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Building your own deep learning computer is 10x cheaper than AWS

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Re: Building your own deep learning computer is 10x cheaper than AWS

#201

Earlier quoted context omitted.

They seriously can't buy a graphics card and slap it in the PCIe slot?

You're neglecting management costs. IT teams don't buy hardware with corporate credit cards, they have to work through pre-existing requisition processes that properly budget for the hardware, make sure support contracts are in place, etc. You have to migrate whatever workload off the server where you installed the GPU (politically problematic since Murphy promises you that your users will be connecting to the server…

How often do employers wait for their employees' approval before making a decision?

Re: Building your own deep learning computer is 10x cheaper than AWS

#202
post #73

Earlier quoted context omitted.

They seriously can't buy a graphics card and slap it in the PCIe slot?

They seriously can't buy a graphics card and slap it in the PCIe slot? Well I mean they obviously can... First of all you're assuming IT will just let you have a decent machines with PCIe slots. It's all about laptops don't you know. Workstations are so 2012. Secondly while they might, after much begging, let me have one graphics card to put into this one old workstation I've scavenged, they certainly won't let me ha…

Not to mention you'll be on some awful Dell tower with no 6/8-pin power connectors and the case won't fit anything reasonably powerful, and the IT gatekeepers have no idea what you mean when you explain this problem.

Re: Building your own deep learning computer is 10x cheaper than AWS

#203
post #122

Earlier quoted context omitted.

That's very passive aggressive way to deal with it. If that's your only option you are really a cubicle slave in corporate hell. In my opinion it's better to escalate upwards with proposals and not back down easily. You just have to frame it correctly and use right names and terms. * Usually big companies understand the concept of "a lab" that has infrastructure managed outside the corporate IT. Once you fight the ha…

I wanted to put an Ubuntu partition on my work PC for Python deep learning work, as I'm significantly faster and happier on it. When I mentioned it to the sysadmin, he said "I'm not allowing that. Linux is like Wikipedia, any idiot can contribute to it. Windows is made by professionals so it has to be better."

Wow. Sometimes I wonder how these people even get hired. I guess a decent workaround would be if you can just get Docker approved, then you can do what you want.

Re: Building your own deep learning computer is 10x cheaper than AWS

#204
post #122

Earlier quoted context omitted.

That's very passive aggressive way to deal with it. If that's your only option you are really a cubicle slave in corporate hell. In my opinion it's better to escalate upwards with proposals and not back down easily. You just have to frame it correctly and use right names and terms. * Usually big companies understand the concept of "a lab" that has infrastructure managed outside the corporate IT. Once you fight the ha…

I wanted to put an Ubuntu partition on my work PC for Python deep learning work, as I'm significantly faster and happier on it. When I mentioned it to the sysadmin, he said "I'm not allowing that. Linux is like Wikipedia, any idiot can contribute to it. Windows is made by professionals so it has to be better."

And they call themselves a "sysadmin" !

Re: Building your own deep learning computer is 10x cheaper than AWS

#205
post #86

Earlier quoted context omitted.

This has less to do with cloud vs own hardware and more about how the company is structured. I've worked in companies before with an ops department: a few people responsible for managing all the cloud servers. All the devs (like me at the time) work locally and have access to an isolated dev/uat environment provided by that team. That team had most of the show automated, they weren't really provisioning any machines…

> I guess this is what you are calling a pre-devops workflow? In a lot of fields not all devs are allowed to see/touch the complete production environment. Not everyone can go the netflix way of "everyone pushes to production and we'll just fix it when it breaks" Unwillingness shouldn't be confused with ability. Most companies can do this if they're not handling PII/PHI. It takes investment in smart people and time b…

Very much so when I was arguably doing devops for BT using PR1ME super minis' back in the day. A mate who was in operations for an IBM shop was horrified that our team were allowed to write our own JCL.

Re: Building your own deep learning computer is 10x cheaper than AWS

#206
post #29

You're forgetting the cost of fighting IT in a bureaucratic corporation to get them to let you buy/run non-standard hardware Much easier to spend huge amounts of money of Azure/AWS and politely tell them it's their own fucking fault when they complain about the costs. (what me? no I'm not bitter, why do you ask?)

You always have at least two options, and this case is not extreme -

1. You can blow up any amount if you like to. 2. Or, you can figure out what you are trying to do. Then, learn how to do it better. There is a cheaper way to run in the cloud too - https://twitter.com/troyhunt/status/968407559102058496

Re: Building your own deep learning computer is 10x cheaper than AWS

#207
post #79

Earlier quoted context omitted.

Respectfully, those companies' cloud architects suck. If someone goes to the trouble to migrate onto cloud, and then replicates pre-devops workflows... wow.

why not cut out the middle man and just say those companies suck, and the cloud isn't going to fix it for you.

Because that seems overly reductive.

There are things you can do in cloud (especially as an SMB) that weren't possible on-prem.

To look at those new opportunities and say "No, how can we do things exactly the way we were?" seems like the real mistake.

Re: Building your own deep learning computer is 10x cheaper than AWS

#208

Earlier quoted context omitted.

> I guess this is what you are calling a pre-devops workflow? In a lot of fields not all devs are allowed to see/touch the complete production environment. Not everyone can go the netflix way of "everyone pushes to production and we'll just fix it when it breaks" Unwillingness shouldn't be confused with ability. Most companies can do this if they're not handling PII/PHI. It takes investment in smart people and time b…

Very much so when I was arguably doing devops for BT using PR1ME super minis' back in the day. A mate who was in operations for an IBM shop was horrified that our team were allowed to write our own JCL.

One of my pet peeves is that there's no mandatory "History of ideas in software development" course in most CS curriculums.

Re: Building your own deep learning computer is 10x cheaper than AWS

#209

>"Nvidia contractually prohibits the use of GeForce and Titan cards in datacenters. So Amazon and other providers have to use the $8,500 datacenter version of the GPUs, and they have to charge a lot for renting it." I wonder if someone might provide some clarification on this. Is this to say only if a reseller buys directly from Nvidia they are compelled by some agreement they signed with Nvidia? How else would this…

Another comment in this thread said that it's due to the license on Nvidia's drivers. So technically you can use the hardware in a datacenter, just not with the official drivers. Unfortunately it seems that the open-source drivers aren't usable for most datacenter purposes, so this effectively limits how you can use the hardware (at least in North America, where they can enforce it).

Re: Building your own deep learning computer is 10x cheaper than AWS

#210
seems logical.. i would argue the inflection point in the ubiquity of pooled compute services came as bandwidth needs increased due to more businesses interacting directly, remotely and in real time, with customers, be they consumers or other businesses

if compute needs were all internal, as they are with desktop apps.. namely, compilation.., and with the majority of computationally expensive machine learning demands.. namely, training.. then i'd argue the pooled compute model would have remained niche

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