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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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.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: ${{ env.pythonLocation }}
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.github/workflows/build-docs.yml
run: python ./scripts/docs.py update-languages - uses: actions/cache@v4 with: key: mkdocs-cards-${{ 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/en/docs/deployment/docker.md
In this case, if you had **multiple containers**, by default, when Prometheus came to **read the metrics**, it would get the ones for **a single container each time** (for the container that handled that particular request), instead of getting the **accumulated metrics** for all the replicated containers.
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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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docs/zh/docs/deployment/docker.md
你还可能有**其他原因**,这将使你更容易拥有一个带有**多个进程**的**单个容器**,而不是拥有每个容器中都有**单个进程**的**多个容器**。 例如(取决于你的设置)你可以在同一个容器中拥有一些工具,例如 Prometheus exporter,该工具应该有权访问**每个请求**。 在这种情况下,如果你有**多个容器**,默认情况下,当 Prometheus 来**读取metrics**时,它每次都会获取**单个容器**的metrics(对于处理该特定请求的容器),而不是获取所有复制容器的**累积metrics**。 在这种情况, 这种做法会更加简单:让**一个容器**具有**多个进程**,并在同一个容器上使用本地工具(例如 Prometheus exporter)收集所有内部进程的 Prometheus 指标并公开单个容器上的这些指标。 ---
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docs/en/docs/async.md
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docs/tr/docs/async.md
* **Bilgisayar görüsü**: bir görüntü milyonlarca pikselden oluşur, her pikselin 3 değeri / rengi vardır, bu pikseller üzerinde aynı anda bir şeyler hesaplamayı gerektiren işleme. * **Makine Öğrenimi**: Çok sayıda "matris" ve "vektör" çarpımı gerektirir. Sayıları olan ve hepsini aynı anda çarpan büyük bir elektronik tablo düşünün.
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