Installation Guide
==================

This page gives instructions of how to build and install the mxnet package from
scratch on various systems. It consists of two steps, first we build the shared
library from the C++ codes (`libmxnet.so` for linux/osx and `libmxnet.dll` for
windows). Then we install the language, e.g. Python, packages. If the
instructions on this page do not work for you, please feel free to ask questions
at [mxnet/issues](https://github.com/dmlc/mxnet/issues), or even better to send
pull request if you can fix the problem.

## Contents
- [Build the Shared Library](#build-mxnet-library)
  - [Prerequisites](#prerequisites)
  - [Building on Ubuntu/Debian](#building-on-ubuntu-debian)
  - [Building on OSX](#building-on-osx)
  - [Building on Windows](#building-on-windows)
  - [Installing pre-built packages on Windows](#installing-pre-built-packages-on-windows)
  - [Customized Building](#customized-building)
- [Python Package Installation](#python-package-installation)
- [R Package Installation](#r-package-installation)
- [Julia Package Installation](#julia-package-installation)
- [Docker Images](#docker-images)

## Build the Shared Library

Our goal is to build the shared library:
- On Linux/OSX the target library is ```libmxnet.so```
- On Windows the target libary is ```libmxnet.dll```

The minimal building requirement is

- A recent c++ compiler supporting C++ 11 such as `g++ >= 4.8` or `clang`
- A BLAS library, such as `libblas`, `libblas`, `openblas` `intel mkl`

Optional libraries

- `CUDA Toolkit >= v7.0` to run on nvidia GPUs
  - Requires GPU with support for `Compute Capability >= 2.0`
- CUDNN to accelerate the GPU computation (only CUDNN 3 is supported)
- opencv for image augmentation

We can edit `make/config.mk` to change the compile options, and then build by
`make`. If everything goes well, we can go the
[language package installation](#install-language-packages) step.

On the remaining of this section, we provide instructions to install the
dependencies and build mxnet from scratch for various systems.

### Building on Ubuntu/Debian

On Ubuntu >= 13.10, one can install the dependencies by

```bash
sudo apt-get update
sudo apt-get install -y build-essential git libatlas-base-dev libopencv-dev
```

Then build mxnet
```bash
git clone --recursive https://github.com/dmlc/mxnet
cd mxnet; make -j4
```

### Building on OSX

On OSX, we can install the dependencies by

```bash
brew update
brew tap homebrew/science
brew info opencv
brew install opencv
```

Then build mxnet

```bash
git clone --recursive https://github.com/dmlc/mxnet
cd mxnet; cp make/osx.mk ./config.mk; make -j4
```

Troubleshooting:

Some of the users might meet the link error `ld: library not found for -lgomp`, indicating that the GNU implementation of OpenMP is not in the library path of operating system.

To resolve this issue, run the following commands:

```
sudo launchctl load -w /System/Library/LaunchDaemons/com.apple.locate.plist # this creates the locate database if it does not exist

locate libgomp.dylib #copy the path which is generated by this command, say path1

ln -s path1 /usr/local/lib/libgomp.dylib

```

then run `make -j4` again.


### Building on Windows

Firstly, we should make your Visual Studio 2013 support more C++11 features.

 - Download and install [Visual C++ Compiler Nov 2013 CTP](http://www.microsoft.com/en-us/download/details.aspx?id=41151).
 - Copy all files in `C:\Program Files (x86)\Microsoft Visual C++ Compiler Nov 2013 CTP` (or the folder where you extracted the zip archive) to `C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC` and overwrite all existed files. Don't forget to backup the original files before copying.

Secondly, fetch the third-party libraries, including [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download), [CuDNN](https://developer.nvidia.com/cudnn) and [OpenBlas](http://sourceforge.net/projects/openblas/files/v0.2.14/)(ignore this if you have MKL).

 - NOTICE: You need to register as a NVIDIA community user to get the download link of CuDNN.

Finally, use CMake to create a Visual Studio solution in `./build/`. During configuration, you may need to set the path of each third-party library, until no error is reported. Open the solution and compile, you will get a `mxnet.dll` in `./build/Release` or `./build/Debug`.

### Installing pre-built packages on Windows

Mxnet also provides pre-built packages on Windows. The pre-built package includes pre-build MxNet library, the dependent thrid-party libraries, a sample C++ solution in Visual Studio and the Python install script.

You can download the packages from the [Releases tab](https://github.com/dmlc/mxnet/releases) of MxNet. There are two variants provided: one with GPU support (using CUDA and CUDNN v3) and one without GPU support. You can choose one that fits your hardward configuration.

After download, unpack the package into a folder, say D:\MxNet, then install the package by double clicking the setupenv.cmd inside the folder. It will setup environmental variables needed by MxNet. After that, you should be able to usee the provided VS solution to build C++ programs, or to [install Python package](#python-package-installation).

### Customized Building

The configuration of mxnet can be modified by ```config.mk```
- modify the compiling options such as compilers, CUDA, CUDNN, Intel MKL,
various distributed filesystem such as HDFS/Amazon S3/...
- First copy [make/config.mk](../make/config.mk) to the project root, on which
  any local modification will be ignored by git, then modify the according flags.

#### Building with Intel MKL Support
First, `source /path/to/intel/bin/compilervars.sh` to automatically set environment variables. Then, edit [make/config.mk](../make/config.mk), let `USE_BLAS = mkl`. `USE_INTEL_PATH = NONE` is usually not necessary to be modified.


## Python Package Installation

The python package is located at [python/mxnet](../python/mxnet). It requires
`python>=2.7` and `numpy`. To install the latter, if `pip` is available, then

```bash
sudo pip install numpy
```

otherwise use your package manager, e.g.

```bash
sudo apt-get install python-numpy # for debian
sudo yum install python-numpy # for redhat
```

To have a quick test of the python package, we can train
a MLP on the mnist dataset:

```bash
python example/image-classification/train_mnist.py
```

or train a convolution neural network using GPU 0 if we set `USE_CUDA=1` during
compiling:

```bash
python example/image-classification/train_mnist.py --network lenet --gpus 0
```

There are several ways to install the package:

1. Install system-widely, which requires root permission

   ```bash
   cd python; sudo python setup.py install
   ```

   You will however need Python `distutils` module for this to
   work. It is often part of the core python package or it can be installed using your
   package manager, e.g. in Debian use

   ```bash
   sudo apt-get install python-setuptools
   ```
2. Only set the environment variable `PYTHONPATH` to tell python where to find
   the library. For example, assume we cloned `mxnet` on the home directory
   `~`. then we can added the following line in `~/.bashrc`
   It is ***recommended for developers*** who may change the codes. The changes will be immediately reflected once you pulled the code and rebuild the project (no need to call ```setup``` again)

    ```bash
    export PYTHONPATH=~/mxnet/python
    ```

3. Install only for the current user.

    ```bash
    cd python; python setup.py develop --user
    ```

4. Copy the package into the working directory which contains the mxnet
   application programs. In this approach we don't need to change the system,
   and therefore is recommended for distributed training.

   Assume we are on the working directory, and `mxnet` is cloned on the home
   directory `~`.

   ```bash
   cp -r ~/mxnet/python/mxnet .
   cp ~/mxnet/lib/libmxnet.so mxnet/
   ```

## R Package Installation

For Windows/Mac users, we provide pre-built binary package using CPU.
You can install weekly updated package directly in R console:

```r
install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("mxnet")
```

To install the R package. First finish the [Build MXNet Library](#build-mxnet-library) step.
Then use the following command to install dependencies and build the package at root folder

```bash
Rscript -e "install.packages('devtools', repo = 'https://cran.rstudio.com')"
cd R-package
Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); install_deps(dependencies = TRUE)"
cd ..
make rpkg
```

Now you should have the R package as a tar.gz file and you can install it as a normal package by (the version number might be different)

```bash
R CMD INSTALL mxnet_0.5.tar.gz
```

Note on Library Build:

We isolate the library build with Rcpp end to maximize the portability
  - MSVC is needed on windows to build the mxnet library, because of CUDA compatiblity issue of toolchains.

## Julia Package Installation

The Julia package is hosted in a separate repository [MXNet.jl](https://github.com/dmlc/MXNet.jl). To use the Julia binding with an existing libmxnet installation, set the following environment variable

```bash
export MXNET_HOME=/path/to/libmxnet
```

The path should be the root directory of libmxnet, in other words, `libmxnet.so` should be found at `$MXNET_HOME/lib`. You might want to add it to your `.bashrc`. Then the Julia package could be installed via

```julia
Pkg.add("MXNet")
```

in a Julia console. For more details, please refer to the [full documentation of MXNet.jl](http://mxnetjl.readthedocs.org/en/latest/user-guide/install.html).

## Scala Package Installation

For Linux/Mac users, we provide pre-built binary packages, with GPU or CPU-only supported.
You can use the following dependency in maven, change the artifactId according to your own architecture, e.g., `mxnet-full_2.10-osx-x86_64-cpu` for OSX (and cpu-only).

```HTML
<dependency>
  <groupId>ml.dmlc.mxnet</groupId>
  <artifactId>mxnet-full_2.10-linux-x86_64-gpu</artifactId>
  <version>0.1.1</version>
</dependency>
```

In case your native environment is slightly different from which the assembly package provides, e.g., you use `openblas` instead of `atlas`, a more recommended way is to use `mxnet-core` and put the compiled Java native library somewhere in your load path.

```HTML
<dependency>
  <groupId>ml.dmlc.mxnet</groupId>
  <artifactId>mxnet-core_2.10</artifactId>
  <version>0.1.1</version>
</dependency>
```

To build with your own environment. First finish the [Build MXNet Library](#build-mxnet-library) step.
Then run following command from the root directory.

```bash
make scalapkg
```

Now you will find jars for `assembly`, `core` and `example` modules.
Also it produces the native library in `native/{your-architecture}/target`, which you can use to cooperate with the `core` module.

To install the scala package into your local maven repository, run

```bash
make scalainstall
```

## Docker Images

Builds of MXNet are available as [Docker](https://www.docker.com/whatisdocker) images:
[MXNet Docker (CPU)](https://hub.docker.com/r/kaixhin/mxnet/) or [MXNet Docker (CUDA)](https://hub.docker.com/r/kaixhin/cuda-mxnet/).
These are updated on a weekly basis with the latest builds of MXNet. Examples of running bash in a Docker container
are as follows:

```bash
sudo docker run -it kaixhin/mxnet
sudo docker run -it --device /dev/nvidiactl --device /dev/nvidia-uvm --device /dev/nvidia0 kaixhin/cuda-mxnet:7.0
```

For a guide to Docker, see the [official docs](https://docs.docker.com/userguide/). For more details on how to use the
MXNet Docker images, including requirements for CUDA support, consult the [source project](https://github.com/Kaixhin/dockerfiles).
