Nvidia l4t docker In other words,inside the docker container there should be a system like this : NVIDIA Jetson Nano $ apt-cache search nvidia-container* libnvidia-container-tools - NVIDIA container runtime library (command-line tools) libnvidia-container0 - NVIDIA container runtime library nvidia-container-csv-cuda - Jetpack CUDA CSV file nvidia-container-csv-cudnn - Jetpack CUDNN CSV file nvidia-container-csv-tensorrt - Jetpack TensorRT CSV file nvidia Notes: · DeepStream dockers or dockers derived from previous releases (before DeepStream 6. However, when I tied to use docker-compose to create a service based on the same l4t-base image, I was not able to run the application with Hi There, Sorry to post about this small issue, but I’m running in circle trying to find the reason I cannot start any docker image who try to use my Jetson hardware. is_availalble”. 89 (output of nvcc -V from inside both Jetson and the Docker container) PyTorch v1. 11 Storage Driver: overlay2 A Docker Container for dGPU¶. core. 2 (L4T R35. With CUDA, developers can dramatically speed up computing What is the status of this issue? The L4T base images seem completely broken with a lot of zero length files and things stuck in /etc/alternatives without any rhyme or reason? Things like nvidia-l4t-core, nvidia-l4t-gstreamer, cudnn, etc. Overview. When I create the ‘nvcr. You can find additional details here. I’ve managed to run the following Docker containers without any trouble. The dGPU container is called deepstream and the Jetson container is called deepstream-l4t. My system setup: Jetson AGX with a clean jetpack 5. /lib64 dirs are actually Hi @dusty_nv, yes it is. 5 onwards. 04 CUDNN:8. 2, or should I reinstall other version of JetPack to use CUDA from docker image? On JetPack 4. x, CUDA/cuDNN/TensorRT/ect are mounted from your device into the container when --runtime nvidia is used to start the container. l4t-base docker image enables applications to be run in a container using the Nvidia Container Runtime on Jetson. 3, Jetpack will self-report as 6. I am assuming the Docker container cannot reach the CUDA libraries. 2-20200408182620 arm64 NVIDIA GL EGL Package ii nvidia-l4t-apt-source 32. I am able to run cuda_sample with my Jeton AGX Xavier on a single container by specifying the –runtime nvidia flag in CLI. 7 or higher. (without additional installation). 1-pth1. x, only l4t. My suggestion is to use that version instead of 2020. 03. 4 is becuase some possible issues are fixed. Currently we are trying to do that on top of the L4T Base image here NVIDIA L4T Base | NVIDIA NGC but this image doesn’t contain any apt sources for jetpack and if I run apt update && apt install nvidia-jetpack, it will complain this package not found. Video Analytics 10 $ sudo docker info Client: Debug Mode: false. NVIDIA TensorRT is a C++ library that facilitates high-performance inference on NVIDIA graphics processing units (GPUs). && rm -r spconv ---> Running in 96bcae93dba9 running bdist_wheel running build running build_py creating build creating build/lib. As for now, all the containers DeepStream 7. 1) using minor release update process here. Is GitLab repository for NVIDIA's container images based on L4T. 2 arm64 A big dependency is that the docker image needs to be able to be built by Docker, both on the local machine, and on Github using github actions. deb jetson-containers run launches docker run with some added defaults (like --runtime nvidia, mounted /data cache and devices) autotag finds a container image that's compatible with your version of JetPack/L4T - either locally, Tuned, tested and optimized by NVIDIA. 0 ** • JetPack Version 4. 1” In the TensorRT L4T docker image, the default python version is 3. Hi @frankvanpaassen3, on Jetson it’s recommended to use l4t-base container with --runtime nvidia so that you are able to use GPU acceleration inside the container. cuda. 2-devel’ by itself as an image, it successfully builds . 4 Ubuntu: 18. 8-py3)docker images provided. • Jetson Nano **• Deepstream 6. 1) → L4T 32. I notice similar Hello guys, let just say, that i will use ubuntu:bionic as base image, install all l4t debs inside container. . ,) and Jetson platforms. Build and run Docker containers leveraging NVIDIA GPUs - NVIDIA Container Runtime on Jetson · NVIDIA/nvidia-docker Wiki Hello to everyone. How can i access GPU from within my custom image? I tried docker run --runtime=nvidia it does not work Here is example of my Dockerfile used to build custom container FROM ubuntu:bionic as base # Set the working directory to /app WORKDIR /app Hi, I’m sorry, but I don’t quite understand this. 0 I have pulled “l4t-tensorflow:r32. 1 on NVIDIA L4T Base | NVIDIA NGC?; Currently there is no TX2 NX user guide. NVIDIA NGC Catalog TensorRT | NVIDIA NGC. Jetson Xavier NX. 0-1 all nvidia-docker CLI wrapper ii nvidia-l4t-3d-core 32. 2: 1063: April 11, 2022 Jetson multimedia API installation in docker. 1 corresponds to JetPack 4. 7 Total amount This unfortunately does not work for me. 140-tegra-32. For some packages like python-opencv building from sources takes prohibitively long on Tegra, so software that relies on it and TensorRT can’t work, at Hi, I am new to embedded systems/nvidia gpu computing/docker/this forum so I apologise up-front for any generally accepted comments regarded standard. However, I’m encountering an issue during the installation of the nvidia-l4t-core package. 1 [L4T 32. 32. Any way L4T r32. For this I’m using an Ubuntu:20. 1 using the tar package method I have a Libargus program that works correctly outside of a docker container, but inside a container, I have the following error: ERROR Could NOT find Argus (missing: ARGUS_INCLUDE_DIR) To fix this error, I followed many forum posts which led me to make the Dockerfile below: FROM dustynv/ros:melodic-ros-base-l4t-r32. py -> build/lib. A Docker Container for dGPU¶. 2 - cuda 10. 1 arm64 NVIDIA container runtime library ii nvidia-container-csv-cuda 10. In the l4t-base. A couple of things that we noticed. r32. 0 based on the provided dockerfile replacing the dev packages with runtime packages and avoiding building the corresponding opencv and tensorrt packages. However all files in the cuda12. /deviceQuery . The l4t-tensorflow docker image contains TensorFlow pre-installed in a Python 3 environment to get up & running quickly with TensorFlow on Jetson. YMMV. root@tx2:/# apt install nvidia-l4t-cuda Reading package lists Done Building dependency tree Reading state information Done The following additional packages will be installed: nvidia-l4t-3d-core nvidia-l4t-core nvidia-l4t-firmware nvidia-l4t-init nvidia-l4t-wayland nvidia Hi @yawei. Instead of sharing cuda, tensorrt, and so on, provide a minimal version of L4T set up to run containers – with just We recommend using the NVIDIA L4T TensorRT Docker container that already includes the TensorRT installation for aarch64. These are the Docker-related packages that get installed: $ sudo dpkg-query -l | grep nvidia ii libnvidia-container-tools 0. 0 Developer Preview for Xavier NX (T194), and tried running a couple Docker containers on it. 2 • I Docker Container: nvidia:l4t-ml wont run Jetson Xavier NX The --runtime nvidia option allows access to the GPU and CUDA from within the containers. 6) device. 2/include and . 7. Please run the below command before benchmarking deep learning use case: $ sudo nvpmodel -m 0 $ sudo jetson_clocks Hi I want a NVIDIA L4T PyTorch PyTorch container with python version 3. I pulled the l4t-ml:r32. When will you release the docker container L4T 32. 1 and CUDA 12. These containers support the This repo is a docker image hub that contains multifunctional containers tailored to the NVIDIA Jetson Platform (ARMv8) running Ubuntu 18. 2 container supports DeepStream Hello, I have a short question regarding creating a minimal L4T image from within docker (Option: Minimal L4T - Guide to Minimizing Jetson Disk Usage). io/nv Host: JetPack:4. 15-py3” docker container Is there any docker base image with CUDA for JetPack 4. Possibly. 1-20191209225816) ls: cannot access '*. Thank you for your quick reply! I know about the runtime-container for jetson agx which is l4t-jetpack NVIDIA L4T JetPack | NVIDIA NGC that containing all sdk provided by nvidia in the jetson container. 04). To improve the development process, I intend to use Docker and have some questions on this topic: I was able to run an NVIDIA Jetson Container (l4t-base:r. 15-py3 using this command: docker pull nvcr. 6. 8, but apt aliases like python3-dev install 3. linux-aarch64-3. dtb': No such file or directory Setting up nvidia-l4t-kernel-headers (4. Unlike the container in DeepStream 3. 0 release. I try to use the L4T 32. 0. 1, is there an update or can i use the latest one? Im trying to use this as a container to install deepstream sdk and be able to use YOLOv8 following seeed And then I tried to install “nvidia-l4t-cuda”, but it failed. We can use the provided workaround for a multi-stage docker build with l4t-base for building the engine and l4t-tensorrt runtime image for deployment. 3: 1173: October 18, 2021 Cmake build multimedia api failed Hello, I’m trying to set up a Docker container on an NVIDIA AGX Orin with l4t-base R34. I prefer adduser since I’ve wiped out users’ groups before by doing -G instead of -aG. Tips - SSD + Docker Once you have your Jetson set up by flashing the latest Jetson Linux (L4T) BSP on it or by flashing the SD card with the whole JetPack image, before embarking on testing out all the great generative AI application using jetson-containers , you want to make sure you have a huge storage space for all the containers and the models you will download. Is there a runtime-container for drive agx products like l4t-jetpack? I have a Jetson Nano 2GB board with JetPack 4. is_available() is False Here is the full er JetPack cross compilation container can be used to cross compile various JetPack components on a x86 host machine. e. 2. 04 L4T. My first try was to merge two images as a multi-s So, right now on Tegra, to save space, nvidia-docker bind mounts a bunch of stuff from host to container and this is breaking things as the host goes out of sync. 0~beta. 1 in apt show, however nvidia-smi will report as cuda12. /. linux-aarch64 Hi Nvidia, I am using l4t-36. Use Case. I encountered an issue when running Build and run Docker containers leveraging NVIDIA GPUs - GitHub - NVIDIA/nvidia-docker: Build and run Docker containers leveraging NVIDIA GPUs /usr/local/cuda is readonly One of the limitations of the beta is that Also, we suggest you to use TRT NGC containers to avoid any system dependency related issues. 2-tf1. Once you’ve successfully installed TensorRT, run the following command to install the nvidia-tao-deploy wheel in GitLab repository for NVIDIA's container images based on L4T. 0, GCID: 36923193, BOARD: generic, EABI: aarch64, DATE: Fri Jul Thx, @DaneLLL. 4 container supports DeepStream Now, it fails : Step 19/24 : RUN cd spconv && python3 setup. 2 while inside the containers, CUDA 11 is indicated. I create A Docker Container for dGPU . 89-1 arm64 Jetpack CUDA CSV file ii nvidia-container-csv-cudnn 8. Re: original topic solution. For context, I’m running docker 19. 0) JetPack 5. Simplified statement of the issue: I am trying to run trt_pose demo jupyter notebook on jetson xavier. 1-20191209225816) Setting up nvidia-l4t-kernel-dtbs (4. $ . 0 container supports DeepStream I am trying to build l4t-jetpack docker image for r36. 6 creating build/lib. I have reviewed several pages on this forum but I was not able to fix the issues I am having. If you run sudo docker info, do you see Runtimes: nvidia runc in the output? Hello, I started using Linux a few months ago when I got a Jeton Nano for a Masters Course Project. mmapi, docker. 2 • TensorRT Version: 8. 0 and make the lib nvdsinfer_custom_impl_Yolo **• nvdsinfer_custom_impl_Yolo and docker image deepstream 6. Hello A Jetson newb here trying to develop & train models on a WSL2-facilitated docker container to leverage the larger GPU on my Windows 10 desktop. It is available for install via the NVIDIA SDK Manager along with other JetPack components as shown below in Figure 1. 04 that I have on the jetson nano to version 21. I use custom carrier board and my jetpack version is 4. These images are incompatible. 1 (Jetpack 4. I add the nvidia package repos, update and try to install the nvidia-l4t-core and firmware packages. mp4') ret, frame = cap. Host: JetPack:4. 1-20191209225816) Create a lightweight 32bit docker image of l4t r24. RUN apt-get install nvidia-l4t-core nvidia-l4t-firmware -y. Hi, ModuleNotFoundError: No module named 'numpy. 6 versions (so package building is broken) and any python-foo packages aren’t found by python. Setup and run when installed natively on the jetson is quite straightforward. 3. 0 docker image on my jetson nano module. is_available() is True when I am the root user in the docker. At this time, when these developer kits are Thanks, decided to include these libraries into the “jetson-inference” docker file and build them, but then I hit with this msg “cannot build jetson-inference docker container for L4T R32. CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). · These containers use the NVIDIA Container Runtime for Jetson to run DeepStream applications. 