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.github/workflows/test.yml
with: python-version: ${{ matrix.python-version }} # Issue ref: https://github.com/actions/setup-python/issues/436 # cache: "pip" # cache-dependency-path: pyproject.toml - uses: actions/cache@v4 id: cache with: path: ${{ env.pythonLocation }}
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.github/workflows/publish.yml
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.github/workflows/test-redistribute.yml
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docs/en/docs/release-notes.md
* 🔧 Update MkDocs for other languages. PR [#9734](https://github.com/tiangolo/fastapi/pull/9734) by [@tiangolo](https://github.com/tiangolo). * 👷 Refactor Docs CI, run in multiple workers with a dynamic matrix to optimize speed. PR [#9732](https://github.com/tiangolo/fastapi/pull/9732) by [@tiangolo](https://github.com/tiangolo).
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docs/en/docs/async.md
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.github/workflows/build-docs.yml
run: python ./scripts/docs.py update-languages - uses: actions/cache@v4 with: key: mkdocs-cards-${{ matrix.lang }}-${{ github.ref }} path: docs/${{ matrix.lang }}/.cache - name: Build Docs run: python ./scripts/docs.py build-lang ${{ matrix.lang }} - uses: actions/upload-artifact@v3 with: name: docs-site path: ./site/**
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docs/de/docs/async.md
* **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.
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