Lazy is the new fast: How Lazy Imports and Cinder accelerate machine learning at Meta

This post details the adoption of Lazy Imports and the Python Cinder runtime to accelerate machine learning development at Meta. It highlights the challenge of Time to First Batch (TTFB) in ML training and explains how Cinder's transparent, default lazy import mechanism defers all imports until needed, reducing costly runtime imports and streamlining workflows. The post quantifies improvements, noting up to 40% TTFB reduction and 20% faster Jupyter kernel startup times. It also discusses challenges encountered, such as library compatibility issues with PyTorch, Numba, NumPy, and SciPy, the need to balance performance with dependability by disabling lazy imports before the first batch, and the learning curve for developers. Future plans include streamlining onboarding, enhancing debugging tools, and fostering community collaboration.