A Python library for fast, reproducible, and modular Neural Architecture Search (NAS) to generate efficient deep networks.
Archai is a Python library from Microsoft Research that accelerates Neural Architecture Search (NAS) for deep learning. It provides a modular framework to automatically discover efficient neural network architectures by balancing objectives like performance, size, and latency. The tool is designed to make NAS research faster, reproducible, and more accessible.
Machine learning researchers and engineers working on automated model design, particularly those focused on optimizing neural networks for efficiency across NLP, vision, and other domains.
Developers choose Archai for its research-grade reproducibility, flexible modular components, and integrated support for multi-objective Pareto optimization, which simplifies the process of finding optimal architectures without sacrificing experimental rigor.
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
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Allows easy swapping of search spaces, algorithms, and evaluators, as demonstrated in the quickstart with TransformerFlexSearchSpace and EvolutionParetoSearch.
Ensures consistent results through well-defined search spaces and objective functions, supporting rigorous academic and industrial experimentation.
Supports Pareto-frontier searches to balance metrics like model size, latency, and memory, enabling efficient architecture discovery across domains.
Includes ready-to-run tasks for text generation and face segmentation, providing practical showcases to accelerate learning and application.
NAS processes involve iterative search and evaluation, requiring significant GPU resources and time, which may be prohibitive for smaller teams or projects.
Assumes prior knowledge of NAS concepts and PyTorch, with documentation and examples geared towards researchers rather than beginners.
Requires Python 3.8+, PyTorch 1.7.0+, and virtual environments like conda, adding overhead to configuration compared to simpler libraries.