---

name: conversion-and-profiling description: "Convert, benchmark, and profile CVNets models." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Conversion and Profiling

Use this sub-skill when the user wants to convert a CVNets model to CoreML, benchmark throughput, or generate a loss landscape.

This sub-skill owns the export and profiling path around main_conversion.py, main_benchmark.py, and main_loss_landscape.py. It does not own the training loop, dataset design, or the architecture-selection problem except where those choices affect exportability.

Read these first

• ../../references/api-reference.md — public conversion and benchmark entry points.
• ../../references/model-overview.md — whether the chosen family is exportable.
• ../../references/configuration.md — conversion, benchmark, and loss-landscape keys.
• references/workflows.md — command patterns for CoreML export, throughput, and loss landscape.
• references/troubleshooting.md — export, optional dependency, and profiling failures.
• scripts/cvnets_convert.py — bundled conversion wrapper.
• scripts/cvnets_benchmark.py — bundled benchmark wrapper.
• scripts/cvnets_loss_landscape.py — bundled loss-landscape wrapper.

Owns

• PyTorch-to-CoreML conversion and the JIT/JIT-optimized artifacts that go with it.
• Benchmark throughput settings and JIT-vs-plain-model choices.
• Loss-landscape grid settings and the training-engine path used to evaluate them.
• Export-related optional dependency checks and Mac/CoreML caveats.

Excludes

• Choosing the model family; route to models-and-architectures.
• Training, resume, finetuning, and evaluation orchestration; route to training-and-evaluation.
• Config/dataset/tokenizer layout questions; route to data-and-config.

Workflow

1. Confirm the model family and the config first; exportability depends on both.
2. Check whether the model exposes an export-friendly path before trying the conversion wrapper.
3. Use scripts/cvnets_convert.py for CoreML export, scripts/cvnets_benchmark.py for throughput, and scripts/cvnets_loss_landscape.py for loss-landscape runs.
4. Treat full CoreML deployment as an optional/macOS-sensitive path unless the user explicitly asked for it.
5. If the problem is actually a model-family or config issue, switch back to the owning sub-skill rather than debugging export in isolation.

Common signals

• main_conversion.py may emit CoreML, traced JIT, and optimized JIT outputs together.
• main_benchmark.py can benchmark either the raw PyTorch model or the JIT-optimized path.
• main_loss_landscape.py reuses the training engine with a grid over a trained model and validation loader.
• coremltools and its support packages are optional; the repo can still run non-export workflows without them.

When to switch away

• If the export fails because the checkpoint or family is wrong, switch to models-and-architectures.
• If the export fails because the config or data layout is wrong, switch to data-and-config.
• If the export is only one step in a larger train/eval issue, switch to training-and-evaluation.