**M**edical **O**pen **N**etwork for **AI** [](https://opensource.org/licenses/Apache-2.0) [](https://github.com/Project-MONAI/MONAI/commits/master) [](https://docs.monai.io/en/latest/?badge=latest) [](https://codecov.io/gh/Project-MONAI/MONAI) [](https://badge.fury.io/py/monai) MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/master/LICENSE) framework for deep learning in healthcare imaging, part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/). 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. ## Features > _The codebase is currently under active development._ > _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) of the current milestone release._ - 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. ## Installation To install [the current release](https://pypi.org/project/monai/): ```bash pip install monai ``` To install from the source code repository: ```bash pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI ``` Alternatively, pre-built Docker image is available via [DockerHub](https://hub.docker.com/r/projectmonai/monai): ```bash # with docker v19.03+ docker run --gpus all --rm -ti --ipc=host projectmonai/monai:latest ``` For more details, please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html). ## Getting Started [MedNIST demo](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) are available on Colab. Tutorials & examples are located at [monai/examples](https://github.com/Project-MONAI/MONAI/tree/master/examples). Technical documentation is available at [docs.monai.io](https://docs.monai.io). ## Contributing For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/master/CONTRIBUTING.md). ## Links - Website: https://monai.io/ - API documentation: https://docs.monai.io - Code: https://github.com/Project-MONAI/MONAI - Project tracker: https://github.com/Project-MONAI/MONAI/projects - Issue tracker: https://github.com/Project-MONAI/MONAI/issues - Wiki: https://github.com/Project-MONAI/MONAI/wiki - Test status: https://github.com/Project-MONAI/MONAI/actions