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* modify tensor_introduction doc (#4297) * modify tensor_introduction doc * modify according to nielang's advise, change picture quote method * fix bugs * fix some md format bugs * Update tensor images links * fix images link bugs * fix image link bugs * fix image link bugs * fix image link bugs * fix image link bugs * point Tensor's necessity; add the same and difference between tensor and numpy array * Modify some descriptions. * Update tensor_introduction_cn.md * Update tensor_introduction_cn.md * Update tensor_introduction_cn.md * Update tensor_introduction_cn.md * Update tensor_introduction_cn.md Co-authored-by: moguguo <[email protected]> * update hardware_info_cn.md (#4653) * update hardware_info_cn.md 1、更新昆仑2代芯片（R200）支持情况。 2、更新龙芯、飞腾cpu新增支持的型号。 * update index_cn.rst 1、新增飞桨对昆仑2代芯片的支持说明 * delete index_cn.rst 1、层级结构不需要调整，因此删除 * update index_cn.rst 1、新增飞桨对昆仑2代芯片的支持说明 * add file paadle_2.0_xpu2_cn.md 1、新增支持飞桨在昆仑2代芯片上的说明 * add paddle_install_xpu2_cn.md 1、新增飞桨在昆仑2代芯片上的安装说明文档 * add train_example_xpu2_cn.md 新增飞桨对昆仑2代芯片的训练示例 * delete xpu2_train.md 1、目录结构有问题，需要重新调整 * update train_example_xpu2_cn.md 1、更新飞桨在昆仑2代芯片上的训练示例文档 * update index_cn.rst 添加飞桨对硬件支持的索引页 * update install_xpu2_cn.md 更新编译线程 * update index_cn.rst 修改格式 * update paddle_2.0_xpu2_cn.md 添加套件链接、分支链接、单机单卡支持情况 * update install.md 补充文字描述 * update train_example 更新数据集描述 * update fix_code_style 修改代码格式问题 * update order_list 更新序列排序前端展示问题 * Update train_example_xpu2_cn.md * 更新序号问题 更新文档序号问题 * Update train_example_xpu2_cn.md * Update train_example_xpu2_cn.md Co-authored-by: Chen Long <[email protected]> * Update hardware_info_cn.md (#4409) * Update hardware_info_cn.md update several links to hardware installation * Update hardware_info_cn.md Co-authored-by: Chen Long <[email protected]> Co-authored-by: Sing_chan <[email protected]> Co-authored-by: moguguo <[email protected]> Co-authored-by: adaxiadaxi <[email protected]> Co-authored-by: onecatcn <[email protected]>
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docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.md
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| npu_docs/index_cn.rst | ||
| ipu_docs/index_cn.rst | ||
| ipu_docs/index_cn.rst | ||
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| .. _cn_xpu_information: | ||
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| #################### | ||
| 昆仑XPU芯片运行飞桨 | ||
| 昆仑芯片运行飞桨 | ||
| #################### | ||
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| 百度昆仑AI计算处理器（Baidu KUNLUN AI Computing Processor）是百度集十年AI产业技术实践于2019年推出的全功能AI芯片。基于自主研发的先进XPU架构，为云端和边缘端的人工智能业务而设计。 百度昆仑与飞桨及其他国产软硬件强强组合，打造一个全面领先的国产化AI技术生态，部署和应用于诸多 “人工智能+“的行业领域，包括智能云和高性能计算，智慧制造、智慧城市和安防等。更多昆仑XPU芯片详情及技术指标请 `点击这里 <https://cloud.baidu.com/product/kunlun.html>`_ 。 | ||
| 百度昆仑AI计算处理器（Baidu KUNLUN AI Computing Processor）是百度集十年AI产业技术实践于2019年推出的全功能AI芯片。基于自主研发的先进XPU架构，为云端和边缘端的人工智能业务而设计。 百度昆仑与飞桨及其他国产软硬件强强组合，打造一个全面领先的国产化AI技术生态，部署和应用于诸多 “人工智能+“的行业领域，包括智能云和高性能计算，智慧制造、智慧城市和安防等。更多昆仑XPU芯片详情及技术指标请 `点击这里 <https://www.kunlunxin.com.cn/>`_ 。 | ||
| 参考以下内容可快速了解和体验昆仑XPU芯片上运行飞桨： | ||
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| - `飞桨对昆仑XPU芯片的支持 <./paddle_2.0_xpu_cn.html>`_ : 飞桨支持昆仑XPU芯片运行 | ||
| - `飞桨框架昆仑xpu版安装说明 <./paddle_install_cn.html>`_ : 飞桨框架昆仑xpu版安装说明 | ||
| - `飞桨框架昆仑XPU版训练示例 <./train_example_cn.html>`_ : 飞桨框架昆仑XPU版训练示例 | ||
| - `飞桨预测库昆仑XPU版安装及使用 <./inference_install_example_cn.html>`_ : 飞桨预测库昆仑XPU版安装及使用示例 | ||
| 昆仑芯2代芯片： | ||
