=postproc_list,
871 )
872 else:
873 closure = obj.func_closure
File ~/hf/lib/python3.8/site-packages/dill/_dill.py:1153, in _save_with_postproc(pickler, reduction, is_pickler_dill, obj, postproc_list)
1151 dest, source = reduction[1]
1152 if source:
-> 1153 pickler.write(pickler.get(pickler.memo[id(dest)][0]))
1154 pickler._batch_setitems(iter(source.items()))
1155 else:
1156 # Updating with an empty dictionary. Same as doing nothing.
KeyError: 140434581781568
```
</details>
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
cc @lhoestq
|
2022-06-28T14:47:17Z
| 4,520
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2022-06-17T10:47:17Z
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https://api.github.com/repos/huggingface/datasets/issues/4520/comments
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Failure to hash `dataclasses` - results in functions that cannot be hashed or cached in `.map`
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CONTRIBUTOR
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2022-06-28T14:04:29Z
| null |
I_kwDODunzps5L_RzM
|
[
"I think this has been fixed by #4516, let me know if you encounter this again :)\r\n\r\nI re-ran your code in 3.7 and 3.9 and it works fine",
"Thank you!"
] |
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This PR creates separate sections in the guides for audio, vision, text, and general usage so it is easier for users to find loading, processing, or sharing guides specific to the dataset type they're working with. It'll also allow us to scale the docs to additional dataset types - like time series, tabular, etc. - while keeping our docs information architecture.
Some other changes include:
- ~Experimented with decorating text with some CSS to highlight guides specific to each modality. Hopefully, it'll be easier for users to find and realize that these different docs exist!~ Will experiment with this in a different PR.
- Added deprecation warning for Metrics and redirect to Evaluate.
- Updated `set_format` section to recommend using the new `to_tf_dataset` function if you need to convert to a TensorFlow dataset.
- Reorganized `toctree` to nest general usage, audio, vision, and text sections under the how-to guides.
- A quick review and edit to the Load and Process docs for clarity.
|
2022-07-07T15:36:37Z
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2022-06-16T21:38:24Z
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Create new sections for audio and vision in guides
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MEMBER
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2022-07-07T15:24:58Z
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"_The documentation is not available anymore as the PR was closed or merged._",
"Ready for review!\r\n\r\nThe `toctree` is a bit longer now with the sections. I think if we keep the audio/vision/text/dataset repository sections collapsed by default, and keep the general usage expanded, it may look a little cleaner and not as overwhelming. Let me know what you think! 😄 "
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This PR patches testing utilities that would otherwise fail with hfh v0.8.0.
|
2022-06-17T16:15:57Z
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2022-06-16T19:45:32Z
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Patch tests for hfh v0.8.0
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yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json
separate Pull Request will modify dataset_infos.json to add these tags
The Enron dataset (dataset id aeslc) is only tagged with:
arxiv:1906.03497'
languages:en
pretty_name:AESLC
Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance):
'task_ids:summarization'
'task_ids:summarization-other-conversations-summarization'
"task_ids:other-other-query-based-multi-document-summarization"
'task_ids:summarization-other-aspect-based-summarization'
'task_ids:summarization--other-headline-generation'
The subject might also be used for the task_category "task_categories:summarization"
E-mail chains might be used for the task category "task_categories:dialogue-system"
|
2022-07-08T15:14:23Z
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2022-06-16T18:52:25Z
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Add tags for task_ids:summarization-* and task_categories:summarization*
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2022-07-08T15:02:31Z
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"Associated community discussion is [here](https://huggingface.co/datasets/aeslc/discussions/1).\r\nPaper referenced in the `dataset_infos.json` is [here](https://arxiv.org/pdf/1906.03497.pdf). It mentions the _email-subject-generation_ task, which is not a tag mentioned in any other dataset so it was not added in this pull request. The _summarization_ task is mentioned as a related task.",
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In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function.
Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9
To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic.
Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now.
Fix https://github.com/huggingface/datasets/issues/4506
|
2022-06-28T13:33:46Z
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Fix hashing for python 3.9
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Adds the uppercased versions of the image file extensions to the supported extensions.
Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain)
Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too.
Fix #4514.
|
2022-06-16T17:21:53Z
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Add uppercased versions of image file extensions for automatic module inference
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2022-06-16T17:11:41Z
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## Describe the bug
When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed.
## Steps to reproduce the bug
```python
# use bash to create 2 sham datasets with jpeg and JPEG ext
!mkdir dataset_a
!mkdir dataset_b
!wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg
!cp example_img.jpeg ./dataset_a/
!mv example_img.jpeg ./dataset_b/example_img.JPEG
from datasets import load_dataset
# working
df1 = load_dataset("./dataset_a", ignore_verifications=True)
#not working
df2 = load_dataset("./dataset_b", ignore_verifications=True)
# show
print(df1, df2)
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
}) DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
})
```
## Actual results
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.
|
2022-06-20T08:18:46Z
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Allow .JPEG as a file extension
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While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included.
Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage".
Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems.
And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers.
Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:
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2022-06-23T17:05:11Z
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2022-06-23T16:54:59Z
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @stevhliu, I've kept the `>>>` before all the in-line code comments as it was done like that in the default S3 example that was already there, I assume that it's done like that just for readiness, let me know whether we should remove the `>>>` in the Python blocks before the in-line code comments or keep them.\r\n\r\n\r\n",
"Comments are ignored by doctest, so I think we can remove the `>>>` :)",
"Cool I'll remove those now 👍🏻",
"Sure @lhoestq, I just kept that structure as that was the more similar one to the one that was already there, but we can go with that approach, just let me know whether I should change the headers so as to leave all those providers in the same level (`h2`). Thanks!"
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Add links to vision dataset scripts in the ADD_NEW_DATASET template.
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2022-07-08T13:56:23Z
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We usually use -1 to represent a missing label, but we should also support any negative values (some users use -100 for example). This is a regression from `datasets` 2.3
Fix https://github.com/huggingface/datasets/issues/4508
|
2022-07-28T16:03:27Z
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Support all negative values in ClassLabel
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for this fix! I'm not sure what the release timeline is, but FYI #4508 is a breaking issue for transformer token classification using Trainer and PyTorch. PyTorch defaults to -100 as the ignored label for [negative log loss](https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html?highlight=nllloss#torch.nn.NLLLoss), so switching labels to -1 leads to index errors using Trainer defaults.\r\n\r\nAs a workaround, I'm using master branch directly (`pip install git+https://github.com/huggingface/datasets.git@master` for anyone who needs to do the same) until this gets released.",
"The new release `2.4` fixes the issue, feel free to update `datasets` :) \r\n```\r\npip install -U datasets\r\n```"
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As spotted by @cccntu in #4502, there's a logic bug in `ArrowWriter.write_batch` as the if-statement to handle the empty batches as detailed in the docstrings of the function ("Ignores the batch if it appears to be empty, preventing a potential schema update of unknown types."), the current if-statement is not handling properly `writer.write_batch({})` as an error is triggered.
Also, if we add a regression test in `test_arrow_writer.py::test_write_batch` before applying the fix, the test will fail as when trying to write an empty batch as follows:
```
=================================================================================== short test summary info ===================================================================================
FAILED tests/test_arrow_writer.py::test_write_batch[None-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[None-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields1-10] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-None] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-1] - ValueError: Schema and number of arrays unequal
FAILED tests/test_arrow_writer.py::test_write_batch[fields2-10] - ValueError: Schema and number of arrays unequal
======================================================================== 9 failed, 73 deselected, 7 warnings in 0.81s =========================================================================
```
So the batch is not ignored when empty, as `batch_examples={}` won't match the condition `if batch_examples: ...`.
|
2022-06-16T12:38:02Z
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Add regression test for `ArrowWriter.write_batch` when batch is empty
|
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CONTRIBUTOR
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2022-06-16T12:28:19Z
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PR_kwDODunzps45wq6o
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"As mentioned by @lhoestq, the current behavior is correct and we should not expect batches with different columns, in that case, the if should fail, as the values of the batch can be empty, but not the actual `batch_examples` value."
