Difference between revisions of "Monai"
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=== MONAI on HiPerGator Tutorial Recordings and Slides === | === MONAI on HiPerGator Tutorial Recordings and Slides === | ||
| − | * | + | * Recording: [https://ufl.zoom.us/rec/play/2O1PqqMU7DJ2mexyn_Jfki3IGGcu3KjLV8iZ2dcT2Wztxx27dIO8g7W_nlWfEnKmoXASRFNBm7ihGWZU.n-AJRZLetesP4X9v?startTime=1658851332000 MONAI Label for Medical Imaging] and slides: [https://github.com/hw-ju/monai_uf_tutorials/blob/main/slides/MONAICore_tutorial_UF_July_2022.pdf MONAICore_tutorial_UF_July_2022.pdf] |
| − | * | + | * Recording: [https://ufl.zoom.us/rec/share/4VXkVx0gpN1lrLFpmzOKd3CHqsuKFrok8FaJ2aBorm4KLEPFhWlLkBtHP-IQE_2S.5FNwh3n6D-cCTUJj?startTime=1657641745000 MONAI Core] |
| + | |||
| + | * GitHub repository with code examples: [https://github.com/hw-ju/monai_uf_tutorials https://github.com/hw-ju/monai_uf_tutorials] | ||
=== MONAI 3-day Bootcamp Recordings and Learning Materials === | === MONAI 3-day Bootcamp Recordings and Learning Materials === | ||
Revision as of 18:35, 29 August 2022
Description
The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.
Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create annotated datasets, and build AI models in a standardized MONAI paradigm.
Environment Modules
Run module spider monai to find out what environment modules are available for this application.
System Variables
- HPC_MONAI_DIR - installation directory
- HPC_MONAI_BIN - executable directory
- HPC_MONAI_LIB - library directory
- HPC_MONAILABEL_DIR - MONAI label installation directory
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:
ml purge
ml ngc-monailabel/<version>
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!
Learning Materials
MONAI on HiPerGator Tutorial Recordings and Slides
- Recording: MONAI Label for Medical Imaging and slides: MONAICore_tutorial_UF_July_2022.pdf
- Recording: MONAI Core
- GitHub repository with code examples: https://github.com/hw-ju/monai_uf_tutorials
MONAI 3-day Bootcamp Recordings and Learning Materials
MONAI Deploy App SDK
MONAI Packages
If you would like to install MONAI on your own platforms, here are some useful links:
MONAI Core tutorials