ankkhedia 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_r217142317
########## File path: docs/install/windows_setup.md ########## @@ -1,64 +1,94 @@ # Installing MXNet in Windows -On Windows, you can download and install the prebuilt MXNet package, or download, build, and install MXNet yourself. +We provide two 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/). -## Build the Shared Library -You can either use a prebuilt binary package or build from source to build the MXNet shared library - ```libmxnet.dll```. +You can also install MXNet with the following programming language support: +- [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. +**Option 1: Build with Visual Studio 2017** +To build and install MXNet yourself using [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/), you need the following dependencies. Install the required dependencies: - -This produces a library called ```libmxnet.dll```. +1. If [Microsoft Visual Studio 2017](https://www.visualstudio.com/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/files/v3.11/cmake-3.11.0-rc4-win64-x64.msi) if it is not already installed. +3. Download and install [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download). +4. Unzip the OpenCV package. +5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g., ```OpenCV_DIR = C:\utils\opencv\build```). +6. If you don’t have the Intel Math Kernel Library (MKL) installed, download and install [OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.20/OpenBLAS%200.2.20%20version.zip/download). +7. 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```). +8. Download and install CUDA: Install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal), and Download the base installer (e.g., ```cuda_9.1.85_win10.exe```). +9. 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. Review comment: This is required since CUDA require VS integration support when it is being installed so that VS can be used to build binaries with CUDA support ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
