Configure Trainer (STL)

Modified

August 16, 2025

Under the framework of PyTorch Lightning, we use the Lightning Trainer object for all configs related to training process, such as number of epochs, training strategy, device, etc.

Trainer is a sub-config under the experiment index config (STL). To configure a custom trainer, you need to create a YAML file in trainer/ folder. Below shows an example of the Trainer config.

Example

configs
├── __init__.py
├── entrance.yaml
├── experiment
│   ├── example_stl_train.yaml
│   └── ...
├── trainer
│   └── cpu.yaml
...

configs/experiment/example_stl_train.yaml

defaults:
  ...
  - /trainer: cpu.yaml
  ...

configs/trainer/cpu.yaml

_target_: lightning.Trainer # always link to the lightning.Trainer class
default_root_dir: ${output_dir}
log_every_n_steps: 50
accelerator: cpu
devices: 1
max_epochs: 2

Required Config Fields

The _target_: lightning.Trainer field is required and must always be specified. Please refer to the PyTorch Lightning documentation for full information on the available argument (called trainer flags) of lightning.Trainer.

PyTorch Lightning Documentation (Trainer)