PyTorch Tutorial 2nd Edition: A Chinese-Language Path from Basics to TensorRT Deployment

A Free Book and Notebook Set for Chinese-Speaking Engineers

This repository is the companion code and online text for a Chinese-language PyTorch textbook, the second edition. The README describes a three-part structure: basics, industry applications, and deployment. The target reader is explicit: beginners, non-computer-science students, and undergraduates in the first part; then practitioners who want CV, NLP, and LLM projects; finally engineers who need to move models into production with ONNX and TensorRT. The material is free to read online and the code is open, but the license is Creative Commons Attribution-NonCommercial 4.0. That means you can study and share it, but not use it for commercial purposes. If you need a permissive license for a company training course, this is a constraint to check before adopting.

Three Tiers of Content: Foundations, Applications, and Deployment

The structure is a deliberate ladder. The first part covers PyTorch core modules: data handling, model definition, optimization, and visualization. It ends by asking the reader to build their own code structure from the learned pieces. The second part branches into eight CV tasks including image classification, segmentation, detection, tracking, GANs, diffusion models, image captioning, and retrieval. The NLP chapters cover RNN, LSTM, Transformer, BERT, and GPT with five tasks: text classification, machine translation, named entity recognition, QA, and article generation. The third part shifts to ONNX and TensorRT, with a detailed look at quantization concepts, PTQ, and QAT. The README says the goal is to form a closed loop from training to deployment. That is a coherent design, but it is a book's table of contents, not a modular toolkit. You cannot grab one notebook and expect it to work independently of the surrounding chapters.

Notebooks as the Primary Artifact, Not a Package

The primary language is Jupyter Notebook, which tells you a lot about how you will use this material. There is no pip package, no command-line tool, and no library API to import. The repository is a collection of educational notebooks and scripts, plus the online book pages. The README shows GIFs of projects and a star-history chart generated by a script, but it does not list a setup file or a requirements.txt. To run the code, you would need to clone the repository, open the notebooks in Jupyter, and install the dependencies that each chapter assumes. That is fine for learning, but it means the maintenance burden is on you. The last push date is September 2026, which suggests ongoing updates, but the only tagged release, v1.0.0, is from May 2024 and is marked as a PDF and EPUB release. The repository may be active, but the release cadence is not evidence of a stable API.

LLM Chapters Focus on Chinese Open-Source Models

The LLM section is not generic. It names four Chinese open-source models: Qwen, ChatGLM, Baichuan, and Yi. The repository also includes an analysis of GPT Academic, which the README calls an LLM industry application. This is a specific choice that makes the material more useful for a Chinese-speaking audience working with domestic models, but less useful if you plan to use Llama or Mistral. The topics list includes qwen and llm, so the code likely shows how to load and deploy these models. The README mentions 'LLM deployment and code analysis' for the four models, but it does not specify whether the code covers fine-tuning, inference, or both. Given the deployment theme of the third part, it is probably inference-oriented. If you are looking for a model-agnostic LLM tutorial, this is not it.

A Real Limitation: The License and the Language Barrier

Two genuine constraints stand out. First, the license is CC BY-NC 4.0. The 'NC' means non-commercial. If you are an engineer at a company that wants to use this material in internal training, or if you want to adapt the code for a commercial product, you cannot do so without separate permission. The README does not offer a commercial option. Second, the entire text is in Chinese. The README, the book, and presumably the notebook comments are all in Chinese. An English-only reader will struggle. The repository is not translated. That narrows the audience considerably. Also, the README asks readers to join QQ groups for discussion, and the group password is hidden inside a code file. That is an unusual access method, and it may frustrate users who expect a forum or issue tracker. The repository does have an issues page on GitHub, but the README does not highlight it as a support channel.

Alternatives: Compare with a Single-Project Tutorial or a Full Course

The main alternative is a single-project tutorial, such as the official PyTorch tutorials on pytorch.org. Those are in English, permissively licensed, and focused on one task at a time, like a quickstart or a transfer learning example. They are better if you need a concise answer for a specific problem, but they do not offer the same breadth or the progression from basics to TensorRT. Another alternative is a full video course, like Fast.ai's Practical Deep Learning, which is free, in English, and covers modern practice with a top-down approach. Fast.ai starts with a working model and then explains the internals, while this repository is bottom-up: learn the modules first, then apply them. The difference is pedagogical. If you prefer to see a complete project before understanding every tensor operation, Fast.ai fits. If you want a systematic, chapter-by-chapter build-up, this repository is closer to a university textbook.

What You Should Verify Before Using It

The README does not specify the PyTorch version, CUDA version, or Python version required. That is a gap. PyTorch changes fast, and a notebook written for one version may fail on another. Before you commit time, pick one chapter, for example the YOLOv5 or DeepSORT example from the CV topics, and check whether the code imports match your installed environment. The deployment chapters on ONNX and TensorRT are even more sensitive: TensorRT is tied to specific GPU architectures and TensorRT versions. The README mentions PTQ and QAT, but it does not give the exact commands. You will need to read the notebook cells to find the export and calibration steps. Also, the license requires attribution if you share the material, so keep the original author name and the license notice. The repository is not archived, and the last push is recent, so you can check the commit history to see how actively the author fixes issues. Do not assume the code is production-ready just because it covers deployment topics.

Who Benefits Most and Who Should Look Elsewhere

The intended reader is a Chinese-speaking learner who wants a single resource that covers the full journey, from installing PyTorch to quantizing a model for TensorRT. The README claims the book helps readers become a deep learning engineer, and the structure supports that claim with a clear sequence. A beginner will find the basics section gentle, and the later chapters give concrete projects to build. An intermediate user who already knows PyTorch can skip the first part and jump to the LLM or deployment sections. But a production engineer who needs a maintained library, or an English speaker, or anyone with a commercial use case should look elsewhere. The value here is in the curated path, not in reusable code. The notebooks are educational artifacts, and they should be treated as such. If you are in the target audience, clone the repository, open the online book, and work through the chapters in order. Verify each environment as you go, and you will get a solid education.

Editorial conclusion

Adopt this repository if you are a Chinese-speaking beginner or intermediate learner who wants a single, free, well-organized path from PyTorch basics to industrial deployment, and who can tolerate a book-style structure with notebook code. Do not adopt it if you need a maintained library, an English text, or a permissive license for commercial training. Before relying on it, verify that the specific CV, NLP, or LLM example matches your PyTorch version and hardware, since the last push is dated September 2026 and the release is from May 2024. Check each notebook's dependencies and test the ONNX and TensorRT sections on your actual GPU, because those chapters assume a specific deployment stack. The repository is a curriculum with a non-commercial license, so treat it as a study resource, not a codebase to vendor.

Official sources

1. Issues
2. Project website
3. README
4. Releases
5. TingsongYu/PyTorch-Tutorial-2nd on GitHub