5 ms·

The machine used is a 36-core + single gpu. So not quite a home computer yet but this is some serious progress! Paper: https://arxiv.org/abs/2006.11751 https:/

The machine used is a 36-core + single gpu. So not quite a home computer yet but this is some serious progress! Paper: https://arxiv.org/abs/2006.11751 https://arxiv.org/abs/2006.11751 Source: https://github.com/alex-petrenko/sample-factory https://github.com/alex-petrenko/sample-factory

cheez 6y ago
That's my machine!
cbozeman 6y ago
I dunno... $8000 builds a 64c/128t 256 GB RAM workstation with the same GPU these researchers used (https://pcpartpicker.com/list/P6WTL2 https://pcpartpicker.com/list/P6WTL2). That's arguably in the realm of home computer for just about anyone making $90,000 and above, I would think; I would also think anyone working in those fields could command at least that salary or greater, unless they're truly entry level positions. Seems it would be a reasonable investment for someone actively working in the area of machine learning / artificial intelligence.
- eanzenberg 6y ago
  Anyone that can afford a car can afford this.
  - 6510 6y ago
    The interesting places you could take it!
  - deleted 6y ago
    [deleted]
- nqzero 6y ago
  what's the per-hour cost spot price of this machine on AWS ?
  - ChuckMcM 6y ago
    well an m5ad.24xlarge is 96 threads and 384G with your own 2xSSD (saves on EBS bandwidth costs). So fewer threads but a bit more memory. (we'll guess that is a 48 core EPYC 7642 equivalent with 96 threads since there is no 96 core version) That bad boy costs $0.96/hr on the current spot prices (https://aws.amazon.com/ec2/spot/pricing/ https://aws.amazon.com/ec2/spot/pricing/)
    - dmurray 6y ago
      How does having your own SSD save on bandwidth? You have to send the data to and from the cores where the computation is taking place. There are certainly some workloads where it makes sense to own your own storage and rent computation, but you can't assume that by default for a "powerful AI" workload.
      - cavisne 6y ago
        It saves on bandwidth $ costs to EBS (AWS block storage)
    - qayxc 6y ago
      That instance type is missing the RTX 2080 Ti-equivalent GPU, though... The closest in performance to that GPU would be the V100 found in P3-instances.
      - sabalaba 6y ago
        Yea which are $3 / hour. Or, you could, shameless plug, use Lambda's GPU cloud which has V100s for $1.5 / hour! https://lambdalabs.com/service/gpu-cloud https://lambdalabs.com/service/gpu-cloud I'm embarrassed that I feel so compelled to post this--on a Friday night at that--I apologize.
        manjunaths 6y ago
        > I'm embarrassed that I feel so compelled to post this--on a Friday night at that--I apologize. Don't be. Finding gems like this is why some of us read the comments.
- andrewprock 6y ago
  If you don't shoot for top end, you can get get a rocking machine for under $1600: https://pcpartpicker.com/list/DGhwBZ https://pcpartpicker.com/list/DGhwBZ 12c/24t, 64G with a high end video card.
  - godelski 6y ago
    In a ML setup your GPU is going to be the workhorse. I'd spend less on the CPU and memory and spend more on the GPU. Throwing something together REALLY quick (I'm being lazy and not checking) I'd go something closer to this https://pcpartpicker.com/list/T9Ds27 https://pcpartpicker.com/list/T9Ds27 because of the graphics card. But getting a Ti would improve a lot just for GPU memory. You could get a lot done with a machine like this though (I assume it would be good for gaming too. Disclosure: not a gamer). tldr: upgrade GPU, downgrade CPU and ram to keep similar pricing.
    - joshvm 6y ago
      This depends quite a lot on the domain. In some image processing tasks you can actually be cpu-bound during dataloading. So either you get tons of RAM and preload the dataset, or you use more cores to queue up batches. You still need a good GPU and generally I'd agree to prioritise that first.
      - godelski 6y ago
        You can get CPU bound, I am $100 under and you could put money towards that or RAM. I did also leave a path to upgrade RAM. But that said, I've been working with image generation a lot lately and CPU really isn't my bottleneck.
      - mpfundstein 6y ago
        If doing image processing (or NLP with rec nets), I wouldn't save on VRAM. 11GB minimum (2080/1080TI). Otherwise you can't even run the bigger nets with good image resolution.
    - junar 6y ago
- scoopertrooper 6y ago
  They used an Nvidia 2080 TI, which is expensive today, but rumours are that the entry level 3XXX series will be similar to the 2080 TI in performance. That should be dropping in about a month!
gameswithgo 6y ago
that is a home computer bow thanks to threadripper
godelski 6y ago
They mention multiple architectures, including down to a 10 core, 128GB ram, and 1080Ti setup. Next setup is improved CPU, double ram, and a 2080Ti. Third setup is 4 2080Tis. See table A.1
joe_the_user 6y ago
Serious question, is there are reason to have a GPU and that many cores? My assumption would that either the GPU or the CPU is the bottleneck, most likely the GPU. Why not spend money for more GPU and fewer cores?
- datameta 6y ago
  For shallower models CPUs aren't overshadowed by GPUs as much - but beyond a certain # of parameters the CPU loses out as GPUs do vector math highly efficiently. Some experiment results: https://azure.microsoft.com/en-us/blog/gpus-vs-cpus-for-deployment-of-deep-learning-models/ https://azure.microsoft.com/en-us/blog/gpus-vs-cpus-for-depl...
  - joe_the_user 6y ago
    But that's a fight between either CPUs OR GPU. It doesn't given indication that both could be useful simultaneously.
- chrisbroadfoot 6y ago
  "The team also tested their approach on a collection of 30 challenges in DeepMind Lab using a more powerful 36-core 4-GPU machine. The resulting AI significantly outperformed the original AI that DeepMind used to tackle the challenge, which was trained on a large computing cluster." Well, they presumably tested the same CPU with 4 GPUs (2080 Ti I think) - maybe they wanted to compare.

