Pipeline Parallelism vs Model Parallelism

Developers should learn pipeline parallelism when working with large neural networks or complex data processing pipelines that do not fit into a single GPU's memory or require faster throughput meets developers should learn and use model parallelism when training or deploying very large neural network models that exceed the memory capacity of a single gpu or tpu, such as transformer-based models with billions of parameters (e. Here's our take.

Pipeline Parallelism

Nice Pick

Developers should learn pipeline parallelism when working with large neural networks or complex data processing pipelines that do not fit into a single GPU's memory or require faster throughput

Pros

• It is essential for scaling deep learning models like transformers (e
• Related to: distributed-training, model-parallelism

Cons

• Specific tradeoffs depend on your use case

Model Parallelism

Developers should learn and use model parallelism when training or deploying very large neural network models that exceed the memory capacity of a single GPU or TPU, such as transformer-based models with billions of parameters (e

Pros

• g
• Related to: distributed-training, data-parallelism

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Pipeline Parallelism if: You want it is essential for scaling deep learning models like transformers (e and can live with specific tradeoffs depend on your use case.

Use Model Parallelism if: You prioritize g over what Pipeline Parallelism offers.

The Bottom Line

Pipeline Parallelism wins

Developers should learn pipeline parallelism when working with large neural networks or complex data processing pipelines that do not fit into a single GPU's memory or require faster throughput