Excerpt
This section explains how to build an application on an x86_64 platform and run it on an NVIDIA Jetson with ARM architecture.
The main benefits of cross-compilation for Jetson are:
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Speeding up application development: For example, building an application on NVIDIA Jetson Nano can be very slow. Using ARM emulation will allow us to build the application on a fast x86 host and launch it on the Jetson Nano.
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Compiling is very resource-intensive. Platforms such as Jetson Nano are limited in memory and disk space; it may not have the resources to build large and complicated packages with AI features.
## Setting Up ARM Emulation on x86
We’ll be using QEMU and Docker to set up our emulated environment. QEMU is an open source machine emulator and virtualizer. It allows users to to build ARM CUDA binaries on your x86 machine without needing a cross compiler.
First, let’s see what happens before setting up the emulation when trying to execute a program compiled for a different architecture :
```plain text
uname -m # Display the host architecture
#x86_64
docker run --rm -t arm64v8/ubuntu uname -m # Run an executable made for aarch64 on x86_64
#standard_init_linux.go:211: exec user process caused "exec format error"
```
As expected the instructions are not recognized since the packages are not installed yet. Installing the following packages should allow you to enable support for aarch64 containers on your x86 workstation:
```plain text
sudo apt-get install qemu binfmt-support qemu-user-static # Install the qemu packages
docker run --rm --privileged multiarch/qemu-user-static --reset -p yes # This step will execute the registering scripts
docker run --rm -t arm64v8/ubuntu uname -m # Testing the emulation environment
#aarch64
```
The installation was successful, the emulation is working. At this point, we can now run aarch64 programs on the host x86_64 workstation. However, it is not yet possible to build and run CUDA accelerated applications with QEMU. To add this feature, we will need to use a Linux for Tegra (L4T) base image from Nvidia DockerHub (see next section for additional details).
Note: For advanced details on using QEMU and Docker, please refer to this repository on GitHub.
## Deploying your Image to Jetson
We recommend the above workflow for building and deploying your applications with Docker on Jetson. In this workflow your source code and Dockerfiles are present on your local device (host machine). The image is built from the Dockerfile on the host and is pushed on a Docker registry that makes it accessible from a remote device (target machine). On the target machine the Docker image is pulled from the registry and is run in a Docker container.