Metadata-Version: 2.1
Name: ezeeml
Version: 0.1.0
Summary: A high level library for Pytorch
Home-page: https://github.com/EKami/EzeeML
Author: GODARD Tuatini
Author-email: tuatinigodard@gmail.com
License: MIT
Description: ## EzeeML
        
        [![PyPI version](https://badge.fury.io/py/ezeeml.svg)](https://badge.fury.io/py/ezeeml)
        
        EzeeML is a high level library on top of popular machine learning frameworks such as
        pandas, Pytorch and Tensorflow.
        It gives a high layer abstraction of repetitive code used in machine learning for day-to-day data science tasks.
        
        ## Installation
        
        ```
        pip install ezeeml
        ```
        
        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/) if you want to use the `ezeeml.torch`
        package and/or head over to the [Tensorflow install page](https://www.tensorflow.org/install/) if you want to
        use the `ezeeml.tf` package.
        
        ## 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-*` folders. This library is still in
        alpha and few APIs may change in the future. 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) if you're interested by
        the `ezeeml.torch` package. Then execute:
        ```
        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
