TimeoutError [100060]?
Dear@John6666and other community membersTimeoutError: [WinError 10060] A connection attempt failed because the connected party did not properly respond  after a period of time, or established connection failed because connected host has failed to respond  During handling of the above exception, another exception occurred:ConnectionError: (ProtocolError(‘Connection aborted.’, TimeoutError(10060, ‘A connection attempt failed because theconnected party did not properly respond after a period of time, or established connection failed because connectedhost has failed to respond’, None, 10060, None)), ‘(Request ID: 34f638f9-0518-48da-aae7-6dcd50daa757)’)I changed the token, reset the variables, check internet connections, what happened?
2024-11-29T02:39:21Z
What environment?
2024-11-29T02:39:50Z
miniConda, does it do with the environment?
2024-11-29T02:41:50Z
Using Conda probably means you’re working in a local environment. Judging from the error message, it seems to be a Windows environment. I wonder if the connection from the local environment is not working properly.Was it working properly until yesterday?
2024-11-29T02:43:08Z
Last time I ran it, it ran into CPU memory problem, then I ran it again and again and faces that. Should I clean some caches? Sorry for the mislicked as solution, haven’t sleeped
2024-11-29T02:47:18Z
Oh, if it disappears using the Solution function, I think you can just post it again.If it’s not working once, it’s possible that the environment is not working properly…However, if you’re doing it from Python, it’s unlikely to be a browser setting, and it’s often the case that it’s due to VPN, SSL, router settings, or internal network settings, but do you have any ideas?Edit:Because this is an error that has occurred for various reasons since long ago, it is not possible to identify the cause or solution just from the error.github.com/tgalal/yowsupTimeoutError: [WinError 10060] A connection attempt failed because the connected  party did not properly respond after a period of time, or established connectio n failed because connected host has failed to respondopened05:25AM - 03 Sep 14 UTCclosed12:09AM - 26 Nov 14 UTCHarrrykrishnanCommand I tried is 

python yowsup-cli --requestcode voice -c D:\Official\Work\R…TB\CEM\RTB\yowsup-master\yowsup-master\src/yowsup-cli.conf

Getting Following error while connecting.  Request for the help.
# 

  File "D:\Official\Work\RTB\CEM\RTB\yowsup-master\yowsup-master\src\Yowsup\Comm
on\Http\warequest.py", line 194, in sendRequest
    conn.request(reqType, path, params, headers);
  File "C:\Python34\lib\http\client.py", line 1090, in request
    self._send_request(method, url, body, headers)
  File "C:\Python34\lib\http\client.py", line 1128, in _send_request
    self.endheaders(body)
  File "C:\Python34\lib\http\client.py", line 1086, in endheaders
    self._send_output(message_body)
  File "C:\Python34\lib\http\client.py", line 924, in _send_output
    self.send(msg)
  File "C:\Python34\lib\http\client.py", line 859, in send
    self.connect()
  File "C:\Python34\lib\http\client.py", line 1221, in connect
    super().connect()
  File "C:\Python34\lib\http\client.py", line 836, in connect
    self.timeout, self.source_address)
  File "C:\Python34\lib\socket.py", line 509, in create_connection
    raise err
  File "C:\Python34\lib\socket.py", line 500, in create_connection
    sock.connect(sa)
TimeoutError: [WinError 10060] A connection attempt failed because the connected
 party did not properly respond after a period of time, or established connectio
n failed because connected host has failed to respond
2024-11-29T02:51:39Z
Yes that’s the problem, when I tried to look for the solution it went more than just huggingface. Let me check some of these.But it does rains a lot since yesterday, I might need to go out for better connection
2024-11-29T02:54:42Z
HF related items.github.com/huggingface/hf-hubConnection Timeoutopened02:22AM - 13 Jan 24 UTCJordan-HaideeIn my Windows OS, when I run the example:
```rust
use REDACTED_TOKEN::api::sync::Api;…fn main() {
    let api = Api::new().unwrap();

    let repo = api.model("bert-base-uncased".to_string());
    let _filename = repo.get("config.json").unwrap();
}
```
- It always raise `Connect Timeout` exception. I tried to use proxy, but it didn't work too.
- If using Python `huggingface_hub` package, the exception didn't happen.
![image](https://github.com/huggingface/hf-hub/assets/85778126/ddbec343-d0eb-4ff3-972b-c3e3ea2d901c)github.com/huggingface/huggingface_hubConnection Erroropened03:04PM - 09 Jun 24 UTCbaharcosbug### Describe the bug

