MONAI Deploy App SDK offers a framework and associated tools to design

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

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

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

Examples and notebook tutorials are located at Project-MONAI/monai-deploy-app-sdk.

User guide is available at docs.monai.io.