] |
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2022-06-16T08:25:38Z
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Support skipping Parquet to Arrow conversion when using Beam
|
https://api.github.com/repos/huggingface/datasets/issues/4509/events
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2022-11-07T16:22:41Z
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4509). All of your documentation changes will be reflected on that endpoint.",
"When #4724 is merged, we can just pass `file_format=\"parquet\"` to `download_and_prepare` and it will output parquet fiels without converting to arrow",
"I think we can close this one"
] |
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## Describe the bug
A bug occurs when mapping a function to a dataset object. I ran the same code with the same data yesterday and it worked just fine. It works when i run locally on an old version of datasets.
## Steps to reproduce the bug
Steps are:
- load whatever datset
- write a preprocessing function such as "tokenize_and_align_labels" written in https://huggingface.co/docs/transformers/tasks/token_classification
- map the function on dataset and get "ValueError: Class label -100 less than -1" from cast_storage method from datasets.features
# Sample code to reproduce the bug
def tokenize_and_align_labels(examples):
tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True, max_length=38,padding="max_length")
labels = []
for i, label in enumerate(examples[f"labels"]):
word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
previous_word_idx = None
label_ids = []
for word_idx in word_ids: # Set the special tokens to -100.
if word_idx is None:
label_ids.append(-100)
elif word_idx != previous_word_idx: # Only label the first token of a given word.
label_ids.append(label[word_idx])
else:
label_ids.append(-100)
previous_word_idx = word_idx
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
dt = dataset.map(tokenize_and_align_labels, batched=True)
## Expected results
New dataset objects should load and do on older versions.
## Actual results
"ValueError: Class label -100 less than -1" from cast_storage method from datasets.features
## Environment info
everything works fine on older installations of datasets/transformers
Issue arises when installing datasets on google collab under python3.7
I can't manage to find the exact output you're requirering but version printed is datasets-2.3.2
|
2022-06-16T13:54:07Z
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2022-06-15T20:47:22Z
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https://api.github.com/repos/huggingface/datasets/issues/4508/comments
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cast_storage method from datasets.features
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completed
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NONE
|
2022-06-16T13:54:07Z
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I_kwDODunzps5L3CZJ
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[
"Hi! We've recently added a check to the `ClassLabel` type to ensure the values are in the valid label range `-1, 0, ..., num_classes-1` (-1 is used for missing values). The error in your case happens only if the `labels` column is of type `Sequence(ClassLabel(...))` before the `map` call and can be avoided by calling `dataset = dataset.cast_column(\"labels\", Sequence(Value(\"int\")))` beforehand. The token-classification examples in Transformers introduce a new `labels` column, so their type is also `Sequence(Value(\"int\"))`, which doesn't lead to an error as this type unbounded. ",
"I'm fine with re-adding support for all negative values for unknown/missing labels @mariosasko, wdyt ?"
] |
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1,272,615,932
|
https://api.github.com/repos/huggingface/datasets/issues/4507/labels{/name}
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If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair.
Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`?
Many thanks for any help.
|
2022-06-16T10:40:08Z
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2022-06-15T18:56:34Z
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https://api.github.com/repos/huggingface/datasets/issues/4507/comments
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How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script
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2022-06-16T10:40:08Z
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I_kwDODunzps5L2pP8
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[
"Hi @liyucheng09.\r\n\r\nUsers can pass the `split` parameter to `load_dataset`. For example, if your split name is \"train\",\r\n```python\r\nds = load_dataset(\"dataset_name\", split=\"train\")\r\n```\r\nwill return a `Dataset` instance.",
"@albertvillanova Thanks! I can't believe I didn't know this feature till now."
] |
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## Describe the bug
Sometimes I get messages about not being able to hash a method:
`Parameter 'function'=<function StupidDataModule._separate_speaker_id_from_dialogue at 0x7f1b27180d30> of the transform datasets.arrow_dataset.Dataset.
_map_single 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.`
Whilst the function looks like this:
```python
@staticmethod
def _separate_speaker_id_from_dialogue(example: arrow_dataset.Example):
speaker_id, dialogue = tuple(zip(*(example["dialogue"])))
example["speaker_id"] = speaker_id
example["dialogue"] = dialogue
return example
```
This is the first step in my preprocessing pipeline, but sometimes the message about failure to hash is not appearing on the first step, but then appears on a later step.
This error is sometimes causing a failure to use cached data, instead of re-running all steps again.
## Steps to reproduce the bug
```python
import copy
import datasets
from datasets import arrow_dataset
def main():
dataset = datasets.load_dataset("blended_skill_talk")
res = dataset.map(method)
print(res)
def method(example: arrow_dataset.Example):
example['previous_utterance_copy'] = copy.deepcopy(example['previous_utterance'])
return example
if __name__ == '__main__':
main()
```
Run with:
```
python -m reproduce_error
```
## Expected results
Dataset is mapped and cached correctly.
## Actual results
The code outputs this at some point:
`Parameter 'function'=<function method at 0x7faa83d2a160> of the transform datasets.arrow_dataset.Dataset._map_single 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.`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Ubuntu 20.04.3
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Datasets version: 2.3.1
|
2023-02-16T03:14:32Z
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2022-06-15T17:11:31Z
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https://api.github.com/repos/huggingface/datasets/issues/4506/comments
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Failure to hash (and cache) a `.map(...)` (almost always) - using this method can produce incorrect results
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2022-06-28T13:23:05Z
| null |
I_kwDODunzps5L2REf
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[
"Important info:\r\n\r\nAs hashes are generated randomly for functions, it leads to **false identifying some results as already hashed** (mapping function is not executed after a method update) when there's a `pytorch_lightning.seed_everything(123)`",
"@lhoestq\r\nseems like quite critical stuff for me, if I'm not making a mistake",
"Hi ! Thanks for reporting. This bug seems to appear in python 3.9 using dill 3.5.1\r\n\r\nAs a workaround you can use an older version of dill:\r\n```\r\npip install \"dill<0.3.5\"\r\n```",
"installing `dill<0.3.5` after installing `datasets` by pip results in dependency conflict with the version required for `multiprocess`. It can be solved by installing `pip install datasets \"dill<0.3.5\"` (simultaneously) on a clean environment",
"This has been fixed in https://github.com/huggingface/datasets/pull/4516, we will do a new release soon to include the fix :)"
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As mentioned in https://github.com/huggingface/transformers/pull/17715 `data_files` can't find a file if the path contains double dots `/../`. This has been introduced in https://github.com/huggingface/datasets/pull/4412, by trying to ignore hidden files and directories (i.e. if they start with a dot)
I fixed this and added a test
cc @sgugger @ydshieh
|
2022-06-15T17:15:58Z
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Fix double dots in data files
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"_The documentation is not available anymore as the PR was closed or merged._",
"The CI fails are unrelated to this PR (apparently something related to `seqeval` on windows) - merging :)"
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## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)*
- **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose *
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
2023-10-18T18:55:30Z
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Can you please add the Stanford dog dataset?
