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Scaling Model Training with Distributed Computing
Distributed training splits the work across machines, but keeping them in step costs time. In real PyTorch data-parallel runs with one, two and four workers, every replica stayed identical, yet small batches trained slower with more workers and only large batches gained, about one and a half times with four.
Continuous Training: Automating Model Retraining
Continuous training retrains models automatically, but retraining is only as good as the checks around it. On three years of real household energy data, this guide compares four policies: never retraining, monthly retraining, adding data checks, and adding a champion gate that was right only 21 times in 34.