serialization-and-reproducibility

# Albumentations serialization and reproducibility Use this sub-skill when a task involves persisting augmentation pipelines, comparing a loaded pipeline with the original, replaying exact random choices, recording transform parameters, or diagnosing reproducibility surprises. ## Route first - Build or edit the initial `A.Compose` / `A.ReplayCompose` structure with `../pipeline-composition/` before serializing it. - Choose transform families and parameter names with `../transform-catalog/` before saving a config. - Confirm image, mask, bbox, keypoint, volume, and additional-target schemas with `../targets-and-formats/` before replaying across data. - Handle PyTorch `Dataset` and dataloader-worker reproducibility with `../framework-integration/` after the Albumentations pipeline itself is correct. ## Use the bundled materials - `references/serialization-api.md`: Use for `A.to_dict`, `A.from_dict`, `A.save`, `A.load`, JSON/YAML buffers/files, schema shape, custom transform mapping, and Hub save/load methods. - `references/reproducibility.md`: Use for `ReplayCompose`, `ReplayCompose.replay`, `save_applied_params`, `applied_transforms`, `seed`, `set_random_seed`, and exact-output validation checks. - `references/troubleshooting.md`: Use for nonserializable `Lambda` failures, missing `nonserializable` keys, YAML dependency errors, replay misuse, shape changes before replay, and Hugging Face Hub network/auth limitations. - `scripts/roundtrip_pipeline.py`: Run a tiny local smoke check that saves and loads a simple pipeline, resets seeds, verifies equal output, and optionally writes JSON or YAML. ## Standard workflow 1. Make the pipeline serializable: prefer built-in transforms; give `A.Lambda` a stable `name`; keep custom callables available in code that loads the config. 2. Round-trip the configuration with `A.to_dict`/`A.from_dict` or `A.save`/`A.load`, then compare `to_dict()` outputs and seeded augmentation outputs on a tiny fixture. 3. For exact reuse of one random draw, capture `result["replay"]` from `A.ReplayCompose` and pass it to `A.ReplayCompose.replay` with data of compatible targets and shapes. 4. For debugging, use `A.Compose(..., save_applied_params=True)` and inspect `result["applied_transforms"]` for transform names and sampled params. 5. Treat Hub methods as optional sharing helpers: local `save_pretrained`/`from_pretrained` use JSON config files; remote push/download requires `huggingface_hub`, network access, and credentials when needed.