Tensorflow errors after import
Closed this issue · 6 comments
When importing thingsvision I get a bunch of errors that are thrown by tensorflow (see below). These errors somehow come naturally with the library, due to the large number of dependencies that are required to extract features for all models.
2023-03-03 13:39:32.863659: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-03 13:39:33.052225: 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
2023-03-03 13:39:33.640642: 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
2023-03-03 13:39:33.640709: 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
2023-03-03 13:39:33.640720: 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.
2023-03-03 13:39:34.574396: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-03 13:39:36.134643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9159 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Physical devices cannot be modified after being initializedThese are not errors. These are warnings. Do you have a suggestion on how to fix that?
Not yet, thinking aobut it and will come back!
Great!
I think the main reason for the above warnings is the Harmonization dependency which seems to be a bottleneck, especially for running thingsvision on macOS.
This should be fixed now with the most recent thingsvision version. (v2.3.13). Can you confirm @florianmahner?
Yes, the warnings from the Harmonization models have disappeared. Thanks!