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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

#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 egress cost to get the data out of cloud A.

This project is therefore only useful for scenarios where you need to do large amounts of compute on relatively small volumes of data. Is that really a common scenario?

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

#4
At some point people will rediscover that "buying" whole sets of equipment through a lease financing company on a lease-to-own plan with $1 end payment, and colocating it the traditional way can often be significantly cheaper than paying endless "cloud" costs.

naysayers will say, but the cloud allows you abstract away your salary costs of engineers! look at all the people you aren't hiring!

I say: If your needs are sufficiently complex you already have a number of well paid six figure salary people on staff to admin your cloud-based software architecture/setup... Hire people who know how to build infrastructure down to the bare metal.

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

#5
It's fascinating to see the descendants of the original AMP Lab (https://amplab.cs.berkeley.edu/) are still generating value long after it ended. For reference, Spark and Mesos are two other projects that came out of the same effort.

This blog post from the authors https://medium.com/@zongheng_yang/skypilot-ml-and-data-scien...

I believe autostop (simply stopping idle clusters) alone can masively (X times) costs for many organizations.

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

#6
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…

They have a project which addresses this concern as well: https://skyplane.org/en/latest/benchmark.html

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

#8
post #4

At some point people will rediscover that "buying" whole sets of equipment through a lease financing company on a lease-to-own plan with $1 end payment, and colocating it the traditional way can often be significantly cheaper than paying endless "cloud" costs. naysayers will say, but the cloud allows you abstract away your salary costs of engineers! look at all the people you aren't hiring! I say: If your needs are s…

It's funny that that is exactly how disruption works. Use tons of VC money to flood a market with a cheaper product, become dominant, then jack up prices hoping people forgot the alternatives.

It doesn't have to be jacking up prices for exploitation either, just sometimes the costs were always there dictating the price and, eventually, you have to foot the bill for those costs.

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

#9
We’re using SkyPilot and Skyplane at Berkeley and the Department of Defense to scale our damage assessment from satellite imagery machine learning models [0] to run for all of Ukraine, all the time! We’re able to detect instances of war crimes rapidly and use them to help plan the reconstruction of Ukraine.

[0] https://xview2.org/

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

#10
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.

https://vast.ai?
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