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Comment by lqdc13 | Hacker News Reader

Still seems much better to buy your own gtx 1080 for $700 which you would have spent in a month playing with parameters on these instances.

And at the end of the month you still have a modern GPU to play video games on etc.

Of course if you have money to burn, these are great.

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You can set up the volume a CPU only machine (even on a free instance), and then just launch that volume with a machine with these big expensive GPU's on it.

I did it a few months ago with a TensorFlow model I was training. Configured it for free then all the training time was fully used after configuration.

I assume it'll be the same with these new instances. I have a backlog of stuff I wanted to train but had no GPU to train it on (I travel a lot so couldn't buy a desktop, and was almost biting the bullet to buy one of those monster laptops with dual 1080's, although really I was after at least 12GB of VRAM so this is fantastic news for me)

Have you considered buying/building a desktop that acts as a server and then using ssh to connect to it from your laptop?

That's what I do.

I'm not the person you posed the question to, but what's the secure, convenient way to do that? If I want to ssh into my desktop from within my house? If I want to ssh into it from elsewhere?

Am I missing the question? Maybe, maybe not. The simple answer is build your new "server" and install some version linux, like Ubuntu. Set up the accounts and make it so you can login using a private key. Turn off remote login with password so that you can only login with the private key. Install Fail2Ban to block automated hacking attempts---almost the second you expose your server to remote traffic you will start seeing attempts. Configure your internet router so that incoming port 22 traffic is routed to the server (or all ports if you don't care about security). Signup for a free dynamic DNS service for when your IP changes. Now you can login to your home server from anywhere using a your hostname which is tied to your home server external ip address. Its secure because there is no password to brute force and Fail2Ban is killing external attempts. You can secure it further using more research, but this is the basic answer.

Nope, you aren't missing the question. That's just a lot of stuff I didn't know to do (or how to do). Thanks!

You can use ZeroTier if you need to traverse a NAT or don't have a static IP and you don't want to deal with DynDNS. That's what I do to access my work computer. It's free and works perfectly.

Yes, a lot. but I don't really have a permanent location to store the server unfortunately.

While you're right that buying your own GPU makes a lot of sense for personal use or small tasks, if you factor in the cost of keeping a ML team waiting for the network to finish training, then it might be cheaper to invest $7000 and have it run in a few hours instead of weeks.

Also, consider the efficiency of owning such a 16 GPU beast: if you don't have tasks to run 100% of the time, then you're losing a lot by just keeping it idle. Cloud providers can reuse the same GPU for other clients in the meantime.

That's not the use-case for this product. You can fiddle around with a single card till you get it right and then run the real job on EC2 where you most likely order dozens of these machines and run them for weeks/months. It's rarely economical to purchase hardware at that scale, unless if you are AWS renting out computation to others. At that scale you have to think about way more cost factors than raw hardware cost.

By the way, I own a 1080 since two weeks and I can't overstate how powerful this thing is. Even if you are not into gaming, getting a 1080 is still a considerable option if you want to experiment with deep learning.

Get an GTX 1080 for your local box and use it to write and test the model, then run in cloud for training. Both are necessary, in fact, they are complementary. If you want to run random hyperparameter search (train 50 times) then you benefit from many GPUs, but a single 1080 doesn't scale.

I question the amount of people that would be buying these for research AND gaming.

I saw quite a few asking for PC builds for both. Might not be too common, but is also not something that happens only once a year.

The demographic overlap between "gamer" and "machine learning researcher" is likely very high.

Likely how??

I guess: University students.

University students can afford $700 graphic cards?

I would assume they have (or have access to) a computer fitted out in a research lab or something which would be seperate from their "gaming rig".

For many students, I suspect the "computer fitted out in a research lab" is some dell desktop box someone shoved a gaming GPU in 3 years ago and that they have to share with 5-20 other students. Better not break it.

If you are into the topic and want to work on things outside of assigned projects or course work, upgrading a personal machine doesn't seem far-fetched if you can make room in the budget for it. $700 isn't cheap, but doable for many as a one-off purchase if they want it.

> Likely how??

Those who are predisposed to buying & installing high-end graphics cards are likely to be either gamers, interested in machine learning or both

That's not quite correct - it might be fair to say 'machine learning researchers are likely to be gamers', but it's not fair to say 'gamers are likely to be machine learning researchers'

or P(X|Y) != P(Y|X)

Yes, that would have been a better way to state it.

Im assuming most machine learning researchers who have big computers with expensive graphics cards for research wouldn't have it in a personal computer they can easily game on.

While I'm sure your statement is true for persons, I doubt its true for the computers themselves.

This sort of baseless statement happens all the time in our field. Gaming is not technical. Gaming has no relationship with programming, unless you are programming games, which is a completely different subset of people. The misuse of "nerdy" to refer to people who consume a mainstream source of entertainment is baffling to me. Nobody would say someone is nerdy for using social media for hours a day, and yet they both require the same amount of technical proficiency.

Most people I know who got into the field via gaming were either completely incompetent and have since left, or do not label themselves as 'gamers' anymore.

The common label is "graphics programmers", a.k.a. "demoscene people." There's a large overlap between those and a certain kind of gamer—the ones that create Tool-Assisted Speedruns, for example.

It's a simple matter of demographics.

Who are the majority of machine learning researchers? PhD students in CS (aka majority male) between 22 and, what, 26 years?

What percentage of male computer science students between 22 and 26 years DOESN'T play games?

You will need time to buy that gpu, upgrade your PC, realise that it doesn't fit into your existing case and that you need a better power supply. Then some time to find a better place to be than sitting next to that really loud and hot PC. And it all takes optimistically days, while you can start computing on EC2 within in an hour. Plus it doesn't scale, so you cannot just speed up the results by paying some more. You may need to wait a month (and then realise the param was wrong and you need to redo it)

Surely there are some cases when you are better off computing on your own GPU, but I think in all of them you assign pretty low value to the time spent.

If you're a regulated financial institution, say performing complex risk valuations, you need the ECC ram. It's a consumer device vs an enterprise, professional device.

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