diff --git a/docs/install/FAQ.md b/docs/install/FAQ.md deleted file mode 100644 index 1fab96862f8..00000000000 --- a/docs/install/FAQ.md +++ /dev/null @@ -1,90 +0,0 @@ - -# **FAQ** - -- 报错“nccl.h 找不到” - - > 请[安装 nccl2](https://developer.nvidia.com/nccl/nccl-download) - - - -- Ubuntu18.04 下 libidn11 找不到？ - - > 使用以下指令： - - apt install libidn11 - -- Ubuntu 编译时出现大量的代码段不能识别？ - - > 这可能是由于 cmake 版本不匹配造成的，请在 gcc 的安装目录下使用以下指令： - - apt install gcc-4.8 g++-4.8 - cp gcc gcc.bak - cp g++ g++.bak - rm gcc - rm g++ - ln -s gcc-4.8 gcc - ln -s g++-4.8 g++ - -- 遇到 paddlepaddle.whl is not a supported wheel on this platform？ - - > 出现这个问题的主要原因是，没有找到和当前系统匹配的 paddlepaddle 安装包。 请检查 Python 版本是否为 2.7 系列。另外最新的 pip 官方源中的安装包默认是 manylinux1 标准， 需要使用最新的 pip (>9.0.0) 才可以安装。您可以执行以下指令更新您的 pip： - - pip install --upgrade pip - 或者 - - python -c "import pip; print(pip.pep425tags.get_supported())" - - > 如果系统支持的是 linux_x86_64 而安装包是 manylinux1_x86_64 ，需要升级 pip 版本到最新； 如果系统支持 manylinux1_x86_64 而安装包 （本地）是 linux_x86_64， 可以重命名这个 whl 包为 manylinux1_x86_64 再安装。 - -- 使用 Docker 编译出现问题？ - - > 请参照 GitHub 上[Issue12079](https://github.com/PaddlePaddle/Paddle/issues/12079) - -- 可以用 IDE 吗？ - - > 当然可以，因为源码就在本机上。IDE 默认调用 make 之类的程序来编译源码，我们只需要配置 IDE 来调用 Docker 命令编译源码即可。 - 很多 PaddlePaddle 开发者使用 Emacs。他们在自己的 `~/.emacs` 配置文件里加两行 - `global-set-key "\C-cc" 'compile` - `setq compile-command "docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev"` - 就可以按 `Ctrl-C` 和 `c` 键来启动编译了。 - -- 可以并行编译吗？ - - > 是的。我们的 Docker image 运行一个 [Bash 脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/paddle_build.sh)。这个脚本调用`make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。 - -- 在 Windows/macOS 上编译很慢？ - - > Docker 在 Windows 和 macOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考[issue627](https://github.com/PaddlePaddle/Paddle/issues/627)。 - -- 磁盘不够？ - - > 本文中的例子里，`docker run` 命令里都用了 `--rm` 参数，这样保证运行结束之后的 containers 不会保留在磁盘上。可以用 `docker ps -a` 命令看到停止后但是没有删除的 containers。`docker build` 命令有时候会产生一些中间结果，是没有名字的 images，也会占用磁盘。可以参考 [这篇文章](https://zaiste.net/posts/removing_docker_containers) 来清理这些内容。 - -- 在 DockerToolbox 下使用 book 时`http://localhost:8888/`无法打开？ - - > 需要将 localhost 替换成虚拟机 ip，一般需要在浏览器中输入：`http://192.168.99.100:8888/` - -- pip install gpu 版本的 PaddlePaddle 后运行出现 SegmentFault 如下： - - @ 0x7f6c8d214436 paddle::platform::EnforceNotMet::EnforceNotMet() - - @ 0x7f6c8dfed666 paddle::platform::GetCUDADeviceCount() - - @ 0x7f6c8d2b93b6 paddle::framework::InitDevices() - - - > 出现这个问题原因主要是由于您的显卡驱动低于对应 CUDA 版本的要求，请保证您的显卡驱动支持所使用的 CUDA 版本 - - - -- macOS 下使用自定义的 openblas 详见 issue： - - > [ISSUE 13217](https://github.com/PaddlePaddle/Paddle/issues/13721) - -- 已经安装 swig 但是仍旧出现 swig 找不到的问题 详见 issue： - - > [ISSUE 13759](https://github.com/PaddlePaddle/Paddle/issues/13759) - -- 出现 “target pattern contain no '%'.”的问题 详见 issue： - - > [ISSUE 13806](https://github.com/PaddlePaddle/Paddle/issues/13806) diff --git a/docs/install/FAQ_en.md b/docs/install/FAQ_en.md deleted file mode 100644 index c4a5572f3b7..00000000000 --- a/docs/install/FAQ_en.md +++ /dev/null @@ -1,111 +0,0 @@ -*** - - -# **FAQ** - -- Ubuntu18.04 under libidn11 can not be found? - - > Use the following instructions: - - apt install libidn11 - -- When Ubuntu compiles, a lot of code segments are not recognized? - - > This may be caused by a mismatch in the cmake version. Please use the following command in the gcc installation directory: - - apt install gcc-4.8 g++-4.8 - cp gcc gcc.bak - cp g++ g++.bak - rm gcc - rm g++ - ln -s gcc-4.8 gcc - ln -s g++-4.8 g++ - - - - -- Encountered paddlepaddle*.whl is not a supported wheel on this platform? - - > The main reason for this problem is that there is no paddlepaddle installation package that