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>For the V100 experiments, we used a p3.8xlarge instance (Xeon E5–2686@2.30GHz 16 cores, 244 GB memory, Ubuntu 16.04) on AWS with four V100 GPUs (16 GB of memor
by Tenoke 9y ago
>For the V100 experiments, we used a p3.8xlarge instance (Xeon E5–2686@2.30GHz 16 cores, 244 GB memory, Ubuntu 16.04) on AWS with four V100 GPUs (16 GB of memory each). For the TPU experiments, we used a small n1-standard-4 instance as host (Xeon@2.3GHz two cores, 15 GB memory, Debian 9) for which we provisioned a Cloud TPU (v2–8) consisting of four TPUv2 chips (16 GB of memory each).
A bit odd that the TPUs are provisioned on such a weaker machine compared to the V100s, especially when there were comparisons which included augmentation and other processing outside of the TPU.
- elmarhaussmann 9y agoAll of the computation, including pre-processing, is offloaded to the TPU. The weak machine is really just idling. A bigger one will only cost money and have no measurable effect on the performance.
- shaklee3 9y agoThis is why my previous comment mentioned that GCP is a better benchmark for this since you can select the number of CPUs to match with the GPUs to some extent. You can get a rough idea of the savings by looking at their P100 instances.
- KaoruAoiShiho 9y agoWhat is the cost difference between the CPUs on the google cloud vs AWS? How would adjusting for it effect the cost/images ratio?
- deleted 9y ago[deleted]
- puzzle 9y agoThe TPU is not really just the chip. It has an actual machine that is provisioned behind the scenes and accepts RPC calls. Good luck finding out its specs. All you're supposed to care about are the address and port it answers at.