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231 changes: 141 additions & 90 deletions docs/install/build_from_source.md
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# Build MXNet from Source

This document explains how to build MXNet from source code. Building MXNet from source is a two step process.

1. Build the MXNet shared library, `libmxnet.so`, from [C++ source files](#build-the-shared-library)
2. Install the [language bindings](#installing-mxnet-language-bindings) for MXNet. MXNet supports the following languages:
- Python
- C++
- Clojure
- Julia
- Perl
- R
- Scala
This document explains how to build MXNet from source code.


## Overview

Building from source follows this general two-step flow of building the shared library, then installing your preferred language binding. Use the following links to jump to the different sections of this guide.

1. Build the MXNet shared library, `libmxnet.so`.
* [Clone the repository](#clone-the-mxnet-project)
* [Prerequisites](#prerequisites)
* [Math library selection](#math-library-selection)
* [Install GPU software](#install-gpu-software)
* [Install optional software](#install-optional-software)
* [Adjust your build configuration](#build-configurations)
* [Build MXNet](#build-mxnet)
* [with NCCL](#build-mxnet-with-nccl) (optional)
* [for C++](#build-mxnet-with-c++) (optional)
* [Usage Examples](#usage-examples)
* [systems with GPUs and Intel CPUs](#recommended-for-Systems-with-NVIDIA-GPUs-and-Intel-CPUs)
* [GPUs with non-Intel CPUs](#recommended-for-Systems-with-Intel-CPUs)
* [Intel CPUs](#recommended-for-Systems-with-Intel-CPUs)
* [non-Intel CPUs](#recommended-for-Systems-with-non-Intel-CPUs)
2. [Install the language API binding(s)](#installing-mxnet-language-bindings) you would like to use for MXNet.
MXNet's newest and most popular API is Gluon. Gluon is built into the Python binding. If Python isn't your preference, you still have more options. MXNet supports several other language APIs:
- [Python (includes Gluon)](../api/python/index.html)
- [C++](../api/c++/index.html)
- [Clojure](../api/clojure/index.html)
- Java (coming soon)
- [Julia](../api/julia/index.html)
- [Perl](../api/perl/index.html)
- [R](../api/r/index.html)
- [Scala](../api/scala/index.html)

<hr>

## Prerequisites

You need C++ build tools and a BLAS library to build the MXNet shared library. If you want to run MXNet with GPUs, you will need to install [NVDIA CUDA and cuDNN](https://developer.nvidia.com/cuda-downloads) first.
## Build Instructions by Operating System

You may use [GNU Make](https://www.gnu.org/software/make/) to build the library but [cmake](https://cmake.org/) is required when building with MKLDNN
Detailed instructions are provided per operating system. Each of these guides also covers how to install the specific [Language Bindings](#installing-mxnet-language-bindings) you require.
You may jump to those, but it is recommended that you continue reading to understand more general "build from source" options.

* [Amazon Linux / CentOS / RHEL](centos_setup.html)
* [macOS](osx_setup.html)
* [Raspbian](raspian_setup.html)
* [TX2](tx2_setup.html)
* [Ubuntu](ubuntu_setup.html)
* [Windows](windows_setup.html)

### C++ build tools

1. A C++ compiler that supports C++ 11.
[G++ (4.8 or later)](https://gcc.gnu.org/gcc-4.8/) or
[Clang](http://clang.llvm.org/) is required.
<hr>

2. [Git](https://git-scm.com/downloads) for downloading the sources from Github repository.
## Clone the MXNet Project

1. Clone or fork the MXNet project.
```bash
git clone --recursive https://github.com/apache/incubator-mxnet mxnet
cd mxnet
```

<hr>

## Prerequisites

### BLAS library
The following sections will help you decide which specific prerequisites you need to install.

#### Math Library Selection
It is useful to consider your math library selection prior to your other prerequisites.
MXNet relies on the
[BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (Basic
Linear Algebra Subprograms) library for numerical computations.
Those can be extended with [LAPACK (Linear Algebra Package)](https://github.com/Reference-LAPACK/lapack), an additional set of mathematical functions.

