feat: Add YAML/JSON config system for training and inference#20
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KrishanYadav333 wants to merge 1 commit intoML4SCI:mainfrom
Open
feat: Add YAML/JSON config system for training and inference#20KrishanYadav333 wants to merge 1 commit intoML4SCI:mainfrom
KrishanYadav333 wants to merge 1 commit intoML4SCI:mainfrom
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- Add dataclass-based config schema for project/data/model/diffusion/training/logging - Add load/save helpers for YAML and JSON - Add override and validation support for safe config updates - Add default config file at DENOISING_DIFFUSION/configs/base_config.yaml - Add unit tests for default load, overrides, JSON/YAML IO, and validation This PR establishes a small, reusable configuration layer for upcoming model, scheduler, and trainer implementation PRs.
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Pre-GSoC groundwork for the "Denoising Astronomical Observations
of Protoplanetary Disks" project (GSoC 2026).
Started with the config system because everything else — the
U-Net, trainer, inference pipeline — will need a clean way to
load and manage hyperparameters. Felt like the right foundation
to build first before touching any model code.
What's included:
ProjectConfigdataclass that groups all settings (data,model, diffusion, training, logging) in one place
load_config()that reads YAML or JSON and supports nestedoverrides, so running quick experiments doesn't require
editing the base config file
save_config()to snapshot the exact config used in each runFiles changed:
configs/base_config.yaml— default hyperparameterssrc/training/config.py— schema + load/save logicsrc/training/__init__.py— clean exportstests/test_config.py— 6 testsLet me know if anything needs changing!