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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/01/18 21:36:10 UTC
[GitHub] eric-haibin-lin closed pull request #9353: added mac gpu install; refactored windows install
eric-haibin-lin closed pull request #9353: added mac gpu install; refactored windows install
URL: https://github.com/apache/incubator-mxnet/pull/9353
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diff --git a/docs/install/index.md b/docs/install/index.md
index 7cdc44caaa..8caedfb701 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -44,7 +44,7 @@ Indicate your preferred configuration. Then, follow the customized commands to i
<!-- Linux Python GPU Options -->
-<div class="linux macos">
+<div class="linux macos windows">
<div class="python">
<div class="cpu gpu">
<div class="btn-group opt-group" role="group">
@@ -689,11 +689,79 @@ $ bash install-mxnet-osx-python.sh
<div class="python">
<div class="gpu">
-More details and verified installation instructions for macOS, with GPUs, coming soon.
+<div class="pip virtualenv docker">
+</br>
+
+Try the **Build from Source** option for now.
+
+</div>
+
+<div class="build-from-source">
+
+**Step 1** Install prerequisites - Homebrew, python development tools.
+
+```bash
+# Install Homebrew
+$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+$ export PATH=/usr/local/bin:/usr/local/sbin:$PATH
+
+# Install python development tools - python2.7, pip, python-setuptools
+$ brew install python pkg-config graphviz
+```
+
+**Step 2** Install optional components - OpenCV
+
+If you want to use OpenCV you should install it first, then build MXNet with the `USE_OPENCV=1` option in the later steps.
+
+```bash
+brew tap homebrew/science
+brew install opencv
+
+```
+
+**Step 3** Install CUDA and cuDNN
+
+The following instructions are for CUDA 9.1 and cuDNN 7 for macOS 10.12+ and a CUDA-capable GPU. They summarize confirmed successful builds in [#9217](https://github.com/apache/incubator-mxnet/issues/9217).
+Alternatively, you may follow the [CUDA installation instructions for macOS](https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html).
+
+1. [Download Xcode 8.3.3 from Apple](https://developer.apple.com/download/more/). This is the version [NVIDIA specifies in its instructions for macOS](https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html). Unzip and rename to `Xcode8.3.3.app`.
+
+2. Run `sudo xcode-select -s /Applications/Xcode8.3.3.app` or to wherever you have placed Xcode.
+
+3. Run `xcode-select --install` to install all command line tools, compilers, etc.
+
+4. Run `sudo xcodebuild -license accept` to accept Xcode's licensing terms.
+5. Install CUDA for macOS. Specific steps are provided in NVIDIA's [CUDA installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#installation).
-*MXNet* is expected to be compatible on macOS with NVIDIA GPUs. Please install CUDA 9.0 and cuDNN 7, prior to installing GPU version of *MXNet*.
+6. [Download](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#download-mac) and [install](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac) cuDNN for macOS. You will need to [create a free developer account](https://developer.nvidia.com/accelerated-computing-developer) with NVIDIA prior to getting the download link.
+**Step 4** Build MXNet
+
+1. Run `git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet` to get the latest version.
+
+2. Run `cd mxnet`.
+
+3. Edit the `make/osx.mk` file to set the following parameters:
+
+ ```
+ USE_CUDA = 1
+ USE_CUDA_PATH = /usr/local/cuda
+ USE_CUDNN = 1
+ USE_OPENCV = 0 # set to 1 if you want to build with OpenCV
+ ```
+
+4. Copy the `make/osx.mk` to `config.mk`
+
+5. Run `make`. If you previously attempted to compile you might want to do `make clean_all` first. You can also run `make -j` with the number of processors you have to compile with multithreading. There'll be plenty of warnings, but there should be no errors.
+
+6. Once finished, you should have a file called `libmxnet.so` in `lib/`.
+
+7. Do `cd python`.
+
+8. Run `sudo pip install -e .` **Note**: the `.` is part of the command.
+
+</div>
</div>
</div>
</div>
@@ -938,8 +1006,78 @@ Follow the installation instructions [in this guide](./osx_setup.md) to set up M
</div>
<div class="windows">
- <div class="python scala julia perl">
- <div class="gpu">
+ <div class="python">
+ <div class="cpu">
+ <div class="pip">
+<br/>
+
+**Step 1** Install Python.
+
+[Anaconda](https://www.anaconda.com/download/) is recommended.
+
+**Step 2** Install *MXNet*.
+
+```bash
+$ pip install mxnet
+```
+
+</div>
+</div>
+
+ <div class="gpu">
+
+<div class="pip">
+<br/>
+
+**Step 1** Install Python.
+
+[Anaconda](https://www.anaconda.com/download/) is recommended.
+
+**Step 2** Install *MXNet* with GPU support using CUDA 9.0.
+
+```bash
+$ pip install mxnet-cu90
+```
+
+Refer to [#8671](https://github.com/apache/incubator-mxnet/issues/8671) for status on CUDA 9.1 support.
+
+</div>
+<div class="build-from-source">
+<br/>
+To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
+
+1. If [Microsoft Visual Studio 2015](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/) 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```.
+6. 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/).
+7. 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\```.
+8. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64) and [cuDNN](https://developer.nvidia.com/cudnn). To get access to the download link, register as an NVIDIA community user.
+
+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).
+2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
+3. In Visual Studio, open the solution file,```.sln```, and compile it.
+These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
+
+
+
+
+Next, we install the ```graphviz``` library that we use for visualizing network graphs that you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples.
