Metadata-Version: 2.0
Name: torchlite
Version: 0.1.5.7
Summary: A high level library for Pytorch
Home-page: https://github.com/EKami/Torchlite
Author: GODARD Tuatini
Author-email: tuatinigodard@gmail.com
License: MIT
Keywords: development
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 :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Requires-Dist: isoweek
Requires-Dist: tqdm
Requires-Dist: bcolz
Requires-Dist: kaggle-data
Requires-Dist: opencv-python
Requires-Dist: scikit-image
Requires-Dist: setuptools
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: Pillow
Requires-Dist: dask
Requires-Dist: scikit-learn
Requires-Dist: tensorboardX
Requires-Dist: typing
Requires-Dist: PyYAML
Requires-Dist: Augmentor
Requires-Dist: torchlite

## High level library for Pytorch

Torchlite is a high level library meant to be what Keras is for Tensorflow and Theano.
It is not meant to micmic the Keras API at 100% but instead to get the best of both
worlds (Pytorch and Keras API). 
For instance if you used Keras train/validation generators, in Torchlite you would
use Pytorch [Dataset](http://pytorch.org/docs/master/data.html#torch.utils.data.Dataset) and
[DataLoader](http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader).

## Installation

```
pip install torchlite
```

or if you want to run this lib directly to have access to the examples clone this repository and run:

```
pip install -r requirements.txt
```

to install the required dependencies.
Then install pytorch and torchvision from [here](http://pytorch.org/).
Finally install the latest version of imgaug with:
```
pip install git+https://github.com/aleju/imgaug
```
Torchlite will use an outdated version from pypi by default.

## Documentation

For now the library has no complete documentation but you can quickly get to know how
it works by looking at the examples in the `examples` folder. This library is still in
pre-alpha and many things may break for now. The only things which will evolve at the same
pace as the library are the examples, they are meant to always be up to date with
the library.

Few examples will generates folders/files such as saved models or tensorboard logs.
To visualize the tensorboard logs download Tensorflow's tensorboard as well as 
[Pytorch's tensorboard](https://github.com/lanpa/tensorboard-pytorch). Then execute on the
log folder:
```
tensorboard --logdir=./tensorboard
```

## Packaging the project for Pypi deploy

```
pip install twine
pip install wheel
python setup.py sdist
python setup.py bdist_wheel
```

[Create a pypi account](https://packaging.python.org/tutorials/distributing-packages/#id76) and create `$HOME/.pypirc` with:
```
[pypi]
username = <username>
password = <password>
```

Then upload the packages with:
```
twine upload dist/*
```

Or just:
```
pypi_deploy.sh
```


