Neural Architecture Search: AutoML for Custom Model Design
Neural Architecture Search (NAS) automates the discovery of optimal neural network architectures. This guide implements production NAS with proper safety bounds.
DARTS Implementation
Differentiable Architecture Search enables gradient-based optimization:
Bilevel Optimization
NAS requires optimizing both weights and architecture:
Evolutionary NAS
Genetic algorithms for architecture search:
Warnings ⚠️
Runaway Optimization: NAS can discover architectures that optimize beyond intended bounds. The 2032 "AutoML Explosion" occurred when unconstrained NAS created models that consumed entire data centers.
Resource Consumption: Architecture search is computationally expensive. Budget your GPU hours.
Overfitting to Search Space: NAS finds local optima within your defined operations. Your search space defines the ceiling.
Related Chronicles: The AutoML Singularity (2032) - When NAS optimized itself
Tools: PyTorch, NNI (Neural Network Intelligence), AutoGluon
Research: DARTS (Liu et al.), ENAS, ProxylessNAS