How to Configure Experiment

This guide describes how to configure Katib Experiment for neural architecture search (NAS).

Before reading this guide, please follow the guide to configure Experiment for hyperparameter (HP) tuning to understand the common Experiment parameters for NAS.

Configuring the Experiment

You can configure your NAS in Katib Experiment YAML file.

The YAML file defines the range of potential network architectures, configuration for neural network graph, the objective metric to use when determining optimal values, the search algorithm to use during architecture search.

As a reference, you can use the YAML file of the efficient neural architecture search (ENAS).

The list below describes the NAS-specific parameters in the YAML file for an Experiment.

• nasConfig: The configuration for NAS. You can specify the configurations of the neural network design that you want to optimize, including the number of layers in the network, the types of operations, and more.

  • graphConfig: The graph config that defines structure for a directed acyclic graph of the neural network. You can specify the number of layers, input_sizes for the input layer and output_sizes for the output layer.

  • operations: The range of operations that you want to tune for your ML model. For each neural network layer the NAS algorithm selects one of the operations to build a neural network. Each operation contains sets of parameters similar to HP tuning Experiment.

You can find all NAS examples here.

Next steps

• Learn about NAS algorithms.