Reference
Reference pages are for exact lookup. Begin with the Quick Start if you have not built a bundle, or use the User Guides for goal-oriented procedures.
TensorRT-Model-Connect exposes three public entry layers:
| API | Entry point | Best for |
|---|---|---|
| Python build API | python -m tensorrt_model_connect build and tensorrt_model_connect.build() | Resolving a supported checkpoint and building a .bundle. |
| Native CLI | trtmc inspect and task commands such as trtmc run | Inspecting a bundle or invoking one abstract Task interface. |
| C++ Task API | #include <trtmc/runtime/family_loader.h> and trtmc::load_task() | Native applications that need task-specific results. |
The build and runtime entry points are intentionally separate. The Python
builder resolves exactly one families/<family>/support.py, imports only that
family's model.py, and writes a bundle. The native loader reads the bundle's
family, task, and backend, then loads exactly one family DSO and one
backend DSO from the explicit runtime root.
Hugging Face model ID or local snapshot
-> python -m tensorrt_model_connect build
-> model.bundle
-> trtmc::load_task() or trtmc TASK --runtime-root DIR
-> task-specific output
There is no Python runtime wrapper, central model registry, runtime-strategy switch, backend search path, or fallback runtime discovery in the current architecture.