diff --git a/develop/doc_cn/_sources/howto/cross_compiling/cross_compiling_for_android_cn.md.txt b/develop/doc_cn/_sources/howto/cross_compiling/cross_compiling_for_android_cn.md.txt index 90dc84718c9ce1374cda6022de177afeeb60279d..1fc58c37cc9151d5e4d99b939e30c29aa99e04f1 100644 --- a/develop/doc_cn/_sources/howto/cross_compiling/cross_compiling_for_android_cn.md.txt +++ b/develop/doc_cn/_sources/howto/cross_compiling/cross_compiling_for_android_cn.md.txt @@ -1,9 +1,46 @@ # 构建Android平台上的PaddlePaddle库 -用户可通过交叉编译的方式,在用户熟悉的开发平台(Linux,Mac OS X和Windows)上编译Android平台上适用的PaddlePaddle库。 +用户可通过如下两种方式,交叉编译Android平台上适用的PaddlePaddle库: +- 基于Docker容器的编译方式 +- 基于Linux交叉编译环境的编译方式 + +## 基于Docker容器的编译方式 +Docker能在所有主要操作系统(包括Linux,Mac OS X和Windows)上运行,因此,使用基于Docker容器的编译方式,用户可在自己熟悉的开发平台上编译Android平台上适用的PaddlePaddle库。 + +### 构建PaddlePaddle的Android开发镜像 +我们把PaddlePaddle的交叉编译环境打包成一个镜像,称为开发镜像,里面涵盖了交叉编译Android版PaddlePaddle库需要的所有编译工具。 + +```bash +$ git clone https://github.com/PaddlePaddle/Paddle.git +$ cd Paddle +$ docker build -t username/paddle-android:dev . -f Dockerfile.android +``` + +### 编译PaddlePaddle C-API库 +构建好开发镜像后,即可使用开发镜像来编译Android版PaddlePaddle C-API库。 +Android的Docker开发镜像向用户提供两个可配置的参数: + +| Argument | Optional Values | Default | +|-----------------|-------------------------|---------| +|`ANDROID_ABI` |`armeabi-v7a, arm64-v8a` | `armeabi-v7a` | +|`ANDROID_API` |`>= 21` | `21` | + +- 编译`armeabi-v7a`,`Android API 21`的PaddlePaddle库 +```bash +$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev +``` + +- 编译`arm64-v8a`,`Android API 21`的PaddlePaddle库 +```bash +$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev +``` + +执行上述`docker run`命令时,容器默认执行[paddle/scripts/docker/build_android.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build_android.sh)脚本。该脚本中记录了交叉编译Android版PaddlePaddle库常用的CMake配置,并且会根据`ANDROID_ABI`和`ANDROID_API`自动构建独立工具链、进行编译和安装。由于arm64架构要求Android API不小于21。因此当`ANDROID_ABI=arm64-v8a`,`ANDROID_API<21`时,Docker容器中将默认使用`Android API 21`的编译工具链。用户可以参考下文**配置交叉编译参数**章节,根据个人的需求修改定制Docker容器所执行的脚本。编译安装结束之后,PaddlePaddle的C-API库将被安装到`$PWD/install_android`目录,所依赖的第三方库同时也被安装到`$PWD/install_android/third_party`目录。 + +## 基于Linux交叉编译环境的编译方式 本文档将以Linux x86-64平台为例,介绍交叉编译Android平台上适用的PaddlePaddle库的方法和步骤。 -## 准备交叉编译环境 +### 准备交叉编译环境 从源码交叉编译PaddlePaddle,用户需要提前准备好交叉编译环境。Android平台上使用的C/C++交叉编译工具链为[Android NDK](https://developer.android.com/ndk/downloads/index.html?hl=zh-cn),用户可自行前往下载预编译好的版本,也可通过以下命令获取: @@ -13,18 +50,27 @@ unzip -q android-ndk-r14b-linux-x86_64.zip ``` Android NDK中包含了所有Android API级别、所有架构(arm/arm64/x86/mips)需要用到的编译工具和系统库。