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I am trying to train and evaluate the network for a small scale experiment. I have managed to get everything working up to training— setting up environment, rendering input image data, changing configurations, managing dependencies issues...
At the start of training in train.py I get the following error:
RuntimeError: cuda runtime error (8) : invalid device function at /opt/conda/conda-bld/pytorch_1544174967633/work/aten/src/THC/THCGeneral.cpp:405
I am running the training on a single NVIDIA GeForce GPU with 25GB memory usage. Driver Version: 510.60.02 and CUDA Version: 11.6. I suspect that this error comes from some incompatibility between my GPU CUDA version and the PyTorch and other packages versions set up by the environment provided on environment.yml
I was wondering if anyone or the authors have run into a similar issue or have any suggestions on how to manage this possible incompatibility.
I tried to install higher versions of PyTorch, torchvision, and install cudatoolkit on the environment but this lead to incompatibilities with other packages' versions specified in environment.yml. conda update --all also created dependency issues
The text was updated successfully, but these errors were encountered:
Apologize for the late response. I believe the issue can be fixed by installing the correct cudatoolkit version. The cudatoolkit in environment.yml is 10.2, please update this to match yours (version 11.6).
Hi,
I am trying to train and evaluate the network for a small scale experiment. I have managed to get everything working up to training— setting up environment, rendering input image data, changing configurations, managing dependencies issues...
At the start of training in
train.py
I get the following error:RuntimeError: cuda runtime error (8) : invalid device function at /opt/conda/conda-bld/pytorch_1544174967633/work/aten/src/THC/THCGeneral.cpp:405
I am running the training on a single NVIDIA GeForce GPU with 25GB memory usage. Driver Version: 510.60.02 and CUDA Version: 11.6. I suspect that this error comes from some incompatibility between my GPU CUDA version and the PyTorch and other packages versions set up by the environment provided on
environment.yml
I was wondering if anyone or the authors have run into a similar issue or have any suggestions on how to manage this possible incompatibility.
I tried to install higher versions of PyTorch, torchvision, and install cudatoolkit on the environment but this lead to incompatibilities with other packages' versions specified in
environment.yml
.conda update --all
also created dependency issuesThe text was updated successfully, but these errors were encountered: