Metadata-Version: 2.1
Name: lazybox
Version: 0.0.2.5
Summary: A description of your project
Home-page: https://github.com/limiteinductive/Databox/tree/main/
Author: Benjamin Trom
Author-email: benjamintrom@gmail.com
License: MIT License
Keywords: some keywords
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# LazyBox
> A user friendly API to jump-start your Deep Learning project based on Fastai. 


The library is still in early development, but a lot of new features will be added in future

    [ ] Support for non-image Datasets
    [ ] Import NN architecture from the timm library
    [ ] Make it better

## Install

`pip install lazybox`

## How to use

Let's go through a typical workflow for a DeepLearning task. Let's download this Dataset on Kaggle: https://www.kaggle.com/tongpython/cat-and-dog which are images of cat and dogs.

```
# we lazy folks only use wild imports
from lazybox.all import *

path = Path('test_dataset')

path.find('archive')
```




    'test_dataset/archive.zip'



Let's decompress this archive


