use "TF Encrypted" everywhere

closes tf-encrypted#404

Author: mortendahl

Makefile

@@ -129,7 +129,7 @@ endif
 # ###############################################
 # Building Docker Image
 #
-# Builds a docker image for tf-encrypted that can be used to deploy and
+# Builds a docker image for TF Encrypted that can be used to deploy and
 # test.
 # ###############################################
 DOCKER_BUILD=docker build -t mortendahl/tf-encrypted:$(1) -f Dockerfile $(2) .

NOTICE

@@ -1,4 +1,4 @@
-tf-encrypted
+TF Encrypted
 
 All contributions by Morten Dahl:
 Copyright (c) 2018, Morten Dahl.

README.md

@@ -1,8 +1,8 @@
-# tf-encrypted
+# TF Encrypted
 
 ![Status](https://img.shields.io/badge/status-alpha-blue.svg)  [![License](https://img.shields.io/github/license/mortendahl/tf-encrypted.svg)](./LICENSE)  [![PyPI](https://img.shields.io/pypi/v/tf-encrypted.svg)](https://pypi.org/project/tf-encrypted/) [![CircleCI Badge](https://circleci.com/gh/mortendahl/tf-encrypted/tree/master.svg?style=svg)](https://circleci.com/gh/mortendahl/tf-encrypted/tree/master) [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://tf-encrypted.readthedocs.io/en/latest/)
 
-tf-encrypted is a Python library built on top of [TensorFlow](https://www.tensorflow.org) for researchers and practitioners to experiment with privacy-preserving machine learning. It provides an interface similar to that of TensorFlow, and aims at making the technology readily available without first becoming an expert in machine learning, cryptography, distributed systems, and high performance computing.
+TF Encrypted is a Python library built on top of [TensorFlow](https://www.tensorflow.org) for researchers and practitioners to experiment with privacy-preserving machine learning. It provides an interface similar to that of TensorFlow, and aims at making the technology readily available without first becoming an expert in machine learning, cryptography, distributed systems, and high performance computing.
 
 In particular, the library focuses on:
 
@@ -18,7 +18,7 @@ The project has benefitted enormously from the efforts of several contributors f
 
 # Installation
 
-tf-encrypted is available as a package on [PyPI](https://pypi.org/project/tf-encrypted/) supporting Python 3.5+ and TensorFlow 1.12.0+ which can be installed using:
+TF Encrypted is available as a package on [PyPI](https://pypi.org/project/tf-encrypted/) supporting Python 3.5+ and TensorFlow 1.12.0+ which can be installed using:
 
 ```bash
 pip3 install tf-encrypted
@@ -32,17 +32,17 @@ cd tf-encrypted
 pip3 install -e .
 ```
 
-This latter is useful on platforms for which the pip package has not yet been compiled but is also needed for [development](./.github/CONTRIBUTING.md). Note that this will get you a working basic installation, yet [a few more steps](./INSTALL.md) are required to match the performance and security of the version shipped in the pip package.
+This latter is useful on platforms for which the pip package has not yet been compiled but is also needed for [development](./.github/CONTRIBUTING.md). Note that this will get you a working basic installation, yet a few more steps are required to match the performance and security of the version shipped in the pip package, see [INSTALL.md](./docs/INSTALL.md).
 
 ## Custom build of TensorFlow
 
-While tf-encrypted will work with the official release of [TensorFlow](https://pypi.org/project/tensorflow/) (version 1.12+), some features currently depend on improvements that have not yet been shipped. In particular, to get speed improvements by using int64 tensors instead of int100 tensors you currently need a custom build of TensorFlow.
+While TF Encrypted will work with the official release of [TensorFlow](https://pypi.org/project/tensorflow/) (version 1.12+), some features currently depend on improvements that have not yet been shipped. In particular, to get speed improvements by using int64 tensors instead of int100 tensors you currently need a custom build of TensorFlow.
 
