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  1. docs/bigdata/README.md

    ### **4.1 Spark Pi**
    
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  2. README.md

    **Raspberry Pi 0 and 1**      | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi01-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi01-py3.html)           | [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv6l.whl)
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  3. common-protos/k8s.io/apimachinery/pkg/api/resource/generated.proto

    // <sign>            ::= "+" | "-"
    // <signedNumber>    ::= <number> | <sign><number>
    // <suffix>          ::= <binarySI> | <decimalExponent> | <decimalSI>
    // <binarySI>        ::= Ki | Mi | Gi | Ti | Pi | Ei
    //
    // 	(International System of units; See: http://physics.nist.gov/cuu/Units/binary.html)
    //
    // <decimalSI>       ::= m | "" | k | M | G | T | P | E
    //
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  4. docs/pt/docs/deployment.md

    ```Dockerfile
    FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7
    
    COPY ./app /app/app
    ```
    
    #### Raspberry Pi e outras arquiteturas
    
    Se você estiver rodando Docker em um Raspberry Pi (que possui um processador ARM) ou qualquer outra arquitetura, você pode criar um `Dockerfile` do zero, baseado em uma imagem base Python (que é multi-arquitetural) e utilizar Uvicorn sozinho.
    
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  5. okhttp-idna-mapping-table/src/main/resources/okhttp3/internal/idna/IdnaMappingTable.txt

    213C          ; mapped                 ; 03C0          # 4.1  DOUBLE-STRUCK SMALL PI
    213D..213E    ; mapped                 ; 03B3          # 3.2  DOUBLE-STRUCK SMALL GAMMA..DOUBLE-STRUCK CAPITAL GAMMA
    213F          ; mapped                 ; 03C0          # 3.2  DOUBLE-STRUCK CAPITAL PI
    2140          ; mapped                 ; 2211          # 3.2  DOUBLE-STRUCK N-ARY SUMMATION
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  6. docs/de/docs/deployment/docker.md

    Das ist, was Sie in **den meisten Fällen** tun möchten, zum Beispiel:
    
    * Bei Verwendung von **Kubernetes** oder ähnlichen Tools
    * Beim Betrieb auf einem **Raspberry Pi**
    * Bei Verwendung eines Cloud-Dienstes, der ein Containerimage für Sie ausführt, usw.
    
    ### Paketanforderungen
    
    Normalerweise befinden sich die **Paketanforderungen** für Ihre Anwendung in einer Datei.
    
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  7. docs/en/docs/deployment/docker.md

    This is what you would want to do in **most cases**, for example:
    
    * Using **Kubernetes** or similar tools
    * When running on a **Raspberry Pi**
    * Using a cloud service that would run a container image for you, etc.
    
    ### Package Requirements
    
    You would normally have the **package requirements** for your application in some file.
    
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  8. docs/pt/docs/deployment/docker.md

    Isso é o que você quer fazer na **maioria dos casos**, por exemplo:
    
    * Usando **Kubernetes** ou ferramentas similares
    * Quando rodando em uma **Raspberry Pi**
    * Usando um serviço em nuvem que irá rodar uma imagem de contêiner para você, etc.
    
    ### O Pacote Requirements
    
    Você normalmente teria os **requisitos do pacote** para sua aplicação em algum arquivo.
    
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  9. docs/zh/docs/deployment/docker.md

    
    ## 为 FastAPI 构建 Docker 镜像
    
    好吧,让我们现在构建一些东西! 🚀
    
    我将向你展示如何基于 **官方 Python** 镜像 **从头开始** 为 FastAPI 构建 **Docker 镜像**。
    
    这是你在**大多数情况**下想要做的,例如:
    
    * 使用 **Kubernetes** 或类似工具
    * 在 **Raspberry Pi** 上运行时
    * 使用可为你运行容器镜像的云服务等。
    
    ### 依赖项
    
    你通常会在某个文件中包含应用程序的**依赖项**。
    
    具体做法取决于你**安装**这些依赖时所使用的工具。
    
    最常见的方法是创建一个`requirements.txt`文件,其中每行包含一个包名称和它的版本。
    
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  10. docs/ru/docs/deployment/docker.md

    Такой подход сгодится для **большинства случаев**, например:
    
    * Использование с **Kubernetes** или аналогичным инструментом
    * Запуск в **Raspberry Pi**
    * Использование в облачных сервисах, запускающих образы контейнеров для вас и т.п.
    
    ### Установить зависимости
    
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