# Distribution Strategy

> Scales training across multiple processors or machines through parallelism

- **Category**: Machine Learning
- **Subcategory**: Training Process and Optimization
- **Canonical URL**: https://designpattern.fyi/machine_learning/distribution-strategy/

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## Description
**Intent**: Handle modern datasets and models that are too large to train in reasonable time, or too large to even fit in memory, on a single machine or accelerator.

**Context**: Large-scale ML workloads can be constrained by single-device memory limits or training time. Training on a single device may be prohibitive in cost or simply impossible for models that don't fit.

**Solution**: Scale training across multiple processors or machines through data parallelism (replicate model on every worker, split each batch, synchronize gradients) or model parallelism (split model across devices when it doesn't fit on one). Gradient synchronization can be synchronous (deterministic) or asynchronous (higher throughput, noisier).



## Use Cases
- Training large models that don't fit on single device memory
- Reducing training time for large datasets
- Distributed training across multiple GPUs/TPUs
- Any workload where single-device training is prohibitive






## Trade-offs


### Advantages

- Enables training of models too large for single device

- Reduces training time through parallelism

- Scales to use available cluster resources

- Flexible strategies for different bottlenecks




### Considerations & Drawbacks

- Adds infrastructure and orchestration complexity

- Communication overhead can eat into expected speedup

- Requires careful hyperparameter tuning at scale

- Debugging distributed systems is more complex







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**Reference**: [Original Source](https://github.com/GoogleCloudPlatform/ml-design-patterns)