Compile nvidia-container-runtime source code on xavier errors

Robotics & Edge Computing Jetson & Embedded Systems Jetson AGX Xavier
1

nvidia-container-runtime$ make
make -C /home/nvidia/Apollo/apollo_new/nvidia-container-runtime/base ubuntu18.04
make[1]: Entering directory ‘/home/nvidia/Apollo/apollo_new/nvidia-container-runtime/base’
docker build --build-arg VERSION_ID=“18.04”
–build-arg GOLANG_VERSION=“1.10.3”
-t “nvidia/base/ubuntu:18.04” -f Dockerfile.ubuntu .
Sending build context to Docker daemon 9.216kB
Step 1/7 : ARG VERSION_ID
Step 2/7 : FROM ubuntu:${VERSION_ID}
—> 9f020c638543
Step 3/7 : RUN apt-get update && apt-get install -y --no-install-recommends wget ca-certificates git build-essential dh-make fakeroot devscripts lsb-release && rm -rf /var/lib/apt/lists/*
—> Using cache
—> a9e0d8165f5a
Step 4/7 : ARG GOLANG_VERSION=0.0.0
—> Using cache
—> 3310366261ef
Step 5/7 : RUN set -eux; arch=“$(uname -m)”; case “${arch##*-}” in x86_64 | amd64) ARCH=‘amd64’ ;; ppc64el | ppc64le) ARCH=‘ppc64le’ ;; *) echo “unsupported architecture” ; exit 1 ;; esac; wget -nv -O - https://storage.googleapis.com/golang/go${GOLANG_VERSION}.linux-${ARCH}.tar.gz | tar -C /usr/local -xz
—> Running in 542f1cc5aa3c

  • uname -m
    unsupported architecture
  • arch=aarch64
  • echo unsupported architecture
  • exit 1
    The command ‘/bin/sh -c set -eux; arch=“$(uname -m)”; case “${arch##*-}” in x86_64 | amd64) ARCH=‘amd64’ ;; ppc64el | ppc64le) ARCH=‘ppc64le’ ;; *) echo “unsupported architecture” ; exit 1 ;; esac; wget -nv -O - https://storage.googleapis.com/golang/go${GOLANG_VERSION}.linux-${ARCH}.tar.gz | tar -C /usr/local -xz’ returned a non-zero code: 1
    Makefile:13: recipe for target ‘ubuntu18.04’ failed
    make[1]: *** [ubuntu18.04] Error 1
    make[1]: Leaving directory ‘/home/nvidia/Apollo/apollo_new/nvidia-container-runtime/base’
    Makefile:25: recipe for target ‘base-ubuntu18.04’ failed
    make: *** -ubuntu18.04] Error 2
2

Hi,

nvidia-container-runtime is an x86 sample and cannot be used on the Jetson platform.

You will need to use standard docker and mount GPU device to the container directly.
Here is an example for your reference:
[url]https://github.com/Technica-Corporation/Tegra-Docker[/url]

Thanks.