Add Flower Datasets tests as GitHub workflow #2609
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: | |
| 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 }}" |