Calculate epoch timings#324
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sgreenbury merged 9 commits intomainfrom Apr 16, 2026
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- Remove time-epochs and compute_cosine_epochs scripts - Integrate time-epochs functionality into CLI and commands
The timer previously measured only the training loop (on_train_epoch_start to on_train_epoch_end), excluding validation. This caused the time-epochs command to underestimate per-epoch duration and over-predict max_epochs, risking max_time cutting training short before the cosine schedule completes. Now measures from one on_train_epoch_start to the next (with the final epoch closed by on_train_end), capturing training batches, validation batches, and any inter-epoch overhead.
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Correct max_time output to DD:HH:MM:SS and add validation for budget, margin, and num_epochs. Update docs example to match.
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This pull request adds a new workflow command for timing training epochs and recommending
max_epochsfor cosine learning rate schedules, along with related documentation and support code. The main goal is to help users accurately settrainer.max_epochsso that the cosine schedule completes exactly within a given wall-clock budget, with a safety margin. The implementation includes both CLI and SLURM support, and provides detailed user guidance.The most important changes include:
New Feature: Epoch Timing and Schedule Recommendation
time-epochscommand to the CLI (src/autocast/scripts/workflow/cli.py,src/autocast/scripts/workflow/commands.py), which runs a short training job to measure per-epoch duration and computes the recommendedtrainer.max_epochsfor a cosine half-period schedule within a user-specified wall-clock budget. [1] [2] [3]Documentation
docs/SCRIPTS_AND_CONFIGS.mdexplaining how to use the new timing feature, including example commands, SLURM integration, margin selection, and the interaction betweenmax_epochsandmax_time.Training Script Support
TrainingTimerCallbackis always attached in autoencoder training so that per-epoch timing data is available in checkpoints.These changes make it easy and robust to calculate the correct number of epochs for cosine schedules, improving reproducibility and efficient use of compute budgets.