seniorDeep Learning
What is Distributed Training in Deep Learning and why is it necessary?
Updated May 16, 2026
Short answer
Distributed Training spreads model computation across multiple GPUs or machines to accelerate training of large deep learning systems.
Deep explanation
Modern deep learning models often contain billions of parameters and require enormous datasets. Single-machine training becomes impractical due to memory, compute, and time constraints.
Distributed Training solves this using parallel computation.
Main approaches:
- Data Parallelism:
- Replicate model across devices.
- Split mini-batches across GPUs.
- Aggregate gradients globally.
- Model Parallelism:
- Split model layers across devices.
- Useful for extremely large models.
- Pipeline Parallelism:
- Different devices process different stages simultaneously.…
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