Indicate your preferred configuration. Then, follow the customized commands to install MXNet.
The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
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.
$ 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.
$ pip install mxnet
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-mkl
Step 2 Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==1.1.0
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-mkl==1.1.0
Step 2 Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==1.0.0
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-mkl==1.0.0
Step 2 Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==0.12.1
For MXNet 0.12.0 -
$ pip install mxnet==0.12.0
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-mkl==0.12.1
For MXNet 0.12.0 -
$ pip install mxnet-mkl==0.12.0
Step 2 Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==0.11.0
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-mkl==0.11.0
Step 2 Install MXNet with OpenBLAS acceleration.
$ pip install mxnet --pre
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-mkl --pre
Step 1 Install virtualenv for Ubuntu.
$ sudo apt-get update $ sudo apt-get install -y python-dev python-virtualenv
Step 2 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.
$ virtualenv --system-site-packages ~/mxnet
Activate the virtualenv environment created for MXNet.
$ source ~/mxnet/bin/activate
After activating the environment, you should see the prompt as below.
(mxnet)$
Step 3 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.
$ pip install --upgrade pip
Install MXNet with OpenBLAS acceleration.
$ pip install mxnet
Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==1.1.0
Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==1.0.0
Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==0.12.1
For MXNet 0.12.0 -
$ pip install mxnet==0.12.0
Install MXNet with OpenBLAS acceleration.
$ pip install mxnet==0.11.0
Install MXNet with OpenBLAS acceleration.
$ pip install mxnet --pre
Step 4 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 5 Validate the installation by running simple MXNet code described here.
Note You can read more about virtualenv here.
Docker images with MXNet are available at Docker Hub.
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE) to get started with MXNet.
Step 2 [Optional] Post installation steps to manage Docker as a non-root user.
Follow the four steps in this docker documentation to allow managing docker containers without sudo.
If you skip this step, you need to use sudo each time you invoke Docker.
Step 3 Pull the MXNet docker image.
$ docker pull mxnet/python # Use sudo if you skip Step 2
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2 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.
Building MXNet from source is a 2 step process.
libmxnet.so, from the C++ sources.Minimum Requirements
Build the MXNet core shared library
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 and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK 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 liblapack-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. You can clone the repository as described in the following code block, or you may try the download links for your desired MXNet version.
$ 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.
Build the MXNet Python binding
Step 1 Install prerequisites - python, setup-tools, python-pip and libfortran (required for Numpy).
$ sudo apt-get install -y python-dev python-setuptools python-pip libgfortran3
Step 2 Install the MXNet Python binding.
$ cd python $ pip install -e .
Note that the -e flag is optional. It is equivalent to --editable and means that if you edit the source files, these changes will be reflected in the package installed.
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
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:
Note: Make sure to add CUDA install path to LD_LIBRARY_PATH.
Example - export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
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.
$ sudo apt-get update $ sudo apt-get install -y wget python $ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
Step 2 Install MXNet with GPU support using CUDA 9.2
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
$ pip install mxnet-cu92
Refer to pypi for older packages.
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-cu90mkl
Step 2 Install MXNet with GPU support using CUDA 9.1
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
$ pip install mxnet-cu91==1.1.0
Refer to pypi for older packages.
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install MXNet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-cu91mkl==1.1.0
Refer to pypi for older packages.
Step 2 Install MXNet with GPU support using CUDA 9.0
$ pip install mxnet-cu90==1.0.0
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-cu90mkl==1.0.0
Step 2 Install MXNet with GPU support using CUDA 9.0
$ pip install mxnet-cu90==0.12.1
For MXNet 0.12.0 -
$ pip install mxnet-cu90==0.12.0
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-cu90mkl==0.12.1
For MXNet 0.12.0 -
$ pip install mxnet-cu90mkl==0.12.0
Step 2 Install MXNet with GPU support using CUDA 8.0
$ pip install mxnet-cu80==0.11.0
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Experimental Choice If You would like to install MXNet with Intel MKL, try the experimental pip package with MKL:
$ pip install mxnet-cu80mkl==0.11.0
Step 1 Install virtualenv for Ubuntu.
$ sudo apt-get update $ sudo apt-get install -y python-dev python-virtualenv
Step 2 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.
$ virtualenv --system-site-packages ~/mxnet
Activate the virtualenv environment created for MXNet.
