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Cheaper than the $150 Google TPU Dev Board, and looks like it can do training as well as inference. Also, doesn't require you to send your model to their compan
by ipsum2 8y ago
Cheaper than the $150 Google TPU Dev Board, and looks like it can do training as well as inference. Also, doesn't require you to send your model to their company. Nice!
- scruffyherder 8y agoQuad core arm, 4gb of ram and gigE! Seems like a really nice board
- tyingq 8y agoAlso has an M.2 slot for WiFi, as well as both HDMI and DP...supports two simultaneous displays out of the box.
- uncoder0 8y agoWhy would you use the M.2 for wifi and not a super fast disk in that slot? USB bus should be able to handle wifi just fine.
- deleted 8y ago[deleted]
- tyingq 8y agoBecause it's a M.2 E slot, which has no SATA. Maybe there are SSD drives now that can use PCI and fit in the smaller E slots? There didn't used to be. If so, great...
- Zekio 8y agojust no SATA? or no SATA and no NVMe?
- tyingq 8y agoM.2 E has PCIe ×2, USB 2.0, I2C, SDIO, UART and PCM. As far as I know, NVMe drives want 4 PCIe lanes.
- llampx 8y agoThere are recent low-cost NVMe drives that do 2x, I believe.
- sitkack 8y agoIf a PCIe device doesn't fall back to the available channels that is most likely a bug. An NVMe from a reputable manufacturer should work in this slot.
- tyingq 8y agoI really don't think it's that easy. Find a picture of an m2 drive has a connector that would fit an "M.2 E" slot. The connector would have to look like this: https://www.addonics.com/products/diagrams/M2-E-KEY-WIFI-CARD-diagram.jpg https://www.addonics.com/products/diagrams/M2-E-KEY-WIFI-CAR... I don't see any.
- joshvm 8y agoThe development kit specifically lists NVMe as an option https://www.nvidia.com/en-gb/autonomous-machines/embedded-systems/jetson-nano/ https://www.nvidia.com/en-gb/autonomous-machines/embedded-sy...
- tyingq 8y agoTried "search in page" for NVMe, no luck. Not sure what you're seeing.
- joshvm 8y agoWeird. I opened it in reader mode. Doesn't seem to be on the normal page. It's a screenshot, maybe a draft? https://www.nvidia.com/content/dam/en-zz/Solutions/pattern-library/2019-02-28_11-40-50.gif https://www.nvidia.com/content/dam/en-zz/Solutions/pattern-l...
- make3 8y agothere is no reason to train on that thing (except for zero shot classification demos). any cheap/lower end Nvidia gpu would do a much better job, and you would then transfer the model to the embedded thing.
- outlace 8y agoWould it do better than a CPU for training? I do my dev on a MacBook Air and use AWS for training, if this cheap gpu will be a few times faster than my Air CPU than I’d be willing to get it. I usually work with medium sized models/data, too big for CPU but don’t need a multi gpu cluster.
- arbie 8y agoI would stick to AWS for training. Your CPU will be orders of magnitude slower than most GPUs for training.
- make3 8y agoGoogle colab gpu instances are free and likely faster than the jetson (and definitely faster then typical laptop or desktop cpu only training), just save the models to your gdrive https://colab.research.google.com/notebooks/welcome.ipynb#recent=true https://colab.research.google.com/notebooks/welcome.ipynb#re...
- anonymousDan 8y agoCan you recommend a cheap/lower end Nvidia GPU for training for someone who just wants to play around with NNs a bit and isn't interested in peak performance? Could I get something for $100 that would be better than my CPU?
- dagw 8y agoWhile you can in theory run CUDA on an Nvidia 1030 GPU (currently around $100) it's really not worth it. The cheapest card actually worth buying for ML is the 1050 Ti, which can be had for around $200. If your budget is $100 I'd take that money and hunt around for various cloud based solutions. Most have introductory offers and/or cheap/free solutions for hobbyists with modest needs. $100 will go a long way on these services if you're careful. Once you've used up your $100 you'll have a much better idea of what, if anything, you actually need.
- nl 8y agoCheaper than the $150 Google TPU Dev Board, and looks like it can do training as well as inference. Training is a slight win (although it's going to be too slow for anything useful really). But it looks like the TPU will outperform this somewhat for inference. The 256 core Jetson (this has 128 cores) could run MobileNet-v2 at between 12 and 20 ms per image (depending on batch size)[1], while the USB TPU adapter takes 2.3ms per image [2] Also, doesn't require you to send your model to their company. Nor does the TPU dev board. [1] https://arxiv.org/pdf/1810.00736.pdf https://arxiv.org/pdf/1810.00736.pdf (see table 1) [2] https://coral.withgoogle.com/tutorials/edgetpu-faq/ https://coral.withgoogle.com/tutorials/edgetpu-faq/
- remyM 8y agoSeems to lose in Mobilenet and win in other benches according to Nvidia's benchmarks. I'll wait for independent testing before I drop $100. https://devblogs.nvidia.com/jetson-nano-ai-computing/ https://devblogs.nvidia.com/jetson-nano-ai-computing/
- m0zg 8y agoHorseshit in that bench right off the bat: I have a Google Edge TPU board right in front of me and its perf on SSD300 is 70fps, not 48. That's with the browser demo, which (as far as I can tell) includes realtime encoding of h264 for streaming. Almost twice as much as Jetson, and likely in a much more modest power envelope. NVIDIA is known for dishonesty in their benchmarks. Although TPU is, of course, a quantized play, and Maxwell will really suck for that, unless it's been tweaked specifically for this board. OTOH, fp32 models are _much_ easier to work with, and this thing has more RAM so you can waste it on 32 bit weights, and NVIDIA's software toolkit is second to none. So the Jetson looks pretty tempting as well. I just wish they didn't try to insult my intelligence.
- remyM 8y agoOh yeah - I never said to trust their benches. Take OEM benches especially vs competitors with the grainiest grains of salt. When people start getting their hands on them I'll start seeing independent benches, and I think anandtech got their hands on one. Hopefully soon™.
- m0zg 8y agoYeah, but Google Edge TPU can do faster than realtime (about 70fps) 90-class object detection with SSD300. Just plug in a webcam. I wonder how this compares both in terms of the raw throughput and FPS per watt. If it compares favorably in even one of these without being too terrible in the other, this could be pretty cool.
- ashwinne 8y agoA more practical solution is to train your network on other beefy CPU/GPU/TPUs and convert+run that on the Nano using TensorRT. TensorRT supports import of models in Tensorflow (UFF) and ONNX formats.