Overview

1. Basic usage examples demonstrate fundamental usage. Learn how to perform Hyperparameter Optimization (HPO), Neural Architecture Search (NAS), and Joint Architecture and Hyperparameter Search (JAHS). Understand how to analyze runs on a basic level, emphasizing that no neural network training is involved at this stage; the search is performed on functions to introduce NePS.

2. Efficiency examples showcase how to enhance efficiency in NePS. Learn about expert priors, multi-fidelity, and parallelization to streamline your pipeline and optimize search processes.

3. Convenience examples show tensorboard compatibility and its integration, explore the compatibility with PyTorch Lightning, and understand file management within the run pipeline function used in NePS.

4. Experimental examples tailored for NePS contributors. These examples provide insights and practices for experimental scenarios.

5. Templates to find a basic fill-in template to kickstart your hyperparameter search with NePS. Use this template as a foundation for your projects, saving time and ensuring a structured starting point.

6. YAML usage examples to define NePS configurations and search spaces with YAML files, streamlining the setup and execution of experiments.