cheez 6y ago

That's my machine!

cbozeman 6y ago

I dunno... $8000 builds a 64c/128t 256 GB RAM workstation with the same GPU these researchers used (https://pcpartpicker.com/list/P6WTL2 https://pcpartpicker.com/list/P6WTL2). That's arguably in the realm of home computer for just about anyone making $90,000 and above, I would think; I would also think anyone working in those fields could command at least that salary or greater, unless they're truly entry level positions. Seems it would be a reasonable investment for someone actively working in the area of machine learning / artificial intelligence.

eanzenberg 6y ago
Anyone that can afford a car can afford this.
- 6510 6y ago
  The interesting places you could take it!
- deleted 6y ago
  [deleted]
nqzero 6y ago
what's the per-hour cost spot price of this machine on AWS ?
- ChuckMcM 6y ago
  well an m5ad.24xlarge is 96 threads and 384G with your own 2xSSD (saves on EBS bandwidth costs). So fewer threads but a bit more memory. (we'll guess that is a 48 core EPYC 7642 equivalent with 96 threads since there is no 96 core version) That bad boy costs $0.96/hr on the current spot prices (https://aws.amazon.com/ec2/spot/pricing/ https://aws.amazon.com/ec2/spot/pricing/)
  - dmurray 6y ago
    How does having your own SSD save on bandwidth? You have to send the data to and from the cores where the computation is taking place. There are certainly some workloads where it makes sense to own your own storage and rent computation, but you can't assume that by default for a "powerful AI" workload.
    - cavisne 6y ago
      It saves on bandwidth $ costs to EBS (AWS block storage)
  - qayxc 6y ago
    That instance type is missing the RTX 2080 Ti-equivalent GPU, though... The closest in performance to that GPU would be the V100 found in P3-instances.
    - sabalaba 6y ago
      Yea which are $3 / hour. Or, you could, shameless plug, use Lambda's GPU cloud which has V100s for $1.5 / hour! https://lambdalabs.com/service/gpu-cloud https://lambdalabs.com/service/gpu-cloud I'm embarrassed that I feel so compelled to post this--on a Friday night at that--I apologize.
      - manjunaths 6y ago
        > I'm embarrassed that I feel so compelled to post this--on a Friday night at that--I apologize. Don't be. Finding gems like this is why some of us read the comments.
andrewprock 6y ago
If you don't shoot for top end, you can get get a rocking machine for under $1600: https://pcpartpicker.com/list/DGhwBZ https://pcpartpicker.com/list/DGhwBZ 12c/24t, 64G with a high end video card.
- godelski 6y ago
  In a ML setup your GPU is going to be the workhorse. I'd spend less on the CPU and memory and spend more on the GPU. Throwing something together REALLY quick (I'm being lazy and not checking) I'd go something closer to this https://pcpartpicker.com/list/T9Ds27 https://pcpartpicker.com/list/T9Ds27 because of the graphics card. But getting a Ti would improve a lot just for GPU memory. You could get a lot done with a machine like this though (I assume it would be good for gaming too. Disclosure: not a gamer). tldr: upgrade GPU, downgrade CPU and ram to keep similar pricing.
  - joshvm 6y ago
    This depends quite a lot on the domain. In some image processing tasks you can actually be cpu-bound during dataloading. So either you get tons of RAM and preload the dataset, or you use more cores to queue up batches. You still need a good GPU and generally I'd agree to prioritise that first.
    - godelski 6y ago
      You can get CPU bound, I am $100 under and you could put money towards that or RAM. I did also leave a path to upgrade RAM. But that said, I've been working with image generation a lot lately and CPU really isn't my bottleneck.
    - mpfundstein 6y ago
      If doing image processing (or NLP with rec nets), I wouldn't save on VRAM. 11GB minimum (2080/1080TI). Otherwise you can't even run the bigger nets with good image resolution.
  - junar 6y ago
scoopertrooper 6y ago
They used an Nvidia 2080 TI, which is expensive today, but rumours are that the entry level 3XXX series will be similar to the 2080 TI in performance. That should be dropping in about a month!

gameswithgo 6y ago

that is a home computer bow thanks to threadripper

godelski 6y ago

They mention multiple architectures, including down to a 10 core, 128GB ram, and 1080Ti setup. Next setup is improved CPU, double ram, and a 2080Ti. Third setup is 4 2080Tis. See table A.1

joe_the_user 6y ago

Serious question, is there are reason to have a GPU and that many cores? My assumption would that either the GPU or the CPU is the bottleneck, most likely the GPU. Why not spend money for more GPU and fewer cores?

datameta 6y ago
For shallower models CPUs aren't overshadowed by GPUs as much - but beyond a certain # of parameters the CPU loses out as GPUs do vector math highly efficiently. Some experiment results: https://azure.microsoft.com/en-us/blog/gpus-vs-cpus-for-deployment-of-deep-learning-models/ https://azure.microsoft.com/en-us/blog/gpus-vs-cpus-for-depl...
- joe_the_user 6y ago
  But that's a fight between either CPUs OR GPU. It doesn't given indication that both could be useful simultaneously.
chrisbroadfoot 6y ago
"The team also tested their approach on a collection of 30 challenges in DeepMind Lab using a more powerful 36-core 4-GPU machine. The resulting AI significantly outperformed the original AI that DeepMind used to tackle the challenge, which was trained on a large computing cluster." Well, they presumably tested the same CPU with 4 GPUs (2080 Ti I think) - maybe they wanted to compare.