💡 If you want to know more about MONAI Deploy WG vision, overall structure, and guidelines, please read MONAI Deploy main repo first.

MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.

• Build medical imaging inference applications using a flexible, extensible & usable Pythonic API
• Easy management of inference applications via programmable Directed Acyclic Graphs (DAGs)
• Built-in operators to load DICOM data to be ingested in an inference app
• Out-of-the-box support for in-proc PyTorch based inference
• Easy incorporation of MONAI based pre and post transformations in the inference application
• Package inference application with a single command into a portable MONAI Application Package
• Locally run and debug your inference application using App Runner

User guide is available at docs.monai.io.

To install the current release, you can simply run:

pip install monai-deploy-app-sdk  # '--pre' to install a pre-release version.

Getting started guide is available at here.

pip install monai-deploy-app-sdk  # '--pre' to install a pre-release version.

# Clone monai-deploy-app-sdk repository for accessing examples.
git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git
cd monai-deploy-app-sdk

# Install necessary dependencies for simple_imaging_app
pip install scikit-image

# Execute the app locally
python examples/apps/simple_imaging_app/app.py -i examples/apps/simple_imaging_app/brain_mr_input.jpg -o output

# Package app (creating MAP Docker image), using `-l DEBUG` option to see progress.
monai-deploy package examples/apps/simple_imaging_app -c simple_imaging_app/app.yaml -t simple_app:latest --platform x64-workstation -l DEBUG

# Run the app with docker image and an input file locally
## Copy a test input file to 'input' folder
mkdir -p input && rm -rf input/*
cp examples/apps/simple_imaging_app/brain_mr_input.jpg input/
## Launch the app
monai-deploy run simple_app-x64-workstation-dgpu-linux-amd64:latest -i input -o output

Tutorials are provided to help getting started with the App SDK, to name but a few below.

YouTube Video (to be updated with the new version):

• MedNIST Classification Example

YouTube Video (to be updated with the new version):

• Spleen Organ Segmentation - Jupyter Notebook Tutorial
• Spleen Organ Segmentation - Deep Dive

https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples/apps has example apps that you can see.

• ai_livertumor_seg_app
• ai_spleen_seg_app
• ai_unetr_seg_app
• dicom_series_to_image_app
• mednist_classifier_monaideploy
• simple_imaging_app

For guidance on making a contribution to MONAI Deploy App SDK, see the contributing guidelines.

To participate, please join the MONAI Deploy App SDK weekly meetings on the calendar and review the meeting notes.

Join the conversation on Twitter @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAI Deploy App SDK's GitHub Discussions tab.

• Website: https://monai.io
• API documentation: https://docs.monai.io/projects/monai-deploy-app-sdk
• Code: https://github.com/Project-MONAI/monai-deploy-app-sdk
• Project tracker: https://github.com/Project-MONAI/monai-deploy-app-sdk/projects
• Issue tracker: https://github.com/Project-MONAI/monai-deploy-app-sdk/issues
• Wiki: https://github.com/Project-MONAI/monai-deploy-app-sdk/wiki
• Test status: https://github.com/Project-MONAI/monai-deploy-app-sdk/actions
• PyPI package: https://pypi.org/project/monai-deploy-app-sdk