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 run an experiment (training + evaluation):
python -m bootstrap.run
-o myproject/options/sgd.yaml
To display parsed options from the yaml file:
python -m bootstrap.run
-o myproject/options/sgd.yaml
-h
Running an experiment will create 4 files, here is an example with mnist:
- 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 myproject/options/sgd.yaml
--exp.dir logs/custom
To reload an experiment:
python -m bootstrap.run
-o logs/custom/options.yaml
--exp.resume last
Documentation
The package reference is available on the documentation website.
It also contains some notes:
Official project modules
- mnist.bootstrap.pytorch is a useful example for starting a quick project with bootstrap
- vision.bootstrap.pytorch contains utilities to train image classifier, object detector, etc. on usual datasets like imagenet, cifar10, cifar100, coco, visual genome, etc.
- recipe1m.bootstrap.pytorch is a project for image-text retrieval related to the Recip1M dataset developped in the context of a SIGIR18 paper.
- block.bootstrap.pytorch is a project focused on fusion modules related to the VQA 2.0, TDIUC and VRD datasets developped in the context of a AAAI19 paper.
