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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/03/01 00:35:03 UTC
[GitHub] szha closed pull request #9718: organized installation instructions to fix render issue
szha closed pull request #9718: organized installation instructions to fix render issue
URL: https://github.com/apache/incubator-mxnet/pull/9718
This is a PR merged from a forked repository.
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diff --git a/docs/install/index.md b/docs/install/index.md
index 8caedfb7011..ae8cc698b21 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -62,11 +62,46 @@ Indicate your preferred configuration. Then, follow the customized commands to i
<!-- START - Linux Python CPU Installation Instructions -->
<div class="linux">
- <div class="python">
- <div class="cpu">
+<div class="python">
+<div class="cpu">
The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+<div class="pip">
+<br/>
+
+**Step 1** Install prerequisites - wget and latest pip.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command in the terminal.
+
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y wget python gcc
+$ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
+```
+
+**Step 2** Install MXNet with OpenBLAS acceleration.
+
+```bash
+$ pip install mxnet
+```
+
+**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+sudo apt-get install graphviz
+pip install graphviz
+```
+
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+**Experimental Choice** If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
+```bash
+$ pip install mxnet-mkl
+```
+
+</div> <!-- End of pip -->
+
<div class="virtualenv">
<br/>
@@ -121,41 +156,8 @@ pip install graphviz
**Note** You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).
-</div>
-
-<div class="pip">
-<br/>
-
-**Step 1** Install prerequisites - wget and latest pip.
-
-Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command in the terminal.
-
-```bash
-$ sudo apt-get update
-$ sudo apt-get install -y wget python gcc
-$ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
-```
-
-**Step 2** Install MXNet with OpenBLAS acceleration.
-
-```bash
-$ pip install mxnet
-```
-
-**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
-```bash
-sudo apt-get install graphviz
-pip install graphviz
-```
-
-**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
-
-**Experimental Choice** If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
-```bash
-$ pip install mxnet-mkl
-```
+</div> <!-- END of virtualenv -->
-</div>
<div class="docker">
<br/>
@@ -172,7 +174,7 @@ Follow the four steps in this [docker documentation](https://docs.docker.com/eng
If you skip this step, you need to use *sudo* each time you invoke Docker.
-**Step 2** Pull the MXNet docker image.
+**Step 3** Pull the MXNet docker image.
```bash
$ docker pull mxnet/python # Use sudo if you skip Step 2
@@ -187,9 +189,10 @@ REPOSITORY TAG IMAGE ID CREATED
mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
```
-**Step 3** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
+
+</div> <!-- END of docker -->
-</div>
<div class="build-from-source">
<br/>
@@ -264,17 +267,11 @@ pip install graphviz
**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
-</div>
-
-</div>
-</div>
-</div>
-<!-- END - Linux Python CPU Installation Instructions -->
+</div><!-- END of build from source -->
+</div><!-- END of CPU -->
<!-- START - Linux Python GPU Installation Instructions -->
-<div class="linux">
-<div class="python">
<div class="gpu">
The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
@@ -323,7 +320,7 @@ pip install graphviz
$ pip install mxnet-cu90mkl
```
-</div>
+</div> <!-- END of pip -->
<div class="virtualenv">
@@ -380,7 +377,7 @@ pip install graphviz
**Note** You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).
-</div>
+</div> <!-- END of virtualenv -->
<div class="docker">
@@ -417,7 +414,7 @@ mxnet/python gpu 493b2683c269 3 weeks ago
**Step 5** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
-</div>
+</div> <!-- END of docker -->
<div class="build-from-source">
@@ -491,87 +488,162 @@ pip install graphviz
```
**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
-</div>
-</div>
-</div>
-</div>
-<!-- END - Linux Python GPU Installation Instructions -->
+</div> <!-- END of build from source -->
+</div> <!-- END of GPU -->
+</div> <!-- END of Python -->
-<!-- START - MacOS Python CPU Installation Instructions -->
-<div class="macos">
- <div class="python">
- <div class="cpu">
+<div class="r">
+<div class="cpu">
-The following installation instructions have been tested on OSX Sierra and El Capitan.
