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This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch.

• Training Arguments

  • -p, --plugin: Plugin to use. Choices: torch_ddp, torch_ddp_fp16, low_level_zero. Defaults to torch_ddp.
  • -r, --resume: Resume from checkpoint file path. Defaults to -1, which means not resuming.
  • -c, --checkpoint: The folder to save checkpoints. Defaults to ./checkpoint.
  • -i, --interval: Epoch interval to save checkpoints. Defaults to 5. If set to 0, no checkpoint will be saved.
  • --target_acc: Target accuracy. Raise exception if not reached. Defaults to None.

• Eval Arguments

  • -e, --epoch: select the epoch to evaluate
  • -c, --checkpoint: the folder where checkpoints are found

In the demo notebook makeenv.ipynb, it shows how to install and run on Google Colab. Note that the runtime may restart multiple times for installing the dependencies.

The model used in the experiment: ResNet The dataset employed: CIFAR-10 Parallel settings: Only 1 GPU is used Instructions on how to run your code: In makeenv.ipynb Experiment results, presented in a table or figure:

Accuracy of the model on the test images: 70.33 %

The original code runs with 80 epoches, but to reduce the time on Google Colab, only 10 epoches are ran. So the accuracy is 70.33%.