Standard 3 stops to get here · leads to 4

Data Parallelism

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

Your route here

3 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 · you are here ✓ understood

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

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

  • Distributed Training
  • Parallel Training
  • Multi-GPU

Where it sits

Before this

Training GPU
Data Parallelism

Leads to

Pipeline Parallelism Sharded Data Parallelism Tensor Parallelism ZeRO

Explore nearby

Training Sharded Data Parallelism Distributing model states across devices to train models larger than single-device memory. Training Pipeline Parallelism Splitting model layers across devices and processing micro-batches in pipeline fashion. 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 Gradient Accumulation Summing gradients over multiple batches before updating, simulating larger effective batch sizes.