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Linux Deep Learning Rig build with RTX 2080 FE [Non Ti]

I am setting up a Deep Learning system with Nvidia RTX 2080  Founders Edition  [non-Ti]  on   Linux (Most probably Ubuntu).
The purpose is obviously to train Deep learning models. I don't plan to install windows or games as of now.

I am looking for some advice for the best components that will work well without bottlenecking the GPU.

In future I may add one more GPU, so would prefer a mother board with 2x 16 PCIe slots; even if I add NVMe M.2 SSD in future



Nvidia RTX 2080  Founders Edition[non-Ti] 
Ubuntu Linux

Below are my thoughts, please advice me with a better idea:

1. CPU Selection:  
     I am thinking of Ryzen 7 2700X 8 core

2. Mother board:
       As it will depend on the CPU, assuming Ryzen 7 2700x which is the best mother board?
        Is Gigabyte X470 Aorus Ultra a good choices. 
        What are the other best candidates ?

3. RAM:
          I heard that some RAMs are optimised for Ryzen 7 2700x.
       Is this true? what are the best candidates here?
      And what are the factors that I should consider when evaluating this?

4. Cabinet/Case:
       What are the best candidates here?
     Which are the cases with a good airflow design?
     What are the factors that I should consider here?

5. Cooling:
       As I am not planning to overclock my CPU or GPU as of now; can I just go with the stock fans of the CPU and case?
    Is water base cooling necessary for the stock settings of this GPU/CPU

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