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2023-10-18T18:55:30Z
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[
"would you like to give it a try, @dgrnd4? (maybe with the help of the dataset author?)",
"@julien-c i am sorry but I have no idea about how it works: can I add the dataset by myself, following \"instructions to add a new dataset\"?\r\nCan I add a dataset even if it's not mine? (it's public in the link that I wrote on the post)\r\n",
"Hi! The [ADD NEW DATASET](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) instructions are indeed the best place to start. It's also perfectly fine to add a dataset if it's public, even if it's not yours. Let me know if you need some additional pointers.",
"If no one is working on this, I could take this up!",
"@khushmeeet this is the [link](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset) where I added the dataset already. If you can I would ask you to do this:\r\n1) The dataset it's all in TRAINING SET: can you please divide it in Training,Test and Validation Set? If you can for each class, take the 80% for the Training set and the 10% for Test and 10% Validation\r\n2) The images has different size, can you please resize all the images in 224,224,3? Look even at the last dimension \"3\" because some images has dimension 4!\r\n\r\nThank you!!",
"Hi @khushmeeet! Thanks for the interest. You can self-assign the issue by commenting `#self-assign` on it. \r\n\r\nAlso, I think we can skip @dgrnd4's steps as we try to avoid any custom processing on top of raw data. One can later copy the script and override `_post_process` in it to perform such processing on the generated dataset.",
"Thanks @mariosasko \r\n\r\n@dgrnd4 As dataset is there on Hub, and preprocessing is not recommended. I am not sure if there is any other task to do. However, I can't seem to find relevant `.py` files for this dataset in GitHub repo.",
"@khushmeeet @mariosasko The point is that the images must be processed and must have the same size in order to can be used for things for example \"Training\". ",
"@dgrnd4 Yes, but this can be done after loading (`map` to resize images and `train_test_split` to create extra splits)\r\n\r\n@khushmeeet The linked version is implemented as a no-code dataset and is generated directly from the ZIP archive, but our \"GitHub\" datasets (these are datasets without a user/org namespace on the Hub) need a generation script, and you can find one [here](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/image_classification/stanford_dogs.py). `datasets` started as a fork of TFDS, so we share similar script structure, which makes it trivial to adapt it.",
"@mariosasko The point is that if I use something like this:\r\nx_train, x_test = train_test_split(dataset, test_size=0.1) \r\n\r\nto get Train 90% and Test 10%, and then to get the Validation Set (10% of the whole 100%):\r\n\r\n```\r\ntrain_ratio = 0.80\r\nvalidation_ratio = 0.10\r\ntest_ratio = 0.10\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(dataX, dataY, test_size=1 - train_ratio)\r\nx_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=test_ratio/(test_ratio + validation_ratio)) \r\n\r\n```\r\n\r\nThe point is that the structure of the data is:\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['image', 'label'],\r\n num_rows: 20580\r\n })\r\n})\r\n\r\n```\r\n\r\nSo how to extract images and labels?\r\n\r\nEDIT --> Split of the dataset in Train-Test-Validation:\r\n```\r\nimport datasets\r\nfrom datasets.dataset_dict import DatasetDict\r\nfrom datasets import Dataset\r\n\r\npercentage_divison_test = int(len(dataset['train'])/100 *10) # 10% --> 2058 \r\npercentage_divison_validation = int(len(dataset['train'])/100 *20) # 20% --> 4116\r\n\r\ndataset_ = datasets.DatasetDict({\"train\": Dataset.from_dict({\r\n\r\n 'image': dataset['train'][0 : len(dataset['train']) ]['image'], \r\n 'labels': dataset['train'][0 : len(dataset['train']) ]['label'] }), \r\n \r\n \"test\": Dataset.from_dict({ #20580-4116 (validation) ,20580-2058 (test)\r\n 'image': dataset['train'][len(dataset['train']) - percentage_divison_validation : len(dataset['train']) - percentage_divison_test]['image'], \r\n 'labels': dataset['train'][len(dataset['train']) - percentage_divison_validation : len(dataset['train']) - percentage_divison_test]['label'] }), \r\n \r\n \"validation\": Dataset.from_dict({ # 20580-2058 (test)\r\n 'image': dataset['train'][len(dataset['train']) - percentage_divison_test : len(dataset['train'])]['image'], \r\n 'labels': dataset['train'][len(dataset['train']) - percentage_divison_test : len(dataset['train'])]['label'] }), \r\n })\r\n```",
"@mariosasko in order to resize images I'm trying this method: \r\n```\r\nfor i in range(0,len(dataset['train'])): #len(dataset['train'])\r\n\r\n ex = dataset['train'][i] #i\r\n image = ex['image']\r\n image = image.convert(\"RGB\") # <class 'PIL.Image.Image'> <PIL.Image.Image image mode=RGB size=500x333 at 0x7F84F1948150>\r\n image_resized = image.resize(size_to_resize) # <PIL.Image.Image image mode=RGB size=224x224 at 0x7F84F17885D0>\r\n\r\n dataset['train'][i]['image'] = image_resized \r\n```\r\n\r\nBecause the DatasetDict is formed by arrows that are immutable, the changing assignment in the last line of code, doesn't work!\r\nDo you have any idea in order to get a valid result?",
"#self-assign",
"I have raised PR for adding stanford-dog dataset. I have not added any data preprocessing code. Only dataset generation script is there. Let me know any changes required, or anything to add to README.",
"Is this issue still open, i am new to open source thus want to take this one as my start.",
"@zutarich This issue should have been closed since the dataset in question is available on the Hub [here](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset)."
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Related to: #4452 and #3792.
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2022-07-06T11:54:15Z
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Refactor and add metadata to fever dataset
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"But this is somehow fever v3 dataset (see this link https://fever.ai/ under the dropdown menu called Datasets). Our fever dataset already contains v1 and v2 configs. Then, I added this as if v3 config (but named feverous instead of v3 to align with the original naming by data owners).",
"In any case, if you really think this should be a new dataset, then I would propose to create it on the Hub instead, as \"fever/feverous\".",
"> In any case, if you really think this should be a new dataset, then I would propose to create it on the Hub instead, as \"fever/feverous\".\r\n\r\nYea makes sense ! thanks :) let's push more datasets on the hub rather than on github from now on",
"I have added \"feverous\" dataset to the Hub: https://huggingface.co/datasets/fever/feverous\r\n\r\nI change the name of this PR accordingly, as now it only:\r\n- Refactors code and include for both Fever v1.0 and v2.0 specific:\r\n - Descriptions\r\n - Citations\r\n - Homepages\r\n- Updates documentation card aligned with above:\r\n - It was missing v2.0 description and citation.\r\n- Update metadata JSON"
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https://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L475-L488
I got some error, and I found it's caused by `batch_examples` being `{}`. I wonder if the code should be as follows:
```
- if batch_examples and len(next(iter(batch_examples.values()))) == 0:
+ if not batch_examples or len(next(iter(batch_examples.values()))) == 0:
return
```
@lhoestq
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2022-06-18T15:15:51Z
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Logic bug in arrow_writer?
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[
"Hi @cccntu you're right, as when `batch_examples={}` the current if-statement won't be triggered as the condition won't be satisfied, I'll prepare a PR to address it as well as add the regression tests so that this issue is handled properly.",
"Hi @alvarobartt ,\r\nThanks for answering. Do you know when and why an empty batch is passed to this function? This only happened to me when processing with multiple workers, while chunking examples, I think.",
"> Hi @alvarobartt , Thanks for answering. Do you know when and why an empty batch is passed to this function? This only happened to me when processing with multiple workers, while chunking examples, I think.\r\n\r\nSo it depends on how you're actually chunking the data as if you're not handling empty chunks `batch_examples={}` or `batch_examples=None`, you may end up running into this issue. So you could check the chunks before you actually call `ArrowWriter.write_batch`, but anyway the fix you proposed I think improves the logic of `write_batch` to avoid running into these issues.",
"Thanks, I added a if-print and I found it does return an empty examples in the chunking function that is passed to `.map()`.",
"Hi ! We consider an empty batch to look like this:\r\n```python\r\nempty_batch = {\r\n \"column_1\": [],\r\n \"column_2\": [],\r\n ...\r\n}\r\n```\r\n\r\nWhile `{}` corresponds to a batch with no columns.\r\n\r\nTherefore calling this code should fail, because the two batches don't have the same columns:\r\n```python\r\nwriter.write_batch({\"a\": [1, 2, 3]})\r\nwriter.write_batch({})\r\n```\r\n\r\nIf you want to write an empty batch, you should do this instead:\r\n```python\r\nwriter.write_batch({\"a\": [1, 2, 3]})\r\nwriter.write_batch({\"a\": []})\r\n```",
"Makes sense, then the if-statement should remain the same or is it better to handle both cases separately using `if not batch_examples or len(next(iter(batch_examples.values()))) == 0: ...`?\r\n\r\nUpdating the regressions tests with an empty batch formatted as `{\"col_1\": [], \"col_2\": []}` instead of `{}` works fine with the current if, and also with the one proposed by @cccntu.",
"> Makes sense, then the if-statement should remain the same or is it better to handle both cases separately using if not batch_examples or len(next(iter(batch_examples.values()))) == 0: ...?\r\n\r\nThere's a check later in the code that makes sure that the columns are the right ones, so I don't think we need to check for `{}` here\r\n\r\nIn particular the check `if not batch_examples or len(next(iter(batch_examples.values()))) == 0:` doesn't raise an error while it should, that why the old `if` is fine IMO\r\n\r\n> Updating the regressions tests with an empty batch formatted as {\"col_1\": [], \"col_2\": []} instead of {} works fine with the current if, and also with the one proposed by @cccntu.\r\n\r\nCool ! If you want you can update your PR to add the regression tests, to make sure that `{\"col_1\": [], \"col_2\": []}` works but not `{}`",
