81–90 of 95 posts
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You don't even need to do this: qemu support for executing arm64 binaries is fairly good, so you can just run the builds on your beefy x86 server anyways.
What's the single thread performance difference of a fast x86 cpu running emulated ARM code vs a fast ARM server or an AWS ARM instance?
I'm definitely not expert and probably this is a dumb question , but why smart edge things like smart robot and not dumb edge with smart central brain ? Anyway data are useful aggregated central;ly why not incorporate the brain centrally too?
Well, first some clarification - "edge" means "on robot" versus something in the cloud. And the reason you do this is latency and connectivity. I am designing a four wheel drive robot using the NVIDIA AGX Xavier [1] that will follow trails on its own or follow the operator on trails. You don't want your robot to lose cellular coverage and become useless. Even if you had coverage, there would be significant data usage…
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The dev support is also bottom-of-the-barrel even if you’re a high-margin cloud customer. For a generous upper bound of what Nvidia considers “software support,” look at TensorRT, where a majority of the useful stuff has either been written by third parties or scoped out the hard way by people trying to use it. Nvidia isn’t really a software company, and their core product has a very narrow user interface. These fact…
This is so frustrating to hear. I have a bin full of ARM single board computers, and while the hardware on all of them is pretty much up to the task, the software support from all the vendors has been terrible. I'm in the process of switching to Nvidia hoping it would be the exception. If anyone from Nvidia is reading this, please do everything you can to convince the bosses to allocate the resources required to supp…
Also I can see something close to that being a competitor to the XSX and PS5. NVIDIA made the Shield and that led to a design win with the Nintendo switch -- wouldn't it be nice to play Nintendo games in VR?
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The dev support is also bottom-of-the-barrel even if you’re a high-margin cloud customer. For a generous upper bound of what Nvidia considers “software support,” look at TensorRT, where a majority of the useful stuff has either been written by third parties or scoped out the hard way by people trying to use it. Nvidia isn’t really a software company, and their core product has a very narrow user interface. These fact…
This is so frustrating to hear. I have a bin full of ARM single board computers, and while the hardware on all of them is pretty much up to the task, the software support from all the vendors has been terrible. I'm in the process of switching to Nvidia hoping it would be the exception. If anyone from Nvidia is reading this, please do everything you can to convince the bosses to allocate the resources required to supp…
Additionally it seemed like there's a decent ecosystem around it of board suppliers, which then translates into pretty good software support.
But again, not sure if that's still the case. I chose them for a project in 2015 and have not regretted it since - but have been able to use the exact same IC since then for other projects so I'm not sure if the experience would be different if starting from scratch today.
I have one of these powering my open source four wheel drive robot. [1] I've started doing machine learning experiments with it finally. (See [1] for details) There's a few tricks to getting the best performance. You want to convert your neural network to run with NVIDIA's TensorRT library instead of just tensorflow or torch. TensorRT does all the optimized goodness that gets you the most out of the hardware. Not all…
For dealing with layers not supported by TensorRT, you might want to try to export to onnx instead and then use tvm[1] to compile your model for the hardware. I have not used it on nvidia boards yet, but I had good experience on other less powerful ARM boards, and as tvm docs show some examples running on jetson tx I imagine the AGX Xavier is likely fine too.
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There is also the Jetson Nanokit which costs ~120 EUR.
Maxwell does not have unified memory -> custom code compared to latest generation along with performance disadvantages.
My biggest complaint with the Jetson line is it's all ARM. Look, I get it. But the developer experience is horrible. Building Docker containers for ARM devices is a pain. Hell, building anything for a Jetson can be a pain unless it's a pre-packaged NVIDIA thing - really not a fan of building things from source. Add on top of that NVIDIA's very low level documentation for pretty much any tooling they ship, coupled wit…
> Building Docker containers for ARM devices is a pain in the ass. It's not so bad, you just need a beefy ARM machine to build the containers in CI. It would be silly to build a Docker container on the Jetson itself. You would never use an embedded device for compiles and builds, why would you build Docker containers on one?
They seem to offer a cheaper 8 GB model too but unfortunately I see no price for it. I'm curious how much it'll be because, as much as I'd like to toy around with this, the $699 is a little to much for just experimentation. EDIT: The 8GB Module seems to be $679 here[1]. This makes the $699 or the 32 GB Developer Kit seem like a steal. Still, too expensive for play, I guess I'll stick with my Jetson Nanos for a while.…