A comprehensive guide to building a reproducible, production-ready text classification pipeline using PyTorch Lightning, Hydra, and Hugging Face. Covers the full MLOps workflow: structuring a project with LightningDataModule and LightningModule, managing hyperparameters via Hydra YAML configs, training a DistilBERT sentiment classifier on the IMDB dataset, and running offline inference in multiple modes (single, batch, interactive, demo). Key MLOps principles emphasized include separation of concerns, deterministic reproducibility, distributed training readiness (DDP/FSDP), and config-driven experimentation without code changes.
Table of contents
Training with PyTorch Lightning: Structured MLOps DevelopmentWhy PyTorch Lightning Improves Reproducible MLOps Training PipelinesConfiguring Your Development EnvironmentProject StructurePyTorch LightningDataModule Explained: Building Efficient Data PipelinesPyTorch LightningModule Explained: Building Modular Deep Learning ModelsUsing Hydra Python Configuration Files for Reproducible ML TrainingBuilding a PyTorch Lightning Training Pipeline with train.pyRunning Model Inference with PyTorch Lightning and DistilBERTTraining and Evaluating a DistilBERT Model with PyTorch Lightning and HydraSummary846 Impressions