NVIDIA L4T TensorRT image for Jetson Orin Nano

Autonomous Machines Jetson & Embedded Systems Jetson Orin Nano
1

Hi NVIDIA Developer

Currently, I create virtual environment in My Jetson Orin Nano 8 GB to run many computer vision models. There are my setup:

  • Jetson Orin Nano Dev 8 GB
  • Jetpack: 5.1.2 (Installed by NVIDIA SDK Manager Method)
  • TensorRT: 8.5.2.2
  • CUDA: 11.4.315

Other information is attached by this image:

jtop
jtop754×303 49.8 KB

Now I would like to change from virtual environment to docker image and container deployments. My questions are

  1. Can I use installed docker or I need to new install docker in my device ? I ask because when I run command docker – version in terminal it shows Docker version 24.0.5, build 24.0.5-0ubuntu1~20.04.1.

Reference: How To Install and Use Docker on Ubuntu 20.04 | DigitalOcean

  1. After finish Docker installation, Which NVIDIA L4T TensorRT image I should pull between l4t-tensorrt:r8.5.2.2-devel and l4t-tensorrt:r8.5.2-runtime to matchs my setups both TensoRT and CUDA versions ?

Reference: NVIDIA L4T TensorRT | NVIDIA NGC

Thanks

3

Hi,

The installed docker should work.

l4t-tensorrt:r8.5.2-runtime is used for runtime only which means your application is already compiled and only needs to be executed in the environment.
l4t-tensorrt:r8.5.2.2-devel contains header and dev library for developing but the container size is larger.
Please chose one based on your use case.

Thanks.

4

Thanks for your answers.

I have one more question about NVIDIA Container Toolkit (NCT). After flash Jetpack 5.1.2 to my device do I need to install NCT in my devices or I can use the installed NCT ? and which version of NCT I should install ?

Reference: Installing the NVIDIA Container Toolkit — NVIDIA Container Toolkit 1.14.4 documentation

5

Hi,

The default nvidia-container-toolkit should work.
Below is our package which is installed with JetPack 5.1.2:

$ sudo apt show nvidia-container-toolkit
Package: nvidia-container-toolkit
Version: 1.11.0~rc.1-1
Priority: optional
Section: utils
Maintainer: NVIDIA CORPORATION <cudatools@nvidia.com>
Installed-Size: 9,586 kB
Depends: libnvidia-container-tools (>= 1.10.0-1), libnvidia-container-tools (<< 2.0.0), libseccomp2
Breaks: nvidia-container-runtime (<= 3.5.0-1), nvidia-container-runtime-hook
Replaces: nvidia-container-runtime (<= 3.5.0-1), nvidia-container-runtime-hook
Homepage: https://github.com/NVIDIA/nvidia-container-toolkit
Download-Size: 1,827 kB
APT-Manual-Installed: no
APT-Sources: https://repo.download.nvidia.com/jetson/common r35.4/main arm64 Packages
Description: NVIDIA Container toolkit
 Provides tools and utilities to enable GPU support in containers.

Thanks.

6

Thanks for your answers.

I have one more question about nvcc. Why I can’t find nvcc in l4t-tensorrt:r8.5.2-runtime ?

nvcc
nvcc984×758 96.1 KB

7

Hi,

You will need to use the dev container for the compiler and headers.

Thanks.

9

May I ask you something ?

Can I use window 10 host machine (no NVIDIA GPU) to build docker image based on l4t-tensorrt:r8.5.2.2-devel and bring the built image to run in Jetson Orin Nano ?

10

Hi,

We don’t officially support l4t-based images on a desktop environment, especially Windows.
Moreover, since TensorRT optimizes based on the hardware resources, the engine file needs to be generated on the target device directly.

Thanks

12

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