HuggingFace Interface works on Colab but not on local jupyter setup #55
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Hi I am assuming that you downloaded the colab notebook as is and tried to run it locally on jupyterlabs and it didnt work. Please correct me if I am wrong. Regardless,
What would help even more would be if you share a snapshot of your local notebook with the outputs of each cell up until things failed. I did a quick google search on your error and the following seem to have an extensive discussion:
Overall its an environment setup issue and should be solved relatively easily. Answers to these will help us isolate the issue. |
Thanks for sharing the links, I had checked those links, based on those reinstalled, clean installed and then deleted the clean install of the virtual environment. |
Hi - I have Ubuntu23.10 Mantic with a couple of GPUs, 3090 being the biggest one. I want to run a batch of Marathi - English Translation with IndicTrans2, when I try to run the
huggingface_interfaceon my local machine with JupyterLab, it errors out with following error -`
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 2
1 import torch
----> 2 from transformers import AutoModelForSeq2SeqLM, BitsAndBytesConfig
3 from IndicTransTokenizer import IndicProcessor, IndicTransTokenizer
5 BATCH_SIZE = 4
File ~/.local/lib/python3.11/site-packages/transformers/init.py:26
23 from typing import TYPE_CHECKING
25 # Check the dependencies satisfy the minimal versions required.
---> 26 from . import dependency_versions_check
27 from .utils import (
28 OptionalDependencyNotAvailable,
29 _LazyModule,
(...)
47 logging,
48 )
51 logger = logging.get_logger(name) # pylint: disable=invalid-name
File ~/.local/lib/python3.11/site-packages/transformers/dependency_versions_check.py:16
1 # Copyright 2020 The HuggingFace Team. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
15 from .dependency_versions_table import deps
---> 16 from .utils.versions import require_version, require_version_core
19 # define which module versions we always want to check at run time
20 # (usually the ones defined in
install_requiresin setup.py)21 #
22 # order specific notes:
23 # - tqdm must be checked before tokenizers
25 pkgs_to_check_at_runtime = [
26 "python",
27 "tqdm",
(...)
37 "pyyaml",
38 ]
File ~/.local/lib/python3.11/site-packages/transformers/utils/init.py:33
24 from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
25 from .doc import (
26 add_code_sample_docstrings,
27 add_end_docstrings,
(...)
31 replace_return_docstrings,
32 )
---> 33 from .generic import (
34 ContextManagers,
35 ExplicitEnum,
36 ModelOutput,
37 PaddingStrategy,
38 TensorType,
39 add_model_info_to_auto_map,
40 cached_property,
41 can_return_loss,
42 expand_dims,
43 find_labels,
44 flatten_dict,
45 infer_framework,
46 is_jax_tensor,
47 is_numpy_array,
48 is_tensor,
49 is_tf_symbolic_tensor,
50 is_tf_tensor,
51 is_torch_device,
52 is_torch_dtype,
53 is_torch_tensor,
54 reshape,
55 squeeze,
56 strtobool,
57 tensor_size,
58 to_numpy,
59 to_py_obj,
60 transpose,
61 working_or_temp_dir,
62 )
63 from .hub import (
64 CLOUDFRONT_DISTRIB_PREFIX,
65 HF_MODULES_CACHE,
(...)
91 try_to_load_from_cache,
92 )
93 from .import_utils import (
94 ACCELERATE_MIN_VERSION,
95 ENV_VARS_TRUE_AND_AUTO_VALUES,
(...)
200 torch_only_method,
201 )
File ~/.local/lib/python3.11/site-packages/transformers/utils/generic.py:442
438 return tuple(self[k] for k in self.keys())
441 if is_torch_available():
--> 442 import torch.utils._pytree as _torch_pytree
444 def _model_output_flatten(output: ModelOutput) -> Tuple[List[Any], "_torch_pytree.Context"]:
445 return list(output.values()), list(output.keys())
File ~/.local/lib/python3.11/site-packages/torch/utils/init.py:4
1 import os.path as _osp
2 import torch
----> 4 from .throughput_benchmark import ThroughputBenchmark
5 from .cpp_backtrace import get_cpp_backtrace
6 from .backend_registration import rename_privateuse1_backend, generate_methods_for_privateuse1_backend
File ~/.local/lib/python3.11/site-packages/torch/utils/throughput_benchmark.py:2
----> 2 import torch._C
5 def format_time(time_us=None, time_ms=None, time_s=None):
6 """Define time formatting."""
ModuleNotFoundError: No module named 'torch._C'
`
This same code works on Google Colab. I have around 7K articles to translate, Google colab does not stay up for that long and I would like to avoid paying for this.
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