Trainingslogs

Trainingslogs are records that document training activities or processes, created to monitor progress, guide future sessions, and assess performance. They are used in fitness and athletic training as well as in machine learning and other computational domains.

In fitness contexts, a training log typically includes date and duration, workout type, distance or volume, intensity, pace or power, heart rate, subjective measures (such as rate of perceived exertion), and notes on conditions, nutrition, or recovery.

In machine learning or AI experiments, training logs record each training run: model architecture, dataset, preprocessing steps, hyperparameters (learning rate, batch size, optimizer), number of epochs, training time, losses and metrics, validation results, and hardware or software environment. Logs may be generated automatically by training frameworks and experiment-tracking tools.

They serve several purposes and benefits. For individuals, logs support progress tracking, adherence, and plan adjustment. For teams, they enable accountability, collaboration, and long-term trend analysis. In ML, they improve reproducibility and debugging and facilitate comparison across experiments.

Data management and challenges include ensuring data consistency and quality, which can require standard formats (CSV, JSON, or specialized schemas) and disciplined entry. Privacy considerations arise for health or sensitive data, and tools often provide access controls and anonymization options.

See also: workout log, training diary, exercise log, experiment tracking, MLflow, Weights & Biases.