MXNet supports multiple mathematical backends for computations on the CPU:

* [Apple Accelerate](https://developer.apple.com/documentation/accelerate)
* [ATLAS](http://math-atlas.sourceforge.net/)
* [MKL](https://software.intel.com/en-us/intel-mkl) (MKL, MKLML)
* [MKL-DNN](https://github.com/intel/mkl-dnn)
* [OpenBLAS](http://www.openblas.net/)

Usage of these are covered in more detail in the [build configurations](#build-configurations) section.


### Optional

These might be optional, but they're typically desirable.

* [OpenCV](http://opencv.org/) for Image Loading and Augmentation
* [NVDIA CUDA and cuDNN](https://developer.nvidia.com/cuda-downloads) for running MXNet with GPUs


## Build Instructions by Operating System

Detailed instructions are provided per operating system.
You may jump to those, but it is recommended that you continue reading to understand more general build from source options.

| | | | |
|---|---|---|---|
| [macOS](osx_setup.html) | [Ubuntu](ubuntu_setup.html) | [CentOS/*unix](centos_setup.html) | [Windows](windows_setup.html) |
| [raspbian](raspian_setup.html) | [tx2](tx2_setup.html) | | |



## Build

1. Clone the MXNet project.
```bash
git clone --recursive https://github.com/apache/incubator-mxnet mxnet
cd mxnet
```

There is a configuration file for make,
[`make/config.mk`](https://github.com/apache/incubator-mxnet/blob/master/make/config.mk), that contains all the compilation options. You can edit it and then run `make`.


## Build Configurations

`cmake` is recommended for building MXNet (and is required to build with MKLDNN), however you may use `make` instead.


### Math Library Selection
It is useful to consider your math library selection first.

The default order of choice for the libraries if found follows the path from the most
(recommended) to less performant backends.
The following lists show this order by library and `cmake` switch.
Expand Down Expand Up @@ -122,21 +112,21 @@ https://software.intel.com/en-us/mkl
It has following flavors:

* MKL is a complete math library, containing all the functionality found in ATLAS, OpenBlas and LAPACK. It is free under
community support licensing (https://software.intel.com/en-us/articles/free-mkl),
but needs to be downloaded and installed manually.
community support licensing (https://software.intel.com/en-us/articles/free-mkl),
but needs to be downloaded and installed manually.

* MKLML is a subset of MKL. It contains a smaller number of functions to reduce the
size of the download and reduce the number of dynamic libraries user needs.
size of the download and reduce the number of dynamic libraries user needs.

<!-- [Removed until #11148 is merged.] This is the most effective option since it can be downloaded and installed automatically
by the cmake script (see cmake/DownloadMKLML.cmake).-->
<!-- [Removed until #11148 is merged.] This is the most effective option since it can be downloaded and installed automatically
by the cmake script (see cmake/DownloadMKLML.cmake).-->

* MKL-DNN is a separate open-source library, it can be used separately from MKL or MKLML. It is
shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the [MKL-DNN project](https://github.com/intel/mkl-dnn))
shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the [MKL-DNN project](https://github.com/intel/mkl-dnn))

Since the full MKL library is almost always faster than any other BLAS library it's turned on by default,
however it needs to be downloaded and installed manually before doing `cmake` configuration.
Register and download on the [Intel performance libraries website](https://software.seek.intel.com/performance-libraries).
Register and download on the [Intel performance libraries website](https://software.intel.com/en-us/performance-libraries).

Note: MKL is supported only for desktop builds and the framework itself supports the following
hardware:
Expand All @@ -150,6 +140,32 @@ If you have a different processor you can still try to use MKL, but performance
unpredictable.


#### Install GPU Software

If you want to run MXNet with GPUs, you must install [NVDIA CUDA and cuDNN](https://developer.nvidia.com/cuda-downloads).


#### Install Optional Software

These might be optional, but they're typically desirable as the extend or enhance MXNet's functionality.

* [OpenCV](http://opencv.org/) - Image Loading and Augmentation. Each operating system has different packages and build from source options for OpenCV. Refer to your OS's link in the [Build Instructions by Operating System](#build-instructions-by-operating-system) section for further instructions.
* [NCCL](https://developer.nvidia.com/nccl) - NVIDIA's Collective Communications Library. Instructions for installing NCCL are found in the following [Build MXNet with NCCL](#build-mxnet-with-nccl) section.

More information on turning these features on or off are found in the following [build configurations](#build-configurations) section.


<hr>

## Build Configurations

There is a configuration file for make,
[`make/config.mk`](https://github.com/apache/incubator-mxnet/blob/master/make/config.mk), that contains all the compilation options. You can edit it and then run `make` or `cmake`. `cmake` is recommended for building MXNet (and is required to build with MKLDNN), however you may use `make` instead.

<hr>

## Build MXNet

### Build MXNet with NCCL
- Download and install the latest NCCL library from NVIDIA.
- Note the directory path in which NCCL libraries and header files are installed.
Expand Down Expand Up @@ -183,53 +199,88 @@ nosetests --verbose tests/python/gpu/test_nccl.py
**Recommendation to get the best performance out of NCCL:**
It is recommended to set environment variable NCCL_LAUNCH_MODE to PARALLEL when using NCCL version 2.1 or newer.

<hr>

### Build MXNet with C++

### Build MXNet with Language Packages
* To enable C++ package, just add `USE_CPP_PACKAGE=1` when you run `make` or `cmake`.

<hr>

### Usage Examples
* `-j` runs multiple jobs against multi-core CPUs. Example using all cores on Linux:

* `-j` runs multiple jobs against multi-core CPUs.

For example, you can specify using all cores on Linux as follows:

```bash
make -j$(nproc)
cmake -j$(nproc)
```

* Build without using OpenCV:

#### Recommended for Systems with NVIDIA GPUs and Intel CPUs
* Build MXNet with `cmake` and install with MKL DNN, GPU, and OpenCV support:

```bash
make USE_OPENCV=0
cmake -j USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1 USE_MKLDNN=1
```

#### Recommended for Systems with NVIDIA GPUs
* Build with both OpenBLAS, GPU, and OpenCV support:

```bash
make -j USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
cmake -j BLAS=open USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
```

#### Recommended for Systems with Intel CPUs
* Build MXNet with `cmake` and install with MKL DNN, and OpenCV support:

```bash
cmake -j USE_CUDA=0 USE_MKLDNN=1
```

#### Recommended for Systems with non-Intel CPUs
* Build MXNet with `cmake` and install with OpenBLAS and OpenCV support:

```bash
cmake -j USE_CUDA=0 BLAS=open
```

#### Other Examples

* Build without using OpenCV:

```bash
cmake USE_OPENCV=0
```

* Build on **macOS** with the default BLAS library (Apple Accelerate) and Clang installed with `xcode` (OPENMP is disabled because it is not supported by the Apple version of Clang):

```bash
make -j USE_BLAS=apple USE_OPENCV=0 USE_OPENMP=0
cmake -j BLAS=apple USE_OPENCV=0 USE_OPENMP=0
```

* To use OpenMP on **macOS** you need to install the Clang compiler, `llvm` (the one provided by Apple does not support OpenMP):

```bash
brew install llvm
make -j USE_BLAS=apple USE_OPENMP=1
cmake -j BLAS=apple USE_OPENMP=1
```

<hr>

## Installing MXNet Language Bindings
After building MXNet's shared library, you can install other language bindings. (Except for C++. You need to build this when you build MXNet from source.)
After building MXNet's shared library, you can install other language bindings.

