Skip to main content

project-monai

Medical Open Network for AI

Supported Python versions License PyPI version docker conda

premerge postmerge docker Documentation Status codecov

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch 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

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 data parallelism support.

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.

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 @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAI's GitHub Discussions tab.

Release files for monai 1.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for monai 1.1.0
File Interpreter ABI Platform
monai-1.1.0-202212191849-py3-none-any.whl Python 3 none any Details

Release files / monai-1.1.0-202212191849-py3-none-any.whl

Download URL monai-1.1.0-202212191849-py3-none-any.whl
Size 1.2 MB
Tags Python 3
SHA-256 checksum
How to use checksums
28b47fc552309f1974a347fca6771969f25e98c228ef3a53a62eae250f3c0b8d
BLAKE2b-256 checksum
How to use checksums
2ced198fda679711571a11aee6a7916f446d04d942e303e7d76a1ece963cf181
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.28.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.64.1 importlib-metadata/4.10.0 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.13

Release history Release notifications | RSS feed

1.6.1

2 release files

1.6.0

2 release files

1.5.2

2 release files

1.5.1

2 release files

1.5.0

2 release files

1.4.0

2 release files

1.3.2

1 release file

1.3.1

1 release file

1.3.0

1 release file

1.2.0

1 release file

This release

1.1.0 This release

1 release file

1.0.1

1 release file

1.0.0

1 release file

0.9.1

1 release file

0.9.0

1 release file

0.8.1

1 release file

0.8.0

1 release file

0.7.0

1 release file

0.6.0

1 release file

0.5.3

1 release file

0.5.2

1 release file

0.5.1

1 release file

0.5.0

1 release file

0.4.0

1 release file

0.3.0

1 release file

0.2.0

1 release file

0.1.0

2 release files

0.0.1

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page