$ source ~/mxnet/bin/activate
After activating the environment, you should see the prompt as below.
(mxnet)$
Step 3 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.
(mxnet)$ pip install --upgrade pip
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
Install MXNet with GPU support using CUDA 9.2:
(mxnet)$ pip install mxnet-cu92
Refer to pypi for older packages.
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
Install MXNet with GPU support using CUDA 9.1:
(mxnet)$ pip install mxnet-cu91==1.1.0
Refer to pypi for older packages.
Install MXNet with GPU support using CUDA 9.0.
(mxnet)$ pip install mxnet-cu90==1.0.0
Refer to pypi for older packages.
Install MXNet with GPU support using CUDA 9.0.
(mxnet)$ pip install mxnet-cu90==0.12.1
Refer to pypi for older packages.
Install MXNet with GPU support using CUDA 8.0.
(mxnet)$ pip install mxnet-cu80==0.11.0
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
Install MXNet with GPU support using CUDA 9.2.
(mxnet)$ pip install mxnet-cu92 --pre
Refer to pypi for older packages.
Step 4 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 5 Validate the installation by running simple MXNet code described here.
Note You can read more about virtualenv here.
Docker images with MXNet are available at Docker Hub.
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE) to get started with MXNet.
Step 2 [Optional] Post installation steps to manage Docker as a non-root user.
Follow the four steps in this docker documentation to allow managing docker containers without sudo.
If you skip this step, you need to use sudo each time you invoke Docker.
Step 3 Install nvidia-docker-plugin following the installation instructions. nvidia-docker-plugin is required to enable the usage of GPUs from the docker containers.
Step 4 Pull the MXNet docker image.
$ docker pull mxnet/python:gpu # Use sudo if you skip Step 2
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2 REPOSITORY TAG IMAGE ID CREATED SIZE mxnet/python gpu 493b2683c269 3 weeks ago 4.77 GB
Step 5 Validate the installation by running simple MXNet code described here.
Building MXNet from source is a 2 step process.
libmxnet.so, from the C++ sources.Minimum Requirements
Build the MXNet core shared library
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 and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK 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 liblapack-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. You can clone the repository as described in the following code block, or you may try the download links for your desired MXNet version.
$ 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
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.
Install the MXNet Python binding
Step 1 Install prerequisites - python, setup-tools, python-pip and libfortran (required for Numpy)..
$ sudo apt-get install -y python-dev python-setuptools python-pip libgfortran3
Step 2 Install the MXNet Python binding.
$ cd python $ pip install -e .
Note that the -e flag is optional. It is equivalent to --editable and means that if you edit the source files, these changes will be reflected in the package installed.
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
sudo apt-get install graphviz pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
The default version of R that is installed with apt-get is insufficient. You will need to first install R v3.4.4+ and build MXNet from source.
After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that incubator-mxnet is the source directory you used to build MXNet as follows:
$ cd incubator-mxnet $ make rpkg
The default version of R that is installed with apt-get is insufficient. You will need to first install R v3.4.4+ and build MXNet from source.
After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that incubator-mxnet is the source directory you used to build MXNet as follows:
$ cd incubator-mxnet $ make rpkg
You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process.
<dependency> <groupId>org.apache.mxnet</groupId> <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId> </dependency>
You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process.
<dependency> <groupId>org.apache.mxnet</groupId> <artifactId>mxnet-full_2.11-linux-x86_64-cpu</artifactId> </dependency>
Follow the installation instructions in this guide to set up MXNet.
The following installation instructions have been tested on OSX Sierra and El Capitan.
Step 1 Install prerequisites - Homebrew, python development tools.
# 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
Step 2 Install MXNet with OpenBLAS acceleration.
Installing MXNet with pip requires a latest version of pip. Install the latest version of pip by issuing the following command.
$ pip install --upgrade pip $ pip install --upgrade setuptools
Then use pip to install MXNet:
$ pip install mxnet
Then use pip to install MXNet:
$ pip install mxnet==1.1.0
Then use pip to install MXNet:
$ pip install mxnet==1.0.0
Then use pip to install MXNet:
$ pip install mxnet=0.12.1
For MXNet 0.12.0 -
$ pip install mxnet=0.12.0
Then use pip to install MXNet:
$ pip install mxnet==0.11.0
Then use pip to install MXNet:
$ pip install mxnet --pre
Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
$ brew install graphviz
$ pip install graphviz
Step 4 Validate the installation by running simple MXNet code described here.
