This would mean that a single chip could handle half-a-dozen cameras on a car without much trouble, allowing even more analysis of the video streams used for basic people identification. Not sure how to check the load on the GPU or the AI accelerators, but I suspect that was low as well. The platform seemed to have plenty of headroom the Linux load and heat load (based on fan operation) was minor. Getting all of it up and running was just a matter of running through the JupyterLab notebook for the various demos. The example showing off the trained model on the Jetson AGX Orin was able to identify multiple people moving in multiple video streams (Fig. This runs containers either in the cloud or on the system to train or utilize a trained model. Once I did, I was able to check out the pretrained models as well as use the TAO (train, adapt, and optimize) support. Just for the record, my mistake in setup was not getting DeepStream installed properly. Likewise, a bar to the left can cause the block to be expanded or collapsed, which is handy as some results can be pages long. I haven’t delved deeply into JupyterLab at this point, but you can run the scripts in a block of code by simply typing Control-Enter while the cursor is in the block. There’s also a multiuser version called JupyterHub. It interacts well with other open-source platforms like Docker and Kubernetes, which is important for NVIDIA, as these are used both in the cloud and on platforms like Jetson AGX Orin. The system is very nice-it can run things like command line scripts and present the results in the same browser window as the commands. It’s a web-based, notebook-style interactive system that’s been adopted by a number of AI developers and platforms. It’s possible to use all the libraries, etc., without this, but most of the demos and support is done using JupyterLab (Fig. To fully take advantage of the system, you will need to learn about JupyterLab. It works for lower-cost, lower-weight applications that can get by with a little less compute power.Īssuming you didn’t make my mistake and jump ahead, then you could finish testing the system in an afternoon. The figure also shows the smaller Jetson Orin module that has less memory and performance than its big brother. Adding one may be useful for more demanding applications. The extra memory and flash storage meant I didn’t worry about adding a NVMe M.2 card as I’ve done with past Jetson platforms. The development kit is a complete system that has the Jetson AGX Orin module at its core (Fig. It was pushing the hardware to the limits, which wasn’t the case with most of the demos. It was one of the few things that actually made the fan run enough to be noticed. I’ll skip the latter since you can find system specs easily and they, of course, agree with what the hardware spits out. Once I installed the JetPack SDK and DeepStream software, I was able to quickly run through all of the demos and benchmarks. I might be forgiven as the system comes up right out of the box running Ubuntu as noted in my initial Kit Close-Up video. However, due to not reading the directions and doing things in order, I had to go back and forth with tech support to figure out why I could not run the demos. I would have written this review earlier if I’d been able to get things running sooner. Ok, I’ll start first with the last bulleted item above. Why Bill Wong doesn’t follow directions.What is JupyterLab and what does it have to do with Jetson AGX Orin?.What’s up with the Jetson AGX Orin Development Kit.The card measures 100 mm in length, 87 mm in width, and features a igp cooling solution. Jetson AGX Orin 32 GB is connected to the rest of the system using a PCI-Express 4.0 x4 interface. Rather it is intended for use in laptop/notebooks and will use the output of the host mobile device. This device has no display connectivity, as it is not designed to have monitors connected to it. The GPU is operating at a frequency of 930 MHz, memory is running at 1600 MHz. NVIDIA has paired 32 GB LPDDR5 memory with the Jetson AGX Orin 32 GB, which are connected using a 256-bit memory interface. Also included are 56 tensor cores which help improve the speed of machine learning applications. It features 1792 shading units, 56 texture mapping units, and 24 ROPs. Unlike the fully unlocked Jetson AGX Orin 64 GB, which uses the same GPU but has all 2048 shaders enabled, NVIDIA has disabled some shading units on the Jetson AGX Orin 32 GB to reach the product's target shader count. Additionally, the DirectX 12 Ultimate capability guarantees support for hardware-raytracing, variable-rate shading and more, in upcoming video games. This ensures that all modern games will run on Jetson AGX Orin 32 GB. Built on the 8 nm process, and based on the GA10B graphics processor, the chip supports DirectX 12 Ultimate. The Jetson AGX Orin 32 GB was an enthusiast-class mobile graphics chip by NVIDIA, launched in February 2023.
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