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Reading the TOS it seems like this is a really great deal for Google: "When you upload, submit, store, send or receive content to or through our Services, you give Google (and those we work with) a worldwide license to use, host, store, reproduce, modify, create derivative works (such as those resulting from translations, adaptations or other changes we make so that your content works better with our Services), commu…
This is exciting. There are lots of specific reasons to choose Google Cloud over AWS (and vice versa), but proprietary hardware is surely an advantage that is going to be hard to replicate / compete with. If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups.
>If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups. Don't startups want to win a big exit though? Google won't need to buy the startup for billions, because the TOS already grants them permission to use all the models and training data for free. Seems like a Faustian bargain to me.
Earlier quoted context omitted.
Google claims[0] the TPU is many times faster for the workloads they've designed it for. > On our production AI workloads that utilize neural network inference, the TPU is 15x to 30x faster than contemporary GPUs and CPUs. As far as I know this will be the first opportunity for the public to prove those claims, as until now they've not been available on GCP. I don't mean to sound skeptical–I'm quite confident they're…
Keep in mind that what you linked refers to TPUv1, which is built for quantized 8-bit inference. The TPUv2, which was announced in this blog post, is for general purpose training and uses 32-bit weights, activations, and gradients. It will have very different performance characteristics.
This is exciting. There are lots of specific reasons to choose Google Cloud over AWS (and vice versa), but proprietary hardware is surely an advantage that is going to be hard to replicate / compete with. If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups.
Having had the chance to attend a fireside chat with leadership from Google and SAP, I get the sense that the hype is likely to hold up. There are a lot of big bets happening in the Enterprise space around this notion of efficient, easy to implement ML.
Reading the TOS it seems like this is a really great deal for Google: "When you upload, submit, store, send or receive content to or through our Services, you give Google (and those we work with) a worldwide license to use, host, store, reproduce, modify, create derivative works (such as those resulting from translations, adaptations or other changes we make so that your content works better with our Services), commu…
The TOS you are quoting only refers to the information you provide in the survey. Here are the Google Cloud TOS: https://cloud.google.com/terms/ if you're interested in what Cloud does with customers data.
5.2 Use of Customer Data. Google will not access or use Customer Data, except as necessary to provide the Services to Customer.
Your training data and models are secure.
This is exciting. There are lots of specific reasons to choose Google Cloud over AWS (and vice versa), but proprietary hardware is surely an advantage that is going to be hard to replicate / compete with. If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups.
>If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups. Don't startups want to win a big exit though? Google won't need to buy the startup for billions, because the TOS already grants them permission to use all the models and training data for free. Seems like a Faustian bargain to me.
1) AI startups usually don't have a lot of value to potential acquirers based on their data, but based on other things (e.g., talent, customers, business model, platform, brand). That's like saying you shouldn't use AWS because Amazon can just steal and commercialize all your data.
2) There are other companies than Google that acquire startups
Having said that, I highly doubt that Google can just use all the training data to on GCloud to launch their own products with that. They can surely look at it and maybe do stuff with them internally, but I am pretty sure that they can't use them commercially.
That $6.50/hr rate might be the big deal here. Amazon does offer instances with a V100 GPU ( https://aws.amazon.com/ec2/pricing/on-demand/ , the P3 instances), but if you're training something like ImageNet, you'll want the biggest image (p3.16xlarge) at $24.48/hr . Attaching a VM of similar power to a TPU on Google Compute Engine is much cheaper ( https://cloud.google.com/compute/pricing , n1-highmem-64, +$3.78/hr t…
Back of the envelope, a TPU costs a little more than 2x as much as a Volta on AWS P3, and delivers a little less than 2x the performance (180 TOPs for the TPU, 100 for Volta). On a raw performance/$ metric, I'm not sure the TPU is that interesting. It might be worth it if I were willing to pay a huge amount to get back results from an experiment faster, by using lots of TPUs- distributed learning on GPUs doesn't seem…
I know you hedged by saying "back of the envelope", but I'd much rather compare on real benchmarks than based on cited peak performance numbers, which are kind of meaningless.
That $6.50/hr rate might be the big deal here. Amazon does offer instances with a V100 GPU ( https://aws.amazon.com/ec2/pricing/on-demand/ , the P3 instances), but if you're training something like ImageNet, you'll want the biggest image (p3.16xlarge) at $24.48/hr . Attaching a VM of similar power to a TPU on Google Compute Engine is much cheaper ( https://cloud.google.com/compute/pricing , n1-highmem-64, +$3.78/hr t…
Back of the envelope, a TPU costs a little more than 2x as much as a Volta on AWS P3, and delivers a little less than 2x the performance (180 TOPs for the TPU, 100 for Volta). On a raw performance/$ metric, I'm not sure the TPU is that interesting. It might be worth it if I were willing to pay a huge amount to get back results from an experiment faster, by using lots of TPUs- distributed learning on GPUs doesn't seem…
Peak ops/second isn’t the only thing that matters though. You have to be able to feed the units. The V100 does lots of finer-grained matrix multiplies which can make it harder to keep up.
Don’t get me wrong, the V100 is a great chip. And we’re all looking forward to more (preferably third-party) benchmark results, to tease out when one is the better choice for a workload. But don’t just compare ops/second or any other architectural number.
Earlier quoted context omitted.
Do much deep learning training with the FPGAs?
Nope, but I'm sure others do.
https://techcrunch.com/2017/08/22/microsoft-brainwave-aims-t...
This is exciting. There are lots of specific reasons to choose Google Cloud over AWS (and vice versa), but proprietary hardware is surely an advantage that is going to be hard to replicate / compete with. If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups.
>If TPUs hold up to the hype, GCloud may become the de facto for ML/AI startups. Don't startups want to win a big exit though? Google won't need to buy the startup for billions, because the TOS already grants them permission to use all the models and training data for free. Seems like a Faustian bargain to me.
Also, even Google's general consume terms of service really isn't what you think: https://www.google.com/policies/terms/
"You retain ownership of any intellectual property rights that you hold in that content. In short, what belongs to you stays yours."