-
Notifications
You must be signed in to change notification settings - Fork 9
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add models to the algorithm templates #297
Comments
@jmsmkn That's a good suggestion. As you say it should be easy to just support PyTorch and TensorFlow models for this for now. I could create a quick mockup for PyTorch when I find some time. I guess we have to carefully think about the interface for the template. |
Would be really good to know if ONNX could be used, so that we do not have to maintain support for all of the frameworks: https://onnx.ai/supported-tools.html#buildModel |
Ok, let me do some research on ONNX first. |
We now use ONNX Runtime (CPU only) for bodyct-multiview-nodule-detection and this works fine, also model/weigths conversion from PyTorch to ONNX format is very easy to do (probably very similar for TensorFlow). Haven't tested it for the GPUs yet. The only caveat I found for using ONNX Runtime (CPU mode) on grand-challenge is that you must explicitly specify the CPU affinities, since it has no permissions for automatic affinity resolution there. See the following code for creating an |
Templating will be removed from this repo as that has moved to https://github.com/DIAGNijmegen/rse-grand-challenge-forge. |
Request from Erdi:
The text was updated successfully, but these errors were encountered: