Guide to Distributed Training

Distributed training scales deep learning workloads across multiple GPUs or servers to address the memory and speed limitations of single-GPU training, particularly as datasets, batch sizes, and model sizes grow. The commonly used Distributed Data Parallel (DDP) approach replicates the model on each device, divides input data among them, computes losses and gradients independently, then averages gradients during backpropagation so every model copy updates identically. This can provide near-linear training-speed improvements as GPUs are added and is especially effective for large datasets and batches, but it does not solve cases where a model itself cannot fit on one GPU, which require sharding-oriented tools such as DeepSpeed. DDP also introduces concerns including duplicated samples when datasets cannot be evenly divided, possible inaccurate evaluation metrics, communication overhead from synchronizing metrics, and stalled processes, while frameworks such as Lightning automate much of the setup, workload handling, and failure detection.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.