Re: flashing problems. It seemed to me that with the R32.1 release, using JetPack,
it wasn’t possible to ssh to the device, and the usb ‘gadget’ devices (net, serial,
memory) weren’t enabled until after I connected a monitor and keyboard and went through
the Ubuntu setup procedure. Could this be impacting the install procedure?
My solution (described above) was to go through the initial setup once with mouse,
keyboard, and monitor and create some accounts, then save the APP image and use
that as system.img for further booting. Once this was working I didn’t spend any
time investigating alternate procedures, so I’m not 100% sure this is the case.
Cool I will install that. Wasn’t up there when I tried before I figured it was 42 instead of 33 in the link name :) I just went back to the last jetpack so I could get it done in time. Its at the Science museum now as an AI demo. Running a ZED so I’m getting object identification and ranging via the stereo camera.
I just installed TensorFlow last week using the string in the thread dusty_nv mentioned. FYI, dusty_nv’s github is a gold mine.
I’ve been thinking, isn’t referring to the tensor flow package, or anything else, by its associated jetpack version (ie pip3 install …/jp/v42… or pip3 install …/jp33…) a bit of a misnomer? My understanding is that the jetson board doesnt run Jetpack; it runs L4T (ie L4T v32.1 gets installed with jp v4.2). Shouldn’t these packages be refered to their L4T version?
I am new to OpenCV and to the Jetson TX2. I noticed that when I tried to run a python OpenCV script, the GPU was not being used at all. Does the version of OpenCV installed with Jetpack 4.2 support CUDA?
I have seen tutorials on how to build newer versions of OpenCV with CUDA support for the TX2, however they are for older version of the OS, and I am not sure if they are still recommended.
Also, this may be a dumb question, but are newer version of OpenCV compatible with the TX2? More specifically 4.1.0, or should I stick with the older ones?
The “TensorFlow for JetPack” pip wheels are created for a particular JetPack release, and verified to work with the JetPack component versions (i.e., L4T, cuDNN, and TensorRT) included in that release.
@prlawrence, Oh I see. L4T is the OS, which could be downloaded and flashed stand alone. Where as, jetpack is a specific software package including L4T, cuDNN, TensorRT, etc. And the TensorFlow pip wheels are designed and tested to fit into that software environment. Got it, thanks!
Edit to add: This appears to be how our corporate firewalls handle certificates. I am leaving comment in place to illustrate how tools need to account for modern security firewall boundaries at corporate level.
“lspci” works on all Jetsons and releases so far as I know. If no device is detected you might find the power bus disabled and no output, but the result is still accurate (other than not showing the root complex due to being shut down). To get max verbosity you do need sudo. Is lspci returning nothing, or is it actually giving an error message? Returning “nothing” would be correct for no devices plus power off to PCIe as an energy savings measure.