Open YunjieChang opened 2 years ago
Hi Yunjie,
Does TensorFlow see the GPU on your cluster node where you are running the training? I would recommend to start an interactive cluster session and then check if the GPU is available with nvidia-smi
. Then you can check if the installed CUDA is compatible with your TensorFlow installation and finally I would run this TensorFlow installation verification code from their install instructions (https://www.tensorflow.org/install/pip):
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
Cheers!
I'm using cryocare_11, any idea how to solve this problem? Thank you very much in advance! Yours, Linhua Tai
Hi Tim,
I just installed cryoCARE on our HPC following the installation procedure "For CUDA 10" and did not meet any errors during the installation.
However, I got the following message when I tried to run the training process (cryoCARE_train.py --conf train_config.json):
This information says that cryoCARE is not using GPU to do the training, instead it is using CPU, therefore, it is quite slow. My tomogram size is 672672200.
Any idea about this issue? Thanks! Yunjie