This AI Paper Introduces Relax: A Compiler Abstraction for Optimizing End-to-End Dynamic Machine Learning Workloads

Optimizing machine learning models with dynamic shapes can be crucial for achieving better performance and flexibility. Dynamic shapes refer to the ability of a model to handle input data with varying dimensions during runtime. Users utilize frameworks that support dynamic computation graphs, such as TensorFlow’s eager execution or PyTorch. These frameworks allow building models that […]

The post This AI Paper Introduces Relax: A Compiler Abstraction for Optimizing End-to-End Dynamic Machine Learning Workloads appeared first on MarkTechPost.

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