Design a Distributed Model Training Platform — System Design Interview Practice
Design a platform that enables distributed training of large ML models across multiple GPUs and nodes, handles data parallelism and model parallelism, and optimizes training throughput and cost. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Support data-parallel and model-parallel trainingRequirement
- Manage multi-node GPU clusters dynamicallyRequirement
- Handle gradient synchronization across nodesRequirement
- Implement checkpointing and fault toleranceRequirement
- Optimize data loading to avoid GPU starvationRequirement
- Support mixed-precision trainingRequirement
- Track training metrics and resource utilizationRequirement
- Manage training job queues and prioritiesRequirement
- mlopsConcept to explore
- distributed trainingConcept to explore
- gpu clusterConcept to explore
- deep learningConcept to explore
- data parallelismConcept to explore
- model parallelismConcept to explore