https://d2l.ai/chapter_appendix-tools-for-deep-learning/selecting-servers-gpus.html
from https://alexiej.github.io/deepnn/#gpu-servers
from https://course.fast.ai/index.html
https://docs.nvidia.com/deeplearning/frameworks/
- https://docs.nvidia.com/deeplearning/frameworks/mxnet-release-notes/index.html
- https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html
- https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/index.html
NVIDIA Data Center Deep Learning Product Performance
Google pytorch vm
FrameView Performance and Power Benchmarking App
Graphics cards are investments, bought on the promise of delivering excellent performance in that card’s class for at least 2 to 3 years. It’s therefore important that the benchmarks of these cards, which you’re likely using as research, are accurate and cover all the bases, showing frame rates, frame times, power usage, performance per watt, and more.
Comparison of learning and inference speed of different gpu with various cnn models in pytorch
- 1080TI
- TITAN V
- 2080TI