Add PyTorch integration tests with FDS #5527
Workflow file for this run
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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 && github.actor != 'dependabot[bot]' }} | |
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: bare-https | |
- 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 "${{ matrix.directory }}" | |
- name: Run virtual client test | |
run: python simulation.py | |
- name: Run driver test | |
run: ./../test_driver.sh "${{ matrix.directory }}" | |
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 }}" |