JetPack 4.6 Production Release with L4T 32.6.1

Tks for your support. Well, I’m using the L4T 32.6.1 Jetson Pack image. I’m trying to build an app in C++ with Docker. I’ve seen that if I set the “default-runtime”: “nvidia” on the “/etc/docker/daemon.json”, this error is fixed. Whatever, now I’m getting the follow one:

Building wheels for collected packages: abc
  Building wheel for tracktorpy (PEP 517): started
  Running command /usr/bin/python3 /usr/local/lib/python3.6/dist-packages/pip/_vendor/pep517/in_process/ build_wheel /tmp/tmplwc0zzi0
  running bdist_wheel
  running build
  running build_ext
  -- The CXX compiler identification is GNU 7.5.0
  -- The C compiler identification is GNU 7.5.0
  -- Detecting CXX compiler ABI info
  -- Detecting CXX compiler ABI info - done
  -- Check for working CXX compiler: /usr/bin/c++ - skipped
  -- Detecting CXX compile features
  -- Detecting CXX compile features - done
  -- Detecting C compiler ABI info
  -- Detecting C compiler ABI info - done
  -- Check for working C compiler: /usr/bin/cc - skipped
  -- Detecting C compile features
  -- Detecting C compile features - done
  -- Loading submodules
  -- Submodule update
  -- Adding hungarian
  -- pybind11 v2.6.1
  -- Found PythonInterp: /usr/bin/python3 (found version "3.6.9")
  -- Found PythonLibs: /usr/lib/aarch64-linux-gnu/
  -- Performing Test HAS_FLTO
  -- Performing Test HAS_FLTO - Success
  -- Adding core
  -- Adding tracking
  -- Looking for pthread.h
  -- Looking for pthread.h - found
  -- Performing Test CMAKE_HAVE_LIBC_PTHREAD
  -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
  -- Looking for pthread_create in pthreads
  -- Looking for pthread_create in pthreads - not found
  -- Looking for pthread_create in pthread
  -- Looking for pthread_create in pthread - found
  -- Found Threads: TRUE
  -- Found CUDA: /usr/local/cuda-10.2 (found suitable version "10.2", minimum required is "9.0")
  -- Found TBB: /usr/include (found version "2017.0")
  -- Adding Romain-Detector
  -- The CUDA compiler identification is NVIDIA 10.2.300
  -- Detecting CUDA compiler ABI info
  -- Detecting CUDA compiler ABI info - done
  -- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped
  -- Detecting CUDA compile features
  -- Detecting CUDA compile features - done
  -- Found Boost: /prefix/include (found suitable version "1.68.0", minimum required is "1.59") found components: filesystem iostreams system regex
  -- Found OpenCV: /prefix (found suitable version "4.3.0", minimum required is "4.0") found components: core imgproc dnn
  -- Adding tracktor
  -- Found OpenCV: /prefix (found suitable version "4.3.0", minimum required is "4.0") found components: core
  -- Adding tracktorpy
  -- Found PythonInterp: /usr/bin/python3 (found suitable version "3.6.9", minimum required is "3")
  -- Found PythonLibs: /usr/lib/aarch64-linux-gnu/ (found suitable version "3.6.9", minimum required is "3")
  -- Configuring done
  -- Generating done
  -- Build files have been written to: /tmp/pip-req-build-yylq0q4n/build/temp.linux-aarch64-3.6
  [ 12%] Built target core
  [ 15%] Building CXX object modules/tracking/CMakeFiles/tracking.dir/src/baseTracking.cpp.o
  [ 18%] Building CUDA object modules/detection/CMakeFiles/detector.dir/src/
  /tmp/pip-req-build-yylq0q4n/modules/detection/include/detection/chunk.h(56): error: member function declared with "override" does not override a base class member

  /tmp/pip-req-build-yylq0q4n/modules/detection/include/detection/chunk.h(70): error: function "nvinfer1::IPluginV2IOExt::configurePlugin(const nvinfer1::Dims *, int32_t, const nvinfer1::Dims *, int32_t, const nvinfer1::DataType *, const nvinfer1::DataType *, const __nv_bool *, const __nv_bool *, nvinfer1::PluginFormat, int32_t)"
  /usr/include/aarch64-linux-gnu/NvInferRuntimeCommon.h(836): here is inaccessible

I noticed this line:

– Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped

It’s like the nvcc isn’t found. But I launched an docker container and I saw it there. My CMakelists.txt is setted as:



set(CMAKE_CUDA_COMPILER “/usr/local/cuda/bin/nvcc”)

find_package(CUDA 9.0 REQUIRED)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32 --compiler-options ‘-fPIC’)

Hi, @adrianokeufw

It’s essential to add "default-runtime": "nvidia" to enable the nvcc access during docker build operations.
Since your app requires nvcc, it causes some errors if not update the /etc/docker/daemon.json file.

You can find more information in the below GitHub.


Hello again,

I am trying to install Triton inference server from source file (according to published this release: Releases · triton-inference-server/server · GitHub)

Below are my system information:
Nvidia Jetson Nano,
Ubuntu 18.04 LTS
Jetpack 4.6

I have download the file called tritonserver2.12.0-jetpack4.6.tgz file but, I am not be able to compile/install Triton Server.

What should I do with source code file (tar.gz file at the bottom)?

I have attached the image of tritonserver2.12.0-jetpack4.6.tgz file folder.


How can I compile/install and run triton inference server?

There is a problem on Jetson Nano - the latest Jetpack does NOT get installed when you use apt update and apt upgrade.

I just checked my version of L4T and it is dated October last year:

nano@jetson-nano:~$ cat /etc/nv_tegra_release
# R32 (release), REVISION: 4.4, GCID: 23942405, BOARD: t210ref, EABI: aarch64, DATE: Fri Oct 16 19:44:43 UTC 2020

@jetsonnvidia i hope you reffered to the instruciton here

The first step is to upgrade the L4T. Please refer “To update to a new minor release” here:

It involves changing the apt source list to point to the 32.6.1 repo and then doing apt update and apt dist-upgrade

1 Like

Hi @skilic, you should use the file tritonserver2.12.0-jetpack4.6.tgz. All installation instructions for JetPack are written below Jetson Jetpack Support in the release note Release Release 2.12.0 corresponding to NGC container 21.07 · triton-inference-server/server · GitHub

To learn how to deploy your models, please reefer to the Quick Start Guide: server/ at main · triton-inference-server/server · GitHub

1 Like

Thanks, that fixed it. I forgot that you needed to edit the sources.lst.

My upgrade is broken. I made the changes for a minor release so now I have this in /etc/apt/sources.list.d/nvidia-l4t-apt-source.list:

deb r32.6 main
deb r32.6 main

When I try to do a dist-upgrade I get this:

nano@jetson-nano:~$ sudo apt dist-upgrade
Reading package lists... Done
Building dependency tree       
Reading state information... Done
You might want to run 'apt --fix-broken install' to correct these.
The following packages have unmet dependencies.
 cuda-command-line-tools-10-2 : Depends: cuda-nvprof-10-2 (>= 10.2.300) but it is not installed
E: Unmet dependencies. Try 'apt --fix-broken install' with no packages (or specify a solution).

When I try --fix-broken-install I get this:

nano@jetson-nano:~$ sudo apt --fix-broken install
Reading package lists... Done
Building dependency tree       
Reading state information... Done
Correcting dependencies... Done
The following additional packages will be installed:
The following NEW packages will be installed
0 to upgrade, 1 to newly install, 0 to remove and 61 not to upgrade.
22 not fully installed or removed.
Need to get 0 B/1,059 kB of archives.
After this operation, 4,807 kB of additional disk space will be used.
Do you want to continue? [Y/n] Y
debconf: Delaying package configuration, since apt-utils is not installed.
(Reading database ... 172633 files and directories currently installed.)
Preparing to unpack .../cuda-nvprof-10-2_10.2.300-1_arm64.deb ...
Unpacking cuda-nvprof-10-2 (10.2.300-1) ...
dpkg: error processing archive /var/cache/apt/archives/cuda-nvprof-10-2_10.2.300-1_arm64.deb (--unpack):
 trying to overwrite '/usr/local/cuda-10.2/targets/aarch64-linux/include/cudaProfiler.h', which is also in package cuda-misc-headers-10-2 10.2.89-1
dpkg-deb: error: paste subprocess was killed by signal (Broken pipe)
Errors were encountered while processing:
E: Sub-process /usr/bin/dpkg returned an error code (1)

What is going on?

Update: fixed it by doing sudo dpkg -r --force-all cuda-misc-headers-10-2

1 Like

You may post your pipeline for this issue to be reproduced and hopefully a workaround to be found.

Hi @hlacik
Please make a new post with detail information so that we can check and suggest next.

Very happy about this. Thanks!

Any can please help me out? I’m having a problem with first boot of jetson nano 2GB

Try another SD card. Looks like that’s the source of the issue.

1 Like

do you plan in future your NGC containers for l4t deepstream and tensorflow to be based on l4t-cuda or l4t-tensorrt containers? is there a plan to make it similar ==normalize it as on x86_64 architecture, that you provide nvidia cuda containers and depend on them (==host os has only docker + nvidia gpu driver) ?

Yes, I just changed SD card from 64GB Samsung to 64GB HP card and it worked fine. Thank you!

@ShaneCCC i just downloaded them and write a driver for my camera based on imx219 camera. So I wrote dtsi and *.c and *.h, and modified some codes so that I can compile image file and all modules in a hostPC
I had following problems:

  1. I want to flash into an external SD card, but it failed.
  2. I copied Image and dts file into /boot folder, my Jetson Nano can boot but it can not detect my camera. In /Proc/device-tree I can not find my camera in the folder i2c@0 or i2c@1 etc like imx219.
    How can I debug to know if it tries to detect my camera by booting?

Thanks for your help