Bootstrap is a high-level framework for starting deep learning projects.
It aims at accelerating research projects and prototyping by providing a powerful workflow focused on your dataset and model only.
And it is:
- Built for reproducibility
- Easy to log and plot anything
It's not a wrapper over pytorch, it's a powerful extension.
To display parsed options from the yaml file:
python -m bootstrap.run -o mnist/options/sgd.yaml -h
To run an experiment (training + evaluation):
python -m bootstrap.run -o mnist/options/sgd.yaml
Running an experiment will create 4 files:
- options.yaml contains the options used for the experiment,
- logs.txt contains all the information given to the logger.
- logs.json contains the following data: train_epoch.loss, train_batch.loss, eval_epoch.accuracy_top1, etc.
To save the next experiment in a specific directory:
python -m bootstrap.run -o mnist/options/sgd.yaml --exp.dir logs/mnist/custom
To reload an experiment:
python -m bootstrap.run -o logs/mnist/cuda/options.yaml --exp.resume last
The package reference is available on the documentation website.
It also contains some notes: