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Partitioning a model into sequential stages across devices and processing microbatches in a pipeline to increase throughput.
DESCRIPTION
Pipeline parallelism divides layers into stages that run concurrently, each handling different microbatches. It reduces memory pressure, boosts device utilization, and complements data or tensor parallelism. Frameworks like DeepSpeed and PyTorch implement scheduling, inter-stage communication, and bubble reduction. Careful partitioning, batch sizing, and synchronization minimize idle time and preserve convergence. It enables training very large models efficiently on limited device memory.
ALTERNATIVE DEFINITIONS
DeepSpeed
Pipeline parallelism partitions a model into stages and allows micro-batches to be processed in a pipelined fashion across devices.
Emphasizes stage partitioning and micro-batch pipelining for efficiency.
PyTorch
Pipeline parallelism enables training of large models by splitting them into pipeline stages that process inputs concurrently.
Highlights concurrent processing to train large models.
RELATED TERMS
Model Parallelism Micro-batching Distributed Training ZeRO Megatron-LM Gradient Accumulation Checkpointing All-Reduce
COMPETING TERMS
Data Parallelism Tensor Parallelism
RELATIONS
- Broader: Parallelism
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer MLOps Engineer Cloud Architect Hardware Engineer Software Developer
EXTERNAL SOURCES
- Wikipedia - Parallel computing
- PyTorch - Tutorial on pipeline parallelism
- DeepSpeed - DeepSpeed pipeline parallelism guide
- NVIDIA Megatron-LM - Model parallelism approaches including pipeline
TOP QUESTIONS
When should I use Pipeline Parallelism?
Use it for very large models that exceed single-device memory, or to improve throughput by overlapping compute across stages.
Category: Requirements
How does Pipeline Parallelism compare to Data Parallelism?
Data parallelism replicates the model across devices, while pipeline parallelism splits the model into stages to process microbatches concurrently.
Category: Misunderstanding
What are common mistakes with Pipeline Parallelism?
Poor stage balancing and microbatch sizing cause pipeline bubbles and idle devices, reducing overall efficiency.
Category: Mistakes
What risks arise in Pipeline Parallelism?
Communication overhead, stale gradients, and complex scheduling can impair convergence or performance.
Category: Risk
What is a hot topic around Pipeline Parallelism?
Hybrid parallel strategies that combine pipeline, tensor, and data parallelism to train trillion-parameter models.
Category: Topic
How do I partition models for Pipeline Parallelism?
Group layers into similarly weighted stages, minimizing inter-stage communication and balancing computational load.
Category: Management
Does Pipeline Parallelism affect training dynamics?
Yes; microbatching and scheduling can change effective batch sizes and gradient staleness, requiring careful tuning.
Category: Opinion
Where do I start learning Pipeline Parallelism?
Follow PyTorch and DeepSpeed tutorials, then experiment with small models to understand scheduling and partitioning.
Category: Learning
What are alternatives to Pipeline Parallelism?
Tensor parallelism for layer-internal splits and data parallelism for replicated models are common alternatives.
Category: Alternatives