Machine Learning System Resources

PhD student at University of Texas at Austin 🤘. Doing systems for ML.

This is my personal list of resources related to machine learning systems. Feel free to drop me an email if you think there's something worth mentioning. I will try to update this page frequently to include the most recent stuffs in mlsys. Resources Facebook's external large-scale work NGC Container Doc : great for development, without having to manually install CUDA, pytorch, and other dependencies. Awesome-System-for-Machine-Learning : A curated list of research in machine learning systems (MLSys). Paper notes are also provided. Mastering LLM Techniques: Inference Optimization : summary of t

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• Asma Lamgh
• Simon Guo
• Ishaan Panigrahi
• Tanmay Agrawal

related reading

• Modern GPU Programming For MLSys — Modern GPU Programming For MLSysmlc.ai
• GitHub - wafer-ai/gpu-perf-engineering-resources: A curated resource list for learning AI performance engineering, from GPU fundamentals to production inference.github.com
• How To Scale Your Modeljax-ml.github.io
• KernelBench: Can LLMs Write GPU Kernels?scalingintelligence.stanford.edu
• How to Land a Frontier Lab Jobvladfeinberg.com
• Getting Caught Up to Modern LLM Research | Samarth Goeldev.samarthgoel.com
• GenAI Handbookgenai-handbook.github.io
• LLM Resourcesforrestbicker.com
• A friendly introduction to machine learning compilers and optimizershuyenchip.com
• Homehao-ai-lab.github.io
• Understanding LLM System with 3-layer Abstraction – Huizi Mao –ralphmao.github.io
• LLM Inference Performance Engineering: Best Practices | Databricks Blogdatabricks.com