indhub closed pull request #12620: Update and modify Windows docs URL: https://github.com/apache/incubator-mxnet/pull/12620
This is a PR merged from a forked repository. As GitHub hides the original diff on merge, it is displayed below for the sake of provenance: As this is a foreign pull request (from a fork), the diff is supplied below (as it won't show otherwise due to GitHub magic): diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md index 01cd46658a7..87fd1cc07d8 100755 --- a/docs/install/windows_setup.md +++ b/docs/install/windows_setup.md @@ -77,6 +77,7 @@ When using supported NVIDIA GPU hardware, inference and training can be vastly f The following steps will setup MXNet with CUDA. cuDNN can be enabled only when building from source. 1. Install [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/). 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. Download and install [NVIDIA_CUDA_DNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows) 1. Install MXNet with CUDA support with pip: ```bash @@ -93,6 +94,7 @@ The following steps will setup MXNet with CUDA and MKL. 1. Install [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) or [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/). 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. Download and install [NVIDIA_CUDA_DNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows) 1. Install MXNet with MKL support with pip: ```bash @@ -163,7 +165,6 @@ cd C:\build ``` 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" ``` -**Note**: you may add to the cmake compilation options the compiler version to use with: `-T version=14.11` 6. After the CMake successfully completed, compile the the MXNet source code by using following command: ``` msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount @@ -216,7 +217,7 @@ These steps are required after building from source. If you already installed MX ```bash # Assuming you are in root mxnet source code folder cd python - sudo python setup.py install + python setup.py install ``` Done! We have installed MXNet with Python interface. ---------------------------------------------------------------- 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: [email protected] With regards, Apache Git Services
