Name: クラウドGPUチュートリアル（比較＆使用方法 (Cloud GPUs Tutorial (comparing & using))
Uploaded: 2021-01-14T10:36:22.000Z
Duration: 48 min 51 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

様態の副詞

And welcome to a video about kind of an overview of the current high end cloud GPU providers, as well as a bit of a tutorial for how to actually make efficient use of these servers.

程度の副詞

So you're not wasting time and money doing pretty processing or even data set, download or upload to a server, which can take a very, very long time and just waste like hundreds of dollars.

So with that, we've got a lot of information to cover.

So the first thing that we'd want to do is just kind of quickly as quickly as possible.

There's so many numbers that you've got to think about and look at it could get super confusing.

Hopefully, I can make it really easy for you.

First of all, the every provider has released.

Most providers have a cluster of deep user different Jeep use that they offer, but the best, the flagship GPU that pretty much everyone is offering is the V 100 so that Tesla V 100 from NVIDIA this GPU has 16 gigabytes of the ram per jeep.

You there is a 32 gigabyte, very into which you can see an example here.

Um and I think aws is the only one offering it.

I'll try to remember to look as we go through the prices, but you have to be spending a lot of money.

So I don't think many of you guys were looking for that.

I think also eight of us has the most GPU V Ram person singular machine that you could have.

So this is before you might think about doing distributed tense or floor pytorch or whatever, Um, which is always a huge mess.

So if you want the largest models possible, AWS is still gonna be the victor.

But in terms of the 100 purvey 100 GPU $3.6 you can sign up for one year and three year reserve prices, which lowers it.

But you'd be a fool to sign up for a GPU for three years were even probably one year.

So I'm not really gonna look at those prices, uh, moving on so on to azure also V 100 gp use one thing start paying a little bit of attention to his CPU and ram for a lot of deep learning tasks, this might be irrelevant to you, like you might not actually need very much at all.

Like maybe the only thing you're doing is like io or something like that from, like a data set.

But if you're doing something like reinforcement learning, the more V ram the better, and often also with reinforcement learning depending on what sort of environment you're using, more CPU, the better.

But my not also always be the case that Morsi few the better.

So So, uh, that is going to be a per use case scenario that I can't possibly dive in for you guys, but basically, Azure is exactly the same price.

But they do have different very variable, very varying amounts of ram storage.

And how many cores of CPU they all very so.

If if any of those things start to matter to you, pay attention moving along.

Also has, uh, Well, actually, they're cheaper.

I wonder if they were always to 48 anyways, to 48 per jeep.

You so actually cheaper than both aws hand azure.

Um and, uh, they have up to 1 28 gigabytes missing what they had only four times.

So actually, azure is kind of losing in in terms of how much possible, Although I wonder if this this one's a little more expensive.

I wonder if this is your 32 gigabyte variant V 100.

Uh, anyway, moving on to paper space, My longtime favorite cloud she BU provider simply because you spend it up, it's ready to go.

Another reason why I really enjoyed going with them.

Also, they offer other GPU is like the P 6000 which has 24 gigabytes of GPU memory, which is fun to play with.

Oh, that one thing That's kind of a downside for paper space, though, is you only have one GPU per machine.

So you're maxing out and either 16 gigabytes of V Aram here or 24 on the P 6000.

You can't have multiple G pews per machine.

So that's kind of a bummer moving on to Lynn Owed, which just started their GPU plans they have, rather than the V 100 the R T x 6000 which is kind of an interesting move.

Uh, so at first I had to take some time to look in what the difference is.

So the V 100 g pew, obviously 16 gigabytes of the Ram The R T X 6000 is 24 gigabytes of your, um so a bit more v ram.

Also, the other thing that matters is how quickly can we process.

Tensor is basically a raise, so the V 100 has 112 tensor T flops.

So Maur operations per second, more memory for half the price, at least of AWS and azure.

It's a little closer to paper space, but again, you can have up to four of these are t X six thousands.

So, uh, that's a pretty amazing offering.

You've got to be operating at a loss at this stage.

I do have referral link for Linda, and sometimes people get a little fidgety when, uh, have referral links.

Honestly, I have a relationship with Amazon Azure, Google Paper Space, and Lynn owed all of them.

It just so happens that right now, Lin owed is offering the most absurd deal possible.

But if you have a different provider, you're watching this later, and someone else has better offer you can.

You can use the same methodology that we're gonna use on any of these providers.

So, uh, so hopefully it will still be of use even after Maybe Leonard is not offering the best deal possible.

You'll go ahead and need to create account log in again.

If you got a little dot com slash Syntex, you should get here and you could just sign up for an account there.

You'll get a $20 credit, but I think you actually have to spend $20 then you will get your credit.

So So keep that in mind, but you'll still at some point get a $20 credit.

So Ah, once you do that, let me go ahead and log into my account.

You should look at something like this when you get to your dashboard.

So how do we How do we want to efficiently do this?

So your GPU servers coming to be many dollars an hour, as opposed to like If we were to look at Theo, other types of servers were talking like pennies per hour.

So So the first thing we want to do is we're gonna have, like, a simple, um, a simple, virtual private server that is going to serve as our sort of house of data.

So at least on Leonard, if you go to create, we're gonna create a Lynn owed.

We can use 1904 Uh, but hopefully don't forget, but wanting is 18 04 for at least our g p server.