Just signed up and ported my model + data:
- it's indeed noticeably faster than the Google VMs. As usual, I compiled tensorflow for this GPU vs K80 (feature 6.1 vs 3.7).
- ubuntu 16 minimal is indeed "minimal" ! but it worked...
- GTX 1080 (7.92GB) has less GPU RAM than the K80 (11.17GiB) -
this required me to reduce the model design slightly.
For my model/data, Hetzner runs 1 training epoch in 1 hr vs 1.75 hr for Google. I'm moving the rest of my work over tomorrow. When Google has TPUs available, I'll look at it again.
This is presumably just the full board versus half nomenclature noted above. But yes, consumer GPUs are way more cost competitive than Tesla class parts. Being able to train bigger models is valuable to some folks, but not everyone, so I don't begrudge using the GTX line.
(Also, I can no longer edit, but a colleague pointed out that I should have read more carefully. A GTX 1080 is a Pascal part, which compared to the poor old Kepler in K80s, it'll really shine. Volta all the more so in the next year).
For my model/data, Hetzner runs 1 training epoch in 1 hr vs 1.75 hr for Google. I'm moving the rest of my work over tomorrow. When Google has TPUs available, I'll look at it again.
thanks!! for the tip.