I’m trying to set up a specific environment on my university’s HPC, which restricts sudo access. The HPC has Python >=3.9 and CUDA >=11.7. For my project, I need Python 3.6 and PyTorch 0.4.1, compatible with CUDA 9.2 and cuDNN 7.2.1.
What I’ve done:
Created a conda environment with Python 3.6.
Installed cudatoolkit=9.2 and cudnn=7.2.1.
Installed PyTorch 0.4.1 using conda install pytorch=0.4.1 cuda92 -c pytorch.
Issues:
When installing pytorch 0.4.1 in this env i got env conflicts, so i created a python venv inside the conda env and installed 0.4.1 using pip.
When running nvcc --version, it shows CUDA 9.2. Also torch.cuda.version returns 9.2 which is good.
torch.version.cuda shows 9.2, but torch.backends.cudnn.version() returns 7.1 instead of 7.2.1.
During training, I encounter the error: RuntimeError: CuDNN error: CUDNN_STATUS_EXECUTION_FAILED.
Questions:
Why is cuDNN version 7.1 instead of 7.2.1?
How can I correctly set up the environment to avoid conflicts?
I think you will find it much easier implementing the 2d versions yourself from scratch. There are also great repos for all them as well. Especially Mask-RCNN.
Hi @Soumya_Kundu , is there any repo you know that is working on this from scratch. What i wanted was a algorithm that specially targets on medical images. I have tried RetinaNet and Yolo. The result from RetinaNet was good but, i wanted to improve the result by using the model specific to the domain. So i tried MDT using retinaUnet.
yes,
i am working at LVO detection and used various networks. They work good but i want to improve the result. My thoughts are that if the medical images based network will work better in this case.
MDT RetinaUnet and nnDetection were the option but MDT is too old and nnDetection implementation is for 3D only. I was just searching if there is some implementation for 2D.
There is no “medical detection” framework that I know of unfortunately. I think you should take your best performing model and try improving that further by playing around with it a bit more.
Yes, but MedicalDetectionToolkit has used to for detection purpose by taking advantage of Segmentation Supervision of UNET.
Thank you @Soumya_Kundu for your time