HN user
Cloud TPU product manager here. As I said in another thread:
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.
Cloud TPU product manager here.
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.
Looked into this a bit more. The GTX 1080 is based on the Pascal architecture and so will be faster than any Kepler-based K80 on any cloud - even faster than a K80 card with 2 GPUs. The GTX is a consumer board and is less expensive than the datacenter equivalent P100 PCIe card. The P100 has 16 GB ram and HBM2 memory (twice the memory and more than twice the memory bandwidth) and supports ECC if you care about detecting memory corruption. The P100 will be faster than the GTX 1080 once it is available. As I said before, GCP offers K80 GPUs in passthrough mode and you can use a single K80 die ($0.70 / hour billed by the minute) or you can attach up to 8 K80 GPUs to a single VM. Disclaimer: I am a product manager for GPUs in Google Cloud.
Each Google K80s is one GPU or 1/2 of a K80 board, so technically you are correct that a Google K80 GPU is half of a K80 board. However, they are offered in passthrough mode and achieve full performance. If you want a whole K80 board, attach 2 K80 GPUs to a single VM. You can have 1, 2, 4 or 8 K80 GPUs attached to each VM in GCP. (I'm one of the GPU product managers at Google Cloud).