stas00/ml-engineering resource

Health v2 · maintenance only

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Adoption not part of the score

18739 stars · 1211 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An open-source 'open book' of methodologies, scripts, and step-by-step instructions for training, fine-tuning, and running inference on large language and multi-modal models. It distills hands-on experience from projects like BLOOM-176B and IDEFICS-80B into practical guides covering hardware, networking, storage, SLURM orchestration, training, inference, and debugging.

Use cases

• learn how to train large language models on multi-GPU clusters
• debug NCCL and distributed training failures
• decide when to upgrade GPUs for LLM training
• set up SLURM for machine learning workloads
• optimize storage and networking for LLM training
• fine-tune and run inference on LLMs and vision-language models
• troubleshoot OOM and performance bottlenecks in PyTorch training

When to choose

• you are an ML engineer or operator training or fine-tuning LLMs/VLMs on GPU clusters
• you need practical, copy-paste commands and battle-tested debugging recipes
• you want to understand hardware, network, and storage choices for large-scale training

When to avoid

• you are looking for a software library or tool to install and use directly
• you are a beginner seeking introductory ML tutorials
• you need formal, peer-reviewed textbook material rather than practitioner notes

Facets

learning-resource · maturity active

machine-learning llm-training llm-inference developer-tools documentation large-language-models machine-learning deep-learning gpu-computing tutorials developer-tools python cloud open-book llm-training slurm pytorch distributed-training debugging gpu-clusters mlops fine-tuning multi-modal linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="stas00/ml-engineering")