Error when attempting to segmentate with Clara train 4.0 Prostate model

Hi, I uploaded the prostate model from NVIDIA NGC to my Clara AIAA with

curl -X PUT "" -F "config=@prostate/config/config_aiaa.json;type=application/json" -F "data=@prostate/models/model.ts"
{"name": "prostate", "labels": ["prostate central gland", "prostate peripheral zone"], "description": "A pre-trained model for volumetric (3D) segmentation of

, and it shows up in the browser API v1/models/.

However, when trying to segmentate with medical decathlon prostate test data, I get

curl -X POST "" -H "accept: multipart/form-data" -H "Content-Type: multipart/form-data" -F "params={}" -F "image=@prostate/Task05_Prostate/imagesTr/prostate_00.nii.gz;type=application/x-gzip" -o output_image.nii.gz
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 4230k  100   119  100 4230k    543  18.8M --:--:-- --:--:-- --:--:-- 18.8M

And the output file only contains either timeout information, or error message

“{“error”:{“message”:[“theta must be Nx3x3 or Nx4x4, got torch.Size([1, 5, 5]).”],“type”:“ValueError”},“success”:false}”

I’m using Clara Train v4.0 for the first time, RTX 2080 Ti (I tried halving the roi in config_aiaa.json to fit it better in my VRAM), Ubuntu 18.04.5 LTS, NVIDIA-driver 460.80. I’m not sure if I should be running these commands from the Clara SDK docker, or from the standard terminal user? I understood

docker-compose --env-file docker-compose.env -p aiaa_triton up --remove-orphans -d

already starts the TRITON-AIAA, which is then ready to be uploaded models and then files.

Thanks for your interest in Clara Train SDK.
Please check out the notebooks to get you started clara-train-examples/PyTorch/NoteBooks at master · NVIDIA/clara-train-examples · GitHub
There are specific AIAA notebooks that would get you up and running
Also note in V4 we have an easier cli that you can run form within the docker as
AIAA <options> to load, start, stop , delete models
this should be easier than using curl commands

Hope this helps