Step 1 Install prerequisites - Homebrew, python development tools.
# 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
Step 2 Install virtualenv for macOS.
$ pip install virtualenv
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.
$ virtualenv --system-site-packages ~/mxnet
Activate the virtualenv environment created for MXNet.
$ source ~/mxnet/bin/activate
After activating the environment, you should see the prompt as below.
(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.
(mxnet)$ pip install --upgrade pip (mxnet)$ pip install --upgrade setuptools
Install MXNet with OpenBLAS acceleration.
(mxnet)$ pip install mxnet
Install MXNet with OpenBLAS acceleration.
(mxnet)$ pip install mxnet==1.1.0
Install MXNet with OpenBLAS acceleration.
(mxnet)$ pip install mxnet==1.0.0
Install MXNet with OpenBLAS acceleration.
(mxnet)$ pip install mxnet==0.12.1
For MXNet 0.12.0 -
(mxnet)$ pip install mxnet==0.12.0
Install MXNet with OpenBLAS acceleration.
(mxnet)$ pip install mxnet==0.11.0
Install MXNet with OpenBLAS acceleration.
(mxnet)$ pip install mxnet --pre
Step 5 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).
$ brew install graphviz (mxnet)$ pip install graphviz
Step 6 Validate the installation by running simple MXNet code described here.
Note You can read more about virtualenv here.
Docker images with MXNet are available at Docker Hub.
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE) to get started with MXNet.
Step 2 Pull the MXNet docker image.
$ docker pull mxnet/python
You can list docker images to see if mxnet/python docker image pull was successful.
$ 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.
Prerequisites
If not already installed, download and install Xcode (or insall it from the App Store) for macOS. Xcode is an integrated development environment for macOS containing a suite of software development tools like C/C++ compilers, BLAS library and more.
Building MXNet from source is a 2 step process.
libmxnet.so, from the C++ sources.Make sure you have installed Xcode before proceeding further.
All the instructions to build MXNet core shared library and MXNet Python bindings are compiled as one helper bash script. You can use this bash script to build MXNet for Python, from source, on macOS.
Step 1 Download the bash script for building MXNet from source.
$ curl -O https://raw.githubusercontent.com/dmlc/mxnet/master/setup-utils/install-mxnet-osx-python.sh
Step 2 Run the script to get latest MXNet source and build.
# Make the script executable $ chmod 744 install-mxnet-osx-python.sh # Run the script. It takes around 5 mins. $ bash install-mxnet-osx-python.sh
Step 3 Validate the installation by running simple MXNet code described here.
Try the Build from Source option for now.
Step 1 Install prerequisites - Homebrew, python development tools.
# 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 pkg-config graphviz
Step 2 Install optional components - OpenCV
If you want to use OpenCV you should install it first, then build MXNet with the USE_OPENCV=1 option in the later steps.
brew tap homebrew/science brew install opencv
Step 3 Install CUDA and cuDNN
The following instructions are for CUDA 9.1 and cuDNN 7 for macOS 10.12+ and a CUDA-capable GPU. They summarize confirmed successful builds in #9217. Alternatively, you may follow the CUDA installation instructions for macOS.
Download Xcode 8.3.3 from Apple. This is the version NVIDIA specifies in its instructions for macOS. Unzip and rename to Xcode8.3.3.app.
Run sudo xcode-select -s /Applications/Xcode8.3.3.app or to wherever you have placed Xcode.
Run xcode-select --install to install all command line tools, compilers, etc.
Run sudo xcodebuild -license accept to accept Xcode's licensing terms.
Install CUDA for macOS. Specific steps are provided in NVIDIA's CUDA installation instructions.
Download and install cuDNN for macOS. You will need to create a free developer account with NVIDIA prior to getting the download link.
Step 4 Build MXNet
Run git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet to get the latest version.
Run cd mxnet.
Edit the make/osx.mk file to set the following parameters:
USE_CUDA = 1 USE_CUDA_PATH = /usr/local/cuda USE_CUDNN = 1 USE_OPENCV = 0 # set to 1 if you want to build with OpenCV
Copy the make/osx.mk to config.mk
Run make. If you previously attempted to compile you might want to do make clean_all first. You can also run make -j with the number of processors you have to compile with multithreading. There'll be plenty of warnings, but there should be no errors.
Once finished, you should have a file called libmxnet.so in lib/.
Do cd python.
Run sudo pip install -e . Note: the . is part of the command.
Install the latest version (3.5.1+) of R from CRAN. You can build MXNet-R from source, or you can use a pre-built binary:
cran <- getOption("repos") cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/" options(repos = cran) install.packages("mxnet")
Will be available soon.
You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process.
<dependency> <groupId>org.apache.mxnet</groupId> <artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId> </dependency>
Not available at this time.
Follow the installation instructions in this guide to set up MXNet.
Step 1 Install Python.
Anaconda is recommended.
Step 2 Install MXNet.
$ pip install mxnet
Step 2 Install MXNet.
$ pip install mxnet==1.1.0
Step 2 Install MXNet.
$ pip install mxnet==1.0.0
Step 2 Install MXNet.
$ pip install mxnet==0.12.1
For MXNet 0.12.0 -
$ pip install mxnet==0.12.0
Step 2 Install MXNet.
$ pip install mxnet==0.11.0
Step 2 Install MXNet.
$ pip install mxnet --pre
Follow the installation instructions in this guide to set up MXNet.
Step 1 Install Python.
Anaconda is recommended.
Step 2 Install MXNet with GPU support using CUDA 9.2.
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
$ pip install mxnet-cu92
Refer to pypi for older packages.
Step 2 Install MXNet with GPU support using CUDA 9.1.
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
$ pip install mxnet-cu91==1.1.0
Refer to pypi for older packages.
Step 2 Install MXNet with GPU support using CUDA 9.0.
$ pip install mxnet-cu90==1.0.0
Step 2 Install MXNet with GPU support using CUDA 9.0.
$ pip install mxnet-cu90==0.12.1
Install MXNet 0.12.0 with GPU support using CUDA 9.0.
$ pip install mxnet-cu90==0.12.0
Step 2 Install MXNet with GPU support using CUDA 8.0.
$ pip install mxnet-cu80==0.11.0
Step 2 Install MXNet with GPU support using CUDA 9.2.
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
$ pip install mxnet-cu92 --pre
Refer to pypi for older packages.
Refer to #8671 for status on CUDA 9.1 support.
We provide both options to build and install MXNet yourself using Microsoft Visual Studio 2017, and Microsoft Visual Studio 2015.
Option 1
To build and install MXNet yourself using Microsoft Visual Studio 2017, you need the following dependencies. Install the required dependencies:
OpenCV_DIR to point to the OpenCV build directory (e.g., OpenCV_DIR = C:\utils\opencv\build).OpenBLAS_HOME to point to the OpenBLAS directory that contains the include and lib directories (e.g., OpenBLAS_HOME = C:\utils\OpenBLAS).cuda_9.1.85_win10.exe).After you have installed all of the required dependencies, build the MXNet source code:
Start cmd in windows.
Download the MXNet source code from GitHub by using following command:
cd C:\ git clone https://github.com/apache/incubator-mxnet.git --recursive
Follow this link to modify Individual components, and check VC++ 2017 version 15.4 v14.11 toolset, and click Modify.
Change the version of the Visual studio 2017 to v14.11 using the following command (by default the VS2017 is installed in the following path):
"C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.11
mkdir C:\build cd C:\build
cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.1,host=x64 -DUSE_CUDA=1 -DUSE_CUDNN=1 -DUSE_NVRTC=1 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.1 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"
NOTE: make sure the DCUDNN_INCLUDE and DCUDNN_LIBRARY pointing to the “include” and “cudnn.lib” of your CUDA installed location, and the C:\incubator-mxnet is the location of the source code you just git in the previous step
msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
Option 2
To build and install MXNet yourself using Microsoft Visual Studio 2015, you need the following dependencies. Install the required dependencies:
Tools -> Extensions and Updates... | Product Updates menu.OpenCV_DIR to point to the OpenCV build directory (C:\opencv\build\x64\vc14 for example). Also, you need to add the OpenCV bin directory (C:\opencv\build\x64\vc14\bin for example) to the PATH 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\.CUDACXX to point to the CUDA Compiler(C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin\nvcc.exe for example).CUDNN_ROOT to point to the cuDNN directory that contains the include, lib and bin directories (C:\Downloads\cudnn-9.1-windows7-x64-v7\cuda for example).After you have installed all of the required dependencies, build the MXNet source code:
git clone --recurse-submodules)../build..sln, and compile it. These commands produce a library called mxnet.dll in the ./build/Release/ or ./build/Debug folder. Next, we install the graphviz library that we use for visualizing network graphs that you build on MXNet. We will also install Jupyter Notebook which is used for running MXNet tutorials and examples.