-Such builds are available for [macOS](https://storage.googleapis.com/dropoutlabs-tensorflow-builds/tensorflow-1.12.0-cp35-cp35m-macosx_10_7_x86_64.whl) and [Linux](https://storage.googleapis.com/dropoutlabs-tensorflow-builds/tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl) as a temporary solution until the next official release of TensorFlow is out (version 1.13), but no guarantees are made about them and they should be treated as pre-alpha. See more in the [installation instructions](./INSTALL.md#custom-tensorflow).
+Such builds are available for [macOS](https://storage.googleapis.com/dropoutlabs-tensorflow-builds/tensorflow-1.12.0-cp35-cp35m-macosx_10_7_x86_64.whl) and [Linux](https://storage.googleapis.com/dropoutlabs-tensorflow-builds/tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl) as a temporary solution until the next official release of TensorFlow is out (version 1.13), but no guarantees are made about them and they should be treated as pre-alpha. See more in the [installation instructions](./docs/INSTALL.md#custom-tensorflow).
 
 # Usage
 
-The following is an example of simple matmul on encrypted data using tf-encrypted:
+The following is an example of simple matmul on encrypted data using TF Encrypted:
 
 ```python
 import tensorflow as tf
@@ -81,7 +81,7 @@ The following texts provide further in-depth presentations of the project:
 
 # Project Status
 
-tf-encrypted is experimental software not currently intended for use in production environments. The focus is on building the underlying primitives and techniques, with some practical security issues post-poned for a later stage. However, care is taken to ensure that none of these represent fundamental issues that cannot be fixed as needed.
+TF Encrypted is experimental software not currently intended for use in production environments. The focus is on building the underlying primitives and techniques, with some practical security issues postponed for a later stage. However, care is taken to ensure that none of these represent fundamental issues that cannot be fixed as needed.
 
 ## Known limitations
 

.github/CONTRIBUTING.md docs/CONTRIBUTING.md

@@ -1,26 +1,26 @@
 # Contribution Guide
 
-The goal of `tf-encrypted` is to make it easy for researchers and practitioners to experiment with various tricks and techniques for privacy-preserving machine learning. As such, contributions are more than welcome and we're always looking for use cases, feature ideas, cryptographic protocols, or machine learning optimizations!
+The goal of TF Encrypted is to make it easy for researchers and practitioners to experiment with various tricks and techniques for privacy-preserving machine learning. As such, contributions are more than welcome and we're always looking for use cases, feature ideas, cryptographic protocols, or machine learning optimizations!
 
 This document helps you get started on:
 
 - [Submitting a pull request](#submitting-a-pull-request)
 - [Writing documentation](#writing-documentation)
 - [Reporting a bug](#reporting-a-bug)
 
-Please visit the [installation instructions](/INSTALL.md) for help on setting up for development.
+Please visit the [installation instructions](./INSTALL.md) for help on setting up for development.
 
 # Submitting a Pull Request
 
-To contribute, [fork](https://help.github.com/articles/fork-a-repo/) `tf-encrypted`, commit your changes, and [open a pull request](https://help.github.com/articles/using-pull-requests/).
+To contribute, [fork](https://help.github.com/articles/fork-a-repo/) TF Encrypted, commit your changes, and [open a pull request](https://help.github.com/articles/using-pull-requests/).
 
 While you may be asked to make changes to your submission during the review process, we will work with you on this and suggest changes. Consider giving us [push rights to your branch](https://help.github.com/articles/allowing-changes-to-a-pull-request-branch-created-from-a-fork/) so we can potentially also help via commits.
 
 ## Commit history and merging
 
 For the sake of transparency our key rule is to keep a logical and intelligible commit history, meaning anyone stepping through the commits on either the `master` branch or as part of a review should be able to easily follow the changes made and their potential implications.
 
-To this end we ask all contributors to sanitise pull requests before submitting them as our preferred way of merging pull requests is rebasing.
+To this end we ask all contributors to sanitize pull requests before submitting them as our preferred way of merging pull requests is rebasing.
 
 Some guidelines:
 
@@ -73,6 +73,7 @@ make docs
 command which will
 subsequently output the html version of our docs to your `build` folder. You
 can view the docs after their built using your browser by running
+
 ```sh
 open build/html/index.html
 ```

INSTALL.md docs/INSTALL.md

@@ -1,6 +1,6 @@
 # Installation
 
-The most convenient way of installing tf-encrypted is via [the official pip package](https://pypi.org/project/tf-encrypted/):
+The most convenient way of installing TF Encrypted is via [the official pip package](https://pypi.org/project/tf-encrypted/):
 