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
-<div class="virtualenv">
<br/>
-**Step 1** Install prerequisites - Homebrew, python development tools.
+**Build the MXNet core shared library**
+**Step 1** Install build tools and git.
```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
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
```
-**Step 2** Install virtualenv for macOS.
+**Step 2** Install OpenBLAS.
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
```bash
-$ pip install virtualenv
+$ sudo apt-get install -y libopenblas-dev liblapack-dev
```
-**Step 3** Create and activate virtualenv environment for MXNet.
+**Step 3** Install OpenCV.
-Following command creates a virtualenv environment at `~/mxnet` directory. However, you can choose any directory by replacing `~/mxnet` with a directory of your choice.
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
```bash
-$ virtualenv --system-site-packages ~/mxnet
+$ git clone --recursive https://github.com/apache/incubator-mxnet
+$ cd incubator-mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
```
-Activate the virtualenv environment created for *MXNet*.
+*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+
+<br/>
+
+**Build and install the MXNet R binding**
+
```bash
-$ source ~/mxnet/bin/activate
+$ make rpkg
+$ R CMD INSTALL mxnet_current_r.tar.gz
```
-After activating the environment, you should see the prompt as below.
+</div> <!-- END of CPU -->
+
+
+<div class="gpu">
+
+The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+
+**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-linux/).
+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 `LD_LIBRARY_PATH`.
+
+Example - *export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH*
+
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
```bash
-(mxnet)$
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
```
+**Step 2** Install OpenBLAS.
-**Step 4** Install MXNet in the active virtualenv environment.
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev liblapack-dev
+```
-Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command.
+**Step 3** Install OpenCV.
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
```bash
-(mxnet)$ pip install --upgrade pip
-(mxnet)$ pip install --upgrade setuptools
+$ sudo apt-get install -y libopencv-dev
```
-Install *MXNet* with OpenBLAS acceleration.
+**Step 4** Download MXNet sources and build MXNet core shared library.
```bash
-(mxnet)$ pip install mxnet
+$ git clone --recursive https://github.com/apache/incubator-mxnet
+$ cd incubator-mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
```
-**Step 5** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+*Note* - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options in `make/config.mk`. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is - */usr/local/cuda*.
+
+<br/>
+
+**Build and install the MXNet R binding**
+
```bash
-$ brew install graphviz
-(mxnet)$ pip install graphviz
+$ make rpkg
+$ R CMD INSTALL mxnet_current_r.tar.gz
```
-**Step 6** Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).
+</div> <!-- END of GPU -->
+</div> <!-- END of R -->
-**Note** You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).
-</div>
+<div class="scala julia perl">
+<div class="cpu gpu">
+
+Follow the installation instructions [in this guide](./ubuntu_setup.md) to set up MXNet.
+
+</div> <!-- End of cpu gpu -->
+</div> <!-- End of scala julia perl -->
+</div> <!-- END - Linux -->
+
+
+<!-- START - MacOS Python CPU Installation Instructions -->
+
+<div class="macos">
+<div class="python">
+<div class="cpu">
+
+The following installation instructions have been tested on OSX Sierra and El Capitan.
+
<div class="pip">
<br/>
@@ -608,35 +680,105 @@ $ pip install graphviz
**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
-</div>
+</div> <!-- END of pip -->
-<div class="docker">
+
+<div class="virtualenv">
<br/>
-Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
+**Step 1** Install prerequisites - Homebrew, python development tools.
-**Step 1** Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/docker-for-mac/install/#install-and-run-docker-for-mac).
+```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:$PATH
-*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+# Install python development tools - python2.7, pip, python-setuptools
+$ brew install python
+```
-**Step 2** Pull the MXNet docker image.
+**Step 2** Install virtualenv for macOS.
```bash
-$ docker pull mxnet/python
+$ pip install virtualenv
```
-You can list docker images to see if mxnet/python docker image pull was successful.
+**Step 3** Create and activate virtualenv environment for MXNet.
