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README
Apache-2.0

TensorFlow Adapter For Ascend

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简介

TensorFlow Adapter For Ascend(简称TF Adapter)致力于将昇腾AI处理器卓越的运算能力,便捷地提供给使用Tensorflow框架的开发者。 开发者只需安装TF Adapter插件,并在现有TensorFlow脚本中添加少量配置,即可实现在昇腾AI处理器上加速自己的训练任务。

tfadapter

您可以通过阅读昇腾社区文档中心的《TensorFlow 迁移与训练》手册获取更多使用细节。

源码安装

您可以通过此仓中的源代码构建TF Adapter软件包并将其部署在昇腾AI处理器所在环境上。

安装前准备

  1. 通过源码方式安装TF Adaptet前,请确保环境已参见昇腾社区文档中心中心的《CANN软件安装》手册完成以下安装与配置:

    a. 完成gcc、python等依赖的安装。

    b. 完成开发套件包Ascend-cann-toolkit*_{version}_linux-{arch}*.run的安装。

    c. 完成开发套件包的环境变量配置,假设以root用户使用默认安装路径进行安装,则环境变量配置命令:source /usr/local/Ascend/ascend-toolkit/set_env.sh。

  2. TF Adapter 插件与 Tensorflow 有严格的匹配关系,通过源码构建TF Adapter软件包前,您需要确保已经正确安装了 Tensorflow v1.15.0版本 ,安装方式可参见昇腾社区文档中心中心《CANN软件安装》手册中的“安装深度学习框架 > 安装TensorFlow”章节。

  3. TF Adapter源码编译要求CMake软件版本 >=3.14.0,若您系统上的CMake版本不满足要求,可从CMake官网下载配套操作系统的CMake软件包。 安装示例:

    a. 这里是列表文本解压缩CMake软件包

    tar -zxvf cmake-3.19.3-Linux-x86_64.tar.gz

    ​ 解压后当前目录下生成cmake-3.19.3-Linux-x86_64的文件夹。

    b. 设置CMake的环境变量。

    export PATH=/home/xxx/xxx/cmake-3.19.3-Linux-x86_64/bin:$PATH

    ​ 以上路径请替换为CMake的实际部署路径。

    c. 检查是否安装成功。

    ​ 执行echo $PATH查看环境变量是否设置成功。

    ​ 执行如下命令,查看测试是否安装成功:

    cmake --version

  4. TF Adapter源码编译依赖SWIG。 可执行如下命令进行SWIG(http://www.swig.org/download.html))的安装:

    pip3 install swig

TF Adapter源码下载

git clone https://gitee.com/ascend/tensorflow.git
cd tensorflow

TensorFlow源码定制(可选)

在部分场景下,您可能会把自己定制或者修改过的TensorFlow与TF Adapter软件包配合使用,由于TF Adapter默认链接的是TensorFlow官方网站的源码,因此您在使用TF Adapter软件包的时候,可能会因为符号不匹配而出现coredump问题。为了使TF Adapter能适配您的TensorFlow源码,您需要将TF Adapter源码下的tensorflow/cmake/tensorflow.cmake文件稍作修改,详细修改点如下:

修改前TF_Adapter链接的是tensorflow官网源码

修改图中FetchContent_Declare下的URL和URL_HASH MD5,将其替换成您自己环境上的tensorflow软件包的地址和MD5值。 例如,您的tensorflow软件包如果放在/opt/hw路径下,则您此处tensorflow.cmake的源码可以修改为

修改后TF_Adapter链接您环境上的tensorflow定制源码

TF Adapter源码定制(可选)

如果您想对TF Adapter的源码进行修改,比如添加链接路径,或链接其他so等操作,您可以修改TF Adapter源码下的tensorflow/CMakeLists.txt文件,只需要将ENABLE_OPEN_SRC分支下的编译配置做修改,便可以生效

CMakeList.txt文件

编译TF Adapter源码生成安装包

执行如下命令,对TF Adapter源码进行编译:

chmod +x build.sh
./build.sh

请注意:执行编译命令前,请确保环境中已配置开发套件包Ascend-cann-toolkit*_{version}_linux-{arch}*.run的环境变量。

编译结束后,TF Adapter安装包生成在如下路径:

./build/tfadapter/dist/python/dist/npu_bridge-1.15.0-py3-none-any.whl

安装TF Adapter

执行如下命令安装TF Adapter。

pip3 install ./build/tfadapter/dist/python/dist/npu_bridge-1.15.0-py3-none-any.whl --upgrade

执行完成后,TF Adapter相关文件安装到python解释器搜索路径下,例如“/usr/local/python3.7.5/lib/python3.7/siite-packages”路径,安装后文件夹为“npu_bridge”与“npu_bridge-1.15.0.dist-info”。

贡献

欢迎参与贡献。

社区版本规划

https://gitee.com/ascend/tensorflow/wikis/Home?sort_id=3076366

Release Notes

Release Notes请参考RELEASE.

FAQ

1. 执行./build.sh时提示配置swig的路径

需要执行以下命令安装swig

pip3 install swig

2. Ubuntu系统中执行./build.sh时提示“Could not import the lzma module”

​ 执行如下命令进行lzma的安装:

apt-get install liblzma-dev

​ 需要注意,此依赖需要在Python安装之前安装,如果用户操作系统中已经安装满足要求的Python环境,在此之后再安装liblzma-dev,则需要重新编译Python环境。

License

Apache License 2.0

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