I think there should be a disclaimer here that while, yes, this will work , it will quickly become suboptimal (perhaps even dysfunctional) for a lot of work as you scale your hobby into something resembling more professional work. I personally would not start with a thousand dollar machine if you have any intention of doing serious computation in the future. While you can trade out parts pretty easy on a custom built…
so do you earn money out of that 10K investment on machines? I didn't read the article but what's the benchmark on deep learning machines? Still TFLOPS?
Serious question, why build a 10K$ machine like this rather than just spinning up some AWS instances? -edit- I know you mention wanting to pay for electricity rather than AWS. But that doesn't necessarily make financial sense.
Many prioritize protecting their data, eliminating the possibility of using the cloud. This is usually a business use case though rather than personal.
This is correct. I handle my own (daily) backups as a result, which are end-to-end encrypted from the machine.
I think there should be a disclaimer here that while, yes, this will work , it will quickly become suboptimal (perhaps even dysfunctional) for a lot of work as you scale your hobby into something resembling more professional work. I personally would not start with a thousand dollar machine if you have any intention of doing serious computation in the future. While you can trade out parts pretty easy on a custom built…
On the other hand, by the time anyone starting just now learn enough ML to be dangerous, the price of the $10,000 hardware would drop to $5,000 or less, so starting with the cheapest yet decent option seems a better approach...
Fair point. I'm just quibbling about where "decent" begins, and adding in the point about ease of upgrades. Replacing a GPU is pretty easy. Replacing a motherboard because you don't have enough PCIe slots will require tearing down the majority of the machine and rebuilding it.
The latter is more likely to happen if you start down this path at rock bottom prices.
On the other hand, by the time anyone starting just now learn enough ML to be dangerous, the price of the $10,000 hardware would drop to $5,000 or less, so starting with the cheapest yet decent option seems a better approach...
Fair point. I'm just quibbling about where "decent" begins, and adding in the point about ease of upgrades. Replacing a GPU is pretty easy. Replacing a motherboard because you don't have enough PCIe slots will require tearing down the majority of the machine and rebuilding it. The latter is more likely to happen if you start down this path at rock bottom prices.
I began building one a year and a half ago, starting with a 5930k, 1x980ti, 32GB. This put me in a position to incrementally upgrade it starting at ~$2.5k. That being said, at this point the smallest AWS p2 instances are equivalent to my current setup, and it would take 150 days worth of running my hardware to make it cost-effective vs. AWS.
Serious question, why build a 10K$ machine like this rather than just spinning up some AWS instances? -edit- I know you mention wanting to pay for electricity rather than AWS. But that doesn't necessarily make financial sense.
Many prioritize protecting their data, eliminating the possibility of using the cloud. This is usually a business use case though rather than personal.
If it's a business use case severe enough that you can't trust AWS, you aren't going to wast time scraping together a cheap $1K or even $10K build while your $1K-$10K/day team is idle. You'll go looking for accessible powerful systems, and be willing to replace hardware as needed.
Not parent, but I've built my own computers for almost 20 years. As the articles author himself questions, he should have gotten the marginally more expensive CPU, and definitely the GPU with more RAM. * The overclockable CPU doesn't just mean that you very easily could get a 10%+ performance boost without much work, but also that you often (depending on your specific chip) can lower the voltage and make it run much…
Was years ago when I build my last PC and I'm out of the game. I'm tempted in build a hackintosh: https://www.tonymacx86.com/threads/hackintosh-cutting-edge-k... - Intel i7 Kaby Lake - No decided on motherboard. The one that cause me less trouble (for hackintosh) is fine. - GTI 750ti (have) or buy a pascal nvidia. - NVMe drive if possible - 32 GB RAM. - Probably a Thermaltake CORE P3 case. Not decided. I was thinking…
Just FYI, 10 series cards aren't supported by hackintosh currently (and for the foreseeable future).
I'm not sure why mITX was chosen - in my experience it's almost always more expensive and leaves no room for additional GPU expansion (Or network adapters/whatever card you might need) in the future. You can get 1151 mATX motherboards for more like $60-70 on Amazon instead of the $125 they paid. Also, paying $125 for 16GB of ram but not spending the extra $20 to have your CPU be able to overclock? I'm not sure where…
I was wondering that too. For something like this maximum room for future expansion seems good. In the past I've used mITX to build low-power, low-noise machines, rather than building a high-powered processing beast. On the other hand, it made the article more interesting for me. I can't really justify a home machine learning rig, but I was thinking I might soon replace that old mITX machine. (edit - just reached the…
I also prefer mITX and I have one I've tried to optimize for low noise and efficiency. Just seems ill-suited for this purpose.
I think there should be a disclaimer here that while, yes, this will work , it will quickly become suboptimal (perhaps even dysfunctional) for a lot of work as you scale your hobby into something resembling more professional work. I personally would not start with a thousand dollar machine if you have any intention of doing serious computation in the future. While you can trade out parts pretty easy on a custom built…
I wouldn't recommend 4 GPUs unless you are very confident the algorithms you are going to run will parallelize well, or that you need to run extensive parallel trials for various hyperparameters. It takes special effort to make use of multiple GPUs.
Fair point. I'm just quibbling about where "decent" begins, and adding in the point about ease of upgrades. Replacing a GPU is pretty easy. Replacing a motherboard because you don't have enough PCIe slots will require tearing down the majority of the machine and rebuilding it. The latter is more likely to happen if you start down this path at rock bottom prices.
I began building one a year and a half ago, starting with a 5930k, 1x980ti, 32GB. This put me in a position to incrementally upgrade it starting at ~$2.5k. That being said, at this point the smallest AWS p2 instances are equivalent to my current setup, and it would take 150 days worth of running my hardware to make it cost-effective vs. AWS.
When you build your first DL box you also get a moral boost. If you think every second is counted and penalized against you, you might use it less (such as when you're using cloud servers). But if it is already yours bought and paid for, then you can afford to run any crazy idea. A good compromise would be a local DL box and extra virtual machines on demand, for when you need to run extensive hyperparameter searches. If you make the leap and invest, at least you commit your money into the direction of ML, strengthening your resolve.
Many prioritize protecting their data, eliminating the possibility of using the cloud. This is usually a business use case though rather than personal.
If it's a business use case severe enough that you can't trust AWS, you aren't going to wast time scraping together a cheap $1K or even $10K build while your $1K-$10K/day team is idle. You'll go looking for accessible powerful systems, and be willing to replace hardware as needed.
This rationale is exactly what the AWS team has seeded in the community. I think it's time we quit parroting an inevitable conclusion that all things will and must go to the cloud.