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PYGLOVE
Evolutionary neural architecture search and hyperparameter optimization
Apache-2.0
ABOUT
Designing neural architectures and tuning hyperparameters manually is a trial-and-error process that requires extensive domain expertise and compute resources, often yielding suboptimal results. PyGlove provides a unified framework for AutoML that represents neural architectures and hyperparameters as manipulable Python objects, enabling developers to define and explore combinatorial search spaces with evolutionary algorithms, genetic programming, and reinforcement learning — all with distributed execution and built-in experiment tracking.
INSTALL
pip install pygloveINTEGRATION GUIDE
1. Evolve neural network architectures automatically with genetic programming and mutation operators
2. Search hyperparameter spaces for deep learning models using evolutionary and RL-based optimization strategies
3. Define complex, multi-objective AutoML pipelines that optimize for accuracy, latency, and model size simultaneously
4. Distribute architecture search across multiple workers and accelerators with built-in parallelism support
5. Compare and visualize search results with integrated experiment logging and reward curve analysis
TAGS
automlnashyperparameter-optimizationevolutionary-searcharchitecture-searchpython