* 🔧 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).

        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 }}

        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/**

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.

* **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.

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。

---

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

* **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.