MXNet currently supports Python, R, Julia, Scala, and Perl. For users of Python and 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 5.
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-v5.1-ga.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 .
To clone the MXNet source code repository to your computer, use git.
# Install git if not already installed. sudo apt-get update sudo apt-get -y install git
Clone the MXNet source code repository to your computer, run the installation script, and refresh the environment variables. In addition to installing MXNet, the script installs all MXNet dependencies: Numpy, LibBLAS and OpenCV. It takes around 5 minutes to complete the installation.
# Clone mxnet repository. In terminal, run the commands WITHOUT "sudo" git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive # If building with GPU, add configurations to config.mk file: cd ~/mxnet cp make/config.mk . echo "USE_CUDA=1" >>config.mk echo "USE_CUDA_PATH=/usr/local/cuda" >>config.mk echo "USE_CUDNN=1" >>config.mk # Install MXNet for Python with all required dependencies cd ~/mxnet/setup-utils bash install-mxnet-ubuntu-python.sh # We have added MXNet Python package path in your ~/.bashrc. # Run the following command to refresh environment variables. $ source ~/.bashrc
You can view the installation script we just used to install MXNet for Python here.
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:
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:
Git (to pull code from GitHub)
libatlas-base-dev (for linear algebraic operations)
libopencv-dev (for computer vision operations)
Install these dependencies using the following commands:
sudo apt-get update sudo apt-get install -y build-essential git libatlas-base-dev libopencv-dev
After installing the dependencies, use the following command to pull the MXNet source code from GitHub
# Get MXNet source code git clone https://github.com/dmlc/mxnet.git ~/mxnet --recursive # Move to source code parent directory cd ~/mxnet cp make/config.mk . echo "USE_BLAS=openblas" >>config.mk echo "ADD_CFLAGS += -I/usr/include/openblas" >>config.mk echo "ADD_LDFLAGS += -lopencv_core -lopencv_imgproc -lopencv_imgcodecs" >>config.mk
If building with GPU support, run below commands to add GPU dependency 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
Then build mxnet:
make -j$(nproc)
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:
Next, we install Python interface for MXNet. Assuming you are in ~/mxnet directory, run below commands.
# Install MXNet Python package cd python sudo python setup.py install
Check if MXNet is properly installed.
# You can change mx.cpu to mx.gpu python >>> import mxnet as mx >>> a = mx.nd.ones((2, 3), mx.cpu()) >>> print ((a * 2).asnumpy()) [[ 2. 2. 2.] [ 2. 2. 2.]]
If you don't get an import error, then MXNet is ready for python.
Note: You can update mxnet for python by repeating this step after re-building libmxnet.so.
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 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:
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.