Metadata-Version: 2.1
Name: torchlite
Version: 0.1.5.6
Summary: A high level library for Pytorch
Home-page: https://github.com/EKami/Torchlite
Author: GODARD Tuatini
Author-email: tuatinigodard@gmail.com
License: MIT
Description: ## 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        Torchlite will use an outdated version from pypi by default.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
        ```
        
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
