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Frequently Asked Questions

Common build and runtime questions, distilled from GitHub issues.

Windows: linking fails with LNK2001: unresolved external symbol __std_find_trivial_8

Unresolved __std_* symbols (__std_find_trivial_*, __std_max_element_*, __std_search_*, ...) mean the linker's MSVC STL is too old. The prebuilt ONNX Runtime binaries that ort-sys downloads are compiled with Visual Studio 2022 (MSVC v143) and reference vectorized STL helpers that do not exist in the VS 2019 (v142) link libraries.

Fix:

  1. Install Visual Studio 2022 or Build Tools for Visual Studio 2022 with the "Desktop development with C++" workload (MSVC v143 + Windows SDK).
  2. Run cargo clean and rebuild — rustc picks the newest installed MSVC toolset automatically.

VS 2019 and VS 2022 build tools can coexist; only the newer one needs to be present for linking. (See issue #105.)

GPU inference is slower than CPU for PP-OCRv6 tiny/small

Expected. The tiny/small models are so small that per-call overhead — host↔device tensor copies, kernel launches, CPU/GPU synchronization, plus pre/post-processing that always runs on the CPU — outweighs the compute the GPU saves. Measured on an RTX 4090 + i9-13900KF (single image, warmup excluded):

Model CPU GPU (CUDA EP)
tiny 34 ms/img 44 ms/img
small 59 ms/img 77 ms/img
medium 404 ms/img 173 ms/img (2.3× faster)

Guidelines:

  • For tiny/small, use the default CPU mode (the simd feature is on by default).
  • Use the medium model, or batch several images per predict() call, when GPU acceleration matters.
  • Exclude the first call when benchmarking: it includes cuDNN initialization and algorithm selection (~5× slower than steady state).

Also note that requesting OrtExecutionProvider::CUDA without building with --features cuda makes the pipeline builder return an error — check the Result of .build(). Without the cuda feature, the downloaded ONNX Runtime is CPU-only and the GPU is never used. (See issue #151.)