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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/09/16 03:23:14 UTC
[GitHub] yajiedesign commented on a change in pull request #12504: [WIP]
replacing windows setup with newer instructions
yajiedesign commented on a change in pull request #12504: [WIP] replacing windows setup with newer instructions
URL: https://github.com/apache/incubator-mxnet/pull/12504#discussion_r217900937
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File path: docs/install/windows_setup.md
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@@ -1,64 +1,196 @@
-# Installing MXNet in Windows
+# Installing MXNet on Windows
-On Windows, you can download and install the prebuilt MXNet package, or download, build, and install MXNet yourself.
+The following describes how to install with pip for computers with CPUs, Intel CPUs, and NVIDIA GPUs. Further along in the document you can learn how to build MXNet from source on Windows, or how to install packages that support different language APIs to MXNet.
-## Build the Shared Library
-You can either use a prebuilt binary package or build from source to build the MXNet shared library - ```libmxnet.dll```.
+- [Prerequisites](#prerequisites)
+- [Install MXNet with Python](#install-mxnet-with-python)
+ - [Install with CPUs](#install-with-cpus)
+ - [Install with Intel CPUs](#install-with-intel-cpus)
+ - [Install with GPUs](#install-with-gpus)
+ - [Notes on the Python Packages](#notes-on-the-python-packages)
+- [Build from Source](#build-from-source)
+- Install MXNet with a Programming Language API
+ - [Python](#install-the-mxnet-package-for-python)
+ - [R](#install-mxnet-package-for-r)
+ - [Julia](#install-the-mxnet-package-for-julia)
-### Installing the Prebuilt Package on Windows
-MXNet provides a prebuilt package for Windows. The prebuilt package includes the MXNet library, all of the dependent third-party libraries, a sample C++ solution for Visual Studio, and the Python installation script. To install the prebuilt package:
-1. Download the latest prebuilt package from the [Releases](https://github.com/dmlc/mxnet/releases) tab of MXNet.
-2. Unpack the package into a folder, with an appropriate name, such as ```D:\MXNet```.
-3. Open the folder, and install the package by double-clicking ```setupenv.cmd```. This sets up all of the environment variables required by MXNet.
-4. Test the installation by opening the provided sample C++ Visual Studio solution and building it.
+## Prerequisites
+### Minimum System Requirements
-
-This produces a library called ```libmxnet.dll```.
+* Windows 7<sup><a href="#fn1" id="ref1">1</a></sup>, 10, Server 2012 R2, or Server 2016
+* Python 2.7 or 3.6
+* pip
-### Building and Installing Packages on Windows
+<sup id="fn1">1. There are [known issues](https://github.com/apache/incubator-mxnet/issues?utf8=%E2%9C%93&q=is%3Aissue+windows7+label%3AWindows+) with Windows 7. <a href="#ref1" title="Return to source text.">↩</a></sup>
-To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
+### Recommended System Requirements
-1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is not already installed, download and install it. You can download and install the free community edition.
-2. Download and Install [CMake](https://cmake.org/) if it is not already installed.
-3. Download and install [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
-4. Unzip the OpenCV package.
-5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (```C:\opencv\build\x64\vc14``` for example). Also, you need to add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for example) to the ``PATH`` variable.
-6. If you have Intel Math Kernel Library (MKL) installed, set ```MKL_ROOT``` to point to ```MKL``` directory that contains the ```include``` and ```lib```. Typically, you can find the directory in
-```C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl```.
-7. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBlas](http://sourceforge.net/projects/openblas/files/v0.2.14/).
-8. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Program files (x86)\OpenBLAS\```.
-9. Download and install [CuDNN](https://developer.nvidia.com/cudnn). To get access to the download link, register as an NVIDIA community user.
+* Windows 10, Server 2012 R2, or Server 2016
+* At least one [NVIDIA CUDA-enabled GPU](https://developer.nvidia.com/cuda-gpus)
+* MKL-enabled CPU: Intel® Xeon® processor, Intel® Core™ processor family, Intel Atom® processor, or Intel® Xeon Phi™ processor
+* Python 2.7 or 3.6
+* pip
+
+
+## Install MXNet with Python
+
+The easiest way to install MXNet on Windows is by using a [Python pip package](https://pip.pypa.io/en/stable/installing/).
+
+### Install with CPUs
+
+Install MXNet with CPU support with Python:
+
+```bash
+pip install mxnet
+```
+
+Now [validate your MXNet installation with Python](validate_mxnet.md).
+
+### Install with Intel CPUs
+
+MXNet has experimental support for Intel [MKL](https://software.intel.com/en-us/mkl) and [MKL-DNN](https://github.com/intel/mkl-dnn). When using supported Intel hardware, inference and training can be vastly faster when using MXNet with [MKL](https://software.intel.com/en-us/mkl) or [MKL-DNN](https://github.com/intel/mkl-dnn).
+
+The following steps will setup MXNet with MKL. MKL-DNN can be enabled only when building from source.
+1. Download and install [Intel MKL](https://software.intel.com/en-us/mkl/choose-download/windows) (registration required).
+1. Install MXNet with MKL support with Python:
+
+```bash
+pip install mxnet-mkl
+```
+
+Now [validate your MXNet installation with Python](validate_mxnet.md).
+
+### Install with NVIDIA GPUs
+
+When using supported NVIDIA GPU hardware, inference and training can be vastly faster with [NVIDIA CUDA](https://developer.nvidia.com/cuda-toolkit) and [cuDNN](https://developer.nvidia.com/cudnn). You have two options for installing MXNet with CUDA support with a Python package.
+- [Install with CUDA support](#install-with-cuda-support)
+- [Install with CUDA and MKL support](#install-with-cuda-and-mkl-support)
+
+#### Install with CUDA Support
+
+The following steps will setup MXNet with CUDA. cuDNN can be enabled only when building from source.
+1. Download and install [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal). CUDA versions 9.2 or 9.0 are recommended. Some [issues with CUDA 9.1](https://github.com/apache/incubator-mxnet/labels/CUDA) have been identified in the past.
+1. Install MXNet with CUDA support with pip:
+
+```bash
+pip install mxnet-cu92
+```
+
+Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
+
+#### Install with CUDA and MKL Support
+
+You can also use a combination of CPU/GPU enhancements provided by Intel and NVIDIA.
+
+The following steps will setup MXNet with CUDA and MKL.
+1. Download and install [Intel MKL](https://software.intel.com/en-us/mkl/choose-download/windows) (registration required).
+1. Download and install [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal).
+1. Install MXNet with MKL support with pip:
+
+```bash
+pip install mxnet-cu92mkl
+```
+
+Once you have installed a version of MXNet, [validate your MXNet installation with Python](validate_mxnet.md).
+
+### Notes on the Python Packages
+To get further enhancements for deep neural networks, you may want to enable MKL-DNN and/or cuDNN. Each of these require you to [build from source](#build-from-source) and to enable the build flags for each.
+
+Check the chart below for other options or refer to [PyPI for other MXNet pip packages](https://pypi.org/project/mxnet/).
+
+
+
+
+## Build from Source
+
+**IMPORTANT: It is recommended that you review the [build from source guide](build_from_source.md) first.** It describes many of the build options that come with MXNet in more detail. You may decide to install additional dependencies and modify your build flags after reviewing this material.
+
+We provide two primary options to build and install MXNet yourself using [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/).
+
+**NOTE:** Visual Studio 2017's compiler is `vc15`. This is not to be confused with Visual Studio 2015's compiler, `vc14`.
+
+You also have the option to install MXNet with MKL or MKLDNN. In this case it is recommended that you refer to the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md).
+
+**Option 1: Build with Microsoft Visual Studio 2017 (VS2017)**
+
+To build and install MXNet yourself using [VS2017](https://www.visualstudio.com/downloads/), you need the following dependencies. You may try a newer version of a particular dependency, but please open a pull request or [issue](https://github.com/apache/incubator-mxnet/issues/new) to update this guide if a newer version is validated.
