Tutorial
In our tutorial we show how to use Ax to run multi-objective NAS for a simple neural network model on the popular MNIST dataset. While the underlying methodology can be used for more complicated models and larger datasets, we opt for a tutorial that is easily runnable end-to-end on a laptop in less than an hour. In our example, we will tune the widths of two hidden layers, the learning rate, the dropout probability, the batch size, and the number of training epochs. The goal is to trade off performance (accuracy on the validation set) and model size (the number of model parameters) using multi-objective Bayesian optimization.
The tutorial makes use of the following PyTorch libraries:


PyTorch Lightning (specifying the model and training loop)


TorchX (for running training jobs remotely / asynchronously)

