MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging. Its ambitions are:

  • developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
  • creating state-of-the-art, end-to-end training workflows for healthcare imaging;
  • providing researchers with the optimized and standardized way to create and evaluate deep learning models.


The codebase is currently under active development.

  • flexible pre-processing for multi-dimensional medical imaging data;
  • compositional & portable APIs for ease of integration in existing workflows;
  • domain-specific implementations for networks, losses, evaluation metrics and more;
  • customizable design for varying user expertise;
  • multi-GPU data parallelism support.


To install the current release:

pip install monai

To install from the source code repository:

pip install git+

Alternatively, pre-built Docker image is available via DockerHub:

  # with docker v19.03+
  docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest

Getting Started

Tutorials & examples are located at monai/examples.

Technical documentation is available via Read the Docs.


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

  • Website:
  • API documentation:
  • Code:
  • Project tracker:
  • Issue tracker:
  • Wiki:
  • Test status: