- #CUDA DRIVER VERSION IS INSUFFICIENT FOR CUDA RUNTIME VERSION MAC OS X 10.13.4 FULL#
- #CUDA DRIVER VERSION IS INSUFFICIENT FOR CUDA RUNTIME VERSION MAC OS X 10.13.4 PRO#
READ THE WHOLE BLOGPOST before you buy a Jetson Nano or begin to work with your own system.ĭisclaimer: The information in this article is offered "as-is," with all faults, errors and omissions, and no warranty whatsoever. This article is meant to capture some of what we've learned in that process, so if you decide you want to experiment with one of your own you can have a quick(er) and smoother start. One comparison of those alternatives can be seen in "Battle of Edge AI - Nvidia vs Google vs Intel"Īnyhow, having recently received a Jetson Nano as a Christmas gift from a family member (thank you Bev!), we've begun to experiment a little with it.
#CUDA DRIVER VERSION IS INSUFFICIENT FOR CUDA RUNTIME VERSION MAC OS X 10.13.4 PRO#
tax/VAT/shipping is described as "Almost twice as fast as the MacBook Pro 13", for VR, AR, and AI projects," according to the vendor. The Google Coral USB Accelerator, $74.99 from Cora.ai ("An individual Edge TPU is capable of performing 4 trillion operations (tera-operations) per second (TOPS.)") Coral also offers a dev board version for those who'd prefer that to a USB stick form factor, $149.99Īnd in a completely different price class, the UDOO Bolt V8, $418.00 excl.
#CUDA DRIVER VERSION IS INSUFFICIENT FOR CUDA RUNTIME VERSION MAC OS X 10.13.4 FULL#
The Intel Neural Compute Stick 2, $73.27 from Amazon ("Intel claims the chipset can hit 4 teraflops of compute and 1 trillion operations per second of dedicated neural net compute at full blast.") Of course, the Jetson Nano is not the only device competing in this space, see also, among others: On the other hand, the Neural Network/Deep Learning model we'd built was not purely a floating point computational exercise – it also involved non-parallel computation and I/O, which might take longer on the slower ARM CPU. We had high hopes for a speed-up since our early 2015-era 3.1 GHz Dual-Core Intel Core i7 Macbook Pro laptop (as used for the earlier runs) peaked at just 79.3 GFLOP under Catalina (when tested with the Intel LINPACK benchmark, roughly 1/6th the quoted performance of the Jetson Nano.
We were particularly interested in whether the neural network models described in our earlier Farsight blog article entitled 'A Second Approach To Automating Detection of "Random-Looking" Domain Names: Neural Networks/Deep Learning' could be run faster on the inexpensive Jetson Nano.
While the device is largely intended for developers working to develop machine learning/neural networks at the edge on low-power mobile devices, we were intrigued in the device's low cost, its relatively high-performance claims, and its NVIDIA GPU, all conveniently running on top of a tailored version of Ubuntu Linux. The Jetson Nano will certainly open up NVIDIA Tegra SoCs to appearing in more low-cost DIY projects and other hobbyist use-cases as well as opening up GPU/CUDA acceleration that until now has really not been possible on the low-cost boards." Phoronix benchmarked and reported on the Jetson Nano, stating that "overall, this is arguably the best sub-$100 ARM developer board we've seen to date depending upon your use-cases.
This is an intriguing little system claiming 472 GFLOP of performance via a 128-core NVIDIA Maxwell GPU, a quad core ARM A57 processor, 4GB of RAM, and gigabit Ethernet – and all at a sub-$100 price point. On March 18th, 2019, NVIDIA pre-announced their new "Jetson Nano" GPU development board, with shipments then-scheduled to begin June 2019.
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