Fast training and near-linear scaling with DataParallel in Amazon SageMaker
summary
SageMaker Distributed DataParallel removes network bottlenecks by offloading AllReduce to CPUs so the cluster’s CPUs act as CPUs-as-parameter-servers, shards fused gradient buffers with a Balanced Fusion Buffer, and overlaps backward pass with gradient sync. It plugs into PyTorch DDP and TensorFlow/horovod-style APIs with minimal edits (distribution arg, wrap model, pin local rank, save leader checkpoints). Benchmarks show 20–40% faster than NCCL/MPI and improve BERT scaling efficiency from ~55% to ~84% on 4-node P3dn.24xlarge clusters; Turbine AI cites up to 90% training time reduction.
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chapters (10)
- 0:00Intro
- 0:51Long training times slow down development
- 1:23Scaling to more GPUs can reduce training time
- 2:51Quick overview of data parallelism
- 3:38Gradient synchronization
- 5:31Infrastructure
- 10:00DataParallel in SageMaker Distributed
- 14:15Using data parallelism with PyTorch
- 16:51Using data parallelism with TensorFlow
- 21:11Distributed training on SageMaker
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