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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.
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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.
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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.
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Experimental examples tailored for NePS contributors. These examples provide insights and practices for experimental scenarios.
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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.
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YAML usage examples to define NePS configurations and search spaces with YAML files, streamlining the setup and execution of experiments.
neps_examples
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