A modern Deep Learning experiment template with PyTorch Lightning
Click on Use this template to initialize new repository.
- π UV Environment Management - Fast and efficient dependency management
- π― Typer CLI - Modern command line interface
- βοΈ YAML Config - Flexible experiment configuration
- π Lightning Components
- DataModule for clean data handling
- Model with built-in training logic
- Trainer with all the bells and whistles
- π Weights & Biases Integration
- Experiment tracking and visualization
- Hyperparameter optimization with sweeps
- Dataset analysis and exploration
- π¨ Clean Project Structure
- Modular and maintainable codebase
- Easy to extend and customize
uv sync --dev
Explore and analyze your dataset with built-in EDA tools:
python -m ailab.main -c config/resnet.yml --eda
Start training your model with a single command:
python -m ailab.main -c config/resnet.yml --train
Evaluate your trained model:
python -m ailab.main -c config/resnet.yml --eval --run-id n8fjnlyi
Optimize your model with W&B Sweeps:
python -m ailab.main -c config/resnet.yml --sweep --sweep-config config/sweep/mlp.yml
This project is licensed under the MIT License - see the LICENSE file for details.
Made with β€οΈ for the ML community