"Great thanks for the response! So I'll just add that regression test and remove the current if-statement.",
"Hi @lhoestq ,\r\n\r\nThanks for your explanation. Now I get it that `{}` means the columns are different. But wouldn't it be nice if the code can ignore it, like it ignores `{\"a\": []}`?\r\n\r\n\r\n--- \r\nBTW, \r\n> There's a check later in the code that makes sure that the columns are the right ones, so I don't think we need to check for {} here\r\n\r\nI remember the error happens around here:\r\nhttps://github.com/huggingface/datasets/blob/88a902d6474fae8d793542d57a4f3b0d187f3c5b/src/datasets/arrow_writer.py#L506-L507\r\nThe error says something like `arrays` and `schema` doesn't have the same length. And it's not very clear I passed a `{}`.\r\n\r\nedit: actual error message\r\n```\r\nFile \"site-packages/datasets/arrow_writer.py\", line 595, in write_batch\r\n pa_table = pa.Table.from_arrays(arrays, schema=schema)\r\n File \"pyarrow/table.pxi\", line 3557, in pyarrow.lib.Table.from_arrays\r\n File \"pyarrow/table.pxi\", line 1401, in pyarrow.lib._sanitize_arrays\r\nValueError: Schema and number of arrays unequal\r\n```",
"> But wouldn't it be nice if the code can ignore it, like it ignores {\"a\": []}?\r\n\r\nI think it would make things confusing because it doesn't follow our definition of a batch: \"the columns of a batch = the keys of the dict\". It would probably break certain behaviors as well. For example if you remove all the columns of a dataset (using `.remove_colums(...)` or `.map(..., remove_columns=...)`), the writer has to write 0 columns, and currently the only way to tell the writer to do so using `write_batch` is to pass `{}`.\r\n\r\n> The error says something like arrays and schema doesn't have the same length. And it's not very clear I passed a {}.\r\n\r\nYea the message can actually be improved indeed, it's definitely not clear. Maybe we can add a line right before the call `pa.Table.from_arrays` to make sure the keys of the batch match the field names of the schema"
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`concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets`
Fix https://github.com/huggingface/datasets/issues/2564
I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals
And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now)
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Add `concatenate_datasets` for iterable datasets
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks ! I addressed your comments :)\r\n\r\n> There is a slight difference in concatenate_datasets between the version for map-style datasets and the one for iterable datasets\r\n\r\nIndeed, here is what I did to fix this:\r\n\r\n- axis 0: fill missing columns with None.\r\n(I first iterate over the input datasets to infer their columns from the first examples, then I set the features of the resulting dataset to be the merged features)\r\nThis is consistent with non-streaming concatenation\r\n\r\n- axis 1: **fill the missing rows with None**, for consistency with axis 0\r\n(but let me know what you think, I can still revert this behavior and raise an error when one of the dataset runs out of examples)\r\nWe might have to align the non-streaming concatenation with this behavior though, for consistency. What do you think ?",
"Added more comments as suggested, and some typing\r\n\r\nWhile factorizing _apply_features_types for both IterableDataset and TypedExamplesIterable, I fixed a missing `token_per_repo_id` that was not passed to TypedExamplesIteable\r\n\r\nLet me know what you think now @mariosasko "
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fix ETT m1/m2 test/val dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thansk for the fix ! Can you regenerate the datasets_infos.json please ? This way it will update the expected number of examples in the test and val splits",
"ah yes!"
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## Describe the bug
It seems that in some cases in which the `prediction` is longer than the `reference` we may have word/character error rate higher than 1 which is a bit odd.
If it's a real bug I think I can solve it with a PR changing [this](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py#L105) line to
```python
return min(incorrect / total, 1.0)
```
## Steps to reproduce the bug
```python
from datasets import load_metric
wer = load_metric("wer")
wer_value = wer.compute(predictions=["Hi World vka"], references=["Hello"])
print(wer_value)
```
## Expected results
```
1.0
```
## Actual results
```
3.0
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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"WER can have values bigger than 1.0, this is expected when there are too many insertions\r\n\r\nFrom [wikipedia](https://en.wikipedia.org/wiki/Word_error_rate):\r\n> Note that since N is the number of words in the reference, the word error rate can be larger than 1.0"
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https://github.com/huggingface/datasets/pull/4384 moved `datasets.utils.download_manager` to `datasets.download.download_manager`
This breaks `evaluate` which imports `DownloadMode` from `datasets.utils.download_manager`
This PR re-adds `datasets.utils.download_manager` without circular imports.
We could also show a message that says that accessing it is deprecated, but I think we can do this in a subsequent PR, and just focus on doing a patch release for now
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the fix.\r\n\r\nI'm wondering how this fixes backward compatibility...\r\n\r\nExecuting this code:\r\n```python\r\nfrom datasets.utils.download_manager import DownloadMode\r\n```\r\nwe will have\r\n```python\r\nDownloadMode = None\r\n```\r\n\r\nIf afterwards we use something like:\r\n```python\r\nif download_mode == DownloadMode.FORCE_REDOWNLOAD\r\n```\r\nthat will raise an exception.",
"It works fine on my side:\r\n```python\r\n>>> from datasets.utils.download_manager import DownloadMode\r\n>>> DownloadMode is not None\r\nTrue\r\n```",
"As reported in https://github.com/huggingface/evaluate/pull/143\r\n```python\r\nfrom datasets.utils import DownloadConfig\r\n```\r\nis also missing, I'm re-adding it",
"Took the liberty of merging this one, to do a patch release soon. If we think of a better approach we can improve it later"
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As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose.
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2022-07-07T17:06:51Z
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Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity
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Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true
When trying to patch `scipy.io.loadmat`:
```python
ModuleNotFoundError: No module named 'scipy'
```
Instead it shouldn't raise an error and do nothing
Bug introduced by #4375
Fix https://github.com/huggingface/datasets/issues/4494
|
2022-06-15T16:40:49Z
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Fix patching module that doesn't exist
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Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true
When trying to patch `scipy.io.loadmat`:
```python
ModuleNotFoundError: No module named 'scipy'
```
Instead it shouldn't raise an error and do nothing
We use patching to extend such functions to support remote URLs and work in streaming mode
|
2022-06-15T08:54:09Z
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Patching fails for modules that are not installed or don't exist
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As suggested by @mariosasko in https://github.com/huggingface/datasets/pull/4411, we should include the `@transmit_format` decorator to `flatten`, `rename_column`, and `rename_columns` so as to ensure that the value of `_format_columns` in an `ArrowDataset` is properly updated.
**Edit**: according to @mariosasko comment below, the decorator `@transmit_format` doesn't handle column renaming, so it's done manually for those instead.
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2022-09-27T11:37:25Z
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Add `@transmit_format` in `flatten`
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2022-09-27T10:48:54Z
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[
"@mariosasko please let me know whether we need to include some sort of tests to make sure that the decorator is working as expected. Thanks! 🤗 ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4493). All of your documentation changes will be reflected on that endpoint.",
"Hi, thanks for working on this! Yes, please add (simple) tests so we can avoid any unexpected behavior in the future.\r\n\r\n`@transmit_format` doesn't handle column renaming, so I removed it from `rename_column` and `rename_columns` and added a comment to explain this.",
"Oops, I thought this PR was already merged and deleted from the source repository, I'll be creating a new branch out of `main` so as to re-create this PR... My bad :weary:"
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Use the commit sha in the data files URLs of the imagenet-1k download script, in case we want to restructure the data files in the future. For example we may split it into many more shards for better paralellism.
cc @mariosasko
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2022-06-14T17:35:13Z
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Pin the revision in imagenet download links
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### Link
https://huggingface.co/datasets/Pavithree/test
### Description
I have extracted the subset of original eli5 dataset found at hugging face. However, while loading the dataset It throws ArrowNotImplementedError: Unsupported cast from string to null using function cast_null error. Is there anything missing from my end? Kindly help.
### Owner
_No response_
|
2022-06-14T14:37:21Z
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[
"This issue can be resolved according to this post https://stackoverflow.com/questions/70566660/parquet-with-null-columns-on-pyarrow. It looks like first data entry in the json file must not have any null values as pyarrow uses this first file to infer schema for entire dataset."
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Use `torch.nested_tensor` for arrays of varying length in `TorchFormatter`.
