The InnerEye models on AzureML are composed of two parts in order to reproduce the same settings at inference and training time:
- Model: The model is registered in the AzureML registry and contains the code used at training time and pytorch checkpoint
- Environment: The Azure ML environment used to train the model. This contains the docker image with all the dependencies that were used for training
If you want to export a model from one Workspace to another you can use the following command to download and upload a
model from an AzureML workspace. This script does not use settings.yml, it uses interactive authentication, and the
workspace specified in the parameters. The model will be written to the path in --path parameter with two folders one
for the MODEL and one for the ENVIRONMENT files.
-
Download to path:
python InnerEye/Scripts/move_model.py -a download --path ./ --workspace_name "<name>" --resource_group "<name>" --subscription_id "<sub_id>" --model_id "name:version" -
Upload from path:
python InnerEye/Scripts/move_model.py - upload --path ./ --workspace_name "<name>" --resource_group "<name>" --subscription_id "<sub_id>" --model_id "name:version"
Once in place you may want to run inference on the model with partial test data, i.e. test data from patients for whom
some of the labels are missing. Normally inference on partial test data would raise an exception. To allow inference to continue over partial test data add the flag --allow_incomplete_labels to your inference call, for example python InnerEye/ML/runner.py --allow_incomplete_labels --train=False --azureml --model=<model_name> --run_recovery_id=<recovery_id>