ML models are typically implemented in Python using frameworks like TensorFlow, ensuring efficient execution on hardware while offering expressive model architecture.
OpenXLA provides a system where high-level model definitions translate to StableHLO, enabling diverse hardware execution without necessitating a Python-based infrastructure.
The OpenXLA stack comprises frameworks defining ML models, a compiler translating HLO to machine code, and a runtime for managing hardware devices and data.
The complexity of compiling and running ML models on various hardware is significant. Generally, developers should avoid re-implementing lower-level components without compelling reasons.
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