The PyTorch API of nested tensors is in the prototype stage, so wait for it to become more mature.
|
2023-07-07T13:02:58Z
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Use `torch.nested_tensor` for arrays of varying length in torch formatter
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[
"What's the current behavior?",
"Currently, we return a list of Torch tensors if their shapes don't match. If they do, we consolidate them into a single Torch tensor."
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Add SV-Ident dataset
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[
"Hi @e-tornike, thanks a lot for adding this interesting dataset.\r\n\r\nRecently at Hugging Face, we have decided to give priority to adding datasets directly on the Hub. Would you mind to transfer your loading script to the Hub? You could create a dedicated org namespace, so that your dataset would be accessible using `vadis/sv_ident` or `sdproc/sv_ident` or `coling/sv_ident` (as you prefer).\r\n\r\nYou have an example here: https://huggingface.co/datasets/projecte-aina/catalan_textual_corpus",
"Additionally, please feel free to ping us if you need assistance/help in creating this dataset.\r\n\r\nYou could put the link to your Hub dataset here in this Issue discussion page, so that we can follow the progress. :)",
"Hi @albertvillanova, thanks for the feedback! Uploading via the Hub is a lot easier! \r\n\r\nI've uploaded the dataset here: https://huggingface.co/datasets/vadis/sv-ident, but it's reporting a \"Status400Error\". Is there any way to see the logs of the dataset script and what is causing the error?",
"Hi @e-tornike, good job at https://huggingface.co/datasets/vadis/sv-ident.\r\n\r\nNormally, you can run locally the loading of the dataset by passing `streaming=True` (as the previewer does):\r\n```python\r\nds = load_dataset(\"path/to/sv_ident.py, split=\"train\", streaming=True)\r\nitem = next(iter(ds))\r\nitem\r\n```\r\n\r\nLet me have a look and open a discussion on your Hub repo! ;)",
"I've opened an Issue: \r\n- #4527 "
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Update the PASS dataset to version v3 (the newest one) from the [version history](https://github.com/yukimasano/PASS/blob/main/version_history.txt).
PS: The older versions are not exposed as configs in the script because v1 was removed from Zenodo, and the same thing will probably happen to v2.
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Update PASS dataset version
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This PR:
- Adds support for streaming UDHR dataset
- Adds the BCP 47 language code as feature
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Support streaming UDHR dataset
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As described in #4075
I could not generate the dummy data. Also, on the data repository isn't provided the split IDs, but I copy the functions that provide the correct data split. In summary, to have a better distribution, the data in this dataset should be separated based on the amount of NORs in each image.
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Add CCAgT dataset
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2022-07-04T14:25:45Z
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Hi! Excellent job @johnnv1! There were typos/missing words in the card, so I took the liberty to rewrite some parts to make them easier to understand. Let me know if you are ok with the changes. Also, feel free to add some info under the `Who are the annotators?` section.\r\n\r\nAdditionally, I fixed the issue with streaming and renamed the `digits` feature to `objects`.\r\n\r\n@lhoestq Are you ok with skipping the dummy data test here as it's tricky to generate it due to the splits separation logic?",
"I think I can also add instance segmentation: by exposing the segment of each instance, so it will be similar with object detection:\r\n\r\n- `instances`: a dictionary containing bounding boxes, segments, and labels of the cell objects \r\n - `bbox`: a list of bounding boxes\r\n - `segment`: a list of segments in format of `[polygon]`, where each polygon is `[x0, y0, ..., xn, yn]`\r\n - `label`: a list of integers representing the category\r\n\r\nDo you think it would be ok?",
"Don't you think it makes sense to keep the same category IDs for all approaches? \r\n\r\nNow we have:\r\n - nucleus category ID equals 0 for object detection and instance segmentation\r\n - background category ID equals 0 (on the masks) for semantic segmentation",
"I find it weird to have a dummy label in object detection just to align the mapping with semantic segmentation. Instead, let's explain in the card (under Data Fields -> annotation) what the pixel values mean (background + object detection labels)",
"Ok, I can do that in the next few days. I will create a `lapix` organization, and I will add this dataset and also #4565",
"So, I think we can close this PR? I have already moved these files there.\r\n\r\nThe link of CCAgT dataset is: https://huggingface.co/datasets/lapix/CCAgT\r\n\r\n🤗 ",
"Woohoo awesome !\r\n\r\nclosing this PR :)"
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It currently fails with `ArrowNotImplementedError` instead of `TypeError` when one tries to cast integer to null type.
Because if this, type inference breaks when one replaces null values with integers in `map` (it first tries to cast to the previous type before inferring the new type).
Fix https://github.com/huggingface/datasets/issues/4483
|
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Fix cast to null
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When a depenency is missing for a dataset script, an ImportError message is shown, with a tip to install the missing dependencies. This message is not ideal at the moment: it may show duplicate dependencies, and is not very readable.
I improved it from
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
```
to
```
ImportError: To be able to use bigbench, you need to install the following dependency: bigbench.
Please install it using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"' for instance'
```
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2022-07-08T14:30:44Z
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Better ImportError message when a dataset script dependency is missing
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"_The documentation is not available anymore as the PR was closed or merged._",
"Discussed offline with @mariosasko, merging :)",
"Fwiw, i think this same issue is occurring on the datasets website page, where preview isn't available due to the `bigbench` import error",
"For the preview of BigBench datasets, we're just waiting for bigbench to have a stable version on PyPI, instead of the one hosted on GCS ;)"
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## Describe the bug
Dataset.map throws pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null when converting from a type of 'empty lists' to 'lists with some type'.
This appears to be due to the interaction of arrow internals and some assumptions made by datasets.
The bug appeared when binarizing some labels, and then adding a dataset which had all these labels absent (to force the model to not label empty strings such with anything)
Particularly the fact that this only happens in batched mode is strange.
## Steps to reproduce the bug
```python
import numpy as np
ds = Dataset.from_dict(
{
"text": ["the lazy dog jumps over the quick fox", "another sentence"],
"label": [[], []],
}
)
def mapper(features):
features['label'] = [
[0,0,0] for l in features['label']
]
return features
ds_mapped = ds.map(mapper,batched=True)
```
## Expected results
Not crashing
## Actual results
```
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2346: in map
return self._map_single(
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:532: in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:499: in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/fingerprint.py:458: in wrapper
out = func(self, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/arrow_dataset.py:2751: in _map_single
writer.write_batch(batch)
../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:503: in write_batch
arrays.append(pa.array(typed_sequence))
pyarrow/array.pxi:230: in pyarrow.lib.array
???
pyarrow/array.pxi:110: in pyarrow.lib._handle_arrow_array_protocol
???
../.venv/lib/python3.8/site-packages/datasets/arrow_writer.py:198: in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper
return func(array, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/table.py:1812: in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper
return func(array, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/table.py:1843: in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
../.venv/lib/python3.8/site-packages/datasets/table.py:1675: in wrapper
return func(array, *args, **kwargs)
../.venv/lib/python3.8/site-packages/datasets/table.py:1752: in array_cast
return array.cast(pa_type)
pyarrow/array.pxi:915: in pyarrow.lib.Array.cast
???
../.venv/lib/python3.8/site-packages/pyarrow/compute.py:376: in cast
return call_function("cast", [arr], options)
pyarrow/_compute.pyx:542: in pyarrow._compute.call_function
???
pyarrow/_compute.pyx:341: in pyarrow._compute.Function.call
???
pyarrow/error.pxi:144: in pyarrow.lib.pyarrow_internal_check_status
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E pyarrow.lib.ArrowNotImplementedError: Unsupported cast from int64 to null using function cast_null
pyarrow/error.pxi:121: ArrowNotImplementedError
```
## Workarounds
* Not using batched=True
* Using an np.array([],dtype=float) or similar instead of [] in the input
* Naming the output column differently from the input column
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: Ubuntu
- Python version: 3.8
- PyArrow version: 8.0.0
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CONTRIBUTOR
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2022-06-14T13:34:14Z
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I_kwDODunzps5Lp0bQ
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[
"Hi @sanderland ! Thanks for reporting :) This is a bug, I opened a PR to fix it. We'll do a new release soon\r\n\r\nIn the meantime you can fix it by specifying in advance that the \"label\" are integers:\r\n```python\r\nimport numpy as np\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"text\": [\"the lazy dog jumps over the quick fox\", \"another sentence\"],\r\n \"label\": [[], []],\r\n }\r\n)\r\n# explicitly say that the \"label\" type is int64, even though it contains only null values\r\nds = ds.cast_column(\"label\", Sequence(Value(\"int64\")))\r\n\r\ndef mapper(features):\r\n features['label'] = [\r\n [0,0,0] for l in features['label']\r\n ]\r\n return features\r\n\r\nds_mapped = ds.map(mapper,batched=True)\r\n```"
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| false
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1,269,237,447
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https://api.github.com/repos/huggingface/datasets/issues/4482/labels{/name}
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TF takes some time to be imported, and also uses some GPU memory.
