Difference between revisions of "Monai"

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[[Category:Software]][[Category:AI]]
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[[Category:Software]][[Category:Healthcare]]
 
{|<!--CONFIGURATION: REQUIRED-->
 
{|<!--CONFIGURATION: REQUIRED-->
 
|{{#vardefine:app|monai}}
 
|{{#vardefine:app|monai}}
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* HPC_{{uc:{{#var:app}}}}_BIN - executable directory
 
* HPC_{{uc:{{#var:app}}}}_BIN - executable directory
 
* HPC_{{uc:{{#var:app}}}}_LIB - library directory
 
* HPC_{{uc:{{#var:app}}}}_LIB - library directory
 +
 +
* HPC_{{uc:{{#var:app}}}}LABEL_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==
 
==Learning Materials==
 +
 +
=== 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 ===
 
* [https://urldefense.proofpoint.com/v2/url?u=https-3A__www.youtube.com_playlist-3Flist-3DPLtoSVSQ2XzyCobzE6NvwjNpITsQyPUtfs&d=DwMGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=NSXeOzyqbRdsXM127ZiphQ&m=e1aztAOhx1tyaakucfLf_fPfneoSqzwX6c1D_MpVwvY&s=u-HgmwAUKaQ8wM-Mbt-ePxWJczHlPy4DqujxvvNYrI8&e= Bootcamp Playlist]
 
* [https://urldefense.proofpoint.com/v2/url?u=https-3A__www.youtube.com_playlist-3Flist-3DPLtoSVSQ2XzyCobzE6NvwjNpITsQyPUtfs&d=DwMGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=NSXeOzyqbRdsXM127ZiphQ&m=e1aztAOhx1tyaakucfLf_fPfneoSqzwX6c1D_MpVwvY&s=u-HgmwAUKaQ8wM-Mbt-ePxWJczHlPy4DqujxvvNYrI8&e= Bootcamp Playlist]
Line 44: Line 86:
  
 
* [https://urldefense.proofpoint.com/v2/url?u=https-3A__www.youtube.com_watch-3Fv-3DWwjilJFHuU4&d=DwMGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=NSXeOzyqbRdsXM127ZiphQ&m=e1aztAOhx1tyaakucfLf_fPfneoSqzwX6c1D_MpVwvY&s=k10xZSVemV69BMYtvaRmUtuITA8HhFOgF5-QILbLBxw&e= MedNIST Classification]
 
* [https://urldefense.proofpoint.com/v2/url?u=https-3A__www.youtube.com_watch-3Fv-3DWwjilJFHuU4&d=DwMGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=NSXeOzyqbRdsXM127ZiphQ&m=e1aztAOhx1tyaakucfLf_fPfneoSqzwX6c1D_MpVwvY&s=k10xZSVemV69BMYtvaRmUtuITA8HhFOgF5-QILbLBxw&e= MedNIST Classification]
 +
 +
=== MONAI Packages ===
 +
If you would like to install MONAI on your own platforms, here are some useful links:
 +
* [https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.monai.io_en_latest_whatsnew-5F0-5F7.html&d=DwMGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=NSXeOzyqbRdsXM127ZiphQ&m=e1aztAOhx1tyaakucfLf_fPfneoSqzwX6c1D_MpVwvY&s=m_sJqlsaQQW5dRdUmTcTY6ysyaaCaUPnVVliC6W9SsM&e= MONAI Core]
 +
 +
* [https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.monai.io_projects_label_en_0.2.0_whatsnew.html&d=DwMGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=NSXeOzyqbRdsXM127ZiphQ&m=e1aztAOhx1tyaakucfLf_fPfneoSqzwX6c1D_MpVwvY&s=XH8yqUKN66lK7HGZnuY-gskf665Joyv04YiC5gh3M3k&e= MONAI Label]
 +
 +
* [https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.monai.io_projects_monai-2Ddeploy-2Dapp-2Dsdk_en_latest_release-5Fnotes_v0.1.0.html&d=DwMGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=NSXeOzyqbRdsXM127ZiphQ&m=e1aztAOhx1tyaakucfLf_fPfneoSqzwX6c1D_MpVwvY&s=_pzytNZK2W9-eQosc_GnDfRp_DuD7R-yPMTCX9IRDEk&e= MONAI Deploy App SDK]
 +
 +
=== MONAI Core tutorials ===
 +
* [https://github.com/Project-MONAI/tutorials Tutorials]
  
 
<!--Configuration-->
 
<!--Configuration-->

Latest revision as of 18:35, 29 August 2022

Description

monai website  

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

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