Gotcha. Mining cases are not a good option.

Open frames like this https://www.amazon.com/Mining-Computer-Currency-Bitcoin-Accessories/dp/B09CNG58R1/ should work. Building your own base on available space is best though.

Chassis is not a problem, it's the heat.

Generally anything above 2x 3090 will need to be underclocked or in an open case to be under 90°C.

I don't think a 4x 3090 rig is possible without water cooling, since even with riser cables and an open case the cards are going to be fairly close to one another. The cards will need to be underclocked heavily and you will need the best power supply on the market, and you would still risk shutdowns or even hardware failure if 4 cards go into a transient spike at the same time. I would not risk it if you're building 2 rigs anyways, there is little benefit from a 4x and 2x configuration instead of a 3x and 3x configuration.

NVLink probably won't matter much since your CPU will be bottlenecked trying to send 5.2 TB/s of data to your GPUs. But again, there are no benchmarks to show how much, maybe the gains from NVLink will be noticeable.

My point is that OP can not just pick any mining rig and expect it to work. Mining rig chassis are designed so that you can use those USB risers that are long and easy to route. Now when you have the wide and relatively short risers it can limit the physical configuration.

Exactly why I suggest Cooler Master x16 risers. (they don't have x1...) I use the 20 and 30 cm PCIe 3.0 ones as my motherboard does not support PCIe 4.0.

Chassis is the same as a mining rig.

To avoid taking too much advice from miners, the thing with mining rigs is that the amount of data transfer between CPU and GPU is minimal so they can get away with 1x risers, which definitely won't be a good idea on a deep learning rig. This means pretty much no mining rig would function as a deep learning rig.

Mining community has been building these for quite some time now. Depending on you TR mobo, you might get away with pcie 3.0 risers. Cooler master has some quality models.

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That’s super impressive! Congrats, I genuinely hope the novel is interesting

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Did you do that over the last week since the GPT bot is available? Or is it one of the previous models of theirs?

I am kind of facing the same issue. I partially want to try some moderate deep learning project, while also interested in other aspect of the GPU, for example, video editing, GPU acceleration for data processing (Matlab, python, etc), etc. just got a computer with i7-12700k, to upgrade GPU.

now 3090 Ti FE is available at $1099, while 4080 costs about $1199 (but less availability). it seems 4080 is better than 3090 Ti in almost every aspect excluding unknown performance in deep learning (didn't find any benchmark/comparison on DL).

so not sure how to choose. worth it to trade VRAM/deep learning for other performance?

Thank you, but I think I've found a few resources for calculus and maths in general.

Again, Thank you :)

Most DL books have an appendix with the linear algebra and calculus needed to understand what's in the book.

I've not seen it yet, but maybe you can take a look at the new course by Sebastian Raschka on the ligthning website?

I can't help with calcululs and algebra because I already know those subjects, so I can't tell you where to study such notions...

>Yeah, this likely breaks some terms of service.

Which ones ? Can you be please be specific ? The whole idea of gpt-3 was to create the content it generated for commercial purposes and the entity which generates the content to own the output.

"As between the parties and to the extent permitted by applicable law, you own all Input, and subject to your compliance with these Terms, OpenAI hereby assigns to you all its right, title and interest in and to Output."

Reference link : https://openai.com/api/policies/terms/

In other words .. the OP has the right to the content he has generated using GPT-3 ( see screenshot -1 )

screenshot -1 https://imgur.com/UM6RrOF

As long it does not violate its general terms and conditions ( see the screenshot -2 )

screenshot -2

https://imgur.com/a/YWLJQHq

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I may do better going through a vendor, honestly. System76 doesn't do dual 4090s, but I think Exxact does.

Best of luck! 4090s at MSRP have been a challenge to find. Hopefully, supply will get better Q1 2023.

I think their performance is good! My concern would be finding places for all the radiators, such that the aio pump would be able to effectively do its job. It may also cramp the case if you decide to also water cool your CPU with another aio.

I am also interested! I'm going in circles trying to decide, and I think a 2x4090 would be the best for me, too. Though I'm more likely to have it built at MicroCenter to save myself the stress.

There are so many kinds of data and so many kinds of data annotations.

For deep learning, the quantity required is high. The question is always “how much is enough?”

You kind of need to narrow your question.

If I was you, I’d look at your competitors and see what they claim to do. Often it’s shipping customers’ image data off to somewhere for cheap labor. And requires the customer to look at the annotations to verify they’re as expected. In my limited experience, there’s a LOT of review.

Data is everything.

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Yeah, this likely breaks some terms of service.

In academia we usually have the data already labeled, but I did one unfortunate project where the annotation is absolutely garbage (too many mistakes). Ensuring the correctness of labeling should be one of the priorities. From my limited experience you would want collaborators with domain knowledge of the data to make sure the processing is absolutely correct.

Recent developments in self supervised learning and generalized pretrained big models may lower the amount of labeled samples needed, not sure what that would affect your product, but it seems related.

Sure, but do the companies neglect the importance? For example, At ur workplace what's the use & quality of data?

Without big data there would not be deep learning, just plain old machine learning.