I just added a test to make sure that in the future it's never imported by default when
```python
import datasets
```
is called.
Right now this fails because `huggingface_hub` does import tensorflow (though this is fixed now on their `main` branch)
I'll mark this PR as ready for review once `huggingface_hub` has a new release
|
2023-10-12T06:31:39Z
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Test that TensorFlow is not imported on startup
|
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2023-10-11T09:11:56Z
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Should we close this PR?",
"I'm closing this PR. Feel free to reopen it if necessary."
] |
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The files were moved to google drive, I hosted them on the Hub instead (ok according to the license)
I also updated the `datasets_infos.json`
|
2022-10-26T09:09:31Z
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2022-06-13T09:51:21Z
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Fix iwslt2017
|
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"CI fails are just abut missing tags in the dataset card, merging !",
"FYI, \r\n\r\nThe checksums have not been edited from the changes of .tgz to .zip files, and as a result a `ExpectedMoreDownloadedFiles` error occurs. Updating them in the `dataset_infos.json` should fix the error. ",
"Thanks for reporting and sorry for the delay, I opened https://huggingface.co/datasets/iwslt2017/discussions/2 to fix this"
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## Describe the bug
Loading bigbech
```py
from datasets import load_dataset
dataset = load_dataset("bigbench","swedish_to_german_proverbs")
```
tries to use gpu and fails with OOM with the following error
```
Downloading and preparing dataset bigbench/swedish_to_german_proverbs (download: Unknown size, generated: 68.92 KiB, post-processed: Unknown size, total: 68.92 KiB) to /home/ceyda/.cache/huggingface/datasets/bigbench/swedish_to_german_proverbs/1.0.0/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0...
Generating default split: 0%| | 0/72 [00:00<?, ? examples/s]2022-06-13 14:11:04.154469: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-06-13 14:11:05.133600: F tensorflow/core/platform/statusor.cc:33] Attempting to fetch value instead of handling error INTERNAL: failed initializing StreamExecutor for CUDA device ordinal 3: INTERNAL: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 25396838400
Aborted (core dumped)
```
I think this is because bigbench dependency (below) installs tensorflow (GPU version) and dataloading tries to use GPU as default.
`pip install bigbench@https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz`
while just doing 'pip install bigbench' results in following error
```
File "/home/ceyda/.local/lib/python3.7/site-packages/datasets/load.py", line 109, in import_main_class
module = importlib.import_module(module_path)
File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/ceyda/.cache/huggingface/modules/datasets_modules/datasets/bigbench/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0/bigbench.py", line 118, in <module>
class Bigbench(datasets.GeneratorBasedBuilder):
File "/home/ceyda/.cache/huggingface/modules/datasets_modules/datasets/bigbench/7d2f6e537fa937dfaac8b1c1df782f2055071d3fd8e4f4ae93d28012a354ced0/bigbench.py", line 127, in Bigbench
BigBenchConfig(name=name, version=datasets.Version("1.0.0")) for name in bb_utils.get_all_json_task_names()
AttributeError: module 'bigbench.api.util' has no attribute 'get_all_json_task_names'
```
## Steps to avoid the bug
Not ideal but can solve with (since I don't really use tensorflow elsewhere)
`pip uninstall tensorflow`
`pip install tensorflow-cpu`
## Environment info
- datasets @ master
- Python version: 3.7
|
2022-06-14T19:45:24Z
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2022-06-13T05:24:06Z
|
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Bigbench tensorflow GPU dependency
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CONTRIBUTOR
|
2022-06-14T19:45:23Z
| null |
I_kwDODunzps5LojTf
|
[
"Thanks for reporting ! :) cc @andersjohanandreassen can you take a look at this ?\r\n\r\nAlso @cceyda feel free to open an issue at [BIG-Bench](https://github.com/google/BIG-bench) as well regarding the `AttributeError`",
"I'm on vacation for the next week, so won't be able to do much debugging at the moment. Sorry for the inconvenience.\r\nBut I did quickly take a look:\r\n\r\n**pypi**:\r\nI managed to reproduce the above error with the pypi version begin out of date. \r\nThe version on `https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz` should be up to date, but it was my understanding that there was some issue with the pypi upload, so I don't even understand why there is a version [on pypi from April 1](https://pypi.org/project/bigbench/0.0.1/). Perhaps @ethansdyer, who's handling the pypi upload, knows the answer to that?\r\n\r\n**OOM error**:\r\nBut, I'm unable to reproduce the OOM error in a google colab with GPU enabled.\r\nThis is what I ran:\r\n```\r\n!pip install bigbench@https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz\r\n!pip install datasets\r\n\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"bigbench\",\"swedish_to_german_proverbs\")\r\n``` \r\nThe `swedish_to_german_proverbs`task is only 72 examples, so I don't understand what could be causing the OOM error. Loading the task has no effect on the RAM for me. @cceyda Can you confirm that this does not occur in a [colab](https://colab.research.google.com/)?\r\nIf the GPU is somehow causing issues on your system, disabling the GPU from TF might be an option too\r\n```\r\nimport os\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n",
"Solved.\r\nYes it works on colab, and somehow magically on my machine too now. hmm not sure what was wrong before I had used a fresh venv both times with just the dataloading code, and tried multiple times. (maybe just a wrong tensorflow version got mixed up somehow) The tensorflow call seems to come from the bigbench side anyway.\r\n\r\nabout bigbench pypi version update, I opened an issue over there https://github.com/google/BIG-bench/issues/846\r\n\r\nanyway closing this now. If anyone else has the same problem can re-open."
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https://huggingface.co/datasets/super_glue/viewer/record/validation
TLDR: We need to record entity positions, which are included in the source data but excluded by the loading script, to enable efficient and effective training for ReCoRD.
Currently, the loading script ignores the entity positions ("entity_start", "entity_end") and only records entity text. This might be because the training method of the official baseline is to make n training instance from a datapoint by replacing \"\@ placeholder\" in query with each entity individually.
But it increases the already heavy computation by multiple folds. So DeBERTa uses a method that take entity embeddings by their positions in the passage, and thus makes one training instance from one data point. It is way more efficient and proved effective for the ReCoRD task.
Can anybody help me with the dataset card rendering error? Maybe @lhoestq ?
|
2022-08-19T23:23:02Z
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Include entity positions as feature in ReCoRD
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2022-08-19T13:23:48Z
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the reply @lhoestq !\r\n\r\nI have sucessed on `datasets-cli test ./datasets/super_glue --name record --save_infos`,\r\nBut as you can see, the check ran into `FAILED tests/test_dataset_cards.py::test_changed_dataset_card[super_glue] - V...`.\r\nHow can we solve it?",
"That would be neat! Let me implement it."
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## Describe the bug
While migrating towards 🤗 Datasets, I encountered an odd performance degradation: training suddenly slows down dramatically. I train with an image dataset using Keras and execute a `to_tf_dataset` just before training.
First, I have optimized my dataset following https://discuss.huggingface.co/t/solved-image-dataset-seems-slow-for-larger-image-size/10960/6, which actually improved the situation from what I had before but did not completely solve it.
Second, I saved and loaded my dataset using `tf.data.experimental.save` and `tf.data.experimental.load` before training (for which I would have expected no performance change). However, I ended up with the performance I had before tinkering with 🤗 Datasets.
Any idea what's the reason for this and how to speed-up training with 🤗 Datasets?
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
import os
dataset_dir = "./dataset"
prep_dataset_dir = "./prepdataset"
model_dir = "./model"
# Load Data
dataset = load_dataset("Lehrig/Monkey-Species-Collection", "downsized")
def read_image_file(example):
with open(example["image"].filename, "rb") as f:
example["image"] = {"bytes": f.read()}
return example
dataset = dataset.map(read_image_file)
dataset.save_to_disk(dataset_dir)
# Preprocess
from datasets import (
Array3D,
DatasetDict,
Features,
load_from_disk,
Sequence,
Value
)
import numpy as np
from transformers import ImageFeatureExtractionMixin
dataset = load_from_disk(dataset_dir)
num_classes = dataset["train"].features["label"].num_classes
one_hot_matrix = np.eye(num_classes)
feature_extractor = ImageFeatureExtractionMixin()
def to_pixels(image):
image = feature_extractor.resize(image, size=size)
image = feature_extractor.to_numpy_array(image, channel_first=False)
image = image / 255.0
return image
def process(examples):
examples["pixel_values"] = [
to_pixels(image) for image in examples["image"]
]
examples["label"] = [
one_hot_matrix[label] for label in examples["label"]
]
return examples
features = Features({
"pixel_values": Array3D(dtype="float32", shape=(size, size, 3)),
"label": Sequence(feature=Value(dtype="int32"), length=num_classes)
})
prep_dataset = dataset.map(
process,
remove_columns=["image"],
batched=True,
batch_size=batch_size,
num_proc=2,
features=features,
)
prep_dataset = prep_dataset.with_format("numpy")
# Split
train_dev_dataset = prep_dataset['test'].train_test_split(
test_size=test_size,
shuffle=True,
seed=seed
)
train_dev_test_dataset = DatasetDict({
'train': train_dev_dataset['train'],
'dev': train_dev_dataset['test'],
'test': prep_dataset['test'],
})
train_dev_test_dataset.save_to_disk(prep_dataset_dir)
# Train Model
import datetime
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D, BatchNormalization
from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping
from transformers import DefaultDataCollator
dataset = load_from_disk(prep_data_dir)
data_collator = DefaultDataCollator(return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=True,
batch_size=batch_size,
collate_fn=data_collator
)
validation_dataset = dataset["dev"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=False,
batch_size=batch_size,
collate_fn=data_collator
)
print(f'{datetime.datetime.now()} - Saving Data')
tf.data.experimental.save(train_dataset, model_dir+"/train")
tf.data.experimental.save(validation_dataset, model_dir+"/val")
print(f'{datetime.datetime.now()} - Loading Data')
train_dataset = tf.data.experimental.load(model_dir+"/train")
validation_dataset = tf.data.experimental.load(model_dir+"/val")
shape = np.shape(dataset["train"][0]["pixel_values"])
backbone = InceptionV3(
include_top=False,
weights='imagenet',
input_shape=shape
)
for layer in backbone.layers:
layer.trainable = False
model = Sequential()
model.add(backbone)
model.add(GlobalAveragePooling2D())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(10, activation='softmax'))
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
print(model.summary())
earlyStopping = EarlyStopping(
monitor='val_loss',
patience=10,
verbose=0,
mode='min'
)
mcp_save = ModelCheckpoint(
f'{model_dir}/best_model.hdf5',
save_best_only=True,
monitor='val_loss',
mode='min'
)
reduce_lr_loss = ReduceLROnPlateau(
monitor='val_loss',
factor=0.1,
patience=7,
verbose=1,
min_delta=0.0001,
mode='min'
)
hist = model.fit(
train_dataset,
epochs=epochs,
validation_data=validation_dataset,
callbacks=[earlyStopping, mcp_save, reduce_lr_loss]
)
```
## Expected results
Same performance when training without my "save/load hack" or a good explanation/recommendation about the issue.
## Actual results
Performance slower without my "save/load hack".
**Epoch Breakdown (without my "save/load hack"):**
- Epoch 1/10
41s 2s/step - loss: 1.6302 - accuracy: 0.5048 - val_loss: 1.4713 - val_accuracy: 0.3273 - lr: 0.0010
- Epoch 2/10
32s 2s/step - loss: 0.5357 - accuracy: 0.8510 - val_loss: 1.0447 - val_accuracy: 0.5818 - lr: 0.0010
- Epoch 3/10
36s 3s/step - loss: 0.3547 - accuracy: 0.9231 - val_loss: 0.6245 - val_accuracy: 0.7091 - lr: 0.0010
- Epoch 4/10
36s 3s/step - loss: 0.2721 - accuracy: 0.9231 - val_loss: 0.3395 - val_accuracy: 0.9091 - lr: 0.0010
- Epoch 5/10
32s 2s/step - loss: 0.1676 - accuracy: 0.9856 - val_loss: 0.2187 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 6/10
42s 3s/step - loss: 0.2066 - accuracy: 0.9615 - val_loss: 0.1635 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 7/10
32s 2s/step - loss: 0.1814 - accuracy: 0.9423 - val_loss: 0.1418 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 8/10
32s 2s/step - loss: 0.1301 - accuracy: 0.9856 - val_loss: 0.1388 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 9/10
loss: 0.1102 - accuracy: 0.9856 - val_loss: 0.1185 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 10/10
32s 2s/step - loss: 0.1013 - accuracy: 0.9808 - val_loss: 0.0978 - val_accuracy: 0.9818 - lr: 0.0010
**Epoch Breakdown (with my "save/load hack"):**
- Epoch 1/10
13s 625ms/step - loss: 3.0478 - accuracy: 0.1146 - val_loss: 2.3061 - val_accuracy: 0.0727 - lr: 0.0010
- Epoch 2/10
0s 80ms/step - loss: 2.3105 - accuracy: 0.2656 - val_loss: 2.3085 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 3/10
0s 77ms/step - loss: 1.8608 - accuracy: 0.3542 - val_loss: 2.3130 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 4/10
1s 98ms/step - loss: 1.8677 - accuracy: 0.3750 - val_loss: 2.3157 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 5/10
1s 204ms/step - loss: 1.5561 - accuracy: 0.4583 - val_loss: 2.3049 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 6/10
1s 210ms/step - loss: 1.4657 - accuracy: 0.4896 - val_loss: 2.2944 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 7/10
1s 205ms/step - loss: 1.4018 - accuracy: 0.5312 - val_loss: 2.2917 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 8/10
1s 207ms/step - loss: 1.2370 - accuracy: 0.5729 - val_loss: 2.2814 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 9/10
1s 214ms/step - loss: 1.1190 - accuracy: 0.6250 - val_loss: 2.2733 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 10/10
1s 207ms/step - loss: 1.1484 - accuracy: 0.6302 - val_loss: 2.2624 - val_accuracy: 0.0909 - lr: 0.0010
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
- TensorFlow: 2.8.0
- GPU (used during training): Tesla V100-SXM2-32GB
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"Hi ! cc @Rocketknight1 maybe you know better ?\r\n\r\nI'm not too familiar with `tf.data.experimental.save`. Note that `datasets` uses memory mapping, so depending on your hardware and the disk you are using you can expect performance differences with a dataset loaded in RAM",
"Hi @lehrig, I suspect what's happening here is that our `to_tf_dataset()` method has some performance issues when streaming samples. This is usually not a problem, but they become apparent when streaming a vision dataset into a very small vision model, which will need a lot of sample throughput to saturate the GPU.\r\n\r\nWhen you save a `tf.data.Dataset` with `tf.data.experimental.save`, all of the samples from the dataset (which are, in this case, batches of images), are saved to disk. When you load this saved dataset, you're effectively bypassing `to_tf_dataset()` entirely, which alleviates this performance bottleneck.\r\n\r\n`to_tf_dataset()` is something we're actively working on overhauling right now - particularly for image datasets, we want to make it possible to access the underlying images with `tf.data` without going through the current layer of indirection with `Arrow`, which should massively improve simplicity and performance. \r\n\r\nHowever, if you just want this to work quickly but without needing your save/load hack, my advice would be to simply load the dataset into memory if it's small enough to fit. Since all your samples have the same dimensions, you can do this simply with:\r\n\r\n```\r\ndataset = load_from_disk(prep_data_dir)\r\ndataset = dataset.with_format(\"numpy\")\r\ndata_in_memory = dataset[:]\r\n```\r\n\r\nThen you can simply do something like:\r\n\r\n```\r\nmodel.fit(data_in_memory[\"pixel_values\"], data_in_memory[\"labels\"])\r\n```",
"Thanks for the information! \r\n\r\nI have now updated the training code like so:\r\n\r\n```\r\ndataset = load_from_disk(prep_data_dir)\r\ntrain_dataset = dataset[\"train\"][:]\r\nvalidation_dataset = dataset[\"dev\"][:]\r\n\r\n...\r\n\r\nmodel.fit(\r\n train_dataset[\"pixel_values\"],\r\n train_dataset[\"label\"],\r\n epochs=epochs,\r\n validation_data=(\r\n validation_dataset[\"pixel_values\"],\r\n validation_dataset[\"label\"]\r\n ),\r\n callbacks=[earlyStopping, mcp_save, reduce_lr_loss]\r\n)\r\n```\r\n\r\n- Creating the in-memory dataset is quite quick\r\n- But: There is now a long wait (~4-5 Minutes) before the training starts (why?)\r\n- And: Training times have improved but the very first epoch leaves me wondering why it takes so long (why?)\r\n\r\n**Epoch Breakdown:**\r\n- Epoch 1/10\r\n78s 12s/step - loss: 3.1307 - accuracy: 0.0737 - val_loss: 2.2827 - val_accuracy: 0.1273 - lr: 0.0010\r\n- Epoch 2/10\r\n1s 168ms/step - loss: 2.3616 - accuracy: 0.2350 - val_loss: 2.2679 - val_accuracy: 0.2182 - lr: 0.0010\r\n- Epoch 3/10\r\n1s 189ms/step - loss: 2.0221 - accuracy: 0.3180 - val_loss: 2.2670 - val_accuracy: 0.1818 - lr: 0.0010\r\n- Epoch 4/10\r\n0s 67ms/step - loss: 1.8895 - accuracy: 0.3548 - val_loss: 2.2771 - val_accuracy: 0.1273 - lr: 0.0010\r\n- Epoch 5/10\r\n0s 67ms/step - loss: 1.7846 - accuracy: 0.3963 - val_loss: 2.2860 - val_accuracy: 0.1455 - lr: 0.0010\r\n- Epoch 6/10\r\n0s 65ms/step - loss: 1.5946 - accuracy: 0.4516 - val_loss: 2.2938 - val_accuracy: 0.1636 - lr: 0.0010\r\n- Epoch 7/10\r\n0s 63ms/step - loss: 1.4217 - accuracy: 0.5115 - val_loss: 2.2968 - val_accuracy: 0.2182 - lr: 0.0010\r\n- Epoch 8/10\r\n0s 67ms/step - loss: 1.3089 - accuracy: 0.5438 - val_loss: 2.2842 - val_accuracy: 0.2182 - lr: 0.0010\r\n- Epoch 9/10\r\n1s 184ms/step - loss: 1.2480 - accuracy: 0.5806 - val_loss: 2.2652 - val_accuracy: 0.1818 - lr: 0.0010\r\n- Epoch 10/10\r\n0s 65ms/step - loss: 1.2699 - accuracy: 0.5622 - val_loss: 2.2670 - val_accuracy: 0.2000 - lr: 0.0010\r\n\r\n",
"Regarding the new long ~5 min. wait introduced by the in-memory dataset update: this might be causing it? https://datascience.stackexchange.com/questions/33364/why-model-fit-generator-in-keras-is-taking-so-much-time-even-before-picking-the\r\n\r\nFor now, my save/load hack is still more performant, even though having more boiler-plate code :/ ",
"That 5 minute wait is quite surprising! I don't have a good explanation for why it's happening, but it can't be an issue with `datasets` or `tf.data` because you're just fitting directly on Numpy arrays at this point. All I can suggest is seeing if you can isolate the issue - for example, does fitting on a smaller dataset containing only 10% of the original data reduce the wait? This might indicate the delay is caused by your data being copied or converted somehow. Alternatively, you could try removing things like callbacks and seeing if you could isolate the issue there."
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Dataset Viewer issue for fgrezes/WIESP2022-NER
|
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completed
|
NONE
|
2022-07-18T13:07:33Z
| null |
I_kwDODunzps5LmNv6
|
[
"https://huggingface.co/datasets/fgrezes/WIESP2022-NER\r\n\r\nThe error:\r\n\r\n```\r\nMessage: Couldn't find a dataset script at /src/services/worker/fgrezes/WIESP2022-NER/WIESP2022-NER.py or any data file in the same directory. Couldn't find 'fgrezes/WIESP2022-NER' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**test*', '**eval*'] in dataset repository fgrezes/WIESP2022-NER with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']\r\n```\r\n\r\nI understand the issue is not related to the dataset viewer in itself, but with the autodetection of the data files without a loading script in the datasets library. cc @lhoestq @albertvillanova @mariosasko ",
"Apparently it finds `scoring-scripts/compute_seqeval.py` which matches `**eval*`, a regex that detects a test split. We should probably improve the regex because it's not supposed to catch this kind of files. It must also only check for files with supported extensions: txt, csv, png etc."
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**Is your feature request related to a problem? Please describe.**
I have a large dataset that I need to convert part of to pandas to do some further analysis. Calling `to_pandas` directly on it is expensive. So I thought I could simply select the columns that I want and then call `to_pandas`.
**Describe the solution you'd like**
```python
from datasets import Dataset
ds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})
pandas_df = ds.with_format(columns=['a', 'b']).to_pandas()
# I would expect `pandas_df` to only include a,b as column.
```
**Describe alternatives you've considered**
I could remove all columns that I don't want? But I don't know all of them in advance.
**Additional context**
I can probably make a PR with some pointers.
|
2022-06-15T17:41:41Z
| 4,476
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https://api.github.com/repos/huggingface/datasets
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2022-06-10T20:25:31Z
|
https://api.github.com/repos/huggingface/datasets/issues/4476/comments
| null |
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`to_pandas` doesn't take into account format.
|
https://api.github.com/repos/huggingface/datasets/issues/4476/events
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completed
|
CONTRIBUTOR
|
2022-06-15T17:41:41Z
| null |
I_kwDODunzps5Lk_Qr
|
[
"Thanks for opening a discussion :)\r\n\r\nNote that you can use `.remove_columns(...)` to keep only the ones you're interested in before calling `.to_pandas()`",
"Yes I can do that thank you!\r\n\r\nDo you think that conceptually my example should work? If not, I'm happy to close this issue. \r\n\r\nIf yes, I can start working on it.",
"Hi! Instead of `with_format(columns=['a', 'b']).to_pandas()`, use `with_format(\"pandas\", columns=[\"a\", \"b\"])` for easy conversion of the parts of the dataset to pandas via indexing/slicing.\r\n\r\nThe full code:\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({'a': [1,2,3], 'b': [5,6,7], 'c': [8,9,10]})\r\npandas_df = ds.with_format(\"pandas\", columns=['a', 'b'])[:]\r\n```",
"Ahhhh Thank you!\r\n\r\nclosing then :)"
] |
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|
Improve the error message for missing packages from inside a dataset script:
With this change, the error message for missing packages for `bigbench` looks as follows:
```
ImportError: To be able to use bigbench, you need to install the following dependencies:
- 'bigbench' using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz"'
```
And this is how it looked before:
```
ImportError: To be able to use bigbench, you need to install the following dependencies['bigbench', 'bigbench', 'bigbench', 'bigbench'] using 'pip install "bigbench @ https://storage.googleapis.com/public_research_data/bigbench/bigbench-0.0.1.tar.gz" bigbench bigbench bigbench' for instance'
```
|
2022-10-06T13:46:26Z
| 4,475
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2022-06-10T16:59:36Z
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Improve error message for missing pacakges from inside dataset script
|
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[] | null | null |
CONTRIBUTOR
|
2022-06-13T13:16:43Z
|
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PR_kwDODunzps45eufw
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"I opened a PR before I noticed yours ^^' You can find it here: https://github.com/huggingface/datasets/pull/4484\r\n\r\nThe only comment I have regarding your message is that it possibly shows several `pip install` commands, whereas one can run one single `pip install` command with the list of missing dependencies, which is maybe simpler.\r\n\r\nLet me know which one your prefer",
"Closing in favor of #4484. "
] |
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Currently the docs about PyTorch are scattered around different pages, and we were missing a place to explain more in depth how to use and optimize a dataset for PyTorch. This PR is related to #4457 which is the TF counterpart :)
cc @Rocketknight1 we can try to align both documentations contents now I think
cc @stevhliu let me know what you think !
|
2022-06-14T14:40:32Z
| 4,474
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|
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[Docs] How to use with PyTorch page
|
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MEMBER
|
2022-06-14T14:04:33Z
|
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"_The documentation is not available anymore as the PR was closed or merged._"
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