Starter project for lightning CLI training of CIFAR-10 with ResNet18

Current configuration achieves 86.4% accuracy on validation set after 30 epochs.

python -m venv env
source env/bin/activate
pip install --upgrade pip
pip install -e .

dev:

pip install pre-commit
pre-commit install
pre-commit run --all-files

take a look at the config in configs/training.yaml before running and update any values necessary (particularly necessary for wandb logging section).

   python runner.py fit -c configs/training.yaml \
 --trainer.logger.name cifar_resnet \
  --trainer.logger.save_dir weights/cifar_resnet

you can specify args to replace keys within the config, as shown here with trainer.logger.name and trainer.logger.save_dir

convert_checkpoint_to_safetensors.py heavily borrows from https://github.com/huggingface/safetensors/blob/v0.4.3/bindings/python/convert.py

We can use this script to convert to safetensor format. This is useful for sharing models without using pickle (unsafe, can run arbitrary code).

This conversion is lossy and will only maintain the state_dict.

python convert_checkpoint_to_safetensors.py --help

add path to file in config at model.safetensors_path or specify in command line using --model.safetenors_path. E.g.

python runner.py test -c configs/training.yaml \
  --model.safetensors_path /path/to/model.safetensors

A model trained for 30 epochs, achieving 86.4% acc on val, can be found in safetensors format in releases.