Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

project-monai

Medical Open Network for AI

License CI Build Documentation Status codecov PyPI version

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

The codebase is currently under active development. Please see the technical highlights and What's New 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, you can simply run:

pip install monai

For other installation methods (using the default GitHub branch, using Docker, etc.), please refer to the installation guide.

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.

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-weekly 0.9.dev2208

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

Source distribution (sdist)

Source distribution for monai-weekly 0.9.dev2208
File Size Uploaded
monai-weekly-0.9.dev2208.tar.gz 576.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for monai-weekly 0.9.dev2208
File Interpreter ABI Platform
monai_weekly-0.9.dev2208-py3-none-any.whl Python 3 none any Details

Total release size: 1.3 MB

Release files / monai-weekly-0.9.dev2208.tar.gz

Download URL monai-weekly-0.9.dev2208.tar.gz
Size 576.2 kB
Tags Source
SHA-256 checksum
How to use checksums
d65a7efb71c46a79a28997ab6ae79b232865709d7147ff0644ae36ad9cfc1c16
BLAKE2b-256 checksum
How to use checksums
b77ed42fc72daff2c71954f97433edca035d44397d754a4241978507b8ea315f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.10

Release files / monai_weekly-0.9.dev2208-py3-none-any.whl

Download URL monai_weekly-0.9.dev2208-py3-none-any.whl
Size 754.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
fa30b354b0cbd29d8a0eaf58c65cea110a1192f5a7eb240b4843ee1ffeb3ed8e
BLAKE2b-256 checksum
How to use checksums
359982ab07b77ebc2943e17af044efe2b12bf17f8d0a93bad91f0438100500b0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.10

Release history Release notifications | RSS feed

This release

0.9.dev2208 This release

2 release files

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