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Disclaimer: I work on Google Cloud (and launched preemptible VMs as well as contributed to our GPU efforts). That's for 1xV100. The interesting action is in m
by boulos 9y ago
Disclaimer: I work on Google Cloud (and launched preemptible VMs as well as contributed to our GPU efforts).
That's for 1xV100.
The interesting action is in multiple GPUs at once, for say deep learning. The spot market is fairly non-linear in pricing, so the 8xV100 is more than 8x as much, while it's strictly linear for Preemptible.
Similarly, you have to attach a lot of cores (Broadwell only) and RAM to the p3s, which is often unnecessary for GPU applications (you often want 1-2x the total GPU memory depending on your application, but rarely do you need 64 vcpus to go with them).
Additionally, if for some reason you want super fast local SSD to go with your GPUs (say a GPU accelerated database), you can do so on GCE.
tl;dr: linear, flexible pricing that usually wins (plus Skylake, local SSD and more).
- singhrac 9y agoThose are all good points - I was focusing on my use case (i.e. small deep learning jobs), but definitely locking into pre-built configurations can be wasteful. I'll likely use GC anyway, but appreciate the extra reasons given.
- boulos 9y agoEither way, I'd encourage trying out multi-GPU training. The latest NVLINK is pretty impressive, and reducing your iteration time for free (because linear pricing) is pure goodness.