ml-engineering

Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviv

Теми: ai · debugging · gpus · inference · large-language-models · llm · machine-learning · machine-learning-engineering · mlops · network · pytorch · scalability · slurm · storage · training