Hi I am consistently getting this error when I try to us…e a tokenizer, mode or pipeline from pretrained hf models:

> OSError: We couldn't connect to 'https://huggingface.co/' to load this file, couldn't find it in the cached files and it looks like google-bert/bert-base-german-cased is not the path to a directory containing a file named config.json.
Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.

I checked my connection and cleared out the cache. Moreover when I update hugging face_hub[cli] with pip install huggingface_hub[cli], I get the following warning:
> WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7fc032677010>: Failed to establish a new connection: [Errno 101] Network is unreachable')': /simple/huggingface-hub/


The models I try to access were google-bert/bert-base-german-cased and dbmdz/bert-base-german-uncased. I am running the code on google cloud instances on n2d-standard-4 (4 vCPUs, 16 GB RAM) machine with transformers version 4.40.2. Could you please let me know what leads to this error and help me solve it?

### Reproduction

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-german-cased")
model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-german-cased")

### Logs

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/urllib3/connection.py:198, in HTTPConnection._new_conn(self)
    197 try:
--> 198     sock = connection.create_connection(
    199         (self._dns_host, self.port),
    200         self.timeout,
    201         source_address=self.source_address,
    202         socket_options=self.socket_options,
    203     )
    204 except socket.gaierror as e:

File /opt/conda/lib/python3.10/site-packages/urllib3/util/connection.py:85, in create_connection(address, timeout, source_address, socket_options)
     84 try:
---> 85     raise err
     86 finally:
     87     # Break explicitly a reference cycle

File /opt/conda/lib/python3.10/site-packages/urllib3/util/connection.py:73, in create_connection(address, timeout, source_address, socket_options)
     72     sock.bind(source_address)
---> 73 sock.connect(sa)
     74 # Break explicitly a reference cycle

OSError: [Errno 101] Network is unreachable

The above exception was the direct cause of the following exception:

NewConnectionError                        Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/urllib3/connectionpool.py:793, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
    792 # Make the request on the HTTPConnection object
--> 793 response = self._make_request(
    794     conn,
    795     method,
    796     url,
    797     timeout=timeout_obj,
    798     body=body,
    799     headers=headers,
    800     chunked=chunked,
    801     retries=retries,
    802     response_conn=response_conn,
    803     preload_content=preload_content,
    804     decode_content=decode_content,
    805     **response_kw,
    806 )
    808 # Everything went great!

File /opt/conda/lib/python3.10/site-packages/urllib3/connectionpool.py:491, in HTTPConnectionPool._make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
    490         new_e = _wrap_proxy_error(new_e, conn.proxy.scheme)
--> 491     raise new_e
    493 # conn.request() calls http.client.*.request, not the method in
    494 # urllib3.request. It also calls makefile (recv) on the socket.

File /opt/conda/lib/python3.10/site-packages/urllib3/connectionpool.py:467, in HTTPConnectionPool._make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
    466 try:
--> 467     self._validate_conn(conn)
    468 except (SocketTimeout, BaseSSLError) as e:

File /opt/conda/lib/python3.10/site-packages/urllib3/connectionpool.py:1099, in HTTPSConnectionPool._validate_conn(self, conn)
   1098 if conn.is_closed:
-> 1099     conn.connect()
   1101 # TODO revise this, see https://github.com/urllib3/urllib3/issues/2791

File /opt/conda/lib/python3.10/site-packages/urllib3/connection.py:616, in HTTPSConnection.connect(self)
    615 sock: socket.socket | ssl.SSLSocket
--> 616 self.sock = sock = self._new_conn()
    617 server_hostname: str = self.host

