Highlevel framework for starting Deep Learning projects (lightweight, flexible, easy to extend)
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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:

  • Scalable
  • Modular
  • Shareable
  • Extendable
  • Uncomplicated
  • Built for reproducibility
  • Easy to log and plot anything

It's not a wrapper over pytorch, it's a powerful extension.

Quick tour

To display parsed options from the yaml file:

python -m bootstrap.run
       -o mnist/options/sgd.yaml

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.
  • view.html contains training and evaluation curves with javascript utilities (plotly).

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: