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Time per epoch

As explained in Graph Neural Networks in Action

Average time per epoch is the mean elapsed time required to complete one full pass of training data during model training.

Time per epoch measures the average elapsed time needed to complete one full pass through the training data. Use it to estimate training timelines and compare scalability across hardware or training strategies. section 7.2.3 section 7.3.2

Apply the metric as a practical benchmarking signal:

  • Benchmark it on a representative dataset to establish a baseline for predicting experimentation and project timelines. section 7.3.2
  • Read it alongside memory utilization, FLOP/s, and time to convergence rather than treating runtime as the only scalability constraint. section 7.2.3 section 7.3.2
  • Use changes in the metric to evaluate whether distributed training justifies its added complexity, debugging effort, and compute cost. section 7.8.2

DDP feasibility study

A team evaluating Distributed Data Parallelism tracks convergence time and average time per epoch during a one-week feasibility study. The measurements support a data-driven decision about deployment before committing additional resources. section 7.8.2

How Graph Neural Networks in Action covers it

How deep it goes
Defines the metric and places it in a benchmarking workflow, but does not specify a calculation procedure or timing implementation.
Examples
Uses representative-dataset baselines and a DDP feasibility study that records average epoch time.
What it assumes
Assumes familiarity with datasets, training epochs, hardware resources, and basic machine-learning benchmarking.
What you can do after
Evaluate training strategies and estimate project timelines from measured epoch durations.
How it connects
Connects time per epoch with memory usage, FLOP/s, convergence speed, and distributed data parallelism.
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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.