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61 lines
1.7 KiB
Markdown
61 lines
1.7 KiB
Markdown
# Training config reference
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`TrainingConfig` controls the training loop, optimisation, data loading, losses,
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and validation tasks.
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Defined in `batdetect2.train.config`.
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## Top-level fields
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- `compile_model`
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- compile the detector before training starts. This is off by default.
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- `train_loader`
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- training data loading and clipping settings.
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- `val_loader`
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- validation data loading and clipping settings.
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- `optimizer`
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- optimiser type and learning rate settings.
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- `scheduler`
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- learning-rate schedule settings.
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- `loss`
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- detection, classification, and size loss settings.
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- `trainer`
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- PyTorch Lightning trainer settings such as `max_epochs`.
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- `labels`
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- target label generation settings.
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- `validation`
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- evaluation tasks used during validation.
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- `checkpoints`
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- checkpoint saving settings.
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## What this config controls
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Use `TrainingConfig` when you want to change things like:
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- batch size,
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- augmentation,
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- optimiser and scheduler settings,
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- runtime options such as model compilation,
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- number of epochs,
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- validation frequency,
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- checkpoint behaviour.
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## Runtime options
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Use `compile_model: true` to call `torch.compile` on the detector used during
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training. This can help on longer runs with stable tensor shapes, but it may be
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slower for short CPU-only experiments because PyTorch has to compile the graph
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before it can reuse it.
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Example files live under `example_data/configs/`, including
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`example_data/configs/training.yaml`.
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## Related pages
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- Evaluation config:
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{doc}`../evaluation/evaluation-config`
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- Train command reference:
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{doc}`../../cli/train`
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- Fine-tune from a checkpoint:
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{doc}`../../../how_to/training/fine-tune-from-a-checkpoint`
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