PyTorch: A Reference Language
The article explores the dual role of PyTorch as both a reference language for deep learning research and an implementation language for production systems. It discusses the tension between high-level API clarity and the need for optimized kernel performance.
A reference implementation is a simplified but complete version of a system that trades performance in return for clarity. We might then say a reference "language" is the fabric of APIs and conventions from which these implementations are cut. At first glance, PyTorch obviously is a reference language: it is, after all, commonly called the lingua franca of modern deep learning. But upon a closer look, there is confusion:
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