I recently upgraded my hardware from D3 SerDes card to "DESIGNCORE® NVIDIA® JETSON AGX XAVIER FPD-LINK™ III INTERFACE CARD".
Hardware I have now:
1> Jetson Xavier AGX installed with jetpack v5.0.2
2> DESIGNCORE® NVIDIA® JETSON AGX XAVIER FPD-LINK™ III INTERFACE CARD
Question: Does D3 BSP v6.0.0 support above specified hardware with jetpack 5.0.2?
I am trying to install D3 BSP v6.0.0. but after installing it I verified using uname -a.
It did not give me desired response and still showing nvidia hardware rather than showing D3, as specified D3 BSP v6.0.0 release note.pdf.
Related
I am working on a mobile robot that consists of Jetson Xavier NX (aarch64 aarch64 GNU/Linux) and Nucleo board F429ZI (STM32). I am on Ubuntu 18.04 and ROS melodic. The motors and IMU are interfaced with the Nucleo board. The Nucleo board is publishing the Joint state, IMU values, and other sensors information. It is also receiving the wheels velocities from a topic to drive motors. The rosserial library is used for communication between Xavier and Nucleo board.
Everything works well when I bringup the robot but after 5-10 minutes I start receiving one of the following errors/info.
wrong checksum for topic id and msg
unable to sync with the device; possible link problem or link software version mismatch such as hydro rosserial_python with groovy Arduino
no sync with device try restarting
Mismatched protocol version in packet: lost sync or rosserial_python is from different ros release than the rosserial client
After the error except error 4, the joint states are no more published. However, I can still send the commands to wheels through joystick or keyboard.
Additionally, I have tested the same setup on my laptop Dell precision 3551 ubuntu 18.04 (\x86_64 x86_64 x86_64 GNU/Linux) and no such info/error appears.
Do you have any expert opinion? Please let me know if you need any additional information
Thanks
I have had many many errors when trying to install tensorflow on my new M1 pro chip computer. Any tips for a smooth installation??
Can we connect Microsoft's azure Kinect DK camera with Nvidia Jetson TX2 board DK together? Do these development kits support each other? Can we work on them?
Not yet, but we are working on it. Is should be ready soon though.
You can see our ARM proposal here: https://github.com/microsoft/Azure-Kinect-Sensor-SDK/blob/develop/proposals/ARM-support.md
I'm using Nvidia Jetson TX2 with Ubuntu 16.04 aarch64 in my project. I need to subscribe data from other system using Connext DDS Pro v5.2 running on Windows x86 or x64.
But RTI Connext DDS Pro didn't supply official pre-built package for ubuntu with ARMv8, and from "RTI_Connext...PlatformNotes.pdf" manual, there is also no support for ARMv8.
Is it possible to support ARMv8 CPU through building from source?
TX2's CPU info as below:
nvidia#tegra-ubuntu:~$ lscpu
Architecture: aarch64
Byte Order: Little Endian
CPU(s): 6
On-line CPU(s) list: 0,3-5
Off-line CPU(s) list: 1,2
Thread(s) per core: 1
Core(s) per socket: 4
Socket(s): 1
Model name: ARMv8 Processor rev 3 (v8l)
CPU max MHz: 2035.2000
CPU min MHz: 345.6000
L1d cache: 32K
L1i cache: 48K
L2 cache: 2048K
gcc info:
nvidia#tegra-ubuntu:~$ gcc --version
gcc (Ubuntu/Linaro 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
RTI has a pre-built library for 64 bit ARMv8 with Ubuntu 16.04 (gcc 5.4.0) for the latest Connext DDS Pro version 5.3. This library was tested on NVIDIA Drive PX 2. Though it has not been tested specifically on NVIDIA Jetson TX2, from the spec you listed above it should also work on TX2.
You could contact sales#rti.com for access to this library.
TensorFlow fails to use nvidia card though nvidia driver, cuda toolkit, cudnn installed and configured.
One thing that I suspect is the reason is the nvidia card on my laptop is connected to pci as 3d controller instead of VGA:
00:02.0 VGA compatible controller: Intel Corporation Sky Lake Integrated Graphics (rev 07)
Subsystem: ASUSTeK Computer Inc. Skylake Integrated Graphics
Kernel driver in use: i915_bpo
01:00.0 3D controller: NVIDIA Corporation GK208M [GeForce 920M] (rev a1)
Subsystem: ASUSTeK Computer Inc. GK208M [GeForce 920M]
Kernel modules: nvidiafb, nouveau, nvidia_304
Even the Nvidia xserver settings don't see the GPU:
Is this true that tensorflow can only use the graphic card as VGA?
After three month, I finally figured out even first what the issue is and resolved it. It turned out to be a nvidia issue with Secure Boot.
Feel obliged to thank jorgemf and Yao Zhang for your help at a time I couldn't even good articulate the problem.
Meanwhile I hope my case can help other people having a same problem.
All started with my attempt to install nvidia driver again today. The installation seemed successful but in the end, it says,
Unable to load the “nvidia-drm” kernel module.
So I thought maybe I could manually load the kernel with
modprobe mvidia-drm
but got an error says something like "required key not applicable". Wonder what that meant so googled a bit. It turned out to be application not registered! So that module has been stopped by Secure Boot!
Went back to boot settings and disabled secure boot. Installed nvidia driver again, successful! Now in Nvidia settings it looks like this:
See now the gpu device shows there.
Head further to install cuda and cudnn. Found this github gist super useful: https://gist.github.com/wangruohui/df039f0dc434d6486f5d4d098aa52d07
Last step, just followed the installation on Tensorflow home page. Tested it did run on GPU!
The take-home message is if you fail to install Nvidia driver on linux system, you probably need to disable Secure Boot. Personal opinion, Windows turned this good idea into a nightmare for linux users!