Scaling Book project is a freely available interactive online resource dedicated to scaling machine learning.
💡 What's inside?
Covers key methods, practices, and architectural approaches that help build scalable, high-performance ML systems.
— Basics of model scaling and training
— Data parallelism, parameter parallelism, and mixed strategies
— Distributed training technologies (TPUs/GPUs)
— Computation and memory optimization
— Practical examples on JAX and other stack tools
— Schemes, codes, and visualizations for specific training patterns
📍 Why it's useful:
— Suitable for both experienced ML engineers and those who want to move from prototypes to industrial ML systems
— Combines the theory and practice of distributed training
— Discusses the real limitations of architectures and ways to address them
— Shows how to think systematically about scaling, rather than just copying hacks
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