matches the current system. Please check if the Python version is 2.7 series. In addition, the latest pip official source installation package defaults to the manylinux1 standard, you need to use the latest pip (>9.0.0) to install. You can update your pip by following these instructions: - - pip install --upgrade pip - or - - python -c "import pip; print(pip.pep425tags.get_supported())" - - > If the system supports linux_x86_64 and the installation package is manylinux1_x86_64, you need to upgrade the pip version to the latest; if the system supports manylinux1_x86_64 and the installation package (local) is linux_x86_64, you can rename this whl package to manylinux1_x86_64 and install it again. - -- Is there a problem with Docker compilation? - - > Please refer to [Issue12079](https://github.com/PaddlePaddle/Paddle/issues/12079) on GitHub. - -- What is Docker? - - > If you haven't heard of Docker, you can think of it as a virtualenv-like system, but it virtualises more than the Python runtime environment. - -- Is Docker still a virtual machine? - - > Someone uses a virtual machine to analogize to Docker. It should be emphasized that Docker does not virtualize any hardware. The compiler tools running in the Docker container are actually run directly on the native CPU and operating system. The performance is the same as installing the compiler on the machine. - -- Why use Docker? - - > Installing the tools and configurations in a Docker image standardizes the build environment. This way, if you encounter problems, others can reproduce the problem to help. In addition, for developers accustomed to using Windows and macOS, there is no need to configure a cross-compilation environment using Docker. - -- Can I choose not to use Docker? - - > Of course you can. You can install development tools to the machine in the same way that you install them into Docker image. This document describes the Docker-based development process because it is easier than the other methods. - -- How hard is it to learn Docker? - - > It's not difficult to understand Docker. It takes about ten minutes to read this [article](https://zhuanlan.zhihu.com/p/19902938). - This can save you an hour of installing and configuring various development tools, as well as the need for new installations when switching machines. Don't forget that PaddlePaddle updates may lead to the need for new development tools. Not to mention the benefits of simplifying the recurrence of problems. - -- Can I use an IDE? - - > Of course, because the source code is on the machine. By default, the IDE calls a program like make to compile the source code. We only need to configure the IDE to call the Docker command to compile the source code. - Many PaddlePaddle developers use Emacs. They add two lines to their `~/.emacs` configuration file. - `global-set-key "\C-cc" 'compile` - `setq compile-command "docker run --rm -it -v $(git rev-parse --show- Toplevel): /paddle paddle:dev"` - You can start the compilation by pressing `Ctrl-C` and` c`. - -- Can I compile in parallel? - - > Yes. Our Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/paddle_build.sh). This script calls `make -j$(nproc)` to start as many processes as the CPU cores to compile in parallel. - -- Docker needs sudo? - - > If you develop with your own computer, you will naturally have admin privileges (sudo). If you are developing from a public computer, you need to ask the administrator to install and configure Docker. In addition, the PaddlePaddle project is working hard to support other container technologies that don't require sudo, such as rkt. - -- Is compiling slow on Windows/macOS? - - > Docker runs on