Xbox Project Scorpio Specs Exposed Eight CPU Cores, 40 GPU Cores, 12GB of GDDR5
hothardware.com
hothardware.com
And HN discussion: https://news.ycombinator.com/item?id=14050338
Well, that's the plan, but NVidia is kind of wiping the floor with them.
And while it technically has 12GB of ram, a big chunk of that is reserved by the OS. In practical terms you would only be able to GPU on 8GB of it. That's not even considering how narrow the niche is that absolutely needs more than 8GB but fits under 12GB.
GPUs with 4GB of RAM like the Nvidia GTX 1050Ti aren't expensive.
I just built a few Kaby Lake workstations with NVMe SSDs and those GPUs, and it is a slick little platform.
I did this to a 6 year old Dell XPS 8300, and now that thing rocks!
(I do a little bit of Tensorflow playing around on it).
For machine learning, you'll want as much RAM on the GPU as possible. I can't recommend a 2GB GPU card. 4GB cards can be less than $140, and then there is a jump in price to the 8GB cards (6GB cards aren't cheaper enough to consider, in my opinion).
Check that your power supply has the extra PCIe 8-pin power connector, though some of the GTX 10xx series cards don't require any additional power.
8GB system RAM minimum is also what I'd recommend. Some NN applications can benefit significantly from a faster CPU, even though it is mostly running on the GPU. For example, for training a Caffe model, an Ivy Bridge i7 was vastly superior to a Kaby Lake i3, using the same GPU card.
* i7-6900K CPU
* 4x GTX 1080 GPUs
* X99-E WS/3.1 Motherboard
* 128GB DDR4 2400Mhz RAM (8x 16GB sticks)
* 14 TB between SSD and HDD (Plex, datasets and applications on Ubuntu 14)
* 14x 120mm fans run at lowest RPM
* Custom liquid cooling loop with dual pumps
* 4x Radiators (1x 120mm, 1x 240mm, 2x 480mm)
It's not just deep learning, a lot of what I like to do with this is research or projects parallelizing things like cryptanalysis. For the rest I do financial modeling (which has a fair overlap with the cryptanalysis from a signal processing perspective anyway). I used to work primarily in application security, but have been slowly moving into cryptography and data analysis; the latter mostly as a supplement for cryptography as a research interest.
What kind of modeling are you doing with a beast of a setup like that?
1. Use one stock that I believe I have unique and actionable data on as the input barometer. Take it as a premise that this stock is currently mispriced by the market, and I have an idea of what it should be. We'll call this the primary stock.
2. Find all stocks that have a consistent positive or negative correlation with the input stock, (do they share volatility?), at different significance levels. (This is not particularly hard, it's just getting accurate data with real granularity that is the challenge.) These are the corollary stocks. If there is a calendar event upcoming, I restrict the backtest to the values of other stocks on similar days.
3. For each correlated stock found, calculate an options model with a new terminal price distribution (as opposed to what is currently priced by market consensus).
4. Simulate different options trading strategies and their outcomes across different projected prices for each stock and present the ones with the highest probability of success versus return. At a minimum, the primary and corollary underlyings should have a large enough misprice that trading them can be profitable across the bid/ask spread. So for example, a $1 increase or decrease will not generally be meaningful enough to make the trade, especially if there is a lot of volatility priced in (such as around earnings releases).
You'll notice that there is nothing truly sophisticated about any of this. It's just a bunch of computation used to extrapolate the best leverage available using derivatives. The core thesis involves having material data that the rest of the market has not yet priced in, and I don't often have that (or rather, enough of a directional conclusion based on it). When I do, I like to do this because 1) I don't trust most profitability calculators which assume constant volatility and 2) it maximizes potential results by not just looking at the primary stock I know about.
tl;dr: Assuming one stock is mispriced, do a bunch of derivative re-pricing and choose an optimal one.
I wouldn't expend that much money right now. I think we are just starting to see the evolution in the hardware for deep learning and that in the next 3-4 years we are going to see a really big jump in terms of performance and performance per watio. So I would expend less money and update the system more frequently. I would want to know what do you think after taking the decision of expending so much money in a system like that. Just see how in a year nvidia released 1080, Tital P and 1080Ti. I consider this the first generation designed with DeepLearning in mind.
Look MS, maybe you can't keep growing this market indefinitely.
But for many keeping there old console/games and using that or indeed buying a replacement second-hand old console to utilise their game investment is always an option.
Gets down to choice. Though the aspect of having to repurchase the games is somewhat a crux and does in effect reduce the option of older games to in effect buying the game again. So more for those who never had the original game/console I surmise. More so if they add no new features to those games to make them seem like value for money.
I have not noticed any difference between playing, say, Burnout Paradise on my 360 vs. on my One. So it seems like MS actually did a decent job on it.
'Course, you're probably referring to how you have to buy Playstation 1 games to play them on the Playstation 3 or 4, right? I have a PS3, but I've never had any PS1 games. They really don't let you just stick the disc into the PS3 to confirm you own it and then download the emulated game from the Playstation store?
There are three levels of backward compatibility for the PS3: - PS2 in hardware / PS1 in software - PS2 in software / PS1 in software - PS1 in software
The PS2 in hardware was only select launch consoles (20 GB and 60 GB). PS2 in software were the 80 GB consoles, but that feature was patched out in an OS update some time ago. All PS3s have PS1 backwards compatibility for games from the PlayStation Store and the physical media. I was playing Chrono Cross the other day on my PS3 slim with no issues.
I never heard of that happening, when was that?
Wikipedia says that those 80GB consoles still had hardware PS2 GPUs and only did the CPUs in software, and that later models took out everything. No mention of software downgrades.
There is an 80 GB model that is backwards compatible with disc based games (though it's backwards compatibility is only listed as "limited"), and, according to Sony, all PS3s can play PS2 classics downloaded from Sony. See this article for more details:
https://support.us.playstation.com/articles/en_US/KC_Article...