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Unread 28-06-2016, 07:55
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Re: Methods for deploying/(cross-)compiling vision code for NVidia Jetson

Quote:
Originally Posted by Wasabi Fan View Post
My team has been working with the Jetson TK1 recently for on-board vision processing. The vision code that we (or, in reality, I -- I am the vision team) write has been primarily using OpenCV functions for the heavy lifting, but we also have written custom CUDA kernels to perform some more specialized processing when OpenCV isn't doing what we want.

Up until now, I've been using NVidia's Nsight Eclipse Edition tooling, which supports built-in remote compilation and debugging of both plain C++ and CUDA code. However, after spending more time troubleshooting sync errors than writing code this past season, I am looking for alternatives to the system I'm currently using.

So, I'd like to hear from others that are using the Jetson to see what they are using for development tools and deployment. I've seen some posted code that indicates that they just copy their code to the remote device and then compile manually, but given the caliber of some of the vision systems I've seen, I doubt that is the state of the art. Additionally, I have yet to see anyone that implemented their own CUDA kernels in addition to the OpenCV ones; having to compile CUDA code manually would get to be a real pain. Are others having success with the project sync facilities in Nsight? Or are there home-grown solutions that teams are using instead? I'd really like to hear what others are having success with!
We compile on our Jetsons for the most part. It might not be "state of the art" but the Jetson's are cheap(ish) and we have enough of them to give them to individual (or pairs of) students to use. We don't transfer the code. It is written on them as well. Basically, we use them as cheap desktops that the students can experiment with.

We aren't doing anything with custom CUDA kernels that I'm aware of... I'm not sure why you would but perhaps you can share your use case. We are using systems others have built to take advantage of CUDA already, both with OpenCV and with the neural network programming we have been doing.
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