both Windows and macOS. However, it is actually running on a Linux virtual machine. It may be necessary to pay attention to allocate more CPU and memory to this virtual machine to ensure efficient compilation. Please refer to [issue627](https://github.com/PaddlePaddle/Paddle/issues/627) for details. - -- Not enough disk? - - > In the example in this article, the `--rm` parameter is used in the `docker run`command to ensure that containers after the end of the run are not retained on disk. You can use the `docker ps -a` command to see containers that are stopped but not deleted. The `docker build` command sometimes produces some intermediate results, an image with no name, and it also occupies the disk. You can refer to this [article](https://zaiste.net/removing_docker_containers/) to clean up this content. - -- Can't I open `http://localhost:8888/` when using the book under DockerToolbox? - - > You need to replace localhost with virtual machine ip. Generally type this in the browser: `http://192.168.99.100:8888/` - -- After the pip install gpu version of PaddlePaddle runing, the SegmentFault appears as follows: - - @ 0x7f6c8d214436 paddle::platform::EnforceNotMet::EnforceNotMet() - - @ 0x7f6c8dfed666 paddle::platform::GetCUDADeviceCount() - - @ 0x7f6c8d2b93b6 paddle::framework::InitDevices() - - > The main reason for this problem is that your graphics card driver is lower than the corresponding CUDA version. Please ensure that your graphics card driver supports the CUDA version used. - -- Use customized openblas under macOS. See issue for details: - - >[ISSUE 13217](https://github.com/PaddlePaddle/Paddle/issues/13721) - -- Swig has been installed but there is still a problem that swig can't find. See issue for details: - - >[ISSUE 13759](https://github.com/PaddlePaddle/Paddle/issues/13759) - -- The question "target pattern contain no '%'." appears. See issue for details: - - >[ISSUE 13806](https://github.com/PaddlePaddle/Paddle/issues/13806) diff --git a/docs/install/install_script.md b/docs/install/install_script.md deleted file mode 100644 index 72300a97a7e..00000000000 --- a/docs/install/install_script.md +++ /dev/null @@ -1,52 +0,0 @@ -# 辅助安装脚本 - -## 使用方法 - -下载脚本至本地后，使用命令`/bin/bash fast_install.sh`启动脚本 - -### Ubuntu 和 CentOS - -脚本会执行以下几步： - -1. GPU 检测 - - 检测您的机器是否含有我们支持的 GPU，如果有，会安装 GPU 版本的 PaddlePaddle，否则会安装 CPU 版本。 - （PaddlePaddle 目前支持 NVIDIA[官网](https://developer.nvidia.com/cuda-gpus#collapseOne)列出的，算力 7.0 以下的 GPU 和 v100 系列的 GPU） - -2. CUDA，cuDNN 检测 - - 检测您的机器是否安装我们支持的 CUDA，cuDNN，具体地： - - 1. 在`/usr/local/` 及其子目录下寻找 `cuda10.1/cuda10.2/cuda11.0/cuda11.2` 目录下的`version.txt`文件（通常如果您以默认方式安装了 CUDA）。 如果提示未找到 CUDA 请使用命令`find / -name version.txt`找到您所需要的 CUDA 目录下的“version.txt”路径，然后按照提示输入。 - 2. 在`/usr` 及其子目录下寻找文件 `cudnn.h` , 如果您的 cuDNN 未安装在默认路径请使用命令`find / -name cudnn.h`寻找您希望使用的 cuDNN 版本的`cudnn.h`路径并按提示输入 - - 如果未找到相应文件，则会安装 CPU 版本的 PaddlePaddle - -3. 选择数学库 -脚本默认会为您安装支持[MKL](https://software.intel.com/en-us/mkl)数学库的 PaddlePaddle，如果您的机器不支持`MKL`，请选择安装支持[OPENBLAS](https://www.openblas.net)的 PaddlePaddle - -4. 选择 PaddlePaddle 版本 -我们为您提供 2 种版本：开发版和稳定版，推荐您选择测试验证过的稳定版 - -5. 选择 Python 版本 -脚本默认会使用您机器中的 Python，您也可以输入您希望使用的 Python 的路径 - -6. 检查[AVX](https://zh.wikipedia.org/zh-hans/AVX 指令集)指令集 - -7. 使用[Python virtualenv](https://virtualenv.pypa.io/en/latest/) -脚本也支持按您的需求创建 Python 的虚拟环境 - -以上检查完成后就会为您安装对应您系统的 PaddlePaddle 了，安装一般需要 1~2 分钟会根据您的网络来决定，请您耐心等待。 - - -### macOS - -脚本会执行以下几步： - -1. 选择 PaddlePaddle 版本 -我们为您提供 2 种版本：开发版和稳定版，推荐您选择测试验证过的稳定版 - -2. 检查 Python 版本 -由于 macOS 自带的 Python 通常依赖于系统环境，因此我们不支持 macOS 自带的 Python 环境，请重新从 Python.org 安装 Python，然后根据提示输入您希望使用的 Python 的路径 - -3. 检查是否支持[AVX](https://zh.wikipedia.org/zh-hans/AVX 指令集)指令集