用户可根据自己的编译目标架构、所需支持的最低Android API级别,构建[独立工具链](https://developer.android.google.cn/ndk/guides/standalone_toolchain.html?hl=zh-cn)。 -比如: + +- 构建`armeabi-v7a`、 `Android API 21`的独立工具链: ```bash your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \ - --arch=arm --platform=android-21 --install-dir=your/path/to/my_standalone_toolchain + --arch=arm --platform=android-21 --install-dir=your/path/to/arm_standalone_toolchain ``` -此命令将在your/path/to/my_standalone_toolchain目录生成一套编译工具链,面向架构为32位ARM架构,支持的最小的Android API级别为21,使用的编译器为arm-linux-androideabi-gcc (GCC) 4.9。 +此命令将在`your/path/to/arm_standalone_toolchain`目录生成一套独立编译工具链,面向架构为32位ARM架构,支持的最小的Android API级别为21,支持编译器`arm-linux-androideabi-gcc (GCC) 4.9`和`clang 3.8`。 -注意:**PaddlePaddle要求使用的编译工具链所支持的Andoid API级别不小于21**。 +- 构建`arm64-v8a`、 `Android API 21`的独立工具链: +```bash +your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \ + --arch=arm64 --platform=android-21 --install-dir=your/path/to/arm64_standalone_toolchain +``` -## 配置交叉编译参数 +此命令将在`your/path/to/arm64_standalone_toolchain`目录生成一套独立编译工具链,面向架构为64位ARM64架构,支持的最小Android API级别为21,支持编译器`arm-linux-androideabi-gcc (GCC) 4.9`和`clang 3.8`。 + +注意:**PaddlePaddle要求使用的编译工具链所支持的Android API级别不小于21**。 + +### 配置交叉编译参数 CMake系统对交叉编译提供了支持[cmake-toolchains](https://cmake.org/cmake/help/v3.0/manual/cmake-toolchains.7.html#cross-compiling)。为了简化cmake配置,PaddlePaddle为交叉编译提供了工具链配置文档[cmake/cross_compiling/android.cmake](https://github.com/PaddlePaddle/Paddle/blob/develop/cmake/cross_compiling/android.cmake),以提供一些默认的编译器和编译参数相关配置。注意,从CMake 3.7版本开始,CMake官方对Android平台的交叉编译提供了通用的支持。PaddlePaddle若检测到用户使用的CMake版本不低于3.7时,将会将用户传进来的配置参数传递CMake系统,交由CMake系统本身来处理。有关参数配置的详细说明见[cmake-toolchains](https://cmake.org/cmake/help/v3.7/manual/cmake-toolchains.7.html#cross-compiling)。 @@ -36,32 +82,57 @@ CMake系统对交叉编译提供了支持[cmake-toolchains](https://cmake.org/cm Android平台可选配置参数: - `ANDROID_STANDALONE_TOOLCHAIN`,独立工具链所在的绝对路径,或者相对于构建目录的相对路径。PaddlePaddle的CMake系统将根据该值自动推导和设置需要使用的交叉编译器、sysroot、以及Android API级别;否则,用户需要在cmake时手动设置这些值。无默认值。 -- `ANDROID_ABI`,目标架构ABI。目前只支持`armeabi-v7a`,默认值为`armeabi-v7a`。 +- `ANDROID_TOOLCHAIN`,目标工具链。可设置`gcc/clang`,默认值为`clang`。 + - CMake 3.7以上,将会始终使用`clang`工具链;CMake 3.7以下,可设置`ANDROID_TOOLCHAIN=gcc`以使用`gcc`工具链。 + - Android官方提供的`clang`编译器要求系统支持`GLIBC 2.15`以上。 +- `ANDROID_ABI`,目标架构ABI。目前支持`armeabi-v7a`和`arm64-v8a`,默认值为`armeabi-v7a`。 - `ANDROID_NATIVE_API_LEVEL`,工具链的Android API级别。