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        "author": "The TF Encrypted Authors",
        "author_email": "contact@tf-encrypted.io",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Security :: Cryptography"
        ],
        "description": "# TF Big\n\nTF Big adds big number support to TensorFlow, allowing computations to be performed on arbitrary precision integers. Internally these are represented as variant tensors of [GMP](https://gmplib.org/) values, and exposed in Python through the `tf_big.Tensor` wrapper for convenience. For importing and exporting, numbers are typically expressed as strings.\n\n[![PyPI](https://img.shields.io/pypi/v/tf-big.svg)](https://pypi.org/project/tf-big/) [![CircleCI Badge](https://circleci.com/gh/tf-encrypted/tf-big/tree/master.svg?style=svg)](https://circleci.com/gh/tf-encrypted/tf-big/tree/master)\n\n## Usage\n\n```python\nimport tensorflow as tf\nimport tf_big\n\n# load large values as strings\nx = tf_big.constant([\"100000000000000000000\", \"200000000000000000000\"])\n\n# load ordinary TensorFlow tensors\ny = tf_big.convert_to_tensor(tf.constant([3, 4]))\n\n# perform computation as usual\nz = x * y\n\n# use TensorFlow sessions to evalutate the results\nwith tf.Session() as sess:\n  res = sess.run(z)\n  print(res)\n```\n\n## Installation\n\nPython 3 packages are available from [PyPI](https://pypi.org/project/tf-big/):\n\n```\npip install tf-big\n```\n\nSee below for further instructions for setting up a development environment.\n\n## Development\n\n### Requirements\n\nWe recommend using [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/distribution/) to set up and use a Python 3.5 or 3.6 environment for all instructions below:\n\n```\nconda create -n tfbig-dev python=3.6\nsource activate tfbig-dev\n```\n\n#### Ubuntu\n\nThe only requirement for Ubuntu is to have [docker installed](https://docs.docker.com/install/linux/docker-ce/ubuntu/). This is the recommended way to [build custom operations for TensorFlow](https://github.com/tensorflow/custom-op). We provide a custom development container for TF Big with all dependencies already installed.\n\n#### macOS\n\nSetting up a development environment on macOS is a little more involved since we cannot use a docker container. We need four things:\n\n- Python (>= 3.5)\n- [Bazel](https://www.bazel.build/) (>= 0.15.0)\n- [GMP](https://gmplib.org/) (>= 6.1.2)\n- [TensorFlow](https://www.tensorflow.org/) (== 1.13.1)\n\nUsing [Homebrew](https://brew.sh/) we first make sure that both [Bazel](https://docs.bazel.build/versions/master/install-os-x.html#install-with-installer-mac-os-x) and GMP are installed:\n\n```\nbrew tap bazelbuild/tap\nbrew install bazelbuild/tap/bazel\nbrew install gmp\nbrew install mmv\n```\n\nThe remaining PyPI packages can then be installed using:\n\n```\npip install -r requirements-dev.txt\n```\n\n### Testing\n\n#### Ubuntu\n\nRun the tests on Ubuntu by running the `make test` command inside of a docker container. Right now, the docker container doesn't exist on docker hub yet so we must first build it:\n\n```\ndocker build -t tf-encrypted/tf-big:0.1.0 .\n```\n\nThen we can run `make test`:\n\n```\nsudo docker run -it \\\n  -v `pwd`:/opt/my-project -w /opt/my-project \\\n  tf-encrypted/tf-big:0.1.0 /bin/bash -c \"make test\"\n```\n\n#### macOS\n\nOnce the development environment is set up we can simply run:\n\n```\nmake test\n```\n\nThis will install TensorFlow if not previously installed and build and run the tests.\n\n### Building pip package\n\nCircleCI currently builds the pip packages for us. If you have a need to do it on your own you can just run `make build`. For linux, doing it inside the tensorflow/tensorflow:custom-op container is recommended.\n\n\n",
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        "license": "Apache License 2.0",
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