---

name: lightning-hydra-template description: "Operate Lightning-Hydra-Template projects: Hydra configs, Lightning training/evaluation, data/model customization, tests, CI, and template maintenance." disable-model-invocation: true metadata: disco-role: operating license: MIT

Lightning-Hydra-Template Repo Skill

Use this skill when a task involves a project based on Lightning-Hydra-Template: a PyTorch Lightning + Hydra template with configs/, src/train.py, src/eval.py, Lightning DataModule/LightningModule examples, pytest smoke tests, and MLOps-style logging/callback/sweep configuration.

This skill is self-contained operating guidance. Treat paths such as configs/train.yaml and src/train.py as files in the user's target checkout or derived project, not as links to the construction repository.

First checks

1. Confirm you are in a target project that resembles the template: it should have configs/, src/, tests/, .project-root, requirements.txt or environment.yaml, and usually setup.py.
2. Install or verify the project environment. The public template path is usually:

   pip install -r requirements.txt
   pip install -e .
   

   Use the project's own environment policy if it has diverged from the template.
3. Run a no-training inspection before expensive work:

   python <this-skill>/scripts/check_lightning_hydra_project.py --repo-root . --config-name train.yaml --instantiate
   python <this-skill>/scripts/check_lightning_hydra_project.py --repo-root . --config-name eval.yaml --override ckpt_path=/tmp/dummy.ckpt --instantiate
   

4. For a current/staleness check on this generated skill, read repository provenance.
5. For environment setup, dependency groups, and public import checks, read quickstart and environment. For the template file layout, read template file map. For cross-cutting failures, read troubleshooting.

Route by task

User task	Read next	Why
Edit Hydra defaults, choose config groups, convert CLI overrides to experiment YAML, debug MissingConfigException, set log paths, or run multiruns/Optuna sweeps.	configure-experiments	Owns Hydra composition, overrides, debug configs, sweeps, tags, and path interpolation.
Train, resume, evaluate checkpoints, choose callbacks/loggers/accelerators, inspect CLI help, or build safe repeated-run commands.	train-evaluate	Owns src.train, src.eval, train_command, eval_command, checkpoint/logging semantics, and accelerator choices.
Add a new dataset, replace MNIST, add a LightningModule or network component, rename the src package, or fix _target_ imports.	customize-data-model	Owns DataModule/LightningModule/component APIs, optimizer/scheduler partials, target strings, and data/model config wiring.
Pick no-network tests, adapt pytest fixtures, update CI/pre-commit, diagnose RunIf skips, or maintain package metadata after template changes.	test-maintain-template	Owns pytest/CI/Makefile workflow, package rename checklist, and maintenance gates.

Public runtime surfaces verified for this skill

• Distribution metadata: default template distribution src==0.0.1.
• Import package: src with src.train, src.eval, src.data.mnist_datamodule, src.models.mnist_module, and src.models.components.simple_dense_net.
• Console entry points from setup.py: train_command = src.train:main and eval_command = src.eval:main.
• Main config groups visible through Hydra help: data, model, callbacks, logger, trainer, paths, extras, hydra, debug, experiment, and hparams_search.
• Safe config/API inspection composes train/eval configs and instantiates datamodule, model, and trainer without calling prepare_data(), fit(), or test().

Safe operating rules

• Do not treat default training commands as no-network: the MNIST example can download data.
• Prefer config composition, CLI --help, and target-import checks before running training.
• Disable online loggers in smoke tests unless credentials and optional packages are explicitly available: use logger=null or logger=csv.
• Keep accelerator claims scoped. CPU config/API checks are portable; CUDA, MPS, TPU, DDP, and online loggers require hardware, packages, services, or credentials.
• If the user has renamed the default src package, update all _target_ strings, imports, setup.py entry points, and CI coverage paths together.

Bundled scripts

• scripts/check_lightning_hydra_project.py: shared target-checkout inspector; composes configs, checks imports/entry points, optionally instantiates objects and probes CUDA.
• Sub-skill scripts provide narrower helpers for config rendering, scheduled command previews, _target_ scanning, and smoke-test selection.