It really depends on how deep you want to go when it comes to your deep learning projects, and how long you are willing to wait, which could be a long time- depending on your project and how much computing it requires to output qualitative results.
My primary workstation is a Dual E5-2697 v2, 128 GB RAM, Dual TITAN GPUs, and an 8x1TB SSD RAID 0 Array over SAS. It's a workhorse; the CPU rates 22000 using Passmark and is in the 99th Percentile. The 2X GPUs rate at 92nd Percentile...
That said, it's a heavy duty system (The original build was roughly $20,000- though it could be built now using the same components and buying used for far less)- but it even wouldn't really be a "great" system for workload heavy deep learning setups, such as video/image/audio projects.
Your best bet would be to buy a good system, I would say don't buy "best", but 6 months post release- it's so much cheaper that way. Go single processor, highest OC you can afford, and a decent GPU.
Then use the money saved to run your actual deep learning projects on Google Cloud Compute or Amazon's AWS.
You can build your VM Instance on Google's Cloud Compute Platform, keep it off which will only incur minimal charges for the IP and system image; then power it up with 1, 2, 4, or even multiple VMs running NVIDIA V100 GPUs.
The V100s will really give you the power you want and while the 2080 card may appear nearly as fast on benchmark charts, it simply can't do what a V100 can, such as FP64, it can host 32GB of RAM which vastly improves it's ability to handle large data sets rather than having to pipe in and out smaller subsets, and you can rent them at the pre-emptable price of roughly $0.80 per hour. (GPU only)
The 2080 may be touted as "80% as fast" as a V100 but if your project would benefit from FP64 and larger ram allocations, it will rip past what your 2080 can do- it's why the card costs 8 times what a single 2080 is priced at.
Add to that you can instantly launch a 2 x V100, or 4x V100 VM Instance, and suddenly you are machine learning speeds are 20x, 30x, or far more, than what your system could do.
Ultimately, it will boil down to: I can run this machine learning project on my system and obtain the results I want in 9 days, OR, I can launch a VM, pay $8.00 an hour, and get my results back in 7 hours.
Using this method you can build your projects on your system, then when you actually are ready to let the machine go to work and do the work, you can put your VM online, transfer your project and assets, and put the V100s to work for a few hours, or a few days.
But a high end deep learning system on Google's platform - using the pre-emptable price; may cost you $4 an hour; what it will be able to do in 12 hours will far surpass what you would be able to do in days, if not weeks and weeks of runtime.
The cost of one V100 is over $8000. Save the cash, rent the cards by the minute when you need them. And costs will drop as newer cards become available.