refactor: modularize training code for LatentEvolution #127
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extract shared training utilities and configuration classes into separate modules to prepare for latent trajectory model implementation. created new modules: - training_utils.py: LossAccumulator (enum-based), seed_everything, get_device - training_config.py: DataSplit, ProfileConfig, TrainingConfig, CrossValidationConfig moved code between modules: - ModelParams: latent.py → eed_model.py (model-specific config) - load_column_slice, load_metadata: zarr_io.py → load_flyvis.py (flyvis-specific) - DataSplit: load_flyvis.py → training_config.py (training concept) updated imports: - chunk_streaming.py, post_run_analyze.py, benchmark_training.py: zarr_io → load_flyvis - latent.py: added load_dataset and load_val_only functions, updated to use new modules - zarr_io.py: removed flyvis-specific dependencies benefits: - no circular dependencies - proper module layering (general doesn't depend on specific) - shared code extracted for reuse across models - LossAccumulator is now generic and reusable 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Results identical up to epoch 5 |
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extract shared training utilities and configuration classes into separate modules to prepare for latent trajectory model implementation.
created new modules:
moved code between modules:
updated imports:
benefits:
🤖 Generated with Claude Code