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name: export-and-speed description: "Routes Ultra-Fast-Lane-Detection TorchScript export, synthetic speed checks, and deployment benchmarking workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

export-and-speed

Use this sub-skill when a task is about exporting a lane model for deployment, timing its throughput, or understanding the C++/LibTorch demo path.

Read this when

• You need to export a checkpoint to TorchScript.
• You need a quick synthetic speed benchmark.
• You need to explain the C++/LibTorch deployment example.
• You need to reason about cls_dim, anchors, device choice, or half precision for export and timing.

What this sub-skill owns

• TorchScript export commands and model input shape.
• Synthetic throughput timing.
• Deployment caveats for the C++ demo.
• Speed-related checkpoint, device, and dimension assumptions.

What it does not own

• Training command construction: see training.
• Dataset layout and conversion: see data-and-config.
• Accuracy evaluation and demo metrics: see evaluation-and-visualization.

Start here

• Read references/export-workflows.md for the export and benchmark flow.
• Read references/speed-and-deployment.md for the LibTorch/OpenCV notes and practical timing caveats.
• Read references/api-reference.md for the verified model signature and dimension assumptions.
• Read references/troubleshooting.md for hardcoded path, checkpoint, and device issues.
• Run scripts/export_torchscript.py when you need a portable TorchScript file.
• Run scripts/benchmark_synthetic.py when you need a configurable synthetic timing check.

Typical flow

1. Decide whether the user wants a CPU or CUDA export/benchmark path.
2. Confirm the checkpoint file and model dimensions.
3. Export or benchmark using a helper that accepts explicit paths and device options.
4. If the user wants C++ deployment, read the deployment caveats before building anything.

Caution points

• The source export.py has a hardcoded checkpoint path and CUDA map_location.
• The source speed scripts are demos, not robust command-line tools.
• The C++ example hardcodes LibTorch and OpenCV paths.
• cls_dim must match the chosen dataset family and backbone setup.

Reference and script links

• references/export-workflows.md for export patterns and safe invocation advice.
• references/speed-and-deployment.md for deployment and benchmark caveats.
• references/api-reference.md for the verified signature notes.
• references/troubleshooting.md for export and timing failures.
• scripts/export_torchscript.py for a parameterized export helper.
• scripts/benchmark_synthetic.py for a configurable speed helper.