nishide-dev

Format Python code with ruff formatter and optionally fix auto-fixable linting issues. Use when formatting code, preparing code for commit, or ensuring consistent code style across the project.

Comprehensive validation of ML project structure, configurations, code quality, and training readiness. Use when setting up a new project, before training runs, or debugging configuration issues. Validates config loading, data pipeline, model architecture, and dependencies.

Initialize a new ML research project using the ML Research template with PyTorch Lightning, Hydra, and modern Python tooling. Use when starting a new ML project from scratch.

Create and manage data loading, preprocessing, and augmentation pipelines (DataModule, transforms, data loaders). Use when implementing DataModules, setting up data loaders, or optimizing data pipelines for computer vision, NLP, or graph ML tasks.

Manage ML experiments, track results, and compare performance across different configurations. Use when setting up experiment tracking, creating experiment configs, comparing runs, or analyzing experiment results with W&B, TensorBoard, or MLflow.

Profile ML training performance to identify bottlenecks (data loading, compute, memory usage) and optimize GPU utilization. Use when training is slow, GPU utilization is low, or experiencing memory issues.

Comprehensive guide for PyTorch Lightning - LightningModule, Trainer, distributed training, PyTorch 2.0 torch.compile integration, Lightning Fabric, and production best practices

Generate and manage Hydra configuration files for machine learning experiments. Use when creating new configs (model, data, trainer, logger, experiment, sweep), organizing config hierarchies, or setting up hyperparameter sweeps with Optuna.

Comprehensive guide for Hydra configuration management, hierarchical configs, experiment management, Optuna integration, and Lightning integration patterns