Monai Usage: Difference between revisions
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==MONAI label== | ==MONAI label== | ||
NGC container usage: | NGC container usage: | ||
=== 1. Server - to start the server as a slurm job: === | |||
*Load modules: | |||
ml purge | |||
ml ngc-monailabel/<version> | |||
*Copy to your directory the file: | |||
/apps/nvidia/containers/monai/start_monai_server_readonly.sh | |||
*Copy e.g. these directories to a place you own (may vary by use case): | |||
cp -r /apps/nvidia/containers/monai/apps/deepedit <my_place> | |||
cp -r /apps/nvidia/containers/monai/datasets/Task09_Spleen <my_place> | |||
*Modify the start_monai_server_readonly.sh line to read: | |||
singularity exec -B /apps/nvidia/containers/monai /apps/nvidia/containers/monai/monailabel/ monailabel start_server --app <my_place>/deepedit --studies <my_place>/Task09_Spleen/imagesTr | |||
or, for a newer version e.g. (the apps/... and datasets/... directories may be different) | |||
singularity exec -B /apps/nvidia/containers/monai /apps/nvidia/containers/monai/monailabel.0.6.0/0.6.0 monailabel start_server --app <my_place>/deepedit --studies <my_place>/Task09_Spleen/imagesTr | |||
*Start server as a batch job: | |||
sbatch start_monai_server_readonly.sh | |||
*Note server address from job output: used in next step | |||
=== 2. 3DSlicer client === | |||
*Start Open On Demand (OOD) session | |||
*Start Console in hwgui with 1 GPU: gpu:geforce:1 | |||
*In console: load & start Slicer | |||
ml qt/5.15.4 slicer/4.13.0 | |||
vglrun -d :0.$CUDA_VISIBLE_DEVICES Slicer | |||
*In Slicer GUI: | |||
**Select module: Active Learning -> MONAILabel | |||
**Fill in server address, e.g.: http://c1007a-s17:8000/ | |||
**Click on refresh button next to server address | |||
**Load Next Sample | |||
*You are good to go! Enjoy! |
Latest revision as of 16:41, 2 August 2023
Back to Monai
MONAI core
NGC container usage:
ml purge ml ngc-monai/<version> python <your__python_script>
MONAI label
NGC container usage:
1. Server - to start the server as a slurm job:
- Load modules:
ml purge ml ngc-monailabel/<version>
- Copy to your directory the file:
/apps/nvidia/containers/monai/start_monai_server_readonly.sh
- Copy e.g. these directories to a place you own (may vary by use case):
cp -r /apps/nvidia/containers/monai/apps/deepedit <my_place> cp -r /apps/nvidia/containers/monai/datasets/Task09_Spleen <my_place>
- Modify the start_monai_server_readonly.sh line to read:
singularity exec -B /apps/nvidia/containers/monai /apps/nvidia/containers/monai/monailabel/ monailabel start_server --app <my_place>/deepedit --studies <my_place>/Task09_Spleen/imagesTr or, for a newer version e.g. (the apps/... and datasets/... directories may be different) singularity exec -B /apps/nvidia/containers/monai /apps/nvidia/containers/monai/monailabel.0.6.0/0.6.0 monailabel start_server --app <my_place>/deepedit --studies <my_place>/Task09_Spleen/imagesTr
- Start server as a batch job:
sbatch start_monai_server_readonly.sh
- Note server address from job output: used in next step
2. 3DSlicer client
- Start Open On Demand (OOD) session
- Start Console in hwgui with 1 GPU: gpu:geforce:1
- In console: load & start Slicer
ml qt/5.15.4 slicer/4.13.0 vglrun -d :0.$CUDA_VISIBLE_DEVICES Slicer
- In Slicer GUI:
- Select module: Active Learning -> MONAILabel
- Fill in server address, e.g.: http://c1007a-s17:8000/
- Click on refresh button next to server address
- Load Next Sample
- You are good to go! Enjoy!