55 lines
1.7 KiB
Markdown
55 lines
1.7 KiB
Markdown
# Training Models
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This guide covers how to configure and run training experiments for the Brittle Star project using Hydra-based configurations.
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## Configuration
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The project uses a modular configuration system powered by [Hydra](https://hydra.cc/). Instead of passing many command-line flags, you select and override configuration groups.
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### Creating a Custom Experiment
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1. **Create a new experiment file:**
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Create a file at `configs/experiment/my_experiment.yaml`. You can copy an existing one as a template:
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```bash
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cp configs/experiment/base.yaml configs/experiment/my_experiment.yaml
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```
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2. **Edit `configs/experiment/my_experiment.yaml`** to set your experiment parameters:
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```yaml
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# @package _global_
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experiment:
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exp_name: "my_custom_run"
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seed: 42
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```
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## Training Execution
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To start a training run with the default settings defined in `configs/main_config.yaml`:
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```bash
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uv run python scripts/train.py
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```
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### Using a Custom Experiment Configuration
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To run with your custom experiment file:
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```bash
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uv run python scripts/train.py experiment=my_experiment
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```
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```bash
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uv run python scripts/train.py ppo.learning_rate=0.001 ppo.num_envs=32 logging.track=true
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```
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## Evaluation During Training
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By default, the trainer saves checkpoints but does not evaluate them. To enable automatic headless evaluation of every saved checkpoint, set `evaluation.evaluate_checkpoints=true`:
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```bash
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uv run python scripts/train.py evaluation.evaluate_checkpoints=true
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```
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For more details on evaluation metrics and comparison tools, see [Evaluation](./evaluation.md).
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For more details on tracking your experiments, see [Tracking & Monitoring](./tracking.md).
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