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*Able to train multiple networks at once (either copies of the same network or modified networks). Allows for running long experiments while running new ones
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Pros of multiple GPUs:
*Adds a lot Able to train multiple networks at once (either copies of complexitythe same network or modified networks).Cons of multiple GPUs:Allows for running long experiments while running new ones
*Possible speed ups if the network can be split up (and is big enough), but tensorflow is not great for this
*More memory for huge batches (not sure if necessary)
Cons of multiple GPUs:
*Adds a lot of complexity.
* Network card?
* DVD drive?
* How much RAM/storage needed?
==Single GPU Build==
==Double GPU Build==
[https://pcpartpicker.com/list/ZQjKf8 PC Partpicker build] missing a lot of pieces
===Motherboard===
===GPU===
* 2x GTX 1080 Ti for computation* Video card for monitorAspeed AST2400 with 32MB VRAM (comes with motherboard)
===RAM===
*At least twice as much RAM as GPUs, probably twice that much (2 * Number of cards 2 * 11 GB [GTX 1080 Ti size]= 32 GB)
*RAM: Crucial DDR4 RDIMM [http://www.newegg.com/Product/Product.aspx?Item=9SIA0ZX39C3002], 2133Mhz , Registered (buffered) and ECC, comes in packs of 4 x 32GB
