Skip to main content

MONAI NVIDIA FLARE integration

Project description

MONAI Integration

Objective

Integration with MONAI's federated learning capabilities.

Add ClientAlgoExecutor class to allow using MONAI's ClientAlgo class in federated scenarios.

Goals:

Allow the use of bundles from the MONAI model zoo or custom configurations with NVFlare.

Background

MONAI allows the definition of AI models using the "bundle" concept. It allows for easy experimentation and sharing of models that have been developed using MONAI. Using the bundle configurations, we can use MONAI's MonaiAlgo (the implementation of ClientAlgo) to execute a bundle model in a federated scenario using NVFlare.

Federated Learning Module in MONAI (https://docs.monai.io/en/stable/modules.html#federated-learning)

Description

NVFlare executes the ClientAlgo class using the ClientAlgoExecutor class provided with this package.

Examples

For an example of using NVIDIA FLARE to train a medical image analysis model using federated averaging (FedAvg) and MONAI Bundle, see the examples.

Requirements

We recommend following the instructions for setting up a virtual environment, and using it in JupyterLab for running the notebooks the MONAI integration examples.

Install MONAI-NVFlare integration from PyPI:

pip install monai_nvflare

(Optional) Install MONAI-NVFlare integration from source:

pip install -e .

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

monai_nvflare-0.2.12-py3-none-any.whl (15.1 kB view details)

Uploaded Python 3

File details

Details for the file monai_nvflare-0.2.12-py3-none-any.whl.

File metadata

File hashes

Hashes for monai_nvflare-0.2.12-py3-none-any.whl
Algorithm Hash digest
SHA256 92c46c6adf275cffd04970daf7027d2060c1aa0f107cba4c5b2ce9ac62476a5e
MD5 1f441aacaddff60f15aaf301729ce0b6
BLAKE2b-256 ea7f97fea5ed9222568c2d6efbf711258700769ec9a2fd2e1797e68775d1b161

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page