File /opt/conda/lib/python3.10/site-packages/urllib3/connection.py:213, in HTTPConnection._new_conn(self)
    212 except OSError as e:
--> 213     raise NewConnectionError(
    214         self, f"Failed to establish a new connection: {e}"
    215     ) from e
    217 # Audit hooks are only available in Python 3.8+

NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7fa15b2b3880>: Failed to establish a new connection: [Errno 101] Network is unreachable

The above exception was the direct cause of the following exception:

MaxRetryError                             Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/requests/adapters.py:486, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
    485 try:
--> 486     resp = conn.urlopen(
    487         method=request.method,
    488         url=url,
    489         body=request.body,
    490         headers=request.headers,
    491         redirect=False,
    492         assert_same_host=False,
    493         preload_content=False,
    494         decode_content=False,
    495         retries=self.max_retries,
    496         timeout=timeout,
    497         chunked=chunked,
    498     )
    500 except (ProtocolError, OSError) as err:

File /opt/conda/lib/python3.10/site-packages/urllib3/connectionpool.py:847, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
    845     new_e = ProtocolError("Connection aborted.", new_e)
--> 847 retries = retries.increment(
    848     method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
    849 )
    850 retries.sleep()

File /opt/conda/lib/python3.10/site-packages/urllib3/util/retry.py:515, in Retry.increment(self, method, url, response, error, _pool, _stacktrace)
    514     reason = error or ResponseError(cause)
--> 515     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
    517 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)

MaxRetryError: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /google-bert/bert-base-german-cased/resolve/main/config.json (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fa15b2b3880>: Failed to establish a new connection: [Errno 101] Network is unreachable'))

During handling of the above exception, another exception occurred:

ConnectionError                           Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1722, in _get_metadata_or_catch_error(repo_id, filename, repo_type, revision, endpoint, proxies, etag_timeout, headers, local_files_only, relative_filename, storage_folder)
   1721 try:
-> 1722     metadata = get_REDACTED_TOKEN_metadata(url=url, proxies=proxies, timeout=etag_timeout, headers=headers)
   1723 except EntryNotFoundError as http_error:

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114, in validate_REDACTED_TOKEN_args.<locals>._inner_fn(*args, **kwargs)
    112     kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1645, in get_REDACTED_TOKEN_metadata(url, token, proxies, timeout, library_name, library_version, user_agent, headers)
   1644 # Retrieve metadata
-> 1645 r = _request_wrapper(
   1646     method="HEAD",
   1647     url=url,
   1648     headers=headers,
   1649     allow_redirects=False,
   1650     follow_relative_redirects=True,
   1651     proxies=proxies,
   1652     timeout=timeout,
   1653 )
   1654 REDACTED_TOKEN_for_status(r)

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:372, in _request_wrapper(method, url, follow_relative_redirects, **params)
    371 if follow_relative_redirects:
--> 372     response = _request_wrapper(
    373         method=method,
    374         url=url,
    375         follow_relative_redirects=False,
    376         **params,
    377     )
    379     # If redirection, we redirect only relative paths.
    380     # This is useful in case of a renamed repository.

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:395, in _request_wrapper(method, url, follow_relative_redirects, **params)
    394 # Perform request and return if status_code is not in the retry list.
--> 395 response = get_session().request(method=method, url=url, **params)
    396 REDACTED_TOKEN_for_status(response)

File /opt/conda/lib/python3.10/site-packages/requests/sessions.py:589, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
    588 send_kwargs.update(settings)
--> 589 resp = self.send(prep, **send_kwargs)
    591 return resp

File /opt/conda/lib/python3.10/site-packages/requests/sessions.py:703, in Session.send(self, request, **kwargs)
    702 # Send the request
--> 703 r = adapter.send(request, **kwargs)
    705 # Total elapsed time of the request (approximately)

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_http.py:66, in UniqueRequestIdAdapter.send(self, request, *args, **kwargs)
     65 try:
---> 66     return super().send(request, *args, **kwargs)
     67 except requests.RequestException as e:

File /opt/conda/lib/python3.10/site-packages/requests/adapters.py:519, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
    517         raise SSLError(e, request=request)
--> 519     raise ConnectionError(e, request=request)
    521 except ClosedPoolError as e:

ConnectionError: (MaxRetryError("HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /google-bert/bert-base-german-cased/resolve/main/config.json (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fa15b2b3880>: Failed to establish a new connection: [Errno 101] Network is unreachable'))"), '(Request ID: 8df1d542-4895-405c-b79d-4d2ca4001529)')

The above exception was the direct cause of the following exception:

LocalEntryNotFoundError                   Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:398, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)
    396 try:
    397     # Load from URL or cache if already cached
--> 398     resolved_file = REDACTED_TOKEN_download(
    399         path_or_repo_id,
    400         filename,
    401         subfolder=None if len(subfolder) == 0 else subfolder,
    402         repo_type=repo_type,
    403         revision=revision,
    404         cache_dir=cache_dir,
    405         user_agent=user_agent,
    406         force_download=force_download,
    407         proxies=proxies,
    408         resume_download=resume_download,
    409         token=token,
    410         local_files_only=local_files_only,
    411     )
    412 except GatedRepoError as e:

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114, in validate_REDACTED_TOKEN_args.<locals>._inner_fn(*args, **kwargs)
    112     kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1221, in REDACTED_TOKEN_download(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, proxies, etag_timeout, token, local_files_only, headers, endpoint, legacy_cache_layout, resume_download, force_filename, local_dir_use_symlinks)
   1220 else:
-> 1221     return _REDACTED_TOKEN_download_to_cache_dir(
   1222         # Destination
   1223         cache_dir=cache_dir,
   1224         # File info
   1225         repo_id=repo_id,
   1226         filename=filename,
   1227         repo_type=repo_type,
   1228         revision=revision,
   1229         # HTTP info
   1230         headers=headers,
   1231         proxies=proxies,
   1232         etag_timeout=etag_timeout,
   1233         endpoint=endpoint,
   1234         # Additional options
   1235         local_files_only=local_files_only,
   1236         force_download=force_download,
   1237     )

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1325, in _REDACTED_TOKEN_download_to_cache_dir(cache_dir, repo_id, filename, repo_type, revision, headers, proxies, etag_timeout, endpoint, local_files_only, force_download)
   1324     # Otherwise, raise appropriate error
-> 1325     _raise_on_head_call_error(head_call_error, force_download, local_files_only)
   1327 # From now on, etag, commit_hash, url and size are not None.

File /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1826, in _raise_on_head_call_error(head_call_error, force_download, local_files_only)
   1824 else:
   1825     # Otherwise: most likely a connection issue or Hub downtime => let's warn the user
-> 1826     raise LocalEntryNotFoundError(
   1827         "An error happened while trying to locate the file on the Hub and we cannot find the requested files"
   1828         " in the local cache. Please check your connection and try again or make sure your Internet connection"
   1829         " is on."
   1830     ) from head_call_error

LocalEntryNotFoundError: An error happened while trying to locate the file on the Hub and we cannot find the requested files in the local cache. Please check your connection and try again or make sure your Internet connection is on.

The above exception was the direct cause of the following exception:

OSError                                   Traceback (most recent call last)
Cell In[13], line 3
      1 from transformers import AutoTokenizer, AutoModelForMaskedLM
----> 3 tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-german-cased")
      4 model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-german-cased")

File /opt/conda/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:819, in AutoTokenizer.from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs)
    817 if config_tokenizer_class is None:
    818     if not isinstance(config, PretrainedConfig):
--> 819         config = AutoConfig.from_pretrained(
    820             pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
    821         )
    822     config_tokenizer_class = config.tokenizer_class
    823     if hasattr(config, "auto_map") and "AutoTokenizer" in config.auto_map:

File /opt/conda/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:928, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    925 trust_remote_code = kwargs.pop("trust_remote_code", None)
    926 code_revision = kwargs.pop("code_revision", None)
--> 928 config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
    929 has_remote_code = "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]
    930 has_local_code = "model_type" in config_dict and config_dict["model_type"] in CONFIG_MAPPING

File /opt/conda/lib/python3.10/site-packages/transformers/configuration_utils.py:631, in PretrainedConfig.get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    629 original_kwargs = copy.deepcopy(kwargs)
    630 # Get config dict associated with the base config file
--> 631 config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
    632 if "_commit_hash" in config_dict:
    633     original_kwargs["_commit_hash"] = config_dict["_commit_hash"]

File /opt/conda/lib/python3.10/site-packages/transformers/configuration_utils.py:686, in PretrainedConfig._get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    682 configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME)
    684 try:
    685     # Load from local folder or from cache or download from model Hub and cache
--> 686     resolved_config_file = cached_file(
    687         pretrained_model_name_or_path,
    688         configuration_file,
    689         cache_dir=cache_dir,
    690         force_download=force_download,
    691         proxies=proxies,
    692         resume_download=resume_download,
    693         local_files_only=local_files_only,
    694         token=token,
    695         user_agent=user_agent,
    696         revision=revision,
    697         subfolder=subfolder,
    698         _commit_hash=commit_hash,
    699     )
    700     commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
    701 except EnvironmentError:
    702     # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
    703     # the original exception.

File /opt/conda/lib/python3.10/site-packages/transformers/utils/hub.py:441, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)
    435     if (
    436         resolved_file is not None
    437         or not _raise_exceptions_for_missing_entries
    438         or not _raise_exceptions_for_connection_errors
    439     ):
    440         return resolved_file
--> 441     raise EnvironmentError(
    442         f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this file, couldn't find it in the"
    443         f" cached files and it looks like {path_or_repo_id} is not the path to a directory containing a file named"
    444         f" {full_filename}.\nCheckout your internet connection or see how to run the library in offline mode at"
    445         " 'https://huggingface.co/docs/transformers/installation#offline-mode'."
    446     ) from e
    447 except EntryNotFoundError as e:
    448     if not _raise_exceptions_for_missing_entries:

OSError: We couldn't connect to 'https://huggingface.co/' to load this file, couldn't find it in the cached files and it looks like google-bert/bert-base-german-cased is not the path to a directory containing a file named config.json.
Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.

### System info

```shell
- huggingface_hub version: 0.23.1
- Platform: Linux-5.10.0-29-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.10.14
- Running in iPython ?: Yes
- iPython shell: ZMQInteractiveShell
- Running in notebook ?: Yes
- Running in Google Colab ?: No
- Token path ?: /home/jupyter/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers: 
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.3.0
- Jinja2: 3.1.4
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: 10.3.0
- REDACTED_TOKEN: N/A
- gradio: N/A
- tensorboard: 2.6.2.2
- numpy: 1.26.4
- pydantic: 1.10.15
- aiohttp: 3.9.5
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /home/jupyter/.cache/huggingface/hub
- HF_ASSETS_CACHE: /home/jupyter/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/jupyter/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10
```
2024-11-29T03:07:30Z
Used cat5 cable and the error become thisCalledProcessError: Command ‘[‘git’, ‘pull’]’ returned non-zero exit status 128.During handling of the above exception, another exception occurred:OSError: fatal: refusing to merge unrelated histories
2024-11-29T03:10:29Z
Well, you need a cat5e cable, but that’s beside the point.The fact that the symptoms change means that the problem is inside the router, including the router. It doesn’t seem like someone is blocking it. It can probably be resolved, but there is some kind of problem lurking in your environment itself.
2024-11-29T03:11:51Z
Is there a solution to that “Refusing to merge unrelated histories” tho, it also went far beyond huggingface scope.Yea I think I’ll try using the cable or try going out
2024-11-29T03:13:47Z
If you’re trying to use git on Windows, it’s possible to use it as standard, but you’ll need to install these manually to make it practically usable. Let’s install them.gitforwindows.orgGit for WindowsWe bring the awesome Git VCS to WindowsGit Large File StorageGit Large File StorageGit Large File Storage (LFS) replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server like GitHub.com or GitHub Enterprise.
2024-11-29T03:15:50Z
I didn’t use any git commands as far as I can see, is it on the huggingface functions or something?I obviously would know if I used one and will check my git connections, but it doesn’t seem so. Is it on thedatasettaking?But if it’s the case then this code means nothing, this codes normally would means every variables are local now. I don’t even know why I have internet problems# load dataset from huggingface
REDACTED_TOKEN_identifier = "seand0101/manggarai-watergate"
ds = load_dataset(REDACTED_TOKEN_identifier)# To configure the model, extract the number of unique labels

repo_id = f"datasets/{REDACTED_TOKEN_identifier}"
filename = "id2label.json" # Define the filename containing label information

# Download the 'id2label.json' file
id2label = json.load(open(REDACTED_TOKEN_download(repo_id=REDACTED_TOKEN_identifier, filename=filename, repo_type="dataset"), "r"))

# Convert the keys in 'id2label' from strings to integers and store the result in 'id2label'
id2label = {int(k): v for k, v in id2label.items()}

# Create a 'label2id' dictionary by reversing the key-value pairs in 'id2label'
label2id = {v: k for k, v in id2label.items()}

# Calculate the number of unique labels in the dataset
num_labels = len(id2label)
2024-11-29T03:17:17Z
What? I think you’ve got a git error though.
2024-11-29T03:19:36Z
For now, I think it would be a good idea to update the libraries for HF communications-related things.pip install -U datasets huggingface_hubEdit:If you set this to a local path, it should work even without an internet connection, but it’s better to think about it after it’s working with an internet connection.# load dataset from huggingface
REDACTED_TOKEN_identifier = "seand0101/manggarai-watergate"
ds = load_dataset(REDACTED_TOKEN_identifier)
2024-11-29T03:21:45Z
Will try that, got it. Do you mind checking mydatasetand .json too probably there’s something wrong with my first curated dataset ever.
2024-11-29T03:28:31Z
From DatasetViewer, it looks like a normal image dataset, withpixel_values as the image and labels as the masked image. The authors of DatasetViewer and the datasets library are to some extent the same, so if it looks normal in DatasetViewer, it can generally be handled normally in the datasets library.However, the way you handle it (how you write the code) will naturally change depending on the structure of the dataset.Your dataset is a combination of images and images, so it’s a little unusual. Datasets that only contain text and images, just texts, and just images are the majority. It should be possible to use it, but you’ll have to search to find out how to use it.huggingface.coImage DatasetWe’re on a journey to advance and democratize artificial intelligence through open source and open science.huggingface.coSemantic segmentationWe’re on a journey to advance and democratize artificial intelligence through open source and open science.
2024-11-29T03:40:31Z
I thought when compared to that sidewalk dataset, they also include masking to their second class. How to convert these masks into “labels” like they do? Noted will check that out thanks again John6666
2024-11-29T03:42:03Z
John6666:Edit:If you set this to a local path, it should work even without an internet connection, but it’s better to think about it after it’s working with an internet connection.Oh so it doesn’t matter if I cached it first in Jupyter Notebook it will still look for online repo for variable when training?
2024-11-29T03:51:10Z
How to convert these masks into “labels” like they do?I have no idea! A mask is a mask. It’s normal to use a dataset that combines images and masks.Can it be used to train an image segmentation model?Basically, you need to specify what you want the AI to learn in some way. If AI doesn’t know what to learn, it’s very inefficient. Of course, it is possible to automate the labeling process to some extent using AI. You can have a smarter, already-trained large AI model do the labeling, and then use that as a dataset to train smaller models. But this wouldn’t be called a conversion. It would be a process.Oh so it doesn’t matter if I cached it first in Jupyter Notebook it will still look for online repo for variable when training?No, it’s not that specific. We were talking about how it would be inconvenient if you couldn’t use HF because of your local environment, so let’s fix that first.
No date available
No reply text available