new warning messages from notebooks

Describe the bug

02:28:58  Running ./3d_segmentation/unet_segmentation_3d_ignite.ipynb
02:28:58  Checking PEP8 compliance...
02:28:59  Running notebook...
02:28:59  Before:
02:28:59      "max_epochs = 10\n",
02:28:59  After:
02:28:59      "max_epochs = 1\n",
02:29:03  MONAI version: 1.0.0rc1+9.ge98226dd
02:29:03  Numpy version: 1.22.4
02:29:03  Pytorch version: 1.10.2+cu102
02:29:03  MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
02:29:03  MONAI rev id: e98226dd6d0fb56ff344bc9122c68f9c8a3af96e
02:29:03  MONAI __file__: /home/jenkins/agent/workspace/Monai-notebooks/MONAI/monai/__init__.py
02:29:03  
02:29:03  Optional dependencies:
02:29:03  Pytorch Ignite version: 0.4.10
02:29:03  Nibabel version: 4.0.2
02:29:03  scikit-image version: 0.19.3
02:29:03  Pillow version: 7.0.0
02:29:03  Tensorboard version: 2.10.0
02:29:03  gdown version: 4.5.1
02:29:03  TorchVision version: 0.11.3+cu102
02:29:03  tqdm version: 4.64.0
02:29:03  lmdb version: 1.3.0
02:29:03  psutil version: 5.9.1
02:29:03  pandas version: 1.1.5
02:29:03  einops version: 0.4.1
02:29:03  transformers version: 4.21.3
02:29:03  mlflow version: 1.28.0
02:29:03  pynrrd version: 0.4.3
02:29:03  
02:29:03  For details about installing the optional dependencies, please visit:
02:29:03      https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies
02:29:03  
02:29:04  /opt/conda/lib/python3.8/site-packages/papermill/iorw.py:153: UserWarning: the file is not specified with any extension : -
02:29:04    warnings.warn(
02:29:25  
Executing:   0%|          | 0/29 [00:00<?, ?cell/s]
Executing:   3%|▎         | 1/29 [00:01<00:36,  1.32s/cell]
Executing:  10%|█         | 3/29 [00:05<00:52,  2.01s/cell]
Executing:  17%|█▋        | 5/29 [00:09<00:44,  1.84s/cell]
Executing:  38%|███▊      | 11/29 [00:16<00:26,  1.45s/cell]
Executing:  45%|████▍     | 13/29 [00:21<00:25,  1.62s/cell]
Executing:  52%|█████▏    | 15/29 [00:21<00:17,  1.22s/cell]2022-09-09 01:29:25.282097: 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 AVX512_VNNI FMA
02:29:25  To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
02:29:25  2022-09-09 01:29:25.450820: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
02:29:25  2022-09-09 01:29:25.490201: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
02:29:26  2022-09-09 01:29:26.138841: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/x86_64-linux-gnu:/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib/python3.8/site-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
02:29:26  2022-09-09 01:29:26.138936: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/x86_64-linux-gnu:/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib/python3.8/site-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
02:29:26  2022-09-09 01:29:26.138944: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

Commencez par 3d_segmentation/unet_segmentation_3d_ignite.ipynb et reproduisez son exécution avec papermill en utilisant les versions des dépendances indiquées dans le rapport. Examinez quels messages proviennent de l’exécution du notebook et lesquels proviennent de l’environnement TensorFlow et CUDA ; l’issue ne définit pas quels avertissements doivent être supprimés ni quel devrait être le comportement une fois terminé.