Distributed training is a critical technique for accelerating the training of large-scale machine learning models by leveraging multiple computing devices. It enables parallel processing of data, model parameters, or both, significantly reducing training time and improving efficiency.

Common Methods of Distributed Training

  1. Data Parallelism 🔄

    • Split the dataset across multiple devices.
    • Each device computes gradients independently and aggregates them via AllReduce.
    • Data_Parallelism

  2. Model Parallelism 🧱

    • Partition the model itself across devices (e.g., layers).
    • Devices collaborate to compute different parts of the model.
    • Model_Parallelism

  3. Hybrid Parallelism 🔄🧱

    • Combines data and model parallelism for complex architectures.
    • Distributed_Training_Architecture

Key Applications

  • Large Model Training: Training models with billions of parameters (e.g., GPT, BERT).
  • Real-Time Data Processing: Handling high-throughput datasets with low latency.
  • Multi-GPU/TPU Environments: Optimizing resource utilization across clusters.

Best Practices & Considerations

  • Hardware Compatibility: Ensure devices support efficient communication (e.g., NVLink, InfiniBand).
  • Communication Overhead: Minimize synchronization delays with advanced frameworks.
  • Load Balancing: Distribute workloads evenly to avoid underutilized resources.

For deeper insights into parallelism strategies, check our parallelism tutorial. 📚