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.

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

• 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