NOTE: For MXNet with Python installation, please refer to the new install guide.
MXNet currently supports Python, R, Julia, Scala, and Perl. For users of R on Ubuntu operating systems, MXNet provides a set of Git Bash scripts that installs all of the required MXNet dependencies and the MXNet library.
The simple installation scripts set up MXNet for Python and R on computers running Ubuntu 12 or later. The scripts install MXNet in your home folder ~/mxnet
.
If you plan to build with GPU, you need to set up the environment for CUDA and CUDNN.
First, download and install CUDA 8 toolkit.
Then download cudnn 6.
Unzip the file and change to the cudnn root directory. Move the header and libraries to your local CUDA Toolkit folder:
tar xvzf cudnn-8.0-linux-x64-v6.0.tgz sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* sudo ldconfig
Finally, add configurations to config.mk file:
cp make/config.mk .
MXNet requires R-version to be 3.2.0 and above. If you are running an earlier version of R, run below commands to update your R version, before running the installation script.
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 sudo add-apt-repository ppa:marutter/rdev sudo apt-get update sudo apt-get upgrade sudo apt-get install r-base r-base-dev
To install MXNet for R:
# Clone mxnet repository. In terminal, run the commands WITHOUT "sudo" git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive cd ~/mxnet cp make/config.mk . # If building with GPU, add configurations to config.mk file: echo "USE_CUDA=1" >>config.mk echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk echo "USE_CUDNN=1" >>config.mk cd ~/mxnet/setup-utils bash install-mxnet-ubuntu-r.sh
The installation script to install MXNet for R can be found here.
Installing MXNet is a two-step process:
Note: To change the compilation options for your build, edit the make/config.mk
file and submit a build request with the make
command.
On Ubuntu versions 13.10 or later, you need the following dependencies:
Step 1 Install build tools and git.
sudo apt-get update sudo apt-get install -y build-essential git
Step 2 Install OpenBLAS.
MXNet uses BLAS library for accelerated numerical computations on CPU machine. There are several flavors of BLAS libraries - OpenBLAS, ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
sudo apt-get install -y libopenblas-dev
Step 3 Install OpenCV.
MXNet uses OpenCV for efficient image loading and augmentation operations.
sudo apt-get install -y libopencv-dev
Step 4 Download MXNet sources and build MXNet core shared library.
If building on CPU:
git clone --recursive https://github.com/dmlc/mxnet cd mxnet make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
If building on GPU:
git clone --recursive https://github.com/dmlc/mxnet cd mxnet make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
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
.
Executing these commands creates a library called libmxnet.so
.
Next, we install graphviz
library that we use for visualizing network graphs you build on MXNet. We will also install Jupyter Notebook used for running MXNet tutorials and examples.
sudo apt-get install -y python-pip sudo pip install graphviz sudo pip install Jupyter
We have installed MXNet core library. Next, we will install MXNet interface package for programming language of your choice:
Run the following commands to install the MXNet dependencies and build the MXNet R package.
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
Note: R-package is a folder in the MXNet source.
These commands create the MXNet R package as a tar.gz file that you can install as an R package. To install the R package, run the following command, use your MXNet version number:
R CMD INSTALL mxnet_current_r.tar.gz
The MXNet package for Julia is hosted in a separate repository, MXNet.jl, which is available on GitHub. To use Julia binding it with an existing libmxnet installation, set the MXNET_HOME
environment variable by running the following command:
export MXNET_HOME=/<path to>/libmxnet
The path to the existing libmxnet installation should be the root directory of libmxnet. In other words, you should be able to find the libmxnet.so
file at $MXNET_HOME/lib
. For example, if the root directory of libmxnet is ~
, you would run the following command:
export MXNET_HOME=/~/libmxnet
You might want to add this command to your ~/.bashrc
file. If you do, you can install the Julia package in the Julia console using the following command:
Pkg.add("MXNet")
For more details about installing and using MXNet with Julia, see the MXNet Julia documentation.
There are two ways to install the MXNet package for Scala:
Use the prebuilt binary package
Build the library from source code
For Linux users, MXNet provides prebuilt binary packages that support computers with either GPU or CPU processors. To download and build these packages using Maven
, change the artifactId
in the following Maven dependency to match your architecture:
<dependency> <groupId>ml.dmlc.mxnet</groupId> <artifactId>mxnet-full_<system architecture></artifactId> <version>0.1.1</version> </dependency>
For example, to download and build the 64-bit CPU-only version for Linux, use:
<dependency> <groupId>ml.dmlc.mxnet</groupId> <artifactId>mxnet-full_2.10-linux-x86_64-cpu</artifactId> <version>0.1.1</version> </dependency>
If your native environment differs slightly from the assembly package, for example, if you use the openblas package instead of the atlas package, it's better to use the mxnet-core package and put the compiled Java native library in your load path:
<dependency> <groupId>ml.dmlc.mxnet</groupId> <artifactId>mxnet-core_2.10</artifactId> <version>0.1.1</version> </dependency>
Before you build MXNet for Scala from source code, you must complete building the shared library. After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Scala package:
make scalapkg
This command creates the JAR files for the assembly, core, and example modules. It also creates the native library in the native/{your-architecture}/target directory
, which you can use to cooperate with the core module.
To install the MXNet Scala package into your local Maven repository, run the following command from the MXNet source root directory:
make scalainstall
Before you build MXNet for Perl from source code, you must complete building the shared library. After you build the shared library, run the following command from the MXNet source root directory to build the MXNet Perl package:
sudo apt-get install libmouse-perl pdl cpanminus swig libgraphviz-perl cpanm -q -L "${HOME}/perl5" Function::Parameters MXNET_HOME=${PWD} export LD_LIBRARY_PATH=${MXNET_HOME}/lib export PERL5LIB=${HOME}/perl5/lib/perl5 cd ${MXNET_HOME}/perl-package/AI-MXNetCAPI/ perl Makefile.PL INSTALL_BASE=${HOME}/perl5 make install cd ${MXNET_HOME}/perl-package/AI-NNVMCAPI/ perl Makefile.PL INSTALL_BASE=${HOME}/perl5 make install cd ${MXNET_HOME}/perl-package/AI-MXNet/ perl Makefile.PL INSTALL_BASE=${HOME}/perl5 make install
**Note - ** You are more than welcome to contribute easy installation scripts for other operating systems and programming languages, see community page for contributors guidelines.