graphviz by downloading the installer from the Graphviz Download Page. Note Make sure to add the graphviz executable path to the PATH environment variable. Refer here for more details
Install the latest version (3.5.1+) of R from CRAN. You can build MXNet-R from source, or you can use a pre-built binary:
cran <- getOption("repos") cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/" options(repos = cran) install.packages("mxnet")
You can build MXNet-R from source, or you can use a pre-built binary:
cran <- getOption("repos") cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92" options(repos = cran) install.packages("mxnet")
Change cu92 to cu80, cu90 or cu91 based on your CUDA toolkit version. Currently, MXNet supports these versions of CUDA.
MXNet-Scala for Windows is not yet available.
Follow the installation instructions in this guide to set up MXNet.
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.
You can also run distributed deep learning with MXNet on AWS using Cloudformation Template.
MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on Raspberry Pi Devices.
These instructions will walk through how to build MXNet for the Raspberry Pi and install the Python bindings for the library.
The complete MXNet library and its requirements can take almost 200MB of RAM, and loading large models with the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the Raspberry Pi 3 or an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has at least 4 GB of free memory.
Install MXNet
Installing MXNet is a two-step process:
Step 1 Build the Shared Library
On Raspbian versions Wheezy and later, you need the following dependencies:
Git (to pull code from GitHub)
libblas (for linear algebraic operations)
libopencv (for computer vision operations. This is optional if you want to save RAM and Disk Space)
A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source code. Supported compilers include the following:
Install these dependencies using the following commands in any directory:
sudo apt-get update sudo apt-get -y install git cmake build-essential g++-4.8 c++-4.8 liblapack* libblas* libopencv*
Clone the MXNet source code repository using the following git command in your home directory:
git clone https://github.com/apache/incubator-mxnet.git --recursive cd incubator-mxnet
If you aren't processing images with MXNet on the Raspberry Pi, you can minimize the size of the compiled library by building MXNet without the Open Source Computer Vision (OpenCV) library with the following commands:
export USE_OPENCV = 0 make
Otherwise, you can build the complete MXNet library with the following command:
make
Executing either of these commands start the build process, which can take up to a couple hours, and creates a file called libmxnet.so in the mxnet/lib directory.
If you are getting build errors in which the compiler is being killed, it is likely that the compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which have less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi by editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to CONF_SWAPSIZE=1024, then running:
sudo /etc/init.d/dphys-swapfile stop sudo /etc/init.d/dphys-swapfile start free -m # to verify the swapfile size has been increased
Step 2 Install MXNet Python Bindings
To install Python bindings run the following commands in the MXNet directory:
cd python pip install --upgrade pip pip install -e .
Note that the -e flag is optional. It is equivalent to --editable and means that if you edit the source files, these changes will be reflected in the package installed.
You are now ready to run MXNet on your Raspberry Pi device. You can get started by following the tutorial on Real-time Object Detection with MXNet On The Raspberry Pi.
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.
MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices.
These instructions will walk through how to build MXNet for the Pascal based NVIDIA Jetson TX2 and install the corresponding python language bindings.
For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device.
Install MXNet
Installing MXNet is a two-step process:
Step 1 Build the Shared Library
You need the following additional dependencies:
Git (to pull code from GitHub)
libatlas (for linear algebraic operations)
libopencv (for computer vision operations)
python pip (to load relevant python packages for our language bindings)
Install these dependencies using the following commands in any directory:
sudo apt-get update sudo apt-get -y install git build-essential libatlas-base-dev libopencv-dev graphviz python-pip sudo pip install pip --upgrade sudo pip install setuptools numpy --upgrade sudo pip install graphviz jupyter
Clone the MXNet source code repository using the following git command in your home directory:
git clone https://github.com/apache/incubator-mxnet.git --recursive cd incubator-mxnet
Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
cp make/crosscompile.jetson.mk config.mk
Edit the Mshadow Makefile to ensure MXNet builds with Pascal's hardware level low precision acceleration by editing 3rdparty/mshadow/make/mshadow.mk and adding the following after line 122:
MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
Now you can build the complete MXNet library with the following command:
make -j $(nproc)
Executing this command creates a file called libmxnet.so in the mxnet/lib directory.
