Install MXNet on a Jetson
These instructions will walk through how to build MXNet and install MXNet's Python language binding.
For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device. The disk image provided by NVIDIA's getting started guides will have the Jetson toolkit preinstalled, and this also includes CUDA. You should double check what versions are installed and which version you plan to use.
After installing the prerequisites, you have several options for installing MXNet: 1. Use a Jetson MXNet pip wheel for Python development and use a precompiled Jetson MXNet binary. 3. Build MXNet from source * On a faster Linux computer using cross-compilation * On the Jetson itself (very slow and not recommended)
To build from source or to use the Python wheel, you must install the following dependencies on your Jetson. Cross-compiling will require dependencies installed on that machine as well.
To use the Python API you need the following dependencies:
sudo apt update sudo apt -y install \ build-essential \ git \ graphviz \ libatlas-base-dev \ libopencv-dev \ python-pip sudo pip install --upgrade \ pip \ setuptools sudo pip install \ graphviz==0.8.4 \ jupyter \ numpy==1.15.2
If you plan to cross-compile you will need to install these dependencies on that computer as well. If you get an error about something being busy, you can restart the Nano and this error will go away. You can then continue installation of the prerequisites.
Download the source & setup some environment variables:
These steps are optional, but some of the following instructions expect MXNet source files and the
MXNET_HOME environment variable. Also, CUDA commands will not work out of the box without updating your path.
Clone the MXNet source code repository using the following
git command in your home directory:
git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
Setup your environment variables for MXNet and CUDA in your
.profile file in your home directory.
Add the following to the file.
export PATH=/usr/local/cuda/bin:$PATH export MXNET_HOME=$HOME/mxnet/ export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH
You can then apply this change immediately with the following:
Note: Change the
~/.profile steps according to how you prefer to use your shell. Otherwise, your environment variables will be gone after you logout.
You can check to see what version of CUDA is running with
To switch CUDA versions on a device or computer that has more than one version installed, use the following and replace the symbolic link to the version you want. This one uses CUDA 10.0, which is preinstalled on the Nano.
sudo rm /usr/local/cuda sudo ln -s /usr/local/cuda-10.0 /usr/local/cuda
Note: When cross-compiling, change the CUDA version on the host computer you're using to match the version you're running on your Jetson device. Note: CUDA 10.1 is recommended but doesn't ship with the Nano's SD card image. You may want to go through CUDA upgrade steps first.
Option 1. Install MXNet for Python
It should download the required dependencies, but if you have issues, install the dependencies in the prerequisites section, then run the pip wheel.
sudo pip install mxnet-1.4.0-cp27-cp27mu-linux_aarch64.whl
Now use a pre-compiled binary you can download it from S3 which is a patch v1.4.1: * https://s3.us-east-2.amazonaws.com/mxnet-public/install/jetson/1.4.1/libmxnet.so
Place this file in
To use this with the MXNet Python binding, you must match the source directory's checked out version with the binary's source version, then install it with pip.
cd $MXNET_HOME git checkout v1.4.x git submodule update --init --recursive cd python sudo pip install -e .
Refer to the following Conclusion and Next Steps section to test your installation.
Option 2. Build MXNet from Source
Installing MXNet from source is a two-step process:
- Build the shared library from the MXNet C++ source code.
- Install the supported language-specific packages for MXNet.
You can use a Docker method or you can build from source manually.
You must have installed Docker and be able to run
Follow these setup instructions to get to this point.
Then run the following to execute cross-compilation via Docker.
$MXNET_HOME/ci/build.py -p jetson
Step 1 Build the Shared Library
(Skip this sub-step for compiling on the Jetson device directly.) Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
cp $MXNET_HOME/make/crosscompile.jetson.mk config.mk
config.mk to make some additional changes for the Nano. Update the following settings:
- Update the CUDA path.
USE_CUDA_PATH = /usr/local/cuda
-gencode arch=compute-63, code=sm_62to the
- Update the NVCC settings.
NVCCFLAGS := -m64
- (optional, but recommended) Turn on OpenCV.
USE_OPENCV = 1
Now edit the Mshadow Makefile to ensure MXNet builds with Pascal's hardware level low precision acceleration by editing
The last line has
MSHADOW_USE_PASCAL set to
0. Change this to
1 to enable it.
MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
Now you can build the complete MXNet library with the following command:
cd $MXNET_HOME make -j $(nproc)
Executing this command creates a file called
libmxnet.so in the
Step 2 Install MXNet Python Bindings (optional)
To install Python bindings run the following commands in the MXNet directory:
cd $MXNET_HOME/python sudo 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 the MXNet Java & Scala Bindings (optional)
Change directories to
scala-package and run
cd $MXNET_HOME/scala-package mvn install
This creates the required
.jar file to use in your Java or Scala projects.
Conclusion and Next Steps
You are now ready to run MXNet on your NVIDIA Jetson TX2 or Nano device. You can verify your MXNet Python installation with the following:
import mxnet mxnet.__version__
You can also verify MXNet can use your GPU with the following test:
import mxnet as mx a = mx.nd.ones((2, 3), mx.gpu()) b = a * 2 + 1 b.asnumpy()
If everything is working, it will report the version number. For assistance, head over to the MXNet Forum.