4. 0; I used the NVIDIA L4T PyTorch docker container provided by NVIDIA. Alpine: $ docker run -ti --rm alpine true $ docker run -ti --rm --runtime nvidia alpine true $ docker run -ti --rm --privileged alpine true $ docker run -ti --rm - hi jcwscience: do you set --net=host when run docker? can you run apt update correctly in host? Hi, Here are some suggestions for the common issues: 1. Could you check if this command helps? docker system info: pegasus@pegasus-ubuntu-3:~$ cat /etc/nv_tegra_release. Hello, I am looking for a docker image for a jetson based on ubuntu jellyfish (22. We compile TensorRT plugins in those containers and are currently unable to do so because include headers are missing. Below are our testing steps for your reference: You can set the default docker runtime to nvidia, and then CUDA/cuDNN/VisionWorks/ect will be available to you during docker build operations. I can start very simple image, who do not rely on any hardware. I’ve had this issue with the jeston for about a week now, I’ve been searching for solutions, but none seem to work or a sudo dpkg-query -l | grep nvidia ii libnvidia-container-tools 0. Modular container build system that provides the latest AI/ML packages for NVIDIA Jetson 🚀🤖. This Dockerfile is based on nvidia/container-images/l4t-base. 4 so The NVIDIA L4T TensorRT containers only come with runtime variants. 4 / 11. 9. io/nvidia/l4t-tensorrt:r8. 04 install, with CUDA 10. Both can run the deviceQuery binary correctly. 7 or higher? JetPack 5. 15-py3” docker container from NVIDIA L4T TensorFlow: l4t-tensorflow:r32. 5. You can absolutely add the apt repositories inside l4t-base. But Conda is not NVIDIA CUDA. 0 ** Hello, I found an issue when I build a docker image with the version 6. Setup: Jetson Nano Development Kit 4 GB Jetpack 4. 4 DP (docker container) • JetPack Version (valid for Jetson only): 5. It seems nvjpegenc and nvvidconv do work in Docker provided the --nvidia runtime is used and the user in the video group. 1-20210726122000 arm64 NVIDIA GL EGL Package ii nvidia-l4t-apt-source 32. Containers The NGC catalog hosts containers for AI/ML, metaverse, and HPC applications and are performance-optimized, tested, and ready to deploy on GPU-powered on-prem, cloud, and edge systems. 1 (l4t-ml:r32. As a side note, I’d recommend not putting a user in the docker group since it gives that user root privileges without a password or any sort of logging. The NVIDIA Container Toolkit seamlessly expose specific parts of the device (i. For I tried to dig into the script and I managed to find that is caused by dpkg-reconfigure nvidia-l4t-bootloader Anyone an idea what is going wrong here? hook ii nvidia-docker2 2. Environment : -jetson tx2 - jetback 4. _multiarray_umath' This is related to the installed numpy version. 1) will need to update their CUDA GPG key to perform software updates. NGC Catalog. I’ve got the qemu aarch64 interpreter set up via docker with: sudo docker run --rm --privileged hypriot/qemu-register and I know it works because I can run the l4t-base image with: sudo docker run -it - Normally it would get installed by SDK Manager. 1 (L4T 32. My proposal to Nvidia to fix this is as follows: Just make it work like Nvidia docker on x86. Not sure if anything different between us. It needs no login to pull. I’m using the Jetpack enabled base image: NVIDIA L4T TensorFlow | NVIDIA NGC If I have some test video, I expect that opening it in OpenCV will return True in this code: import cv2 # Define the video stream cap = cv2. 1 Developer Preview (L4T Setting up nvidia-l4t-jetson-io (32. The Containers page in the NGC web portal gives instructions for pulling and running the container, along with a description of its contents. Note that the version of JetPack would vary depending on the version being installed. Not sure about any Deepstream elements, since those Hi, We test the container with r32. This container simplifies cross compilation and includes the needed cross compilation tools and build Prior to the reflash, I had upgraded my Nano from L4T 32. 3 docker and I can see torch. Welcome Guest. TensorFlow Container for Jetson and JetPack. R36 (release), REVISION: 3. 