We are pleased to announce JetPack 4.3 supporting Jetson AGX Xavier series, Jetson TX2 series, Jetson TX1, and Jetson Nano.
JetPack 4.3 key features include new versions of TensorRT and cuDNN, Docker support for CSI cameras, Xavier DLA, and Video Encoder from within containers, and a new Debian package server put in place to host all NVIDIA JetPack-L4T components for installation and future JetPack OTA updates.
Support for installation of JetPack components via Debian package archives
JetPack components are provided as Debian packages via a public APT server hosted by NVIDIA, enabling easier upgrades of JetPack components to future versions. Previous versions of JetPack need to be re-flashed to JetPack 4.3, after which the APT server can be used.
Support for CSI, DLA, and Video Encoder from within containers
On all Jetson products: Camera Serial Interface (CSI) and NVENC are now supported from within containers.
On Jetson AGX Xavier series only: NVIDIA Deep Learning Accelerator (DLA) engines are now supported from within containers.
Support for DeepStream 4.0.2
Support for ISAAC SDK version 2019.3
New easy to use Jetson-IO tool to configure 40-pin header on Jetson developer kits
Developer preview of VPI (Vision Programing Interface), a software library that provides Computer Vision and Image Processing algorithms implemented on Xavier Vision Accelerator, GPU and CPU.
GPU and CPU implementations are not optimized for performance in this preview release of VPI. A future release will bring performance optimized GPU and CPU implementations
Just tried it straight from the sdk. Figured I have had enough issues, what one more try. Worked great thank you for fixing the bug. Now I have room to really use it for my project. I’ve already applied for the Jarvis sdk. Any idea on how long that takes to get approved for or is my previous approval to the Isaac sdk messing things up? I would gladly give up my Isaac access to be allowed into the Jarvis sdk. It fits my project better.
$ sudo /opt/nvidia/jetson-io/config-by-pin.py
Traceback (most recent call last):
File “/opt/nvidia/jetson-io/config-by-pin.py”, line 51, in
File “/opt/nvidia/jetson-io/config-by-pin.py”, line 34, in main
jetson = board.Board()
File “/opt/nvidia/jetson-io/Jetson/board.py”, line 149, in init
self.dtb = _board_get_dtb(self.compat, self.model, dtbdir)
File “/opt/nvidia/jetson-io/Jetson/board.py”, line 88, in _board_get_dtb
raise RuntimeError(“No DTB found for %s!” % model)
RuntimeError: No DTB found for NVIDIA Jetson Nano Developer Kit!
Can you tell me exactly how you flashed the board? Or are you using the SD card image? Ah looking at the SD card image there is no /boot/dtb directory so that will be a problem if you are using this image. Please confirm. We will look into a fix for this.