Machine Learning Engineering Open Book: A Comprehensive Resource
The "Machine Learning Engineering Open Book" is an expansive collection of methodologies, tools, and step-by-step instructions aimed at aiding the successful training and inference of large language models (LLMs) and multi-modal models (VLMs). This resource is particularly valuable for those involved in training these types of models, providing an array of scripts and commands to expedite problem-solving processes.
Origin and Purpose
The author, Stas Bekman, leverages extensive experience from working on notable projects such as the BLOOM-176B and IDEFICS-80B models. This repository serves as a repository of accumulated knowledge aimed at benefiting the wider machine learning community by sharing insights and solutions for real-world challenges encountered during model training.
Structure and Contents
The book is structured into various parts, covering a range of topics integral to machine learning model training and operation:
Part 1: Insights
- The AI Battlefield Engineering: Offers essential knowledge for succeeding in the competitive field of AI development.
Part 2: Hardware
- Compute: Discusses the use of accelerators, CPUs, and memory considerations.
- Storage: Details the nuances of local, distributed, and shared file systems.
- Network: Covers both intra- and inter-node networking essentials.
Part 3: Orchestration
- SLURM: An exploration of the main orchestration environment used in model training.
Part 4: Training
- Provides a series of guides and resources related to effective model training techniques.
Part 5: Inference
- Delivers insights into model inference, helping users to apply models effectively post-training.
Part 6: Development
- Debugging and Troubleshooting: Tools and techniques for resolving both straightforward and complex issues.
- Testing: Provides tips and tools to make the test writing process more enjoyable.
Part 7: Miscellaneous
- Resources: Chronicles of LLM/VLM model training logs from a variety of sources.
Additional Resources
Bekman regularly updates the community on significant changes via Twitter, ensuring followers stay informed. A PDF version of the book is also available for download, with instructions for building the latest version accessible online. Discussion forums engage the community further, encouraging the sharing of experiences and ideas.
Gratitude and Acknowledgments
Stas Bekman expresses gratitude to contributors who have assisted in refining the content, as well as HuggingFace for the opportunities and support provided. Special acknowledgments extend to those who facilitated critical steps in his learning journey, including Thom Wolf.
Contribution
The project welcomes community contributions. If you discover errors or have suggestions for improvements, the author encourages opening an issue or submitting a pull request via GitHub.
Licensing and Citation
The content is shared under an Attribution-ShareAlike 4.0 International License. For those citing this work, a BibTeX entry is provided for proper attribution.
In summary, the "Machine Learning Engineering Open Book" is a rich, collaborative resource designed to empower and equip machine learning engineers with the knowledge and tools needed to excel in the evolving field of AI model development.