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UC Berkeley launches SkyPilot to help navigate soaring cloud costs

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Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#41

Post by Ar-Curunir »

Helmut10001 wrote: post #20

I wonder why UC Berkeley doesn't build a proper HPC, they have a Data School and should provide this service for free to their faculties. We have "free" HPC resources at TU Dresden (Germany) (meaning: faculties do not need to pay for using HPC resources and they are not calculated in project budgets). I once applied for a job at University of Virginia, and they didn't have a HPC - everything was bought from AWS. When…

Berkeley CS (and in particular the systems research labs like AmpLAB, RISE lab, etc.) gets enough funding from AWS, GCE, Azure, etc for it to be uneconomical to have a data center administered by the campus.

That being said, there are HPC facilities shared with LBNL, as well as smaller clusters operated by the department.

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#42

Post by reacharavindh »

Fomite wrote: post #38
Helmut10001 wrote: post #20

I wonder why UC Berkeley doesn't build a proper HPC, they have a Data School and should provide this service for free to their faculties. We have "free" HPC resources at TU Dresden (Germany) (meaning: faculties do not need to pay for using HPC resources and they are not calculated in project budgets). I once applied for a job at University of Virginia, and they didn't have a HPC - everything was bought from AWS. When…

At several universities I've been at, HPC groups have been utterly unprepared (and disinterested in becoming prepared) to handle PII or any sort of health or confidential data.

As we are talking anecdotes. Universities I’ve been at that researched sensitive data like human genetics and some commercially sensitive data have been excellent at data security, and provided a centralised HPC cluster at a marginal cost than it would have been at AWS..

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#43

Post by KronisLV »

Aren't AWS, Azure and GCP all comparatively expensive? They are good for certain workloads and it's good that this project exists, but if you wanted cheap cloud resources, you'd probably need to look at one of the smaller cloud vendors: Hetzner, Scaleway, DigitalOcean, Vultr, Contabo and others. Of course, if you need GPUs, things can get limiting.

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#44

Post by covi »

YetAnotherNick wrote: post #16
bashtoni wrote: post #3

From reading this launch post, I'm not convinced this is going to save too much money. The project automatically selects the cheapest cloud to run a job, and does it there - which sounds sensible. In reality though, these jobs presumably need large volumes of input data. If your input data is in cloud A, and you run a job in cloud B, typically any cost saving from running in cloud B will be more than offset by the eg…

I think it’s common to train 100s of models on the same data for experiments. Then you would only need to copy data once to all the cloud storage and run experiments as you wish. Also most cloud provider don’t charge for ingress so you could move the data from something like R2 to cloud as many times you want..

+1. We've heard from some heavy users that Cloudfare R2 is saving them $$$ on egress costs: https://www.cloudflare.com/products/r2/

As outlined in the position paper (linked by another commenter) we believe such tailwinds are increasingly helping foster the "Sky" and making workloads moving between clouds much easier.

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#45

Post by ww520 »

caycep wrote: post #31
ww520 wrote: post #7

I feel that the old SETI@home project can use a comeback. Most home machines have powerful GPU these days. The ML problems are embarrassingly parallel and can be distributed to the home machines. Just need to work out the economy for everyone involved.

why....that sounds like Web3! (I kid I kid...)

Actually it's a good use of Web3, handling of the billing and revenue sharing portion of the problem.

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#46

Post by throwawaysleep »

Fomite wrote: post #38
Helmut10001 wrote: post #20

I wonder why UC Berkeley doesn't build a proper HPC, they have a Data School and should provide this service for free to their faculties. We have "free" HPC resources at TU Dresden (Germany) (meaning: faculties do not need to pay for using HPC resources and they are not calculated in project budgets). I once applied for a job at University of Virginia, and they didn't have a HPC - everything was bought from AWS. When…

At several universities I've been at, HPC groups have been utterly unprepared (and disinterested in becoming prepared) to handle PII or any sort of health or confidential data.

Or any interest in reliability or making it usable. Students are there for passion. People who work in university IT are just utterly unemployable elsewhere.

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#47

Post by laurencerowe »

Helmut10001 wrote: post #20

I wonder why UC Berkeley doesn't build a proper HPC, they have a Data School and should provide this service for free to their faculties. We have "free" HPC resources at TU Dresden (Germany) (meaning: faculties do not need to pay for using HPC resources and they are not calculated in project budgets). I once applied for a job at University of Virginia, and they didn't have a HPC - everything was bought from AWS. When…

It really depends on the workload. GPU clusters are usually cheaper to run in house since Nvidia let you use regular GPUs for research which end up cheaper than cloud GPUs. And often Universities will charge less overhead for capital expenses on a grant which can artificially reduce the cost of running it yourself.

The big downside of institutional HPC is it can be difficult to get stuff running on the ancient distributions they're stuck on. Nowadays perhaps Docker makes things easier but even running a Docker container was a challenge when I worked at a university some 5-10 years ago. As a software engineer who had not used HPC previously I found it much more difficult than just firing up jobs on AWS which had documentation (though for many researchers it was the opposite.)

I also kind of object to the term HPC... with the exception of a small number of shared memory clusters used for physics simulations they're usually just a bunch of standard servers often with incredibly slow network storage. Nothing high performance about them.

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#48

Post by laurencerowe »

reacharavindh wrote: post #42
Fomite wrote: post #38

Earlier quoted context omitted.

At several universities I've been at, HPC groups have been utterly unprepared (and disinterested in becoming prepared) to handle PII or any sort of health or confidential data.

As we are talking anecdotes. Universities I’ve been at that researched sensitive data like human genetics and some commercially sensitive data have been excellent at data security, and provided a centralised HPC cluster at a marginal cost than it would have been at AWS..

While hospital records are protected, traditionally genomics data is not considered PII so is not covered by HIPAA. It does seem a bit of a farce though considering it could be uploaded to GEDmatch and have a good chance of finding relations of person the sample was taken from...

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#49

Post by nix23 »

laurencerowe wrote: post #47
Helmut10001 wrote: post #20

I wonder why UC Berkeley doesn't build a proper HPC, they have a Data School and should provide this service for free to their faculties. We have "free" HPC resources at TU Dresden (Germany) (meaning: faculties do not need to pay for using HPC resources and they are not calculated in project budgets). I once applied for a job at University of Virginia, and they didn't have a HPC - everything was bought from AWS. When…

It really depends on the workload. GPU clusters are usually cheaper to run in house since Nvidia let you use regular GPUs for research which end up cheaper than cloud GPUs. And often Universities will charge less overhead for capital expenses on a grant which can artificially reduce the cost of running it yourself. The big downside of institutional HPC is it can be difficult to get stuff running on the ancient distri…

>since Nvidia let you use regular GPUs for research which end up cheaper than cloud GPUs.

Funfact, that's illegal in Europe, producers have no right to tell you what to do with their products and your property.

Re: UC Berkeley launches SkyPilot to help navigate soaring cloud costs

#50

Post by laurencerowe »

Having worked on large compute intensive projects in both academia and industry I am somewhat sceptical about multi-cloud tools. Normally you do your compute where the data is since egress is so expensive. And it just doesn't seem worth developing against a cloud agnostic API and limiting myself to the lowest common denominator.

If you have a large workload though they may be helpful as a negotiating tactic to play one cloud off against another.

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