Mini toolsDefinition
A method of training AI models across multiple machines or processors simultaneously to handle large models or datasets more efficiently.
Detailed Explanation
A training architecture that splits model training across multiple computational resources, either by distributing the data, the model, or both. It includes strategies for synchronization, parameter averaging, and gradient aggregation to maintain model coherence while leveraging parallel processing.
Use Cases
Training large language models, Processing massive image datasets, High-performance computing clusters
