Bootstrap Documentation

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.

Installation

First, install python3 and pytorch with Anaconda:

Attention: You need pytorch version 0.4.1 or superior in order to use Bootstrap.

There are two ways of using bootstrap.pytorch: (1) as a standalone project, or (2) as a python library.

1. As a standalone project

We advise you to clone bootstrap to start a new project. This way, it will be easier to prototype and debug your code, as you will have direct access to bootstrap core functions:

git clone https://github.com/Cadene/bootstrap.pytorch.git
cd bootstrap.pytorch
pip install -r requirements.txt

2. As a python library

Using bootstrap like a python library is also possible. You can use pip install:

pip install bootstrap.pytorch

Or install from source:

git clone https://github.com/Cadene/bootstrap.pytorch.git
cd bootstrap.pytorch
python setup.py install

Few words from the authors

Bootstrap is the result of the time we spent engineering stuff since the beginning of our PhDs. We have worked with different libraries and languages (Torch7, Keras, Tensorflow, Pytorch, Torchnet, and others), and they all inspired the development of bootstrap.pytorch.

Part of this inspiration also comes from the modularity of modern web frameworks. We came up with a nice workflow and good practicies that we wanted like to share.

Last but not least, criticism is always welcome, feel free to send us a message or a pull request. :-)

Remi Cadene, Micael Carvalho, Hedi Ben-Younes, and Thomas Robert