+- Install the ```graphviz``` by downloading the installer from the [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html).
+**Note** Make sure to add the `graphviz` executable path to the PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
+
+
+
+</div>
+</div>
+</div>
+
+<div class="windows">
+ <div class="scala julia perl">
+ <div class="cpu gpu">
Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
@@ -947,6 +1085,18 @@ Follow the installation instructions [in this guide](./windows_setup.md) to set
</div>
</div>
+<div class="windows">
+ <div class="python">
+ <div class="cpu">
+ <div class="virtualenv docker build-from-source">
+
+Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
+
+</div>
+</div>
+</div>
+</div>
+
<div class="devices">
<div class="raspberry-pi">
@@ -1012,7 +1162,7 @@ If you are getting build errors in which the compiler is being killed, it is lik
**Step 2** Install MXNet Python Bindings
-To install python bindings run the following commands in the MXNet directory:
+To install Python bindings run the following commands in the MXNet directory:
```bash
cd python
@@ -1094,7 +1244,7 @@ Executing this command creates a file called ```libmxnet.so``` in the mxnet/lib
**Step 2** Install MXNet Python Bindings
-To install python bindings run the following commands in the MXNet directory:
+To install Python bindings run the following commands in the MXNet directory:
```bash
cd python
@@ -1120,6 +1270,9 @@ You are now ready to run MXNet on your NVIDIA Jetson TX2 device.
<br/>
+<!-- This # tag restarts the page and allows reuse
+ of the div classes for validation sections, etc -->
+
# Validate MXNet Installation
<div class="linux macos">
@@ -1183,6 +1336,75 @@ array([[ 3., 3., 3.],
</div>
</div>
+<!-- Validate Windows CPU pip install -->
+
+<div class="windows">
+ <div class="python">
+ <div class="cpu">
+ <div class="pip">
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```python
+>>> import mxnet as mx
+>>> a = mx.nd.ones((2, 3))
+>>> b = a * 2 + 1
+>>> b.asnumpy()
+array([[ 3., 3., 3.],
+ [ 3., 3., 3.]], dtype=float32)
+```
+
+</div>
+</div>
+</div>
+</div>
+
+<!-- Mac OS GPU installation validation -->
+
+<div class="macos">
+ <div class="python">
+ <div class="gpu">
+
+<div class="pip virtualenv docker">
+</br>
+
+Will be available soon.
+
+</div>
+
+<div class="build-from-source">
+</br>
+
+From the MXNet root directory run: `python example/image-classification/train_mnist.py --network lenet --gpus 0` to test GPU training.
+
+</div>
+</div>
+</div>
+</div>
+
+<!-- Windows GPU installation validation -->
+
+<div class="windows">
+ <div class="python">
+ <div class="gpu">
+
+<div class="virtualenv docker">
+</br>
+
+Will be available soon.
+
+</div>
+
+<div class="pip build-from-source">
+</br>
+
+From the MXNet root directory run: `python example/image-classification/train_mnist.py --network lenet --gpus 0` to test GPU training.
+
+</div>
+</div>
+</div>
+</div>
+
<!-- Validation for GPU machines -->
<div class="linux">
@@ -1246,15 +1468,10 @@ array([[ 3., 3., 3.],
</div>
</div>
-<div class="macos">
- <div class="python">
- <div class="gpu">
-More details and verified validation instructions for macOS, with GPUs, coming soon.
-</div>
-</div>
-</div>
+
+
<!-- Linux Clean up -->
<div class="linux">
@@ -1450,77 +1667,28 @@ Will be available soon.
</div>
</div>
+<!-- Windows MXNet Installation validation -->
<div class="windows">
<div class="python">
- <div class="cpu">
- </div>
- <div class="gpu">
-
-
-**Prerequisites**
-
-Install the following NVIDIA libraries to setup *MXNet* with GPU support:
-
-1. Install CUDA 9.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows).
-2. Install cuDNN 7 for CUDA 9.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
-
-**Note:** Make sure to add CUDA install path to `PATH`.
+ <div class="cpu">
-<div class="pip">
+<div class="build-from-source virtualenv docker">
<br/>
-
-**Step 1** Install python.
-
-Recommend install ```Anaconda3``` [here](https://www.anaconda.com/download/)
-
-**Step 2** Install *MXNet* with GPU support using CUDA 9.0
-
-```bash
-$ pip install mxnet-cu90
-```
-
+Will be available soon.
</div>
-<div class="build-from-source">
-<br/>
-To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
-
-1. If [Microsoft Visual Studio 2015](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/) 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```.
-6. 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/).
-7. 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\```.
-8. Download and install [CuDNN](https://developer.nvidia.com/cudnn). To get access to the download link, register as an NVIDIA community user.
-
-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).
-2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
-3. In Visual Studio, open the solution file,```.sln```, and compile it.
-These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
-
-
-
-
-Next, we install ```graphviz``` library that we use for visualizing network graphs you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/) used for running MXNet tutorials and examples.
-- Install ```graphviz``` by downloading MSI installer from [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html).
-**Note** Make sure to add graphviz executable path to PATH environment variable. Refer [here for more details](http://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-aft)
-
-
+</div>
</div>
- </div>
- </div>
- <div class="scala julia perl">
- <div class="cpu gpu">
+<div class="scala julia perl">
+<div class="cpu gpu">
Will be available soon.
- </div>
- </div>
</div>
+</div>
+</div>
+<!-- End Windows Installation validation -->
<div class="devices">
<div class="raspberry-pi">
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