Distributed Training
Definition
Distributed training allows multiple devices or computers to jointly train a model.
Training a model with coordinated computing across multiple processes, devices, or computers.
Simply explained
Multiple computers share the learning of a model. They must coordinate their work accordingly.
Why it matters in practice
The term contributes to secure, maintainable, and high-performance web applications as well as resilient technical decisions.
Example from business
An operation distributes image training across multiple GPUs and checks scaling efficiency and reproducibility.
Limits and pitfalls
- More devices do not mean linear acceleration; communication, batch size, failures, and deterministic resumption are critical.
Origin & history
Data and model parallelism became central with large neural networks and GPU clusters.
Use in mid-sized companies
- Possible example: An operation distributes image training across multiple GPUs and checks scaling efficiency and reproducibility.
Frequently asked questions
What does Distributed Training mean?
Training a model with coordinated computing across multiple processes, devices, or computers. What should you pay attention to? More devices do not mean linear acceleration; communication, batch size, failures, and deterministic resumption are critical.
Sources
- Horovod · Sergeev and Del Balso, Horovod
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: