Quick Start Guide for New Projects

0. Prepare the Environment

• Set up your environment:

  conda create -n myenv python=3.9
  conda activate myenv
  pip install -r requirements.txt
  

1. Project Initialization

• Use the Minimal Lightning Hydra Template: Click "Use this template" on GitHub, or clone and remove .git for a fresh start:

  git clone https://github.com/antonibigata/minimal-lightning-hydra-template.git <project-name>
  cd <project-name>
  rm -rf .git
  

2. Data Setup

• Structure data code in src/datamodules and components/dataset.py.
• Configure in configs/datamodule/datamodule.yaml; use default.yaml for shared parameters.
• Example: Adapt FashionMNIST dataset and configurations. Implement and test custom datamodules and datasets.

3. Model and Network Configuration

• Define networks in src/models/components/nets and configure in configs/model/net.
• Create simple models for practice, referencing provided examples. Write unit tests to ensure functionality.

4. Model Creation

• Develop your model in src/models as a PyTorch Lightning LightningModule.
• Use configs/model/default.yaml for base settings; add specific model configs as needed.
• Examine example models and consult PyTorch Lightning documentation for guidance.

5. Training Setup

• Manage training with PyTorch Lightning Trainer and configurations in configs/trainer.
• Adjust training settings via command line, e.g., python src/train.py trainer.max_epochs=20.
• Use configs/train.yaml to encompass datamodule, model, callbacks, logger, trainer, and paths settings.

6. Launch Training

• Start with python src/train.py. Override configs as required, e.g., python src/train.py model/net=conv_net.
• For GPU training, add trainer=gpu.

7. Logging with Wandb

• Install Wandb, sign up, and authenticate. Modify configs/train.yaml for Wandb logging or use python src/train.py logger=wandb.
• Log training metrics, images, and explore advanced Wandb features as per documentation.

8. Evaluation

• Evaluate model performance with python src/eval.py ckpt_path=<path_to_checkpoint>.
• Use test set for unbiased assessment; locate checkpoints in logs/train/runs.

9. Inference

• Perform inference on custom data: python src/inference.py ckpt_path=<checkpoint> image_path=<image_path>.
• Repository includes sample FashionMNIST images in logs/data_check for quick tests.