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Training machine learning models across multiple devices or nodes to scale batch sizes, accelerate computation, and fit larger models.
DESCRIPTION
Distributed training parallelizes model updates across GPUs or machines using data-parallel, model-parallel, or hybrid strategies. Communication backends (e.g., NCCL) and collectives (e.g., AllReduce) synchronize gradients. Frameworks like PyTorch and TensorFlow provide strategies, and Horovod simplifies multi-GPU orchestration. Practical setups optimize batch sizes, learning rates, precision, and communication overlap. Monitoring throughput and convergence ensures performance and correctness.
ALTERNATIVE DEFINITIONS
TensorFlow
Distributed training allows you to train models on multiple devices or machines to speed up training and scale to larger datasets.
Framework-oriented guidance emphasizing speed and scale.
PyTorch
PyTorch distributed provides primitives for training models across multiple processes and machines.
Focuses on distributed primitives and process-based execution.
Horovod
Horovod makes distributed deep learning fast and easy to use by implementing ring-allreduce and other efficient collective operations.
Highlights efficient collectives and ease of use.
RELATED TERMS
Data-Parallel Training Model-Parallel Training Horovod PyTorch Distributed TensorFlow MirroredStrategy Parameter Server All-Reduce GPU Cluster Gradient Synchronization Mixed Precision Training
COMPETING TERMS
Single-GPU Training
RELATIONS
- Broader: Training
KNOWLEDGE AREAS
TARGET AUDIENCE
MLOps Engineer AI Engineer Data Scientist Hardware Engineer Cloud Architect
EXTERNAL SOURCES
- Wikipedia - Distributed machine learning
- PyTorch - Distributed training overview and tutorial
- TensorFlow - Guide to distributed strategies
- NVIDIA - Collective communications library for GPUs
- Microsoft Azure - Azure ML distributed training docs
TOP QUESTIONS
Why use Distributed Training for deep learning?
It accelerates training, enables larger batch sizes, and allows fitting bigger models across multiple GPUs or nodes.
Category: Relevancy
What are common Distributed Training strategies?
Data-parallel replicates models across workers; model-parallel splits models; hybrid combinations leverage both.
Category: Overview
What mistakes occur in Distributed Training setups?
Mismatched seeds, improper gradient synchronization, and unbalanced workloads cause convergence and performance issues.
Category: Mistakes
How does AllReduce work in Distributed Training?
Workers compute gradients locally and collectively average them via efficient communication patterns like ring-AllReduce.
Category: Example
How do you monitor Distributed Training?
Track throughput, GPU utilization, gradient norms, loss convergence, and communication time using dashboards and logs.
Category: Management
What is a competing approach to Distributed Training?
Single-GPU training is simpler but slower and limited for large models.
Category: Alternatives
What infrastructure is required for Distributed Training?
High-speed interconnects, synchronized clocks, robust schedulers, and optimized GPU drivers and libraries (e.g., NCCL).
Category: Requirements
What is the biggest misunderstanding about Distributed Training?
That scaling linearly is guaranteed; communication overhead and optimization settings affect speedups and convergence.
Category: Misunderstanding
Where to start learning Distributed Training?
Follow PyTorch and TensorFlow distributed tutorials, then experiment with Horovod on a small GPU cluster.
Category: Overview