flip-utils 0.1.5

FLIP - Federated Learning and Interoperability Platform library built on NVIDIA FLARE

About flip-utils

flip-utils is the pip-installable distribution published from this repository. Its Python import package is flip, which contains the shared platform logic used by FLIP jobs and services including core training logic, NVFLARE components, and utility helpers.

The FLIP platform uses this package to power federated learning applications across multiple job types: standard federated training, distributed evaluation, diffusion model training, and custom federated optimization.

Hosted documentation is available on Read the Docs.

Installation

Install the published package from PyPI:

pip install flip-utils
# or with uv
uv add flip-utils

To use the latest development version, clone the repository and install from source:

git clone https://github.com/londonaicentre/flip-fl-base.git
git checkout develop   # or the desired branch/commit
cd flip-fl-base
uv sync
# or
pip install .

To build a distributable wheel for development:

uv build

Package Structure & Modules

The flip package is organized into logical modules:

flip.core

Core classes and abstractions:

• FLIPBase — Abstract base class with common FL logic

• FLIPStandardProd — Production implementation using FLIP platform APIs

• FLIPStandardDev — Development implementation using local CSV/filesystem

• FLIP() factory — Automatically selects the correct implementation based on environment

flip.constants

Configuration and enumerations:

• FlipConstants — Pydantic-settings configuration singleton

• ResourceType — Enum for imaging resource types (DICOM, NIFTI, etc.)

• ModelStatus — Enum for model training states

• JobType — Enum for supported FL job types

• PTConstants — PyTorch-specific constants and settings

flip.utils

Utility helpers:

• Utils — General utility functions

• model_weights_handling — Model weight aggregation and manipulation

flip.nvflare

NVFLARE-specific components:

• executors/ — RUN_TRAINER, RUN_VALIDATOR, RUN_EVALUATOR wrappers

• controllers/ — Workflow controllers (ScatterAndGather, CrossSiteModelEval, etc.)

• components/ — Event handlers, persistors, privacy filters, model locators, etc.

• metrics.py — Metrics collection and reporting

Using the FLIP Factory

The FLIP() factory automatically selects between development and production implementations based on the LOCAL_DEV environment variable:

from flip import FLIP

# Uses FLIPStandardProd in production or FLIPStandardDev in local dev
flip = FLIP()
df = flip.get_dataframe(project_id, query)

See the API reference for detailed method documentation.

Job Types

Set the job type via the JOB_TYPE environment variable:

Type	Description
standard	Federated training with FedAvg aggregation (default)
evaluation	Distributed model evaluation without training
diffusion_model	Two-stage training: VAE encoder followed by diffusion model training
fed_opt	Custom federated optimization with flexible aggregation strategies

User Application Requirements

User-provided application code goes in the job’s custom/ directory. The executor wrappers dynamically import these files:

File	Description
trainer.py	Training logic — must export FLIP_TRAINER class
validator.py	Validation logic — must export FLIP_VALIDATOR class
models.py	Model definitions — must export get_model() function
config.json	Hyperparameters — must include LOCAL_ROUNDS and LEARNING_RATE
transforms.py	Data transforms (optional)

Development Mode

To test FL applications locally before deploying to production:

1. Set environment variables in .env.development:

   LOCAL_DEV=true
   DEV_IMAGES_DIR=../data/accession-resources
   DEV_DATAFRAME=../data/sample_get_dataframe.csv
   JOB_TYPE=standard

2. Place your application files in src/<JOB_TYPE>/app/custom/.

3. Run the simulator in Docker:

   make run-container

Running Tests

Run unit tests for the flip package:

make unit-test
# or
uv run pytest -s -vv

Tests use pytest with coverage reporting and are located in tests/unit/.

Building the Docs Locally

From the repository root, run:

make docs

The generated HTML site will be written to docs/_build/html. To clean previous builds:

make docs-clean

How the API Reference is Generated

The API reference is built with sphinx-autoapi and points directly at the flip/ source tree. That keeps the reference pages aligned with the code without maintaining hand-written module stubs. See the API Reference for complete documentation of all public classes and functions.