corridorkey
The corridorkey package is the pipeline library. It exposes the full processing pipeline as a set of composable functions and types that any interface (CLI, GUI, TUI, web) can orchestrate.
Purpose
This package owns the pipeline logic, configuration, runtime, and all stage implementations. It has no dependency on any interface layer. Interfaces import from corridorkey - corridorkey never imports from them.
Package Layout
corridorkey/
__init__.py # single public import surface
errors.py # typed exception hierarchy
events.py # PipelineEvents callbacks
infra/ # configuration, device, logging
config/ # CorridorKeyConfig and per-stage settings
device_utils.py # GPU detection and device resolution
logging.py # file logging setup
model_hub.py # model download and checksum
runtime/ # pipeline orchestration
clip_state.py # ClipEntry state machine
queue.py # BoundedQueue with sentinel shutdown
runner.py # PipelineRunner and MultiGPURunner
worker.py # PreprocessWorker, InferenceWorker, PostWriteWorker
stages/ # one folder per pipeline stage
scanner/ # scan() - clip discovery
loader/ # load(), resolve_alpha() - clip loading
preprocessor/ # preprocess_frame() - tensor preparation
inference/ # load_model(), run_inference()
postprocessor/ # postprocess_frame()
writer/ # write_frame()
Public API
All public symbols are exported from corridorkey.__init__. Do not import from submodules directly.
from corridorkey import (
scan, load, resolve_alpha, preprocess_frame,
load_model, run_inference, postprocess_frame, write_frame,
load_config, setup_logging, resolve_device, detect_gpu,
ClipState, ClipEntry, PipelineRunner,
)
See the API Reference for the full symbol list.
Documents in This Section
- Clip State Machine - How clip lifecycle states are tracked and transitioned.
- Job Queue - Bounded queue and sentinel-based shutdown used between pipeline workers.
- Configuration - All configuration fields, defaults, and sources.
- Stages - Step-by-step breakdown of all six pipeline stages.