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Add table of baselines to docs #3725

Add table of baselines to docs

Add table of baselines to docs #3725

Workflow file for this run

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:
wheel:
runs-on: ubuntu-22.04
name: Build, test and upload wheel
steps:
- uses: actions/checkout@v4
- name: Bootstrap
uses: ./.github/actions/bootstrap
- name: Install dependencies (mandatory only)
run: python -m poetry install
- name: Build wheel
run: ./dev/build.sh
- name: Test wheel
run: ./dev/test-wheel.sh
- name: Upload wheel
if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
id: upload
env:
AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
run: |
cd ./dist
echo "WHL_PATH=$(ls *.whl)" >> "$GITHUB_OUTPUT"
sha_short=$(git rev-parse --short HEAD)
echo "SHORT_SHA=$sha_short" >> "$GITHUB_OUTPUT"
[ -z "${{ github.head_ref }}" ] && dir="${{ github.ref_name }}" || dir="pr/${{ github.head_ref }}"
echo "DIR=$dir" >> "$GITHUB_OUTPUT"
aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/$dir/$sha_short --recursive
outputs:
whl_path: ${{ steps.upload.outputs.WHL_PATH }}
short_sha: ${{ steps.upload.outputs.SHORT_SHA }}
dir: ${{ steps.upload.outputs.DIR }}
frameworks:
runs-on: ubuntu-22.04
timeout-minutes: 10
needs: wheel
# 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: Install Flower wheel from artifact store
if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
run: |
python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
- 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
needs: wheel
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: Install Flower wheel from artifact store
if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
run: |
python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
- 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 }}"