Metadata-Version: 1.1
Name: bootstrap.pytorch
Version: 0.0.13
Summary: High level framework for starting Deep Learning projects
Home-page: https://github.com/cadene/bootstrap.pytorch
Author: Remi Cadene
Author-email: remi.cadene@icloud.com
License: UNKNOWN
Description: <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 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](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
        ```
        
        
        ## Documentation
        
        The 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>
        
Keywords: pytorch framework bootstrap deep learning scaffolding
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
