💡 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

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

Features

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

User guide is available at docs.monai.io.

Installation

To install the current release, you can simply run:

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

Getting Started

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 -t simple_app:latest -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:latest input output