Data Pipeline & Loaders

Deterministic data loading for reproducible PyTorch experiments is achieved by using phase-aware data loaders that enforce a strict loader contract, provide deterministic splits, and generate synthetic dev data to ensure consistent sample shapes across runs.

A dataset registry centrally manages multimodal data by registering, instantiating, listing, downloading, and caching datasets across different training phases, with ready-made collate functions to unify multimodal batches and enforce sample-shape inference.

Creating phase-specific data loaders involves instantiating a base loader contract like SNNPhaseLoader with a dev DataConfig, enabling iteration through phase-aware train loaders that provide specific collate and split utilities for each training stage.

Yes, synthetic data serves as a production fallback when actual data downloads fail, allowing the dataset registry to cache real datasets while automatically generating deterministic synthetic dev data to maintain continuous training pipeline operation.

This data-loading infrastructure fully supports multimodal batch handling in PyTorch by offering ready-made collate functions and sample-shape inference utilities that unify diverse data types within the enforced base loader contract.

Phase-aware splits are needed for brain_ai training pipelines because the seven distinct phases, such as Encoders, HTM, and Active Inference, require specific data configurations to maintain deterministic experiments and reproducible results across multimodal datasets.