Menü
Hydra configuration file structure and conventions for models, datasets, callbacks, and training
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Git operations: branching, commit message format, pre-commit hook handling, and push policy
Full workflow: environment setup, running experiments, reusing and implementing new components (models, datasets, plmodules, callbacks), and testing
How to load a checkpoint and run evaluation (inference only, no training)
Reference code folder conventions — storing external code and documentation for AI-assisted development context
Coding style guidelines and common pitfalls for this project
Data format conventions for datasets and plmodules
| name | configs |
| description | Hydra configuration file structure and conventions for models, datasets, callbacks, and training |
All configuration files live in configs/, organized using the Hydra framework.
accelerate/ — Distributed training accelerationcallbacks/ — Lightning callbacks (e.g., model summary, checkpointing)data/ — Dataset and dataloader configurationsdatamodule/ — PyTorch Lightning datamodule configurationsexperiment/ — Experiment-specific overridesextras/ — Additional utility configurationshydra/ — Hydra framework configurationslogger/ — Logging configurations (e.g., TensorBoard, WandB)loss/ — Loss function configurationsmodel/ — Model configurationsopt/ — Optimizer configurationspaths/ — Path configurationsplmodule/ — PyTorch Lightning module configurationstrainer/ — Trainer configurationsTop-level training config files (e.g., train_nop.yaml, train_nop_rollout.yaml) specify which configs to use for a given project.
There are two types of callback config files in configs/callbacks/:
single_callback_name.yaml — Configuration for a single callback.many_callbacks_project_name.yaml — Lists all callback configs to be used in a project.For a new project, create a many_callbacks_project_name.yaml listing the individual callback configs. If you add a new callback, also create a corresponding single_callback_name.yaml.