Reference 4 stops to get here

Pipeline Parallelism

Splitting model layers across devices and processing micro-batches in pipeline fashion.

Your route here

4 stops · basics first
  1. GPU ✓ understood

    Graphics Processing Unit - hardware accelerator with thousands of cores, essential for parallel computation in deep learning.

  2. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  3. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  4. Data Parallelism ✓ understood

    Replicating the model across devices, each processing different data batches.

  5. Pipeline Parallelism · you are here ✓ understood

Splitting model layers across devices and processing micro-batches in pipeline fashion.

This concept is essential for understanding training & optimization and forms a key part of modern AI systems.

  • Distributed Training
  • Model Parallelism
  • GPipe

Where it sits

Before this

Data Parallelism
Pipeline Parallelism

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Training Tensor Parallelism Splitting individual layers/tensors across devices for very large models. Training ZeRO Zero Redundancy Optimizer - techniques for memory-efficient distributed training by partitioning optimizer states. Training Sharded Data Parallelism Distributing model states across devices to train models larger than single-device memory. Training Gradient Accumulation Summing gradients over multiple batches before updating, simulating larger effective batch sizes.