若没有显式设置,PaddlePaddle将根据`ANDROID_STANDALONE_TOOLCHAIN`的值自动推导得到。 -- `ANROID_ARM_MODE`,是否使用ARM模式。可设置`ON/OFF`,默认值为`ON`。 -- `ANDROID_ARM_NEON`,是否使用NEON指令。目前必须设置成`ON`,默认值为`ON`。 +- `ANROID_ARM_MODE`,是否使用ARM模式。 + - `ANDROID_ABI=armeabi-v7a`时,可设置`ON/OFF`,默认值为`ON`; + - `ANDROID_ABI=arm64-v8a`时,不需要设置。 +- `ANDROID_ARM_NEON`,是否使用NEON指令。 + - `ANDROID_ABI=armeabi-v7a`时,可设置`ON/OFF`,默认值为`ON`; + - `ANDROID_ABI=arm64-v8a`时,不需要设置。 其他配置参数: +- `USE_EIGEN_FOR_BLAS`,是否使用Eigen库进行矩阵计算。可设置`ON/OFF`,默认值为`OFF`。 - `HOST_C/CXX_COMPILER`,宿主机的C/C++编译器。在编译宿主机版protoc可执行文件和目标机版OpenBLAS库时需要用到。默认设置成环境变量`CC`的值;若环境变量`CC`没有设置,则设置成`cc`编译器。 -一种常用的cmake配置如下: +常用的cmake配置如下: ```bash cmake -DCMAKE_SYSTEM_NAME=Android \ - -DANDROID_STANDALONE_TOOLCHAIN=your/path/to/my_standalone_toolchain \ + -DANDROID_STANDALONE_TOOLCHAIN=your/path/to/arm_standalone_toolchain \ -DANDROID_ABI=armeabi-v7a \ -DANDROID_ARM_NEON=ON \ -DANDROID_ARM_MODE=ON \ + -DUSE_EIGEN_FOR_BLAS=ON \ -DCMAKE_INSTALL_PREFIX=your/path/to/install \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ .. ``` +``` +cmake -DCMAKE_SYSTEM_NAME=Android \ + -DANDROID_STANDALONE_TOOLCHAIN=your/path/to/arm64_standalone_toolchain \ + -DANDROID_ABI=arm64-v8a \ + -DUSE_EIGEN_FOR_BLAS=OFF \ + -DCMAKE_INSTALL_PREFIX=your/path/to/install \ + -DWITH_C_API=ON \ + -DWITH_SWIG_PY=OFF \ + .. +``` + 用户还可根据自己的需求设置其他编译参数。比如希望最小化生成的库的大小,可以设置`CMAKE_BUILD_TYPE`为`MinSizeRel`;若希望最快的执行速度,则可设置`CMAKE_BUILD_TYPE`为`Release`。亦可以通过手动设置`CMAKE_C/CXX_FLAGS_MINSIZEREL/RELEASE`来影响PaddlePaddle的编译过程。 -## 编译和安装 +**性能TIPS**,为了达到最快的计算速度,在CMake参数配置上,有以下建议: +- 设置`CMAKE_BUILD_TYPE`为`Release` +- 使用`clang`编译工具链 +- `armeabi-v7a`时,设置`USE_EIGEN_BLAS=ON`,使用Eigen进行矩阵计算;`arm64-v8a`时,设置`USE_EIGEN_FOR_BLAS=OFF`,使用OpenBLAS进行矩阵计算 + +### 编译和安装 CMake配置完成后,执行以下命令,PaddlePaddle将自动下载和编译所有第三方依赖库、编译和安装PaddlePaddle预测库。 @@ -72,4 +143,4 @@ make install 注意:如果你曾经在源码目录下编译过其他平台的PaddlePaddle库,请先使用`rm -rf`命令删除`third_party`目录和`build`目录,以确保所有的第三方依赖库和PaddlePaddle代码都是针对新的CMake配置重新编译的。 -执行完安装命令后,`your/path/to/install`目录中会包含`include`和`lib`目录,其中`include`中包含C-API的头文件,`lib`中包含一个Android版本的库。自此,PaddlePaddle的已经安装完成,用户可将`your/path/to/install`目录下的生成文件用于深度学习相关Android App中,调用方法见C-API文档。 +执行完安装命令后,`your/path/to/install`目录中会包含`include`、`lib`和`third_party`目录,其中`include`中包含C-API的头文件,`lib`中包含若干个不同Android ABI的PaddlePaddle库,`third_party`中包含所依赖的所有第三方库。自此,PaddlePaddle的已经安装完成,用户可将`your/path/to/install`目录下的生成文件用于深度学习相关Android App中,调用方法见C-API文档。 