 ```
 pip3 install tf-encrypted
@@ -44,7 +44,7 @@ We address both below but stress that they can be skipped for initial experiment
 
 # Custom Ops
 
-Certains operations, such as secure randomness generation, rely on C++ extensions of TensorFlow known as [custom ops](https://www.tensorflow.org/guide/extend/op). These come precompiled with the [tf-encrypted pip package](https://pypi.org/project/tf-encrypted/) but need to be manually compiled when installing from source code as done above.
+Certains operations, such as secure randomness generation, rely on C++ extensions of TensorFlow known as [custom ops](https://www.tensorflow.org/guide/extend/op). These come precompiled with the [pip package](https://pypi.org/project/tf-encrypted/) but need to be manually compiled when installing from source code as done above.
 
 On macOS this is straight forward once libtool and automake are installed (see the [detailed instructions](#complete-instructions)):
 
@@ -100,7 +100,7 @@ after making sure flake8 is installed:
 
 # Custom TensorFlow
 
-While tf-encrypted will work with the official release of [TensorFlow](https://pypi.org/project/tensorflow/) (version 1.12+), some features currently depend on improvements that have not yet been shipped. In particular, to get speed improvements from using int64 instead of int100 tensors you currently need a custom build of TensorFlow.
+While TF Encrypted will work with the official release of [TensorFlow](https://pypi.org/project/tensorflow/) (version 1.12+), some features currently depend on improvements that have not yet been shipped. In particular, to get speed improvements from using int64 instead of int100 tensors you currently need a custom build of TensorFlow.
 
 We provide such builds as a temporary solution until the next official release of TensorFlow is out (version 1.13), but no guarantees are made about them and they should be treated as experimental:
 
@@ -115,7 +115,7 @@ In both cases should you end up with a wheel file that you can install using pip
 (venv) ./ $ pip install tensorflow-1.13.0-XXX.whl
 ```
 
-tf-encrypted auto-detects which features are available so no further actions are needed.
+TF Encrypted auto-detects which features are available so no further actions are needed.
 
 # Complete Instructions
 
@@ -213,7 +213,7 @@ We finally close the repository:
 ./ $ cd tf-encrypted
 ```
 
-install tf-encrypted outside a virtual environment and without dependencies:
+install TF Encrypted outside a virtual environment and without dependencies:
 
 ```
 ./tf-encrypted/ $ pip3 install -e . --no-deps

docs/getting_started.md

@@ -1,4 +1,4 @@
-# Getting Started with tf-encrypted
+# Getting Started with TF Encrypted
 
 This guide assumes that you have followed the installation instructions in [README](https://github.com/mortendahl/tf-encrypted).
 
@@ -12,14 +12,14 @@ This guide assumes that you have followed the installation instructions in [READ
 
 ## Introduction to the API
 
-tf-encrypted has a simple API to make it easy for data scientists to make private predictions and training.
+TF Encrypted has a simple API to make it easy for data scientists to make private predictions and training.
 
-To define a machine learning model, tf-encrypted and TensorFlow follow a very similar API.
+To define a machine learning model, TF Encrypted and TensorFlow follow a very similar API.
 
 <table>
 <tr>
 <th>TensorFlow</th>
-<th>tf-encrypted</th>
+<th>TF Encrypted</th>
 </tr>
 <tr>
 <td><pre lang="python">layer0 = tf.matmul(x, w0) + b0
@@ -69,7 +69,7 @@ Two servers and a crypto producer are doing the actual computation on encrypted
 ## Private Predictions
 ### a. Locally
 
-With tf-encrypted, you can very easily make private predictions with a pre-trained model saved as a [protobuf](https://www.tensorflow.org/extend/tool_developers/) file.
+With TF Encrypted, you can very easily make private predictions with a pre-trained model saved as a [protobuf](https://www.tensorflow.org/extend/tool_developers/) file.
 
 1. In the [`examples/`](./examples/) folder run the following line of code in your terminal to train a convolution network on MNIST dataset, then save the model as a protobuf file :
 ```bash