**NOTE:** The C++ API binding must be built when you build MXNet from source. See [Build MXNet with C++](#build-mxnet-with-c++).

The following table provides links to each language binding by operating system:
| | Linux | macOS | Windows |
|---|---|---|---|
| Python | [Linux](ubuntu_setup.html#install-mxnet-for-python) | [macOS](osx_setup.html) | [Windows](windows_setup.html#install-mxnet-for-python) |
| C++ | [Linux](c_plus_plus.html) | [macOS](c_plus_plus.html) | [Windows](c_plus_plus.html) |
| Clojure | [Linux](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | [macOS](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | n/a |
| Julia | [Linux](ubuntu_setup.html#install-the-mxnet-package-for-julia) | [macOS](osx_setup.html#install-the-mxnet-package-for-julia) | [Windows](windows_setup.html#install-the-mxnet-package-for-julia) |
| Perl | [Linux](ubuntu_setup.html#install-the-mxnet-package-for-perl) | [macOS](osx_setup.html#install-the-mxnet-package-for-perl) | [Windows](n/a) |
| R | [Linux](ubuntu_setup.html#install-the-mxnet-package-for-r) | [macOS](osx_setup.html#install-the-mxnet-package-for-r) | [Windows](windows_setup.html#install-the-mxnet-package-for-r) |
| Scala | [Linux](scala_setup.html) | [macOS](scala_setup.html) | n/a |
| | [Ubuntu](ubuntu_setup.html) | [macOS](osx_setup.html) | [Windows](windows_setup.html) |
| --- | ---- | --- | ------- |
| Python | [Ubuntu guide](ubuntu_setup.html#install-mxnet-for-python) | [OSX guide](osx_setup.html) | [Windows guide](windows_setup.html#install-mxnet-for-python) |
| C++ | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) | [C++ guide](c_plus_plus.html) |
| Clojure | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | [Clojure guide](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package) | n/a |
| Julia | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-julia) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-julia) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-julia) |
| Perl | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-perl) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-perl) | n/a |
| R | [Ubuntu guide](ubuntu_setup.html#install-the-mxnet-package-for-r) | [OSX guide](osx_setup.html#install-the-mxnet-package-for-r) | [Windows guide](windows_setup.html#install-the-mxnet-package-for-r) |
| Scala | [Scala guide](scala_setup.html) | [Scala guide](scala_setup.html) | n/a |
8 changes: 4 additions & 4 deletions docs/tutorials/unsupervised_learning/gan.md
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Expand Up @@ -63,7 +63,7 @@ The MNIST dataset contains 70,000 images of handwritten digits. Each image is 28

### 1. Preparing the MNSIT dataset

Let us start by preparing the handwritten digits from the MNIST dataset.
Let us start by preparing the handwritten digits from the MNIST dataset.
```python
import mxnet as mx
import numpy as np
Expand All @@ -75,7 +75,7 @@ mnist_test = mx.gluon.data.vision.datasets.MNIST(train=False)
```python
# The downloaded data is of type `Dataset` which are
# Well suited to work with the new Gluon interface but less
# With the older symbol API, used in this tutorial.
# With the older symbol API, used in this tutorial.
# Therefore we convert them to numpy array first
X = np.zeros((70000, 28, 28))
for i, (data, label) in enumerate(mnist_train):
Expand Down Expand Up @@ -394,7 +394,7 @@ As a result, we have created two neural nets: a Generator, which is able to crea
Along the way, we have learned how to do the image manipulation and visualization that is associated with the training of deep neural nets. We have also learned how to use MXNet's Module APIs to perform advanced model training functionality to fit the model.

## Acknowledgements
This tutorial is based on [MXNet DCGAN codebase](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/dcgan.py),
This tutorial is based on [MXNet DCGAN codebase](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/DCGAN/dcgan.py),
[The original paper on GANs](https://arxiv.org/abs/1406.2661), as well as [this paper on deep convolutional GANs](https://arxiv.org/abs/1511.06434).

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