diff --git a/develop/doc_cn/howto/cross_compiling/cross_compiling_for_android_cn.html b/develop/doc_cn/howto/cross_compiling/cross_compiling_for_android_cn.html index 2a1706facb19410a64757332dc3d5d8e736d0bc9..ab647afc9c288645c7a7881d61c0dce39a621edd 100644 --- a/develop/doc_cn/howto/cross_compiling/cross_compiling_for_android_cn.html +++ b/develop/doc_cn/howto/cross_compiling/cross_compiling_for_android_cn.html @@ -193,26 +193,77 @@
用户可通过交叉编译的方式,在用户熟悉的开发平台(Linux,Mac OS X和Windows)上编译Android平台上适用的PaddlePaddle库。 -本文档将以Linux x86-64平台为例,介绍交叉编译Android平台上适用的PaddlePaddle库的方法和步骤。
+用户可通过如下两种方式,交叉编译Android平台上适用的PaddlePaddle库:
+Docker能在所有主要操作系统(包括Linux,Mac OS X和Windows)上运行,因此,使用基于Docker容器的编译方式,用户可在自己熟悉的开发平台上编译Android平台上适用的PaddlePaddle库。
+我们把PaddlePaddle的交叉编译环境打包成一个镜像,称为开发镜像,里面涵盖了交叉编译Android版PaddlePaddle库需要的所有编译工具。
+$ git clone https://github.com/PaddlePaddle/Paddle.git
+$ cd Paddle
+$ docker build -t username/paddle-android:dev . -f Dockerfile.android
+
构建好开发镜像后,即可使用开发镜像来编译Android版PaddlePaddle C-API库。 +Android的Docker开发镜像向用户提供两个可配置的参数:
+| Argument | Optional Values | Default |
+|—————–|————————-|———|
+|ANDROID_ABI
|armeabi-v7a, arm64-v8a
| armeabi-v7a
|
+|ANDROID_API
|>= 21
| 21
|
armeabi-v7a
,Android API 21
的PaddlePaddle库$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev
+
arm64-v8a
,Android API 21
的PaddlePaddle库$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev
+
执行上述docker run
命令时,容器默认执行paddle/scripts/docker/build_android.sh脚本。该脚本中记录了交叉编译Android版PaddlePaddle库常用的CMake配置,并且会根据ANDROID_ABI
和ANDROID_API
自动构建独立工具链、进行编译和安装。由于arm64架构要求Android API不小于21。因此当ANDROID_ABI=arm64-v8a
,ANDROID_API<21
时,Docker容器中将默认使用Android API 21
的编译工具链。用户可以参考下文配置交叉编译参数章节,根据个人的需求修改定制Docker容器所执行的脚本。编译安装结束之后,PaddlePaddle的C-API库将被安装到$PWD/install_android
目录,所依赖的第三方库同时也被安装到$PWD/install_android/third_party
目录。
本文档将以Linux x86-64平台为例,介绍交叉编译Android平台上适用的PaddlePaddle库的方法和步骤。
从源码交叉编译PaddlePaddle,用户需要提前准备好交叉编译环境。Android平台上使用的C/C++交叉编译工具链为Android NDK,用户可自行前往下载预编译好的版本,也可通过以下命令获取:
wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip
unzip -q android-ndk-r14b-linux-x86_64.zip
Android NDK中包含了所有Android API级别、所有架构(arm/arm64/x86/mips)需要用到的编译工具和系统库。用户可根据自己的编译目标架构、所需支持的最低Android API级别,构建独立工具链。 -比如:
+Android NDK中包含了所有Android API级别、所有架构(arm/arm64/x86/mips)需要用到的编译工具和系统库。用户可根据自己的编译目标架构、所需支持的最低Android API级别,构建独立工具链。
+armeabi-v7a
、 Android API 21
的独立工具链:your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
- --arch=arm --platform=android-21 --install-dir=your/path/to/my_standalone_toolchain
+ --arch=arm --platform=android-21 --install-dir=your/path/to/arm_standalone_toolchain
此命令将在your/path/to/my_standalone_toolchain目录生成一套编译工具链,面向架构为32位ARM架构,支持的最小的Android API级别为21,使用的编译器为arm-linux-androideabi-gcc (GCC) 4.9。
-注意:PaddlePaddle要求使用的编译工具链所支持的Andoid API级别不小于21。
+此命令将在your/path/to/arm_standalone_toolchain
目录生成一套独立编译工具链,面向架构为32位ARM架构,支持的最小的Android API级别为21,支持编译器arm-linux-androideabi-gcc (GCC) 4.9
和clang 3.8
。
arm64-v8a
、 Android API 21
的独立工具链:your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
+ --arch=arm64 --platform=android-21 --install-dir=your/path/to/arm64_standalone_toolchain
+
此命令将在your/path/to/arm64_standalone_toolchain
目录生成一套独立编译工具链,面向架构为64位ARM64架构,支持的最小Android API级别为21,支持编译器arm-linux-androideabi-gcc (GCC) 4.9
和clang 3.8
。
注意:PaddlePaddle要求使用的编译工具链所支持的Android API级别不小于21。
CMake系统对交叉编译提供了支持cmake-toolchains。为了简化cmake配置,PaddlePaddle为交叉编译提供了工具链配置文档cmake/cross_compiling/android.cmake,以提供一些默认的编译器和编译参数相关配置。注意,从CMake 3.7版本开始,CMake官方对Android平台的交叉编译提供了通用的支持。PaddlePaddle若检测到用户使用的CMake版本不低于3.7时,将会将用户传进来的配置参数传递CMake系统,交由CMake系统本身来处理。有关参数配置的详细说明见cmake-toolchains。
交叉编译Android版本的PaddlePaddle库时,有一些必须配置的参数:
Android平台可选配置参数:
ANDROID_STANDALONE_TOOLCHAIN
,独立工具链所在的绝对路径,或者相对于构建目录的相对路径。PaddlePaddle的CMake系统将根据该值自动推导和设置需要使用的交叉编译器、sysroot、以及Android API级别;否则,用户需要在cmake时手动设置这些值。无默认值。ANDROID_ABI
,目标架构ABI。目前只支持armeabi-v7a
,默认值为armeabi-v7a
。ANDROID_TOOLCHAIN
,目标工具链。可设置gcc/clang
,默认值为clang
。clang
工具链;CMake 3.7以下,可设置ANDROID_TOOLCHAIN=gcc
以使用gcc
工具链。clang
编译器要求系统支持GLIBC 2.15
以上。ANDROID_ABI
,目标架构ABI。目前支持armeabi-v7a
和arm64-v8a
,默认值为armeabi-v7a
。ANDROID_NATIVE_API_LEVEL
,工具链的Android API级别。若没有显式设置,PaddlePaddle将根据ANDROID_STANDALONE_TOOLCHAIN
的值自动推导得到。ANROID_ARM_MODE
,是否使用ARM模式。可设置ON/OFF
,默认值为ON
。ANDROID_ARM_NEON
,是否使用NEON指令。目前必须设置成ON
,默认值为ON
。ANROID_ARM_MODE
,是否使用ARM模式。ANDROID_ABI=armeabi-v7a
时,可设置ON/OFF
,默认值为ON
;ANDROID_ABI=arm64-v8a
时,不需要设置。ANDROID_ARM_NEON
,是否使用NEON指令。ANDROID_ABI=armeabi-v7a
时,可设置ON/OFF
,默认值为ON
;ANDROID_ABI=arm64-v8a
时,不需要设置。其他配置参数:
USE_EIGEN_FOR_BLAS
,是否使用Eigen库进行矩阵计算。可设置ON/OFF
,默认值为OFF
。HOST_C/CXX_COMPILER
,宿主机的C/C++编译器。在编译宿主机版protoc可执行文件和目标机版OpenBLAS库时需要用到。默认设置成环境变量CC
的值;若环境变量CC
没有设置,则设置成cc
编译器。一种常用的cmake配置如下:
+常用的cmake配置如下:
cmake -DCMAKE_SYSTEM_NAME=Android \
- -DANDROID_STANDALONE_TOOLCHAIN=your/path/to/my_standalone_toolchain \
+ -DANDROID_STANDALONE_TOOLCHAIN=your/path/to/arm_standalone_toolchain \
-DANDROID_ABI=armeabi-v7a \
-DANDROID_ARM_NEON=ON \
-DANDROID_ARM_MODE=ON \
+ -DUSE_EIGEN_FOR_BLAS=ON \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
..
cmake -DCMAKE_SYSTEM_NAME=Android \
+ -DANDROID_STANDALONE_TOOLCHAIN=your/path/to/arm64_standalone_toolchain \
+ -DANDROID_ABI=arm64-v8a \
+ -DUSE_EIGEN_FOR_BLAS=OFF \
+ -DCMAKE_INSTALL_PREFIX=your/path/to/install \
+ -DWITH_C_API=ON \
+ -DWITH_SWIG_PY=OFF \
+ ..
+
用户还可根据自己的需求设置其他编译参数。比如希望最小化生成的库的大小,可以设置CMAKE_BUILD_TYPE
为MinSizeRel
;若希望最快的执行速度,则可设置CMAKE_BUILD_TYPE
为Release
。亦可以通过手动设置CMAKE_C/CXX_FLAGS_MINSIZEREL/RELEASE
来影响PaddlePaddle的编译过程。
性能TIPS,为了达到最快的计算速度,在CMake参数配置上,有以下建议:
+CMAKE_BUILD_TYPE
为Release
clang
编译工具链armeabi-v7a
时,设置USE_EIGEN_BLAS=ON
,使用Eigen进行矩阵计算;arm64-v8a
时,设置USE_EIGEN_FOR_BLAS=OFF
,使用OpenBLAS进行矩阵计算CMake配置完成后,执行以下命令,PaddlePaddle将自动下载和编译所有第三方依赖库、编译和安装PaddlePaddle预测库。
make
make install
注意:如果你曾经在源码目录下编译过其他平台的PaddlePaddle库,请先使用rm -rf
命令删除third_party
目录和build
目录,以确保所有的第三方依赖库和PaddlePaddle代码都是针对新的CMake配置重新编译的。
执行完安装命令后,your/path/to/install
目录中会包含include
和lib
目录,其中include
中包含C-API的头文件,lib
中包含一个Android版本的库。自此,PaddlePaddle的已经安装完成,用户可将your/path/to/install
目录下的生成文件用于深度学习相关Android App中,调用方法见C-API文档。
执行完安装命令后,your/path/to/install
目录中会包含include
、lib
和third_party
目录,其中include
中包含C-API的头文件,lib
中包含若干个不同Android ABI的PaddlePaddle库,third_party
中包含所依赖的所有第三方库。自此,PaddlePaddle的已经安装完成,用户可将your/path/to/install
目录下的生成文件用于深度学习相关Android App中,调用方法见C-API文档。
IfElse
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","Design Doc: PaddlePaddle Programs","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Session","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design for TensorArray","Background","\u7f16\u8bd1\u5b89\u88c5\u4e0e\u5355\u5143\u6d4b\u8bd5","\u96c6\u7fa4\u8bad\u7ec3\u4e0e\u9884\u6d4b","FAQ","\u672c\u5730\u8bad\u7ec3\u4e0e\u9884\u6d4b","\u6a21\u578b\u914d\u7f6e","\u53c2\u6570\u8bbe\u7f6e","\u7ecf\u5178\u7684\u7ebf\u6027\u56de\u5f52\u4efb\u52a1","PaddlePaddle\u7684\u7f16\u8bd1\u9009\u9879","PaddlePaddle\u7684Docker\u5bb9\u5668\u4f7f\u7528\u65b9\u5f0f","\u5b89\u88c5\u4e0e\u7f16\u8bd1","\u57fa\u672c\u4f7f\u7528\u6982\u5ff5","\u65b0\u624b\u5165\u95e8","\u6784\u5efaAndroid\u5e73\u53f0\u4e0a\u7684PaddlePaddle\u5e93","\u6784\u5efaRaspberry Pi\u5e73\u53f0\u4e0a\u7684PaddlePaddle\u5e93","\u652f\u6301\u53cc\u5c42\u5e8f\u5217\u4f5c\u4e3a\u8f93\u5165\u7684Layer","\u5355\u53cc\u5c42RNN API\u5bf9\u6bd4\u4ecb\u7ecd","RNN\u76f8\u5173\u6a21\u578b","Recurrent 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