Package: bootstrap.pytorch
<a href="http://remicadene.com/bootstrap"><img src="https://github.com/Cadene/bootstrap.pytorch/blob/master/docs/source/_static/img/bootstrap-logo-dark.png" width="50%"/></a><a href="https://travis-ci.org/Cadene/bootstrap.pytorch"><img src="https://api.travis-ci.org/Cadene/bootstrap.pytorch.svg?branch=master"/></a>`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 anythingIt's not a wrapper over pytorch, it's a powerful extension.## Quick tourTo 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](https://github.com/Cadene/mnist.bootstrap.pytorch):- [options.yaml](https://github.com/Cadene/bootstrap.pytorch/blob/master/docs/assets/logs/mnist/sgd/options.yaml) contains the options used for the experiment,- [logs.txt](https://github.com/Cadene/bootstrap.pytorch/blob/master/docs/assets/logs/mnist/sgd/logs.txt) contains all the information given to the logger.- [logs.json](https://github.com/Cadene/bootstrap.pytorch/blob/master/docs/assets/logs/mnist/sgd/logs.json) contains the following data: train_epoch.loss, train_batch.loss, eval_epoch.accuracy_top1, etc.- <a href="http://htmlpreview.github.io/?https://raw.githubusercontent.com/Cadene/bootstrap.pytorch/master/docs/assets/logs/mnist/sgd/view.html">view.html</a> 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```## DocumentationThe package reference is available on the [documentation website](http://remicadene.com/bootstrap).It also contains some notes:- [Installation](http://remicadene.com/bootstrap/#installation)- [Concepts](http://remicadene.com/bootstrap/concepts.html)- [Quickstart](http://remicadene.com/bootstrap/quickstart.html)- [Directories](http://remicadene.com/bootstrap/directories.html)- [Examples](http://remicadene.com/bootstrap/examples.html)## Official project modules- [mnist.bootstrap.pytorch](https://github.com/Cadene/mnist.bootstrap.pytorch) is a useful example for starting a quick project with bootstrap- [vision.bootstrap.pytorch](https://github.com/Cadene/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](https://github.com/Cadene/recipe1m.bootstrap.pytorch) is a project for image-text retrieval related to the Recip1M dataset developped in the context of a [SIGIR18 paper](https://arxiv.org/abs/1804.11146).- [block.bootstrap.pytorch](https://github.com/Cadene/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](http://remicadene.com/pdfs/paper_aaai2019.pdf).## Poster<a href="http://remicadene.com/bootstrap/_static/img/bootstrap_poster.pdf"><img src="http://remicadene.com/bootstrap/_static/img/bootstrap_poster_mini.png" width="20%"/></a>