Building Jetson Container on your x86 Workstation Note: For advanced details on using QEMU and Docker, please refer to this repository on GitHub. 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). However, it is not yet possible to build and run CUDA-accelerated applications with QEMU. At this point, we can now run aarch64 programs on the host x86_64 workstation. The installation was successful, the emulation is working. 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 It allows users to build ARM CUDA binaries on their x86 machine without needing a cross-compiler.įirst, let’s see what happens before setting up the emulation when trying to execute a program compiled for a different architecture :

QEMU is an open-source machine emulator and virtualizer. We’ll be using QEMU and Docker to set up our emulated environment.

Platforms such as Jetson Nano are limited in memory and disk space they may not have the resources to build large and complicated packages with AI features. Using ARM emulation will allow us to build the application on a fast x86 host and launch it on the Jetson Nano.Ĭompiling is very resource-intensive. Speeding up application development: For example, building an application on NVIDIA Jetson Nano can be very slow. The main benefits of cross-compilation for Jetson are: This section explains how to build an application on an x86_64 platform and run it on an NVIDIA Jetson with ARM architecture. Running and Building ARM Docker Containers on x86

Qemu arm tutorial