Step 2 Install MXNet Python Bindings
To install Python bindings run the following commands in the MXNet directory:
cd python pip install --upgrade pip pip install -e .
Note that the -e flag is optional. It is equivalent to --editable and means that if you edit the source files, these changes will be reflected in the package installed.
Add the mxnet folder to the path:
cd .. export MXNET_HOME=$(pwd) echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.bashrc source ~/.bashrc
You are now ready to run MXNet on your NVIDIA Jetson TX2 device.
Start the python terminal.
$ python
Launch a Docker container with mxnet/python image and run example MXNet python program on the terminal.
$ docker run -it mxnet/python bash # Use sudo if you skip Step 2 in the installation instruction # Start a python terminal root@4919c4f58cac:/# python
Activate the virtualenv environment created for MXNet.
$ source ~/mxnet/bin/activate
After activating the environment, you should see the prompt as below.
(mxnet)$
Start the python terminal.
$ python
Run a short MXNet python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
>>> import mxnet as mx >>> a = mx.nd.ones((2, 3)) >>> b = a * 2 + 1 >>> b.asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32)
Run a short MXNet python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
>>> import mxnet as mx >>> a = mx.nd.ones((2, 3)) >>> b = a * 2 + 1 >>> b.asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32)
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From the MXNet root directory run: python example/image-classification/train_mnist.py --network lenet --gpus 0 to test GPU training.
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From the MXNet root directory run: python example/image-classification/train_mnist.py --network lenet --gpus 0 to test GPU training.
Start the python terminal.
$ python
Launch a NVIDIA Docker container with mxnet/python:gpu image and run example MXNet python program on the terminal.
$ nvidia-docker run -it mxnet/python:gpu bash # Use sudo if you skip Step 2 in the installation instruction # Start a python terminal root@4919c4f58cac:/# python
Activate the virtualenv environment created for MXNet.
$ source ~/mxnet/bin/activate
After activating the environment, you should see the prompt as below.
(mxnet)$
Start the python terminal.
$ python
Run a short MXNet python program to create a 2X3 matrix of ones a on a GPU, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use mx.gpu(), to set MXNet context to be GPUs.
>>> import mxnet as mx >>> a = mx.nd.ones((2, 3), mx.gpu()) >>> b = a * 2 + 1 >>> b.asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32)
Exit the Python terminal.
>>> exit() $
Exit the Python terminal and Deactivate the virtualenv MXNet environment.
>>> exit() (mxnet)$ deactivate $
Exit the Python terminal and mxnet/python docker container.
>>> exit() root@4919c4f58cac:/# exit
Exit the Python terminal.
>>> exit() $
Exit the Python terminal and Deactivate the virtualenv MXNet environment.
>>> exit() (mxnet)$ deactivate $
Exit the Python terminal and then the docker container.
>>> exit() root@4919c4f58cac:/# exit
Login to the cloud instance you launched, with pre-installed MXNet, following the guide by corresponding cloud provider.
Start the python terminal.
$ python
Run a short MXNet python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
>>> import mxnet as mx >>> a = mx.nd.ones((2, 3)) >>> b = a * 2 + 1 >>> b.asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32)
Exit the Python terminal.
>>> exit() $
Run a short MXNet python program to create a 2X3 matrix of ones a on a GPU, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use mx.gpu(), to set MXNet context to be GPUs.
>>> import mxnet as mx >>> a = mx.nd.ones((2, 3), mx.gpu()) >>> b = a * 2 + 1 >>> b.asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32)
Run a short MXNet R program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
library(mxnet) a <- mx.nd.ones(c(2,3), ctx = mx.cpu()) b <- a * 2 + 1 b
You should see the following output:
[,1] [,2] [,3] [1,] 3 3 3 [2,] 3 3 3
Run a short MXNet R program to create a 2X3 matrix of ones a on a GPU, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use mx.gpu(), to set MXNet context to be GPUs.
library(mxnet) a <- mx.nd.ones(c(2,3), ctx = mx.gpu()) b <- a * 2 + 1 b
You should see the following output:
[,1] [,2] [,3] [1,] 3 3 3 [2,] 3 3 3
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Download your required version of MXNet.