P2P is disabled on the 4090s so if you needed that expect degraded performance

The proce of 4 rtx4090 is very high. If you plan on spending that much money I suppose it is for business or research. It could make sense to invest in a server grade processor and motherboard with support to many more pcie lanes. Also, servers often use multiple power supplies for redundancy, but in this case they can be helpful for managing the wattage of 4 cards. In my experience limiting the max power does not impact training time in a dramatic way, so I would do that especially if you are planning to air cool them (which is probably the best thing for an offsite server)

You would have to limit the power to 250W. It will overheat without an open case. PCI-E 3 x8 means you are cutting the cards bandwidth in half.

Overall a terrible idea.

They removed NVLink to cut down on costs on a feature that is supported by only a handful of games in existence and otherwise useless.

PCIe extenders always worry me, they tend to catch fire very easilly. There is a large amount of current flowing through them after all

If you have the hardware, and if you have a lot of those input-output examples, you can use alternative smaller models in the gpt family.

Should work reasonably well especially if the variance in the input-output isn't too much. (A lot depends on your dataset here)

Definitely tradeoffs here in terms of model dev, inference and maintenance of it. If the expected costs aren't too high, I'd strongly recommend gpt3 as a base.

[deleted]

Not surprising given that they removed NVLink to force multi-gpu users to purchase A6000s

That means it will need to be paid for each summarization API call, forever, right? Is there an alternative model that one can tune on a couple of high end nvidia cards? Like GPT NeoX?

Create a Corpus C like this

<source text from corpus A> <human generated text from corpus B> . . .

Make sure you add some unique tokens marking the start and end of each example and the input and output within it.

Then, take any pretrained LLM (tuning gpt3 is trivial with ~10-20 lines of code).

For inference, use the tuned model and give it the input and let it complete the output. You can add the "end" marker token to get generation to complete.

[Source: trained/tuned several language models including gpt3]

lol

more like: given item x from Corpus B, predict item y from corpus A

Just to make sure I’m following, steps here could be…

1. Finetune GPT2 or GPT3 on Corpus A
2. Take the finetuned GPT* and continue as a summarization task?

proceed as with summarization. you can train gpt family using open ai or get yourself t5 and train it. training from scratch will be a waste of resources

[removed]

Look into computer vision courses in university websites and you should see the range

My instinct says perception, but I can’t say I’m all too familiar with the full pipeline. Do you have any resources where I could read up on it? Thanks so much

You just use mfcc and then it’s just like image detection

Once trained (and happy w the testing results)…make it fail :) find perturbations and difficult inputs so you understand the limits of your models you can now focus on robustness

Grid search to reduce the search space on the hyperparameters, then random search on the reduced search space.

no you're not annoying at all, thanks for the update :)

Sorry to be annoying but I thought it was nice to give you some news as well. I was confused as to why there isnt yolo in pytorch, here it is why https://github.com/pytorch/vision/issues/6341

Put it to test with real data. Putting a lot of effort into tuning a model on a fixed data set that will eventually be deployed is a waste of time. And dont freak out when it fails! Just add more quality data from when it is deployed.

Rub it in oil and herbs and place it in the oven on broil for 10-12 minutes

Oh I see, I missed the major point that the training data is basically incomplete to model the entire relationship.

Why embed priors into neural networks, doesn’t Bayesian Modeling using MCMC do pretty much what this is attempting to do? We did something similar to this in one of my courses although we didn’t get to spend enough time on it so forgive me if my questions are stupid. I also would need someone to walk me through a motivating example for a PINN because I’d just get lost in generalities otherwise. I get the example, but am failing to see the larger use case.