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Microsoft Readies Azure GPUs

virtualizationpractice.com

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Re: Microsoft Readies Azure GPUs

#31
post #11
post #8

Earlier quoted context omitted.

it sure does. I hope AWS and GCE will step up their GPU instance game

GCE should offer Tensorflow instances sometime soon. Closed alpha now. This will be a nice offering from Google which is no doubt part of the reason they open sourced Tensorflow. Which GPUs they offer and how much, nobody knows.

Nice. Would still prefer GPU instances I can do anything I want with

Re: Microsoft Readies Azure GPUs

#33
post #19
post #4

Not sure about the exact pricing shown here, but it looks like Azure blows AWS GPU pricing out of the water. Comparing specs: AWS g2.2xlarge: * DRAM: 15GB * GPU processor: 1xGK104 * 1536 CUDA cores * 2.29Tflops/s peak single precision throughput * GPU memory: 4GB * 160GB/s peak bandwidth * Price: $0.65/hr (US East, Linux) Azure NC6: * DRAM: 56GB * GPU processors: 2xGK210 * 4992 CUDA cores * 8.73Tflops/s peak single p…

What about comparing against spot market prices? Those AWS GPU instances are frequently $0.10 or $0.20 rather than $0.66. Not clearly a better deal anymore.

Yes, if you are ok with having your neural net suddenly killed mid-training.

Re: Microsoft Readies Azure GPUs

#36
post #20
post #11

Earlier quoted context omitted.

GCE should offer Tensorflow instances sometime soon. Closed alpha now. This will be a nice offering from Google which is no doubt part of the reason they open sourced Tensorflow. Which GPUs they offer and how much, nobody knows.

> Which GPUs they offer and how much, nobody knows. They might not offer GPUs at all. It may be that Google is planning to build a cloud using their own proprietary "Tensor Processing Unit" chip designed for TensorFlow ( https://en.wikipedia.org/wiki/Tensor_processing_unit )

TPUs are only useful for prediction, not for training.

Re: Microsoft Readies Azure GPUs

#37
post #33
post #19

Earlier quoted context omitted.

What about comparing against spot market prices? Those AWS GPU instances are frequently $0.10 or $0.20 rather than $0.66. Not clearly a better deal anymore.

Yes, if you are ok with having your neural net suddenly killed mid-training.

you can set your max spot price high, and/or you can periodically save the trained weights (to an EBS volume) and continue from where you left off, same as recovering from a crash. Probably not a huge deal.

Here's how you do it with Tensorflow:

https://www.tensorflow.org/versions/r0.10/how_tos/variables/...

The sweet spot would be updated AWS GPU instances at spot prices. You could spin up XX instances for a relatively short time to train fast. Needs some Kubernetes etc cluster setup for distributed training but should be doable. If the spot price is $0.1-$0.2/hr that's pretty attractive, considering that is close to the price of electricity alone

Re: Microsoft Readies Azure GPUs

#38

AWS: 4 year-old GPUs Azure: 2 year-old GPUs Wouldn't it be awesome if a cloud provider could keep up with the GPU roadmap and ship something within a year or so of its release?

The GPUs that hosting providers are buying need to support vGPU features, which are only support by the higher-end workstation and server products.

Re: Microsoft Readies Azure GPUs

#39
post #28

Earlier quoted context omitted.

Oh yeah the Kepler 2 K80's blow the K10 out of the water. I wonder just how expensive the enterprise grade Pascal boards are that even Microsoft doesn't offer them as an option. It's really amazing just how much money NVIDIA seems to be squeezing out of the compute market (almost makes you feel bad for AMD) especially considering that Kepler is a 4 almost 5 year old architecture by now. The K80 is still going for lik…

Is there any reason cloud providers don't offer virtualised Titan X, or GTX 1080?

"Gaming" GPU's have reduced compute capabilities (pretty horrible DP/HP compute performance compared to Tesla parts), and their SKU's are not really designed for enterprise integration (power requirements, thermals etc.). As far as "virtualization" goes NVIDIA actually restricts that in the driver, including PCIE passthrough especially to not allow cheaper desktop parts being used for low end computing as the margins per GPU are considerably higher with their enterprise parts.
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