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Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 seconds

Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 seconds

cifar10-fast

Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 seconds.

cifar10-fast

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

  • setup an instance with AMI: Deep Learning AMI (Ubuntu) Version 11.0 (ami-c47c28bc in us-west-2)
  • ssh into the instance: ssh -i $KEY_PAIR [email protected]$PUBLIC_IP_ADDRESS -L 8901:localhost:8901
  • on the remote machine
    • source activate pytorch_p36
    • pip install pydot (optional for network visualisation)
    • git clone https://github.com/davidcpage/cifar10-fast.git
    • jupyter notebook --no-browser --port=8901
  • open the jupyter notebook url in a browser, open demo.ipynb and run all the cells

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.

GitHub