Pipeline

Data ingestion and training orchestration for SNN workflows.

• DataIngestor — Validated multimodal dataset preparation: min-max normalizes each modality to [0, 1], preserves the reserved labels field as labels, and rejects empty, scalar, non-finite, or mismatched sample axes.
• SCTrainingLoop — Standard and RL training orchestration with logging, checkpointing, and early stopping

from sc_neurocore.pipeline import DataIngestor, SCTrainingLoop

dataset = DataIngestor().prepare_dataset(
    {"vision": [[0.0, 1.0], [2.0, 3.0]], "labels": [0, 1]}
)
sample = dataset.get_sample(0)

::: sc_neurocore.pipeline options: show_root_heading: true