PyTorch from Scratch: Volume 2: Master Deep Learning Step by Step: Production & Advanced Applications

From training to production - and into the world of fine-tuned LLMs.Volume 2 of PyTorch from Scratch picks up where Volume 1 left off. You have a model that trains. Now what? This volume shows you how to save it, run it on a GPU, debug it when it breaks, ship it to production, and adapt a 7-billion-parameter language model on a single consumer GPU using QLoRA.What is insideSaving, Loading, and Checkpointing. state_dict anatomy, the three-tier checkpoint strategy, cross-device loading, ONNX export.GPU Acceleration with CUDA. Device management, mixed precision with torch.amp, DistributedDataParallel, and Apple Silicon (MPS).Debugging and Profiling. Indexed by error message - every common PyTorch stack trace explained, plus the Profiler, anomaly detection, and memory-leak hunting.Deploying PyTorch Models. TorchScript, ONNX Runtime, quantization, pruning, knowledge distillation, SWA, and a complete Flask + TorchServe pipeline.End-to-End Project implementation A full image classification app from raw data to a Gradio web demo: ResNet transfer learning, two-phase training, confusion matrices, ROC curves.Efficient Fine-Tuning with Hugging Face. Tokenizers, the Auto classes, LoRA, 4-bit NF4 quantization, QLoRA, and a complete instruction-tuning pipeline you can run on one 24 GB GPU.Where to Go from Here. GANs, VAEs, REINFORCE for RL, Graph Convolutional Networks, PyTorch Lightning, and how to read research papers without drowning.What makes this volume differentEvery code block is walked through line by line - not just what it does, but why it is written that way and what would break if you changed it.Every error gets a callout. If you have ever Googled "RuntimeError: Expected all tensors to be on the same device," this book is indexed for you.Worked numerical examples for every algorithm: LoRA decomposition, distillation loss, mixed precision scaling, quantization math - all with concrete numbers you can verify by hand.150 runnable code listings, 27 diagrams, and 22 reference tables. Every example script is on GitHub and independently runnable.Appendix E - a complete nn.Module reference covering hooks, parameter counting, custom modules, and module deep-dives.Who this is forEngineers and data scientists who can train a model in PyTorch but want to ship one: turn a notebook into an API, a checkpoint into a deployable artifact, and a research-paper idea into running code. Comfortable with Python; Volume 1 or equivalent PyTorch experience is recommended for the full benefit.What you will be able to do after readingSave and resume any training run cleanly across machines and devicesTrain 2 to 3 times faster with mixed precision and proper GPU usageDiagnose a NaN, a shape error, or a memory leak in minutesDeploy a PyTorch model as a REST API or as a Gradio demoFine-tune an open-weight LLM on your own data with QLoRA on consumer hardwareRead modern deep-learning papers and understand what each component is actually doingWritten in plain language, with the understanding that the gap between "trained a model" and "shipped a model" is where most projects quietly die. This book closes that gap. Read more