Getting Started with flip-utils

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, Flower helpers, 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.

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 monorepo and install from source:

git clone https://github.com/londonaicentre/FLIP.git
cd FLIP/flip-utils
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

flip.flower

Flower-specific helpers:

  • metrics.py — Server-side metrics collection and reporting for Flower runs

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; NVFLARE Executor API)

standard_client_api

Federated training via the NVFLARE Client API (Client-API path used for Ark+ etc.)

evaluation

Distributed model evaluation without training (NVFLARE Executor API)

evaluation_client_api

Distributed model evaluation via the NVFLARE Client API (head-only eval path)

diffusion_model

Two-stage training: VAE encoder followed by diffusion model training

fed_opt

Custom federated optimization with flexible aggregation strategies

The Flower backend ships its own standard and evaluation templates under fl-apps/flower/ — selected at the deploy layer by FL_BACKEND=flower.

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 fl-apps/nvflare/<JOB_TYPE>/app/custom/ (e.g. fl-apps/nvflare/standard/app/custom/).

  3. Run one of the shipped tutorials against the NVFLARE simulator from the repository root:

    make -C fl-tutorials run-tutorial TUTORIAL=xray_classification
# list every available tutorial with:
make -C fl-tutorials list-tutorials

    The simulator harness is documented in fl-tutorials/nvflare/testing/ and is driven per-tutorial via that tutorial’s .env.app. See fl-services/nvflare/README.md for building the local :dev FL images the harness uses.

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

flip-utils is documented as part of the FLIP documentation. From the repository root, run:

cd docs && make docs

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

cd docs && make 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 section of the built documentation for complete coverage of all public classes and functions.

Note

The flip-utils package lives under flip-utils/ in the FLIP repository, and its documentation is published as part of the FLIP documentation.