+
+1. Install or update VS2017.
+ - If [VS2017](https://www.visualstudio.com/downloads/) is not already installed, download and install it. You can download and install the free community edition.
+ - When prompted about installing Git, go ahead and install it.
+ - If VS2017 is already installed you will want to update it. Proceed to the next step to modify your installation. You will be given the opportunity to update VS2017 as well
+1. Follow the [instructions for opening the Visual Studio Installer](https://docs.microsoft.com/en-us/visualstudio/install/modify-visual-studio) to modify `Individual components`.
+1. Once in the Visual Studio Installer application, update as needed, then look for and check `VC++ 2017 version 15.4 v14.11 toolset`, and click `Modify`.
+1. Change the version of the Visual studio 2017 to v14.11 using the following command (by default the VS2017 is installed in the following path):
+```
+"C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.11
+```
+1. Download and install [CMake](https://cmake.org/download) if it is not already installed. [CMake v3.11](https://cmake.org/files/v3.11/cmake-3.11.0-rc4-win64-x64.msi) has been tested with MXNet.
+1. Download and run the [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download) package. There are more recent versions of OpenCV, so please create an issue/PR to update this info if you validate one of these later versions.
+1. This will unzip several files. You can place them in another directory if you wish.
+1. Set the environment variable `OpenCV_DIR` to point to the OpenCV build directory that you just unzipped (e.g., `OpenCV_DIR = C:\utils\opencv\build`).
+1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can install it and follow the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md) from here, or you can use OpenBLAS. These instructions will assume you're using OpenBLAS.
+1. Download the [OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.19/OpenBLAS-v0.2.19-Win64-int32.zip/download) package. Later versions of OpenBLAS are available, but you would need to build from source. v0.2.19 is the most recent version that ships with binaries. Contributions of more recent binaries would be appreciated.
+1. Unzip the file. You can place the unzipped files and folders in another directory if you wish.
+1. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS directory that contains the `include` and `lib` directories (e.g., `OpenBLAS_HOME = C:\utils\OpenBLAS`).
+1. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal). If you already had CUDA, then installed VS2017, you should reinstall CUDA now so that you get the CUDA toolkit components for VS2017 integration.
+1. Download and install cuDNN. To get access to the download link, register as an NVIDIA community user. Then Follow the [link](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows) to install the cuDNN.
+1. Download and install [git](https://git-for-windows.github.io/) if you haven't already.
After you have installed all of the required dependencies, build the MXNet source code:
-1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet). Don't forget to pull the submodules:
+1. Start ```cmd``` in windows.
+2. Download the MXNet source code from GitHub by using following command:
```
- git clone https://github.com/apache/incubator-mxnet.git --recursive
+cd C:\
+git clone https://github.com/apache/incubator-mxnet.git --recursive
```
-2. Start a Visual Studio command prompt.
-3. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build``` or some other directory. Make sure to specify the architecture in the
-[CMake](https://cmake.org/) command:
+3. Verify that the `DCUDNN_INCLUDE` and `DCUDNN_LIBRARY` environment variables are pointing to the `include` folder and `cudnn.lib` file of your CUDA installed location, and `C:\incubator-mxnet` is the location of the source code you just cloned in the previous step.
+4. Create a build dir using the following command and go to the directory, for example:
```
- mkdir build
- cd build
- cmake -G "Visual Studio 14 Win64" ..
+mkdir C:\build
+cd C:\build
+```
+5. Compile the MXNet source code with `cmake` by using following command:
+```
+cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.2,host=x64 -DUSE_CUDA=1 -DUSE_CUDNN=1 -DUSE_NVRTC=1 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.2 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"
+```
+6. After the CMake successfully completed, compile the the MXNet source code by using following command:
+```
Review comment:
It is recommended to add a section.the cmake 3.12.2 can use -T version=14.11 to choice vs14.11.
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