This release is a pre-release and may not be stable for production use.
Medical Open Network for AI
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the PyTorch Ecosystem. Its ambitions are as follows:
- 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
Please see the technical highlights and What's New of the milestone releases.
- 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 multi-node data parallelism support.
Requirements
MONAI works with the currently supported versions of Python, and depends directly on NumPy and PyTorch with many optional dependencies.
- Major releases of MONAI will have dependency versions stated for them. The current state of the
devbranch in this repository is the unreleased development version of MONAI which typically will support current versions of dependencies and include updates and bug fixes to do so. - PyTorch support covers the current version plus three previous minor versions. If compatibility issues with a PyTorch version and other dependencies arise, support for a version may be delayed until a major release.
- Our support policy for other dependencies adheres for the most part to SPEC0, where dependency versions are supported where possible for up to two years. Discovered vulnerabilities or defects may require certain versions to be explicitly not supported.
- See the
requirements*.txtfiles for dependency version information.
Installation
To install the current release, you can simply run:
pip install monai
Please refer to the installation guide for other installation options.
Getting Started
MedNIST demo and MONAI for PyTorch Users are available on Colab.
Examples and notebook tutorials are located at Project-MONAI/tutorials.
Technical documentation is available at docs.monai.io.
Docker
The MONAI Docker image is available from Dockerhub,
tagged as latest for the latest state of dev or with a release version. A slimmed down image can also be built
locally using Dockerfile.slim, see that file for instructions.
To get started with the latest MONAI, use docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash.
Citation
If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.
Model Zoo
The MONAI Model Zoo is a place for researchers and data scientists to share the latest and great models from the community. Utilizing the MONAI Bundle format makes it easy to get started building workflows with MONAI.
Contributing
For guidance on making a contribution to MONAI, see the contributing guidelines.
Community
Join the conversation on Twitter/X @ProjectMONAI, LinkedIn, or join our Slack channel.
Ask and answer questions over on MONAI's GitHub Discussions tab.
Links
- Website: https://project-monai.github.io/
- API documentation (milestone): https://monai.readthedocs.io/
- API documentation (latest dev): https://monai.readthedocs.io/en/latest/
- 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
- PyPI package: https://pypi.org/project/monai/
- conda-forge: https://anaconda.org/conda-forge/monai
- Weekly previews: https://pypi.org/project/monai-weekly/
- Docker Hub: https://hub.docker.com/r/projectmonai/monai
1 maintainer
Release files for monai-weekly 1.6.dev2619
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| monai_weekly-1.6.dev2619.tar.gz | 1.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| monai_weekly-1.6.dev2619-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.9 MB
Release files / monai_weekly-1.6.dev2619.tar.gz
| Download URL | monai_weekly-1.6.dev2619.tar.gz |
|---|---|
| Size | 1.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b294e1f64019b54f979905d1a2ec848ac337eb3809aba29b632c9eb51d66f4fe
|
|
BLAKE2b-256 checksum How to use checksums |
48fd7d1e8c423cae7e31bdd6eafc8e89546a5f008bc5894226df0ed93ecc9a43
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Release files / monai_weekly-1.6.dev2619-py3-none-any.whl
| Download URL | monai_weekly-1.6.dev2619-py3-none-any.whl |
|---|---|
| Size | 1.6 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a279add00d14bd15717e35da2caa2cee542e21940e42c38e10123f8651a6ccc5
|
|
BLAKE2b-256 checksum How to use checksums |
71d22e1e015270d5ec7acecf8116ae321fe8386a40035e151ed36b6ed4446472
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