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| - `飞桨对昆仑2代芯片的支持 <./paddle_2.0_xpu_cn.html>`_ : 飞桨支持昆仑2代芯片（R200、R300）运行 | ||
| - `飞桨框架昆仑2代芯片安装说明 <./paddle_install_cn.html>`_ : 飞桨框架昆仑2代芯片（R200、R300）安装说明 | ||
| - `飞桨框架昆仑2代芯片训练示例 <./train_example_cn.html>`_ : 飞桨框架昆仑2代芯片（R200、R300）训练示例 | ||
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| 昆仑芯1代芯片： | ||
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| - `飞桨对昆仑1代芯片的支持 <./paddle_2.0_xpu_cn.html>`_ : 飞桨支持昆仑1代芯片（K100、K200）运行 | ||
| - `飞桨框架昆仑1代芯片安装说明 <./paddle_install_cn.html>`_ : 飞桨框架昆仑1代芯片（K100、K200）安装说明 | ||
| - `飞桨框架昆仑1代芯片训练示例 <./train_example_cn.html>`_ : 飞桨框架昆仑1代芯片（K100、K200）训练示例 | ||
| - `飞桨预测库昆仑1代芯片安装及使用 <./inference_install_example_cn.html>`_ : 飞桨预测库昆仑1代芯片（K100、K200）版安装及使用示例 | ||
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| .. toctree:: | ||
| :hidden: | ||
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| paddle_2.0_xpu2_cn.md | ||
| paddle_install_xpu2_cn.md | ||
| train_example_xpu2_cn.md | ||
| paddle_2.0_xpu_cn.md | ||
| paddle_install_cn.md | ||
| train_example_cn.md | ||
| inference_install_example_cn.md | ||
| inference_install_example_cn.md |
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docs/guides/09_hardware_support/xpu_docs/paddle_2.0_xpu2_cn.md
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| # 飞桨对昆仑2代芯片的支持 | ||
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| 飞桨自2.3rc版本起支持在昆仑2代芯片上（R200，R300）运行，经验证的模型训练的支持情况如下： | ||
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| ## 训练支持 | ||
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| 可进行单机单卡/单机多卡训练的模型，如下所示： | ||
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| | 模型 | 领域 | 编程范式 | 可用的CPU类型 | 单机单卡支持 | 单机多卡支持 | | ||
| | ------------------ | -------- |------------- | ----------------------- | -------------- | -------------- | | ||
| | ResNet50 | 图像分类 | 动态图 | X86（Intel） | 支持 |- | | ||
| | MobileNet_v3 | 图像分类 | 动态图 | X86（Intel） | 支持 |- | | ||
| | UNet | 图像分割 | 动态图 | X86（Intel） | 支持 |- | | ||
| | Yolov3-DarkNet53 | 目标检测 | 动态图 | X86（Intel） | 支持 |- | | ||
| | SSD-ResNet34 | 目标检测 | 动态图 | X86（Intel） | 支持 |支持 | | ||
| | OCR-DB | 文字检测 | 动态图 | X86（Intel） | 支持 |- | | ||
| | Bert-Base | NLP | 静态图 | X86（Intel） | 支持 |支持 | | ||
| | Transformer | NLP | 静态图 | X86（Intel） | 支持 |支持 | | ||
| | GPT-2 | NLP | 动态图 | X86（Intel） | 支持 |- | | ||
| | DeepFM | 推荐 | 动态图 | X86（Intel） | 支持 |- | | ||
| | Wide&Deep | 推荐 | 动态图 | X86（Intel） | 支持 |- | | ||
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| 模型放置在飞桨模型套件中，作为github.com/PaddlePaddle下的独立repo存在，git clone下载即可获取所需的模型文件： | ||
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| | 领域 | 套件名称 | 分支/版本 | | ||
| | -------- | --------------- | ----------- | | ||
| | 图像分类 | [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) | [develop](https://github.com/PaddlePaddle/PaddleClas/tree/develop) | | ||
| | 目标检测 | [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) | [develop](https://github.com/PaddlePaddle/PaddleDetection/tree/develop) | | ||
| | 图像分割 | [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) | [develop](https://github.com/PaddlePaddle/PaddleSeg/tree/develop) | | ||
| | NLP | [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) | [develop](https://github.com/PaddlePaddle/PaddleNLP/tree/develop) | | ||
| | OCR | [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) | [dygraph](https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph) | | ||
| | 推荐 | [PaddleREC](https://github.com/PaddlePaddle/PaddleRec) | [master](https://github.com/PaddlePaddle/PaddleRec/tree/master) | | ||
