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Time to convergence

As explained in Graph Neural Networks in Action

Time to convergence is the elapsed training time required for an optimization process to reach a specified convergence condition.

Time to convergence measures how long training takes to reach a defined convergence condition. It helps assess whether a model, dataset, or hardware setup scales effectively. section 7.2.3 section 7.3.2

Use time to convergence as part of a practical training benchmark:

  • Establish a baseline with a representative dataset. section 7.3.2
  • Record time to convergence alongside memory utilization, time per epoch, and FLOP/s. section 7.2.3 section 7.3.2
  • Compare configurations empirically before committing additional compute or distributed-training complexity. section 7.8.2

DDP feasibility study

A team considering distributed data parallelism ran a one-week feasibility study, tracking convergence time and average time per epoch before deciding whether DDP justified its debugging and resource costs. section 7.8.2

How Graph Neural Networks in Action covers it

How deep it goes
Defines a training metric and pairs it with time per epoch, but does not specify a convergence criterion or measurement formula.
Examples
Uses representative-dataset baselines and a one-week DDP feasibility study tracking convergence time and average time per epoch.
What it assumes
Assumes a training workflow with a representative dataset and measurable training metrics.
What you can do after
Evaluate training scalability by recording time to convergence alongside time per epoch and other resource metrics.
How it connects
Connects time to convergence with memory utilization, time per epoch, FLOP/s, benchmarking, and distributed data parallelism.
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.