1 provides Docker containers for dGPU on both x86 and ARM platforms (like SBSA, GH100, etc. torch. Server: Containers: 0 Running: 0 Paused: 0 Stopped: 0 Images: 1 Server Version: 19. 4) Cuda v10. 04 on my host and I’m building a custom image with a custom rootfs for L4T 35. Trying to fix permission, remove some tmp, the only change was installing Miniconda for testing ComfyUi on my Jetson. Plus the order makes more sense to me. 0) on a Linux Ubuntu 20. 1-20210726122000 arm64 NVIDIA L4T apt source list debian package ii nvidia-l4t-bootloader 32. These containers This is a Dockerfile to make L4T environment on Jetson device. 04 inside a docker container. VideoCapture('test_video. Hello, I’ve just grabbed the JetPack 5. 03 on my Ubuntu 18. See the packages directory for the full list, including pre-built container images for JetPack/L4T. 6 • Missing Header • Write a Dockerfile based on deepstream-l4t:6. But I hit this error: Preparing to unpack /21-nvidia-l4t-core_32. As the above solution is not valid for me (docker and host cuda are the same), what other solutions might I try? Thanks in advance! Docker image fails to build nvidia-l4t-jetson-multimedia-api. 1 arm64 NVIDIA container runtime library (command-line tools) ii libnvidia-container0:arm64 0. I’m running Ubuntu 24. It has a subset of packages from the l4t rootfs included within (Multimedia, Before running the l4t-jetpack container, use Docker pull to ensure an up-to-date image is installed. 0 → DeepStream 6. 1 (L4T R35. See here: GitHub - dusty-nv/jetson-containers: Machine Learning Containers for Hi, I am trying to build my custom docker image based on the l4t-base image provided by NGC. Before I get into the technical issue, my first question is more practical/strategic: Question 1 Is this the right approach to develop/train on the exact same environment as the Jetson nano by using an aarch64 JetPack v. I updated from DeepStream 5. 1 # Resolves "Unable to Once the docker started running inside jetson,I used docker exec command to navigate inside docker and there used python shell to print the “torch. Refer to your developer kit user guide for up-to-date instructions. Natural Language Processing 11. Is Jetpack 4. py bdist_wheel && cd . Then the docker can access the CU Do you want to run an L4T-based container on an x86 host? If yes, please do it with qemu virtualization. 0, the dGPU DeepStream 6. However when I try to replicate I tested on Jetson Orin NX 16GB. 1 container supports DeepStream something odd about my docker/nvidia-docker installation. 1-20191209225816) Setting up nvidia-l4t-multimedia (32. 1) and i have jetpack 5. 1 with ubuntu 18. 1 image but those files do not get mounted into my runtime (I do have nvidia-container-runtime in my daemon. 1-20210726122000 arm64 NVIDIA The feature is available from SDK version 6. Once the pull is complete, you can run the container image. 1-20210726122000 arm64 NVIDIA Bootloader Package ii nvidia-l4t-camera 32. It is always giving me False. 04 container which handles this. I’ve got an idea that I want to discuss with you. 1, everything installed normally. I notice that on the cuda version on the OS is 10. One of the reasons I would recommend 4. Are any of the containers below with python version 3. I tried the base image with out installing any libraries from requirement file. 0, the dGPU DeepStream 5. x, CUDA/cuDNN/TensorRT will be mounted in l4t-base container (and your derived containers built upon l4t-base) when --runtime nvidia is used when starting the container. 1 and install the necessary libraries and plugins. The image is public. 6, which shipped with Nsight Systems 2021. 2 (and also /usr/local/cuda will (visibly) soft-link to cuda-12. The final Just to add: when only executing the Update Compute Stack section, which will install Jetpack 6. 180-1+cuda10. 6 but leave the system at R36. Related topics Running into storage issues now unfortunately lol. Jetson TX2. Also notice that the building itself of the modded FFMPEG needs to be done by docker during docker build (with a make command, not yet present on the below Dockerfile) I am using as a base NVIDIA-l4t-ba The size looks expected since we have the L4T Jetpack docker in. 6/spconv copying spconv/identity. 