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| * 注：支持基于Kermel Primitive算子的昆仑2代芯片支持，[点击这里](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/07_new_op/kernel_primitive_api/index_cn.html)。 |
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| # 飞桨框架昆仑2代芯片安装说明 | ||
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| 在昆仑2代芯片上，飞桨框架支持基于python的训练和原生预测，当前最新版本为2.3rc，提供两种安装方式： | ||
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| **1. 预编译的支持昆仑2代芯片的wheel包** | ||
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| 目前此wheel包只支持一种环境： | ||
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| 英特尔CPU+昆仑2代芯片+Linux操作系统 | ||
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| **2. 源码编译安装** | ||
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| 其他环境请选择源码编译安装。 | ||
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| ## 安装方式一：通过Wheel包安装 | ||
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| ### 下载安装包 | ||
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| **环境1：英特尔CPU+昆仑2代芯片+Linux操作系统** | ||
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| Linux发行版建议选择CentOS 7系统 | ||
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| Python3.7 | ||
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| ``` | ||
| wget https://paddle-inference-lib.bj.bcebos.com/2.3.0-rc0/python/Linux/XPU2/x86-64_gcc8.2_py36_avx_mkl/paddlepaddle_xpu-2.3.0rc0-cp37-cp37m-linux_x86_64.whl | ||
| ``` | ||
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| ``` | ||
| python3.7 -m pip install -U paddlepaddle_xpu-2.3.0rc0-cp37-cp37m-linux_x86_64.whl | ||
| ``` | ||
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| ### 验证安装 | ||
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| 安装完成后您可以使用 python 或 python3 进入python解释器，输入 | ||
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| ``` | ||
| import paddle | ||
| ``` | ||
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| 再输入 | ||
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| ``` | ||
| paddle.utils.run_check() | ||
| ``` | ||
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| 如果出现PaddlePaddle is installed successfully!，说明您已成功安装。 | ||
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| * 注：支持基于Kermel Primitive算子的昆仑2代芯片编译whl包，[点击这里查看](https://www.kunlunxin.com.cn)。 | ||
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| ## 安装方式二：从源码编译支持昆仑XPU的包 | ||
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| ### 环境准备 | ||
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| **英特尔CPU+昆仑2代芯片+CentOS系统** | ||
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| - **处理器：Intel(R) Xeon(R) Gold 6148 CPU @2.40GHz** | ||
| - **操作系统：CentOS 7.8.2003（建议使用CentOS 7）** | ||
| - **Python版本： 3.7 (64 bit)** | ||
| - **pip或pip3版本：9.0.1+ (64 bit)** | ||
| - **cmake版本：3.15+** | ||
| - **gcc/g++版本：8.2+** | ||
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| ### 源码编译安装步骤： | ||
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| （1）Paddle依赖cmake进行编译构建，需要cmake版本>=3.15，如果操作系统提供的源包括了合适版本的cmake，直接安装即可，否则需要 | ||
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| ``` | ||
| wget https://github.com/Kitware/CMake/releases/download/v3.16.8/cmake-3.16.8.tar.gz | ||
| tar -xzf cmake-3.16.8.tar.gz && cd cmake-3.16.8 | ||
| ./bootstrap && make && sudo make install | ||
| ``` | ||
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| （2）Paddle内部使用patchelf来修改动态库的rpath，如果操作系统提供的源包括了patchelf，直接安装即可，否则需要源码安装，请参考 | ||
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| ``` | ||
| ./bootstrap.sh | ||
| ./configure | ||
| make | ||
| make check | ||
| sudo make install | ||
| ``` | ||
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| （3）根据[requirments.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/python/requirements.txt)安装Python依赖库 | ||
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| （4）将Paddle的源代码克隆到当下目录下的Paddle文件夹中，并进入Paddle目录 | ||
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| ``` | ||
| git clone https://github.com/PaddlePaddle/Paddle.git | ||
| cd Paddle | ||
| ``` | ||
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| 使用较稳定的版本编译，建议切换到release2.3分支下： | ||
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| ``` | ||
| git checkout release/2.3 | ||
| ``` | ||
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| （5）进行Wheel包的编译，请创建并进入一个叫build的目录下 | ||
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| ``` | ||
| mkdir build && cd build | ||
| ``` | ||
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| 具体编译选项含义可参见[编译选项表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#Compile) | ||
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| **英特尔CPU+昆仑2代芯+CentOS系统** | ||
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| 链接过程中打开文件数较多，可能超过系统默认限制导致编译出错，设置进程允许打开的最大文件数： | ||
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| ``` | ||
| ulimit -n 4096 | ||
| ``` | ||
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| 执行cmake，完成编译 | ||
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| Python3.7 | ||
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| ``` | ||
| cmake .. -DPY_VERSION=3.7 \ | ||
| -DCMAKE_BUILD_TYPE=Release \ | ||
| -DWITH_GPU=OFF \ | ||
| -DWITH_XPU=ON \ | ||
| -DON_INFER=ON \ | ||
| -DWITH_PYTHON=ON \ | ||
| -DWITH_AVX=ON \ | ||
| -DWITH_MKL=ON \ | ||
| -DWITH_MKLDNN=ON \ | ||
| -DWITH_XPU_BKCL=ON \ | ||
| -DWITH_DISTRIBUTE=ON \ | ||
| -DWITH_NCCL=OFF | ||
| make -j$(nproc) | ||
| ``` | ||
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| （6）编译成功后进入Paddle/build/python/dist目录下找到生成的.whl包 。 | ||
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| （7）将生成的.whl包copy至带有昆仑XPU的目标机器上，并在目标机器上根据[requirments.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/python/requirements.txt)安装Python依赖库。（如果编译机器同时为带有昆仑XPU的目标机器，略过此步） | ||
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| （8）在带有昆仑XPU的目标机器安装编译好的.whl包：pip install -U（whl包的名字）或pip3 install -U（whl包的名字）。恭喜，至此您已完成昆仑XPU机器上PaddlePaddle的编译安装。 | ||
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| **验证安装** | ||
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| 安装完成后您可以使用 python 或 python3 进入python解释器，输入 | ||
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| ``` | ||
| import paddle | ||
| ``` | ||
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| 再输入 | ||
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| ``` | ||
| paddle.utils.run_check() | ||
| ``` | ||
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| 如果出现PaddlePaddle is installed successfully!，说明您已成功安装。 | ||
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| ### 如何卸载 | ||
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| 使用以下命令卸载PaddlePaddle： | ||
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| ``` | ||
| pip uninstall paddlepaddle | ||
| ``` | ||
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| 或 | ||
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| ``` | ||
| pip3 uninstall paddlepaddle | ||
| ``` | ||
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| * 注：支持基于Kermel Primitive算子的昆仑2代芯片源码编译，[点击这里查看](https://www.kunlunxin.com.cn)。 |
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