e2e.yml

name: E2E

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

concurrency:
  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.event.pull_request.number || github.ref }}
  cancel-in-progress: true

env:
  FLWR_TELEMETRY_ENABLED: 0

jobs:
  frameworks:
    runs-on: ubuntu-22.04
    timeout-minutes: 10
    # Using approach described here:
    # https://docs.github.com/en/actions/using-jobs/using-a-matrix-for-your-jobs
    strategy:
      matrix:
        include:
          - directory: bare

          - directory: jax

          - directory: pytorch
            dataset: |
              from torchvision.datasets import CIFAR10
              CIFAR10('./data', download=True)

          - directory: tensorflow
            dataset: |
              import tensorflow as tf
              tf.keras.datasets.cifar10.load_data()
              
          - directory: tabnet
            dataset: |
              import tensorflow_datasets as tfds
              tfds.load(name='iris', split=tfds.Split.TRAIN)

          - directory: opacus
            dataset: |
              from torchvision.datasets import CIFAR10
              CIFAR10('./data', download=True)
              
          - directory: pytorch-lightning
            dataset: |
              from torchvision.datasets import MNIST
              MNIST('./data', download=True)

          - directory: mxnet
            dataset: |
              import mxnet as mx
              mx.test_utils.get_mnist()

          - directory: scikit-learn
            dataset: |
              import openml
              openml.datasets.get_dataset(554)

          - directory: fastai
            dataset: |
              from fastai.vision.all import untar_data, URLs
              untar_data(URLs.MNIST) 

          - directory: pandas
            dataset: |
              from pathlib import Path
              from sklearn.datasets import load_iris
              Path('data').mkdir(exist_ok=True)
              load_iris(as_frame=True)['data'].to_csv('./data/client.csv')

    name: Framework / ${{matrix.directory}}

    defaults:
      run:
        working-directory: e2e/${{ matrix.directory }}

    steps:
      - uses: actions/checkout@v4
      - name: Bootstrap
        uses: ./.github/actions/bootstrap
        with:
          python-version: 3.8
      - name: Install dependencies
        run: python -m poetry install
      - name: Download dataset
        if: ${{ matrix.dataset }}
        run: python -c "${{ matrix.dataset }}"
      - name: Run edge client test
        run: ./../test.sh "${{ matrix.directory }}"
      - name: Run virtual client test
        run: python simulation.py
      - name: Run driver test
        run: ./../test_driver.sh

  strategies:
    runs-on: ubuntu-22.04
    timeout-minutes: 10
    strategy:
      matrix:
        strat: ["FedMedian", "FedTrimmedAvg", "QFedAvg", "FaultTolerantFedAvg", "FedAvgM", "FedAdam", "FedAdagrad", "FedYogi"]

    name: Strategy / ${{ matrix.strat }}

    defaults:
      run:
        working-directory: e2e/strategies

    steps:
      - uses: actions/checkout@v4
      - name: Bootstrap
        uses: ./.github/actions/bootstrap
      - name: Install dependencies
        run: |
          python -m poetry install
      - name: Cache Datasets
        uses: actions/cache@v3
        with:
          path: "~/.keras"
          key: keras-datasets
      - name: Download Datasets
        run: |
          python -c "import tensorflow as tf; tf.keras.datasets.mnist.load_data()"
      - name: Test strategies
        run: |
          python test.py "${{ matrix.strat }}"