Comments (2)
Hi @kimbyungeun ,
- Sorry for the delay, Check your environment variables related to CUDA and cuDNN. Set LD_LIBRARY_PATH to include the directories containing the CUDA and cuDNN libraries.
- Run a script to check if tensorflow can detect your GPU and if it's using the correct CUDA/cuDNN versions.
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
- Clearing the tensorflow cache can resolve intermittent issues. You can do this by deleting the '~/.keras' directory.
- If the issue persists, consider reinstalling TensorFlow and its dependencies from scratch. Use a virtual environment to isolate the installation.
Thank you!
from tensorflow.
Hi @Venkat6871
I am planning to proceed with the following two tasks:
- Set the LD_LIBRARY_PATH environment variable in a Docker container.
- Downgrade CUDA version from 11.3.0 to 11.2.2.
Additionally, I have a question:
- Between CUDA 11.3.1 and CUDA 11.2.2, which direction would be better? Please recommend.
Thank you!
from tensorflow.
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