AI BriefingAccelerating Large-Scale Model Training with DeepSpeed
Key point
It introduces how to apply DeepSpeed ZeRO technology through Hugging Face's Accelerate library to solve memory issues in large-scale model training.
Details
It covers how to leverage DeepSpeed's ZeRO (Zero Redundancy Optimizer) technology to solve the OOM (Out of Memory) problem that occurs during large-scale model training.
ZeRO technology increases memory efficiency through the following stages:
- Stage 1: Partitions Optimizer States across GPUs
- Stage 2: Partitions Optimizer States and Gradients
- Stage 3: Partitions Optimizer States, Gradients, and model parameters all together
- Offload: Offloads gradients and optimizer states to CPU/disk to minimize memory usage
Using Hugging Face's Accelerate library, DeepSpeed ZeRO can be applied immediately through accelerate config settings without any separate code modifications.
Actual benchmark results confirmed that applying DeepSpeed ZeRO Stage-2 allowed a 5x larger batch size per GPU compared to the existing DDP (Distributed Data Parallel) method, greatly improving training efficiency.
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