Example Usage of existing unlearn algorithms

Add the following code snippet to a python script trainer.py. Run the script using python trainer.py.

from mu.algorithms.concept_ablation.algorithm import (
    ConceptAblationAlgorithm,
)
from mu.algorithms.concept_ablation.configs import (
    concept_ablation_train_mu,
    ConceptAblationConfig,
)

if __name__ == "__main__":

    concept_ablation_train_mu.lightning.trainer.max_steps = 5

    algorithm = ConceptAblationAlgorithm(
        concept_ablation_train_mu,
        config_path="mu/algorithms/concept_ablation/configs/train_config.yaml",
        ckpt_path="machine_unlearning/models/compvis/style50/compvis.ckpt",
        prompts="mu/algorithms/concept_ablation/data/anchor_prompts/finetune_prompts/sd_prompt_Architectures_sample.txt",
        output_dir="outputs/concept_ablation/outputs",
        template_name = "Abstractionism", #concept to erase
        dataset_type = "unlearncanvas" ,
        use_sample = True, #train on sample dataset
        # devices="1",
    )
    algorithm.run()

Notes

1. Ensure all dependencies are installed as per the environment file.
2. The training process generates logs in the logs/ directory for easy monitoring.
3. Use appropriate CUDA devices for optimal performance during training.
4. Regularly verify dataset and model configurations to avoid errors during execution.

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