docs/source/api/config/config.rst

@@ -1,7 +1,7 @@
 `Config`
 ============
 
-| Config determines how a session should run in tf-encrypted.
+| Config determines how a session should run in TF Encrypted.
 | There are two primary ways in which config can be used:
 
 .. py:class:: tf_encrypted.config.LocalConfig

docs/source/api/layers/relu.rst

@@ -7,7 +7,7 @@ Classicly, `Relu` computes the following on input:
 
     Relu(x) = max(0, x)
 
-In `tf-encrypted`, how `Relu` behaves will depend on the underlying protocol
+In TF Encrypted, how `Relu` behaves will depend on the underlying protocol
 you are using.
 
 | With :class:`~tf_encrypted.protocol.pond.Pond`, `Relu` will be approximated using `Chebyshev Polynomial Approximation`_

docs/source/api/protocol/index.rst

@@ -1,7 +1,7 @@
 `protocol`
 ===========
 
-In `tf-encrypted`, a protocol represents a certain type of cryptographic protocol
+In TF Encrypted, a protocol represents a certain type of cryptographic protocol
 to achieve security.
 
 The goal is to allow you to easily play with or use different cryptographic methods

docs/source/api/session.rst

@@ -3,7 +3,7 @@
 
 Session is an extension of `tf.Session` that lets the graph run in a secure manner.
 
-The aim of `tf-encrypted` is to look as close to `TensorFlow` as possible.  With this goal in mind,
+The aim of TF Encrypted is to look as close to `TensorFlow` as possible.  With this goal in mind,
 you get and use a session the same way that you're used to with Tensorflow::
 
     import tf_encrypted as tfe

docs/source/guides/mnist.rst

@@ -4,7 +4,7 @@ MNIST
 This tutorial is also available on `Google Collab`_, feel free to follow along there!
 
 In this tutorial, we will train our model in plaintext with Tensorflow, then
-make private predictions with `tf-encrypted`. we will use the `MNIST dataset`_.
+make private predictions with TF Encrypted. we will use the `MNIST dataset`_.
 
 .. _Google Collab: https://colab.research.google.com/drive/1BbOcMc8npAfQH91-2jtCWkTvpXY0aThC
 .. _MNIST dataset: http://yann.lecun.com/exdb/mnist/
@@ -276,7 +276,7 @@ The `PredictionClient` object will provide the private input that will be used t
 
 Once you instantiate the `ModelTrainer` and `PredictionClient` objects, you can very
 easily get the weights trained in plaintext, get the private input from the client
-and finally make private predictions. As you can see, to create a model, `tf-encrypted`
+and finally make private predictions. As you can see, to create a model, TF Encrypted
 and TensorFlow follow a very similar API
 
 .. code-block:: python

docs/source/index.rst

@@ -3,10 +3,10 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-tf-encrypted
+TF Encrypted
 ========================================
 
-tf-encrypted is a Python library built on top of TensorFlow_ for researchers and practitioners to experiment with privacy-preserving machine learning. It provides an interface similar to that of TensorFlow, and aims at making the technology readily available without first becoming an expert in machine learning, cryptography, distributed systems, and high performance computing.
+TF Encrypted is a Python library built on top of TensorFlow_ for researchers and practitioners to experiment with privacy-preserving machine learning. It provides an interface similar to that of TensorFlow, and aims at making the technology readily available without first becoming an expert in machine learning, cryptography, distributed systems, and high performance computing.
 
 In particular, the library focuses on: