# Using transformers on Kaggle **URL:** **Author:** ![PeteBleackley](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/petebleackley/32/51692_2.png) [@PeteBleackley](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FPeteBleackley)%5C **Post date:** [July 29, 2025, 12:31pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-transformers-on-kaggle%2F164960%2F1 "2025-07-29T12:31:53Z") In this [Kaggle kernel](https://www.kaggle.com/code/petebleackley/rule-violation-classifier#The-Encoder-Model) I’m using a ModernBert base model with a custom head. The resulting model inherits from `transformers.PreTrainedModel` This works fine in the draft session, but throws an error when I try to save and commit. ```auto --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) /usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py in __getattr__ (self, name) 2044 try: -> 2045 module = self._get_module(self._class_to_module[name]) 2046 value = getattr(module, name) /usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py in _get_module(self, module_name) 2074 except Exception as e: -> 2075 raise e 2076 /usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py in _get_module(self, module_name) 2072 try: -> 2073 return importlib.import_module("." + module_name, self. __name__ ) 2074 except Exception as e: /usr/lib/python3.11/importlib/ __init__.py in import_module(name, package) 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level) 127 /usr/lib/python3.11/importlib/_bootstrap.py in _gcd_import(name, package, level) /usr/lib/python3.11/importlib/_bootstrap.py in _find_and_load(name, import_) /usr/lib/python3.11/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_) /usr/lib/python3.11/importlib/_bootstrap.py in _load_unlocked(spec) /usr/lib/python3.11/importlib/_bootstrap_external.py in exec_module(self, module) /usr/lib/python3.11/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds) /usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py in 72 ) ---> 73 from .loss.loss_utils import LOSS_MAPPING 74 from .pytorch_utils import ( # noqa: F401 /usr/local/lib/python3.11/dist-packages/transformers/loss/loss_utils.py in 20 ---> 21 from .loss_d_fine import DFineForObjectDetectionLoss 22 from .loss_deformable_detr import DeformableDetrForObjectDetectionLoss, DeformableDetrForSegmentationLoss /usr/local/lib/python3.11/dist-packages/transformers/loss/loss_d_fine.py in 20 from ..utils import is_vision_available ---> 21 from .loss_for_object_detection import ( 22 box_iou, /usr/local/lib/python3.11/dist-packages/transformers/loss/loss_for_object_detection.py in 31 if is_vision_available(): ---> 32 from transformers.image_transforms import center_to_corners_format 33 /usr/local/lib/python3.11/dist-packages/transformers/image_transforms.py in 20 ---> 21 from .image_utils import ( 22 ChannelDimension, /usr/local/lib/python3.11/dist-packages/transformers/image_utils.py in 58 if is_torchvision_available(): ---> 59 from torchvision.transforms import InterpolationMode 60 /usr/local/lib/python3.11/dist-packages/torchvision/ __init__.py in 9 from .extension import _HAS_OPS # usort:skip ---> 10 from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils # usort:skip 11 /usr/local/lib/python3.11/dist-packages/torchvision/_meta_registrations.py in 162 --> 163 @torch.library.register_fake("torchvision::nms") 164 def meta_nms(dets, scores, iou_threshold): /usr/local/lib/python3.11/dist-packages/torch/library.py in register(func) 1022 use_lib = lib -> 1023 use_lib._register_fake(op_name, func, _stacklevel=stacklevel + 1) 1024 return func /usr/local/lib/python3.11/dist-packages/torch/library.py in _register_fake(self, op_name, fn, _stacklevel) 213 --> 214 handle = entry.fake_impl.register(func_to_register, source) 215 self._registration_handles.append(handle) /usr/local/lib/python3.11/dist-packages/torch/_library/fake_impl.py in register(self, func, source) 30 ) ---> 31 if torch._C._dispatch_has_kernel_for_dispatch_key(self.qualname, "Meta"): 32 raise RuntimeError( RuntimeError: operator torchvision::nms does not exist The above exception was the direct cause of the following exception: ModuleNotFoundError Traceback (most recent call last) /tmp/ipykernel_26/2863755039.py in () ----> 1 class EncoderModel(transformers.PreTrainedModel): 2 3 def __init__ (self,path): 4 """ 5 Creates the encoder model /usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py in __getattr__ (self, name) 2046 value = getattr(module, name) 2047 except (ModuleNotFoundError, RuntimeError) as e: -> 2048 raise ModuleNotFoundError( 2049 f"Could not import module '{name}'. Are this object's requirements defined correctly?" 2050 ) from e ModuleNotFoundError: Could not import module 'PreTrainedModel'. Are this object's requirements defined correctly? ``` Has anyone else encountered this issue, and have any idea how to solve it? ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C **Post date:** [July 29, 2025, 10:46pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fusing-transformers-on-kaggle%2F164960%2F2 "2025-07-29T22:46:37Z")
Perhaps the Kaggle Docker image has an outdated version of `torchvision`, causing incompatibility? I think it might work if you fix the `torchvision` version or, if unnecessary, uninstall it altogether… > **[\`transformers\` library works in draft session but not in commit | Kaggle](https://www.kaggle.com/discussions/questions-and-answers/591506)** > > \`transformers\` library works in draft session but not in commit > > > \### Search before asking > > \- \[X\] I have searched the YOLOv8 \[issues\](https://gi…thub.com/ultralytics/ultralytics/issues) and found no similar bug report. > > > \### Bug > > I have tried the old answers but nothing works with torch 2.\* in windows, on linux there is no problem; on windows i have tried installing 2.0.0 2.0.1 and 2.0.2 with associated torchvision and torchaudio for cuda 11.8, changing ultralytics version but nothing. > > update: \*\*If I run the code from the terminal python script it works, but within jupyter it does not work and returns the error.\*\* > > the code is a easy inference: > \`\`\` > from PIL import Image > from ultralytics import YOLO > > reconizer = YOLO("yolov8n.pt") > > image = Image.open("NewPath.png") > output = None > output = reconizer(image) > out = \[\] > for r in output: > boxes = r.boxes > for box in boxes: > if(box.conf\>0.5): > b = box.xyxy\[0\] > c = box.cls > out.append(\[b, reconizer.names\[int(c)\]\]) > > print(out) > > \`\`\` > the error is as follows: > > \------------------------------------------------------------------------ > NotImplementedError Traceback (most recent call last) > Cell In\[3\], line 16 > 14 image = Image.open("NewPath.png") > 15 output = None > \---\> 16 output = reconizer(image) > 17 out = \[\] > 18 for r in output: > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ultralytics\\engine\\model.py:96, in Model.\_\_call\_\_(self, source, stream, \*\*kwargs) > 94 def \_\_call\_\_(self, source=None, stream=False, \*\*kwargs): > 95 """Calls the 'predict' function with given arguments to perform object detection.""" > \---\> 96 return self.predict(source, stream, \*\*kwargs) > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ultralytics\\engine\\model.py:236, in Model.predict(self, source, stream, predictor, \*\*kwargs) > 234 if prompts and hasattr(self.predictor, 'set\_prompts'): # for SAM-type models > 235 self.predictor.set\_prompts(prompts) > \--\> 236 return self.predictor.predict\_cli(source=source) if is\_cli else self.predictor(source=source, stream=stream) > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ultralytics\\engine\\predictor.py:194, in BasePredictor.\_\_call\_\_(self, source, model, stream, \*args, \*\*kwargs) > 192 return self.stream\_inference(source, model, \*args, \*\*kwargs) > 193 else: > \--\> 194 return list(self.stream\_inference(source, model, \*args, \*\*kwargs)) > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\utils\\\_contextlib.py:35, in \_wrap\_generator.\.generator\_context(\*args, \*\*kwargs) > 32 try: > 33 # Issuing \`None\` to a generator fires it up > 34 with ctx\_factory(): > \---\> 35 response = gen.send(None) > 37 while True: > 38 try: > 39 # Forward the response to our caller and get its next request > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ultralytics\\engine\\predictor.py:257, in BasePredictor.stream\_inference(self, source, model, \*args, \*\*kwargs) > 255 # Postprocess > 256 with profilers\[2\]: > \--\> 257 self.results = self.postprocess(preds, im, im0s) > 258 self.run\_callbacks('on\_predict\_postprocess\_end') > 260 # Visualize, save, write results > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ultralytics\\models\\yolo\\detect\\predict.py:25, in DetectionPredictor.postprocess(self, preds, img, orig\_imgs) > 23 def postprocess(self, preds, img, orig\_imgs): > 24 """Post-processes predictions and returns a list of Results objects.""" > \---\> 25 preds = ops.non\_max\_suppression(preds, > 26 self.args.conf, > 27 self.args.iou, > 28 agnostic=self.args.agnostic\_nms, > 29 max\_det=self.args.max\_det, > 30 classes=self.args.classes) > 32 if not isinstance(orig\_imgs, list): # input images are a torch.Tensor, not a list > 33 orig\_imgs = ops.convert\_torch2numpy\_batch(orig\_imgs) > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\ultralytics\\utils\\ops.py:242, in non\_max\_suppression(prediction, conf\_thres, iou\_thres, classes, agnostic, multi\_label, labels, max\_det, nc, max\_time\_img, max\_nms, max\_wh) > 240 c = x\[:, 5:6\] \* (0 if agnostic else max\_wh) # classes > 241 boxes, scores = x\[:, :4\] + c, x\[:, 4\] # boxes (offset by class), scores > \--\> 242 i = torchvision.ops.nms(boxes, scores, iou\_thres) # NMS > 243 i = i\[:max\_det\] # limit detections > 245 # # Experimental > 246 # merge = False # use merge-NMS > 247 # if merge and (1 \< n \< 3E3): # Merge NMS (boxes merged using weighted mean) > (...) > 254 # if redundant: > 255 # i = i\[iou.sum(1) \> 1\] # require redundancy > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torchvision\\ops\\boxes.py:41, in nms(boxes, scores, iou\_threshold) > 39 \_log\_api\_usage\_once(nms) > 40 \_assert\_has\_ops() > \---\> 41 return torch.ops.torchvision.nms(boxes, scores, iou\_threshold) > > File ~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\\_ops.py:502, in OpOverloadPacket.\_\_call\_\_(self, \*args, \*\*kwargs) > 497 def \_\_call\_\_(self, \*args, \*\*kwargs): > 498 # overloading \_\_call\_\_ to ensure torch.ops.foo.bar() > 499 # is still callable from JIT > 500 # We save the function ptr as the \`op\` attribute on > 501 # OpOverloadPacket to access it here. > \--\> 502 return self.\_op(\*args, \*\*kwargs or {}) > > NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: \[CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PythonDispatcher\]. > > CPU: registered at C:\\Users\\circleci\\project\\torchvision\\csrc\\ops\\cpu\\nms\_kernel.cpp:112 \[kernel\] > QuantizedCPU: registered at C:\\Users\\circleci\\project\\torchvision\\csrc\\ops\\quantized\\cpu\\qnms\_kernel.cpp:124 \[kernel\] > BackendSelect: fallthrough registered at ..\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 \[backend fallback\] > Python: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:144 \[backend fallback\] > FuncTorchDynamicLayerBackMode: registered at ..\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:491 \[backend fallback\] > Functionalize: registered at ..\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:280 \[backend fallback\] > Named: registered at ..\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 \[backend fallback\] > Conjugate: registered at ..\\aten\\src\\ATen\\ConjugateFallback.cpp:17 \[backend fallback\] > Negative: registered at ..\\aten\\src\\ATen\\native\\NegateFallback.cpp:19 \[backend fallback\] > ZeroTensor: registered at ..\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 \[backend fallback\] > ADInplaceOrView: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:63 \[backend fallback\] > AutogradOther: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:30 \[backend fallback\] > AutogradCPU: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:34 \[backend fallback\] > AutogradCUDA: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:42 \[backend fallback\] > AutogradXLA: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:46 \[backend fallback\] > AutogradMPS: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:54 \[backend fallback\] > AutogradXPU: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:38 \[backend fallback\] > AutogradHPU: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:67 \[backend fallback\] > AutogradLazy: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:50 \[backend fallback\] > AutogradMeta: fallthrough registered at ..\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:58 \[backend fallback\] > Tracer: registered at ..\\torch\\csrc\\autograd\\TraceTypeManual.cpp:294 \[backend fallback\] > AutocastCPU: fallthrough registered at ..\\aten\\src\\ATen\\autocast\_mode.cpp:487 \[backend fallback\] > AutocastCUDA: fallthrough registered at ..\\aten\\src\\ATen\\autocast\_mode.cpp:354 \[backend fallback\] > FuncTorchBatched: registered at ..\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:815 \[backend fallback\] > FuncTorchVmapMode: fallthrough registered at ..\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:28 \[backend fallback\] > Batched: registered at ..\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1073 \[backend fallback\] > VmapMode: fallthrough registered at ..\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 \[backend fallback\] > FuncTorchGradWrapper: registered at ..\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:210 \[backend fallback\] > PythonTLSSnapshot: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:152 \[backend fallback\] > FuncTorchDynamicLayerFrontMode: registered at ..\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:487 \[backend fallback\] > PythonDispatcher: registered at ..\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:148 \[backend fallback\] > > \### Environment > > OS: windows 11 > Env: jupyter > Python: 3.11.3 > torch: 2.0.1+cu11.8 > RAM: 16.00 GB > VRAM: 4.0 GB > > \### Minimal Reproducible Example > > \_No response\_ > > \### Additional > > \_No response\_ > > \### Are you willing to submit a PR? > > \- \[\] Yes I'd like to help by submitting a PR!