Machine Learning Compiler
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About
The Machine Learning Compiler project provides a comprehensive treatment of machine learning compilation, an emerging field essential for deploying innovative AI models across various production environments. It tackles the challenges of supporting a wide range of models and environments for both training and inference workloads. Key goals addressed include minimizing dependencies, achieving broader model coverage, leveraging emerging hardware primitives for performance, reducing memory footprint, and scaling to larger environments. The solution involves ML programming abstractions, learning-driven search, compilation techniques, and optimized library runtimes. This material serves as a reference for the MLC course, offering systematic study of core elements in ML compilation, automatic optimization, and dependency, memory, and performance optimization in end-to-end ML deployment.
Platform
Task
Features
• optimized library runtime
• ml programming abstractions
• gpu and hardware acceleration
• integration with machine learning frameworks
• dependency, memory, and performance optimization
• automatic program optimization techniques
• compilation for ai models
• learning-driven search for optimization
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