Skip to content

CLI Reference

The ReFedEz CLI provides a simple interface to manage federated learning deployments. It handles building environments, deploying to machines, and controlling the lifecycle of your federated projects.

Getting Started

The CLI workflow follows these steps:

  • Requirements: Ensure your network and machines are set up
  • Configure: Set up project.yml and refedez.yaml
  • Start: Deploy and launch services
  • Execute: Run your federated learning jobs

Commands

See Commands for detailed documentation of all available CLI commands:

  • refedez start - Build and deploy federated services
  • refedez status - Check deployment status
  • refedez stop - Stop running services
  • refedez clean - Clean up temporary files

Project Structure

ReFedEz projects typically include:

  • project.yml - NVIDIA FLARE configuration (participants, certificates, etc.)
  • refedez.yaml - ReFedEz-specific settings (machines, environments)
  • model.py - Your federated learning code
  • .refedez/ - Temporary folder created during deployment

Configuration Files

refedez.yaml

Defines machines and their connection details:

refedez:
  folder: ./.refedez
  capabilties: ./project.yml

machines:
  server.localhost:
    type: local
  client1:
    type: remote
    ip: 192.168.1.100
    user: researcher

project:
  server.localhost:
    folder: .
  client1:
    folder: /home/researcher/project

project.yml

NVIDIA FLARE configuration defining participants and their roles. ReFedEz uses this to provision the federated setup but you generally don't need to modify it manually.