+
+Following command creates a virtualenv environment at `~/mxnet` directory. However, you can choose any directory by replacing `~/mxnet` with a directory of your choice.
```bash
-$ docker images
+$ virtualenv --system-site-packages ~/mxnet
+```
-REPOSITORY TAG IMAGE ID CREATED SIZE
+Activate the virtualenv environment created for *MXNet*.
+
+```bash
+$ source ~/mxnet/bin/activate
+```
+
+After activating the environment, you should see the prompt as below.
+
+```bash
+(mxnet)$
+```
+
+**Step 4** Install MXNet in the active virtualenv environment.
+
+Installing *MXNet* with pip requires a latest version of `pip`. Install the latest version of `pip` by issuing the following command.
+
+```bash
+(mxnet)$ pip install --upgrade pip
+(mxnet)$ pip install --upgrade setuptools
+```
+
+Install *MXNet* with OpenBLAS acceleration.
+
+```bash
+(mxnet)$ pip install mxnet
+```
+
+**Step 5** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
+```bash
+$ brew install graphviz
+(mxnet)$ pip install graphviz
+```
+
+**Step 6** Validate the installation by running simple *MXNet* code described [here](#validate-mxnet-installation).
+
+**Note** You can read more about virtualenv [here](https://virtualenv.pypa.io/en/stable/userguide/).
+
+</div> <!-- End of virtualenv -->
+
+
+<div class="docker">
+<br/>
+
+Docker images with *MXNet* are available at [Docker Hub](https://hub.docker.com/r/mxnet/).
+
+**Step 1** Install Docker on your machine by following the [docker installation instructions](https://docs.docker.com/docker-for-mac/install/#install-and-run-docker-for-mac).
+
+*Note* - You can install Community Edition (CE) to get started with *MXNet*.
+
+**Step 2** Pull the MXNet docker image.
+
+```bash
+$ docker pull mxnet/python
+```
+
+You can list docker images to see if mxnet/python docker image pull was successful.
+
+```bash
+$ docker images
+
+REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
```
**Step 4** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
-</div>
+</div> <!-- END of docker -->
+
<div class="build-from-source">
<br/>
@@ -675,20 +817,12 @@ $ bash install-mxnet-osx-python.sh
**Step 3** Validate the installation by running simple MXNet code described [here](#validate-mxnet-installation).
-</div> <!-- End of source build -->
-
-</div>
-</div>
-</div>
+</div> <!-- END of build from source -->
+</div> <!-- END of CPU -->
-<!-- END - Mac OS Python CPU Installation Instructions -->
<!-- START - Mac OS Python GPU Installation Instructions -->
-
-<div class="macos">
- <div class="python">
- <div class="gpu">
-
+<div class="gpu">
<div class="pip virtualenv docker">
</br>
@@ -761,31 +895,15 @@ Alternatively, you may follow the [CUDA installation instructions for macOS](htt
8. Run `sudo pip install -e .` **Note**: the `.` is part of the command.
-</div>
-</div>
-</div>
-</div>
-
-<!-- END - Mac OS Python GPU Installation Instructions -->
-
-<!-- START - Cloud Python Installation Instructions -->
-
-<div class="cloud">
-
-AWS Marketplace distributes Deep Learning AMIs (Amazon Machine Image) with MXNet pre-installed. You can launch one of these Deep Learning AMIs by following instructions in the [AWS Deep Learning AMI Developer Guide](http://docs.aws.amazon.com/dlami/latest/devguide/what-is-dlami.html).
-
-You can also run distributed deep learning with *MXNet* on AWS using [Cloudformation Template](https://github.com/awslabs/deeplearning-cfn/blob/master/README.md).
