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常见问题解答

未来发展趋势如何？

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普通人应该关注哪些方面？

对于普通读者而言，建议重点关注The JIT path is the fast path — best suited for quick exploration before committing to AOT. Set an environment variable, run your script unchanged, and AITune auto-discovers modules and optimizes them on the fly. No code changes, no setup. One important practical constraint: import aitune.torch.jit.enable must be the first import in your script when enabling JIT via code, rather than via the environment variable. As of v0.3.0, JIT tuning requires only a single sample and tunes on the first model call — an improvement over earlier versions that required multiple inference passes to establish model hierarchy. When a module cannot be tuned — for instance, because a graph break is detected, meaning a torch.nn.Module contains conditional logic on inputs so there is no guarantee of a static, correct graph of computations — AITune leaves that module unchanged and attempts to tune its children instead. The default fallback backend in JIT mode is Torch Inductor. The tradeoffs of JIT relative to AOT are real: it cannot extrapolate batch sizes, cannot benchmark across backends, does not support saving artifacts, and does not support caching — every new Python interpreter session re-tunes from scratch.