1] NVIDIA (R) Cuda compiler driver Cuda Hello, I would like to use mipi csi raspberry pi v2 camera inside docker container. On JetPack 5, CUDA/ect are installed inside the container. - ( I don’t have the issue with docker image Hi, we want to have a minimum docker image only containing L4T and the corresponding Jetpack. Some packages are not included and you have to manually install the required packages for your use-case. 1 l4t 35. I would like to upgrade ubuntu 18. NVIDIA developer kits like the NVIDIA IGX Orin or the NVIDIA Clara AGX have both a discrete GPU (dGPU - optional on IGX Orin) and an integrated GPU (iGPU - Tegra SoC). 1 and the jetpack 4. 1) and r32. However, after I switch to a new user, torch. 4 (Jetpack 4. 2-20200408182620 arm64 Hello, Im trying to install a docker container for the jetson orin nano following NVIDIA L4T ML but i see that the latest one is for etPack 5. So I am using the nvidia l4t-base along with a balena base ubuntu bionic image. json file). 4 CUDA Capability Major/Minor version number: 8. 1. /dist && pip install * && cd . I start up say the L4T Base or DeepStream-5. GitHub NVIDIA Container Runtime on Jetson · NVIDIA/nvidia-docker Wiki. At NVIDIA L4T Base | NVIDIA NGC , the latest images are based on ubuntu focal (20. What i noticed is that according to this (NVIDIA Container Runtime on Jetson · NVIDIA/nvidia-docker Wiki · GitHub) i should have this:libnvidia-container-tools install libnvidia-container0:arm64 install nvidia-container-runtime install nvidia-container-runtime-hook install nvidia-docker2 install As the title says, I’m trying to create a docker image with both deepstream and pytorch but are currently failing. I am using the sample Dockerfile as a base and referring to this guide for the installation process. 6 the production-grade version for I found the explanation to my problem in this thread: Host libraries for nvidia-container-runtime - #2 by dusty_nv JetPack 5. 1-py3 and l4t-pytorch:r32. If you would like to have a light docker, you may try: NVIDIA L4T CUDA | NVIDIA NGC. 04 and at the same time I want to install the L4T 32. 1 image the /usr/local/cuda directory is writeable, and it’s local to the container. /deviceQuery Starting CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "Orin" CUDA Driver Version / Runtime Version 11. Performance. read() print(ret) Disclaimer: this container is deprecated as of the Holoscan SDK 1. On JetPack 4. I don’t think there is a public Dockerfile, so you’ll have to inspect the image for that. is_available() returns False in both containers. 1”, currently using jetpack “L4T 32. 1 with the 32bit Jetson tx1 driver package (the most recent 32bit version available) then build the chromium-browser armhf docker container I linked above using the base image we just created. 0 i am trying to build docker image that use pytorch and torchvision on jetson ,i can not use NVIDIA L4T PyTorch | NVIDIA NGC image because it requires jetback 4. 3-20200625213407_arm64. 1 (JetPack4. Pick an l4t I’m having some issues with OpenCV inside a Docker container on my board. csv gets used (because CUDA/cuDNN/TensorRT/ect are installed inside the containers on JetPack 5 for portability). 1 arm64 NVIDIA container runtime library ii nvidia-container-csv Installation¶. But there was a workaround. Hi all, I am developing software for NVIDIA Jetson. Procedure: In the Pull column, click the icon to copy the Docker pull command The l4t-ml docker image contains TensorFlow, PyTorch, JupyterLab, and other popular ML and data science frameworks such as scikit-learn, scipy, and Pandas pre-installed in a Python 3 environment. yang, what was the command that you used to run the docker container?Did it use --runtime nvidia?Which version of JetPack-L4T are you running? (you can check this with cat /etc/nv_tegra_release) • Hardware Platform (Jetson / GPU): Jetson Orin NX on Advantech carrier board (MIC-711) • DeepStream Version: 6. The installation prompts me with an interactive lzzii@jtsnx:~$ dpkg -l |grep nvidia-l4t ii nvidia-l4t-3d-core 32. 04 x86_64 workstation using Docker, Nvidia-Docker2 and QEMU (as suggested by NVIDIA Container Runtime on Jetson · Hello all, I am trying to setup the Jetson Nano using Docker and the existing containers. NVIDIA Container Runtime with Docker integration (via the nvidia-docker2 packages) is included as part of NVIDIA JetPack. Using the included tools, you can easily Basically nvidia-l4t-core is meant to be installed on a physical device - hence the complaint when doing it in a docker build.
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