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        "author": "GODARD Tuatini",
        "author_email": "tuatinigodard@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Build Tools"
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        "description": "## Torchlite\n![](./docs/sources/img/logo.png)\n[![PyPI version](https://badge.fury.io/py/torchlite.svg)](https://badge.fury.io/py/torchlite)\n\nTorchlite is a high level library on top of popular machine learning frameworks such as\nsklearn, Pytorch and Tensorflow.\nIt gives a high layer abstraction of repetitive code used in machine learning for day-to-day data science tasks.\n\n## Installation\n\n```\npip install torchlite\n```\n\nor if you want to run this lib directly to have access to the examples clone this repository and run:\n\n```\npip install -r requirements.txt\n```\n\nto install the required dependencies.\nBy default **Pytorch 0.4.0+** and **Tensorflow-GPU 1.8.0+** are installed along with this library but it's recommended\nto install them from source from [here](http://pytorch.org/) if you want to use the `torchlite.torch`\npackage and/or head over to the [Tensorflow install page](https://www.tensorflow.org/install/) if you want to\nuse the `torchlite.tf` package.\n\n## Documentation\n\nFor now the library has no complete documentation but you can quickly get to know how\nit works by looking at the examples in the `examples-*` folders. This library is still in\nalpha and few APIs may change in the future. The only things which will evolve at the same\npace as the library are the examples, they are meant to always be up to date with\nthe library.\n\nFew examples will generates folders/files such as saved models or tensorboard logs.\nTo visualize the tensorboard logs download Tensorflow's tensorboard as well as \n[Pytorch's tensorboard](https://github.com/lanpa/tensorboard-pytorch) if you're interested by\nthe `torchlite.torch` package. Then execute:\n```\ntensorboard --logdir=./tensorboard\n```\n\n## Packaging the project for Pypi deploy\n\n```\npip install twine\npip install wheel\npython setup.py sdist\npython setup.py bdist_wheel\n```\n\n[Create a pypi account](https://packaging.python.org/tutorials/distributing-packages/#id76) and create `$HOME/.pypirc` with:\n```\n[pypi]\nusername = <username>\npassword = <password>\n```\n\nThen upload the packages with:\n```\ntwine upload dist/*\n```\n\nOr just:\n```\npypi_deploy.sh\n```\n\n\n",
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