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Jetson AGX Xavier

nvidia.com

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Re: Jetson AGX Xavier

#41

Post by arthurcolle »

king_magic wrote: post #38

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…

Why is it a pain to build Docker container for the ARM architecture? Just curious, haven't had to do it myself.

Re: Jetson AGX Xavier

#42

Post by mappu »

my123 wrote: post #31

For a moddable Arm platform with all batteries included, there isn't really an alternative to this, especially at $699. It's much stronger than an RPi and could fill the gap between RPi and Arm-based server platforms.

In terms of using it as an ARMv8 desktop workstation (with decent CPU performance, real SATA / Ethernet / PCI-e connectors) - some other contenders include the MACCHIATObin (quad A72) and Honeycomb LX2K (16-core A72, 750USD) from Solid-Run.

Re: Jetson AGX Xavier

#43

Post by 01100011 »

epmaybe wrote: post #22
TaylorAlexander wrote: post #11

Earlier quoted context omitted.

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…

This is a bit off topic, but I'm constantly looking at ways to efficiently stream 4K cameras live to local displays as well as remote displays at the highest framerate and resolution possible. How feasible would it be on the xavier to stream 2 4k cameras and display them on at least 2 4k screens? Extra points if you could do that and simultaneously upload to a streaming service, such as twitch.

Best bet is to take the raw video and run it through gstreamer. You should be able to setup a pipeline which displays the raw video locally while sending a compressed stream to the network. I'd bet that Nvidia has gstreamer modules which make use of their compression HW, so it might be possible. To be honest though, that's a lot of data, so I don't know how well dual 4k would work. You can always scale it down in gstreamer before you send it to the compression module.

You'll probably want to use the CSI-2 interfaces to connect the cameras, but that depends. CSI-2 was developed for cell phones and is hard to run over long distances. It's optimized for low-power and designed for very short interconnects. We had a ton of problems using it at the last company I worked for. I really wish there was a competing standard for embedded cameras.

Re: Jetson AGX Xavier

#44

Post by linarism »

0x8BADF00D wrote: post #40
king_magic wrote: post #38

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?

Genuinely curious, what is an example of a beefy ARM machine?

Re: Jetson AGX Xavier

#45

Post by king_magic »

arthurcolle wrote: post #41
king_magic wrote: post #38

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…

Why is it a pain to build Docker container for the ARM architecture? Just curious, haven't had to do it myself.

If your dependencies don’t have ARM builds, you need to build them from source. Which is fine, unless you get unlucky and they don’t build. Which happens way more often than I’d like.

Re: Jetson AGX Xavier

#46

Post by king_magic »

0x8BADF00D wrote: post #40
king_magic wrote: post #38

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?

Spinning up an ARM machine to build a container that needs to be shipped over the network to a Jetson is a pain when developing.

Re: Jetson AGX Xavier

#47

Post by michaelt »

my123 wrote: post #32
fizixer wrote: post #15

Earlier quoted context omitted.

Edge means on-premise (on robot) as you said. But 'edge,' as used in context of AI, is also a wink-and-a-nod that the device is inference-only (no learning, no training). The term "inference only" doesn't sound very marketing-friendly.

AGX Xavier can do training on device just fine - and run every CUDA workload. It's just not the fastest device at that, you'd prefer a desktop GPU if you can for such a purpose.

I assume what fizixer means is, if you're making an Amazon-Alexa-type-thing, training 1 model on 1 million user's data will work better than 1 million models trained on 1 user's data each.

AFAIK the "Roomba learns the layout of your house" type of edge learning is generally done with SLAM rather than neural networks. There might be other applications for edge learning, of course.

Re: Jetson AGX Xavier

#48

Post by 0x8BADF00D »

king_magic wrote: post #46
0x8BADF00D wrote: post #40

Earlier quoted context omitted.

> 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?

Spinning up an ARM machine to build a container that needs to be shipped over the network to a Jetson is a pain when developing.

Welcome to embedded computing :-)

Re: Jetson AGX Xavier

#49

Post by achuwilson »

m463 wrote: post #36
epmaybe wrote: post #22

Earlier quoted context omitted.

This is a bit off topic, but I'm constantly looking at ways to efficiently stream 4K cameras live to local displays as well as remote displays at the highest framerate and resolution possible. How feasible would it be on the xavier to stream 2 4k cameras and display them on at least 2 4k screens? Extra points if you could do that and simultaneously upload to a streaming service, such as twitch.

I've wondered about this too. I think the magic camera interconnect is CSI/CSI2 and it's not really flexible enough. You either have really short copper interconnects, or unavailable fiber interconnects. What would be cool is if csi to ethernet were a thing. either low latency put-it-on-the-wire or compressed. I don't know, maybe it is. But make it a standard like rca jacks.

It is possible to extend CSI through good HDMI cables https://www.robotshop.com/en/arducam-csi-hdmi-cable-extensio...

Re: Jetson AGX Xavier

#50

Post by pram »

linarism wrote: post #44
0x8BADF00D wrote: post #40

Earlier quoted context omitted.

> 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?

Genuinely curious, what is an example of a beefy ARM machine?

Anything with a ThunderX processor is maximum beefy. You can get on-demand servers like that from places like Packet. AWS also has their own A1 instances with lower core counts. These would all be good for cross compiling/builds.

Comedy answer: iPad Pro

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