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.
- 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
To install the current release, you can simply run:
pip install monai-deploy-app-sdk # '--pre' to install a pre-release version.
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
User guide is available at docs.monai.io.
For guidance on making a contribution to MONAI Deploy App SDK, see the contributing guidelines.