Intermediate · Systems
Data parallelism
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Data parallelism scales training by distributing data subsets across replicated models on multiple devices.
Technical Definition
Data parallelism scales training by distributing data subsets across replicated models on multiple devices.
How it works
Data parallelism is a technique used to speed up machine learning model training or inference by replicating the model across multiple processing units (like GPUs). Each replica processes a different subset of the training data simultaneously. This approach is effective for large datasets and batch sizes but requires the model to fit on each individual device.
Related Concepts
- Distributed Training — Training a model across multiple machines or accelerators in parallel.
- GPU — Graphics Processing Unit — a massively parallel processor that powers most modern AI workloads.