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cifar10-fast

Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 seconds as described in this blog series.

<img src="net.svg">

Instructions to reproduce on an AWS p3.2xlarge instance:

In my test, 35 out of 50 runs reached 94% test set accuracy with a median of 94.08%. Runtime for 24 epochs is roughly 79s.

A second notebook experiments.ipynb contains code to reproduce the main results from the posts.

NB: demo.ipynb also works on the latest Deep Learning AMI (Ubuntu) Version 16.0, but some examples in experiments.ipynb trigger a core dump when using TensorCores in versions after 11.0.

DAWNBench

To reproduce DAWNBench timings, setup the AWS p3.2xlarge instance as above but instead of launching a jupyter notebook on the remote machine, change directory to cifar10-fast and run python dawn.py from the command line. Timings in DAWNBench format will be saved to logs.tsv.

Note that DAWNBench timings do not include validation time, as in this FAQ, but do include initial preprocessing, as indicated here. DAWNBench timing is roughly 74 seconds which breaks down as 79s (as above) -7s (validation)+ 2s (preprocessing).

Update 4th Dec 2018