-
-</div>
-
-<!-- END - Cloud Python Installation Instructions -->
+</div> <!-- END of build from source -->
+</div> <!-- END of GPU -->
+</div> <!-- END of Python -->
<!-- START - MacOS R CPU Installation Instructions -->
-<div class="macos">
- <div class="r">
- <div class="cpu">
+<div class="r">
+<div class="cpu">
The CPU version of MXNet R package can be installed in R like other packages
@@ -796,160 +914,118 @@ options(repos = cran)
install.packages("mxnet")
```
-
-</div>
+</div> <!-- END of CPU -->
<div class="gpu">
Will be available soon.
-</div>
-
-</div>
-</div>
-<!-- END - MacOS R CPU Installation Instructions -->
+</div> <!-- END of GPU -->
+</div> <!-- END of R -->
-<div class="linux">
- <div class="r">
- <div class="cpu">
-<br/>
+<div class="scala julia perl">
+<div class="cpu gpu">
-Building *MXNet* from source is a 2 step process.
-1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
-2. Build the language specific bindings.
+Follow the installation instructions [in this guide](./osx_setup.md) to set up MXNet.
-**Minimum Requirements**
-1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
-2. [GNU Make](https://www.gnu.org/software/make/)
+</div> <!-- End of cpu gpu -->
+</div> <!-- End of scala julia perl -->
+</div> <!-- END - Mac OS -->
-<br/>
-**Build the MXNet core shared library**
-**Step 1** Install build tools and git.
-```bash
-$ sudo apt-get update
-$ sudo apt-get install -y build-essential git
-```
-**Step 2** Install OpenBLAS.
-*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
-```bash
-$ sudo apt-get install -y libopenblas-dev liblapack-dev
-```
-**Step 3** Install OpenCV.
-*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
-```bash
-$ sudo apt-get install -y libopencv-dev
-```
-**Step 4** Download MXNet sources and build MXNet core shared library.
-```bash
-$ git clone --recursive https://github.com/apache/incubator-mxnet
-$ cd incubator-mxnet
-$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
-```
-
-*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+<div class="windows">
+<div class="python">
+<div class="cpu">
+<div class="pip">
<br/>
-**Build and install the MXNet R binding**
+**Step 1** Install Python.
+[Anaconda](https://www.anaconda.com/download/) is recommended.
+
+**Step 2** Install *MXNet*.
```bash
-$ make rpkg
-$ R CMD INSTALL mxnet_current_r.tar.gz
+$ pip install mxnet
```
+</div> <!-- End of pip -->
-</div>
-
-<div class="gpu">
-
-The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
+<div class="virtualenv docker build-from-source">
-**Prerequisites**
+Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
-Install the following NVIDIA libraries to setup *MXNet* with GPU support:
+</div> <!-- End of virtualenv docker build-from-source -->
+</div> <!-- End of CPU -->
-1. Install CUDA 9.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
-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 `LD_LIBRARY_PATH`.
-
-Example - *export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH*
+<div class="gpu">
+<div class="pip">
<br/>
-Building *MXNet* from source is a 2 step process.
-1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
-2. Build the language specific bindings.
-
-**Minimum Requirements**
-1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
-2. [GNU Make](https://www.gnu.org/software/make/)
-
-<br/>
+**Step 1** Install Python.
-**Build the MXNet core shared library**
+[Anaconda](https://www.anaconda.com/download/) is recommended.
-**Step 1** Install build tools and git.
-```bash
-$ sudo apt-get update
-$ sudo apt-get install -y build-essential git
-```
-**Step 2** Install OpenBLAS.
+**Step 2** Install *MXNet* with GPU support using CUDA 9.0.
-*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and [LAPACK](https://en.wikipedia.org/wiki/LAPACK) libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
```bash
-$ sudo apt-get install -y libopenblas-dev liblapack-dev
+$ pip install mxnet-cu90
```
-**Step 3** Install OpenCV.
+Refer to [#8671](https://github.com/apache/incubator-mxnet/issues/8671) for status on CUDA 9.1 support.
-*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
-```bash
-$ sudo apt-get install -y libopencv-dev
-```
+</div>
+<div class="build-from-source">
+<br/>
+To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
-**Step 4** Download MXNet sources and build MXNet core shared library.
+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.
