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This was mentioned slightly already, but still I would like to ask: Why would you use a Raspberry PI or Dev Board to run compute intensive AI tasks on it? Why n
by ersiees 9y ago
This was mentioned slightly already, but still I would like to ask: Why would you use a Raspberry PI or Dev Board to run compute intensive AI tasks on it? Why not use the PC you are working with most of the time, that probably is much faster? And might even have a reasonable ggpu!?
- wil421 9y agoWhy not? I have a $35 mini computer to play around with. It has a huge community and tons of options that make it easier for beginners. I plan to make an image recognition system that will be able to count the number of train cars that go past my back yard. Why? Because it’s fun and I can learn a lot about AI and computer vision. My laptop and desktop won’t be able to survive outside. Plus the Pi can run on battery and be charged by a solar panel. No parts will come close to breaking the bank.
- mwexler 9y agoThings make more sense if you look at the top level page: https://microsoft.github.io/ELL/ https://microsoft.github.io/ELL/ talks about their embeddable ML libraries, with the focus on allowing some live training on the embeddable, instead of, say, CoreML which (as of this writing) only allows scoring of trained models on the device. So, showing how this works on a Pi (and where it doesn't quite due to heat) is in scope; I don't think they intended to replace a GPU-based PC training approach.
- guhidalg 9y agoBecause it's way more fun and interesting to work in a resource constrained environment where your main concern isn't what library du jour you us, but writing code that runs in a reasonable time, doesn't consume unnecessary memory, and meets your requirements.