# BDA5.4.1 Batch Size and Data Parallelism

This skill focuses on tuning batch sizes and applying data parallelism to accelerate training across multiple compute units. It covers trade-offs in memory usage, convergence behavior, and hardware utilization.

## Requirements

* External: Familiarity with model training and GPU compute
* Internal: BDA5.3.3 Distributed Training (recommended)

## Learning Outcomes

* Explain how batch size affects training stability, convergence, and throughput.
* Identify the relationship between batch size and memory usage on accelerators.
* Apply data parallelism techniques across GPUs or nodes for scalable training.
* Use gradient accumulation to simulate large batch sizes under memory constraints.
* Evaluate performance trade-offs using throughput and loss convergence metrics.

** Caution: All text is AI generated **