-```bash
-$ git clone --recursive https://github.com/apache/incubator-mxnet
-$ cd incubator-mxnet
-$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
-```
+After you have installed all of the required dependencies, build the MXNet source code:
-*Note* - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options in `make/config.mk`. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is - */usr/local/cuda*.
+1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet).
+2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
+3. In Visual Studio, open the solution file,```.sln```, and compile it.
+These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
-<br/>
-**Build and install the MXNet R binding**
-```bash
-$ make rpkg
-$ R CMD INSTALL mxnet_current_r.tar.gz
-```
+
+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> <!-- End of pip -->
+</div> <!-- End of GPU -->
+</div> <!-- End of Python -->
<!-- START - Windows R CPU Installation Instructions -->
-<div class="windows">
- <div class="r">
- <div class="cpu">
+<div class="r">
+<div class="cpu">
The CPU version of MXNet R package can be installed in R like other packages
@@ -961,9 +1037,7 @@ options(repos = cran)
install.packages("mxnet")
```
-</div>
-
-<!-- END - Windows R CPU Installation Instructions -->
+</div> <!-- END - Windows R CPU -->
<div class="gpu">
@@ -979,123 +1053,30 @@ install.packages("mxnet")
Alternatively, You can also follow the installation instructions [in this guide](./windows_setup.md) to build MXNet from source.
-</div>
-</div>
-</div>
-
-<!-- END - Windows R GPU Installation Instructions -->
-
-<div class="linux">
- <div class="scala julia perl">
- <div class="cpu gpu">
-
-Follow the installation instructions [in this guide](./ubuntu_setup.md) to set up MXNet.
-
-</div>
-</div>
-</div>
-
-<div class="macos">
- <div class="scala julia perl">
- <div class="cpu gpu">
-
-Follow the installation instructions [in this guide](./osx_setup.md) to set up MXNet.
-
-</div>
-</div>
-</div>
-
-<div class="windows">
- <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.
+</div> <!-- END of GPU -->
+</div> <!-- END - Windows R -->
+<div class="scala julia perl">
+<div class="cpu gpu">
-
-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)
+Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
+</div> <!-- End of cpu gpu -->
+</div> <!-- End of scala julia perl -->
+</div> <!-- End of Windows -->
-
-</div>
-</div>
-</div>
-<div class="windows">
- <div class="scala julia perl">
- <div class="cpu gpu">
+<!-- START - Cloud Python Installation Instructions -->
-Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
+<div class="cloud">
-</div>
-</div>
-</div>
+AWS Marketplace distributes Deep Learning AMIs (Amazon Machine Image) with MXNet pre-installed. You can launch one of these Deep Learning AMIs by following instructions in the [AWS Deep Learning AMI Developer Guide](http://docs.aws.amazon.com/dlami/latest/devguide/what-is-dlami.html).
-<div class="windows">
- <div class="python">
- <div class="cpu">
- <div class="virtualenv docker build-from-source">
+You can also run distributed deep learning with *MXNet* on AWS using [Cloudformation Template](https://github.com/awslabs/deeplearning-cfn/blob/master/README.md).
-Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
+</div> <!-- END - Cloud Python Installation Instructions -->
-</div>
-</div>
-</div>
-</div>
<div class="devices">
<div class="raspberry-pi">
@@ -1176,7 +1157,7 @@ You are now ready to run MXNet on your Raspberry Pi device. You can get started
*Note - Because the complete MXNet library takes up a significant amount of the Raspberry Pi's limited RAM, when loading training data or large models into memory, you might have to turn off the GUI and terminate running processes to free RAM.*
-</div>
+</div> <!-- End of raspberry pi -->
<div class="nvidia-jetson-tx2">
@@ -1265,10 +1246,9 @@ Add the mxnet folder to the path:
You are now ready to run MXNet on your NVIDIA Jetson TX2 device.
-</div>
-</div>
+</div> <!-- End of jetson -->
+</div> <!-- End of devices -->
-<br/>
<!-- This # tag restarts the page and allows reuse
of the div classes for validation sections, etc -->
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