Merge pull request #56 from amzn/develop

Release version 0.2.0

Author: zhenwendai

.travis.yml

@@ -25,6 +25,7 @@ install:
   - pip install --upgrade pip
   - pip install -r requirements/requirements.txt
   - pip install -r requirements/test_requirements.txt
+  - pip install .
   - pip freeze
 script:
   - pytest

MANIFEST.in

@@ -6,3 +6,4 @@ include CODE_OF_CONDUCT.md
 include CONTRIBUTING.md
 include LICENSE
 include NOTICE
+include mxfusion/common/config.cfg

README.md

@@ -1,44 +1,41 @@
 # MXFusion
 [![Build Status](https://travis-ci.org/amzn/MXFusion.svg?branch=master)](https://travis-ci.org/amzn/MXFusion) |
-[![Build Status](https://travis-ci.org/amzn/MXFusion.svg?branch=develop)](https://travis-ci.org/amzn/MXFusion/branches) |
 [![codecov](https://codecov.io/gh/amzn/MXFusion/branch/master/graph/badge.svg)](https://codecov.io/gh/amzn/MXFusion) |
 [![pypi](https://img.shields.io/pypi/v/mxfusion.svg?style=flat)](https://pypi.org/project/mxfusion/) |
 [![Documentation Status](https://readthedocs.org/projects/mxfusion/badge/?version=latest)](https://mxfusion.readthedocs.io/en/latest/?badge=latest) |
 [![GitHub license](https://img.shields.io/github/license/amzn/mxfusion.svg)](https://github.com/amzn/mxfusion/blob/master/LICENSE)
 
 ![MXFusion](docs/images/logo/blender-small.png)
 
-[Website](https://github.com/amzn/MXFusion) |
+[Tutorials](https://mxfusion.readthedocs.io/en/latest/tutorials.html) |
 [Documentation](https://mxfusion.readthedocs.io/en/latest/index.html) |
-[Contribution Guide](https://github.com/amzn/MXFusion/CONTRIBUTING.md)
+[Contribution Guide](CONTRIBUTING.md)
 
-MXFusion is a library for integrating probabilistic modelling with deep learning.
+MXFusion is a modular deep probabilistic programming library.
 
 With MXFusion Modules you can use state-of-the-art inference techniques for specialized probabilistic models without needing to implement those techniques yourself. MXFusion helps you rapidly build and test new methods at scale, by focusing on the modularity of probabilistic models and their integration with modern deep learning techniques.
 
-MXFusion uses  [MXNet](https://github.com/apache/incubator-mxnet) as its computational platform to bring the power of distributed, heterogenous computation to probabilistic modelling.
-
-## Vision
-
-TODO
-
-### Why use probabilistic models?
-
-TODO
-
-## Features
-It currently supports modelling of directed probabilistic models, deep learning integration through MXNet, and Variational Inference methods. Gaussian Processes are soon to come.
+MXFusion uses [MXNet](https://github.com/apache/incubator-mxnet) as its computational platform to bring the power of distributed, heterogenous computation to probabilistic modeling.
 
 
 ## Installation
 
 ### Dependencies / Prerequisites
-MXFusion's primary dependencies are MXNet >= 1.2 and Networkx >= 2.1.
+MXFusion's primary dependencies are MXNet >= 1.3 and Networkx >= 2.1.
 See [requirements](requirements/requirements.txt).
 
 ### Supported Architectures / Versions
 
-MXFusion is tested on Python 3.5+ on MacOS and Amazon Linux.
+MXFusion is tested on Python 3.4+ on MacOS and Linux.
+
+### Installation of MXNet
+
+There are multiple PyPi packages of MXNet. A straight-forward installation with only CPU support can be done by:
+```
+pip install mxnet
+```
+
+For an installation with GPU or MKL, detailed instructions can be found on [MXNet site](https://mxnet.apache.org/install/).
 
 ### pip
 If you just want to use MXFusion and not modify the source, you can install through pip:
@@ -54,11 +51,12 @@ pip install .
 
 ## Where to go from here?
 
-[Documentation](https://github.com/amzn/MXFusion/docs)
+[Tutorials](https://mxfusion.readthedocs.io/en/latest/tutorials.html)
+
+[Documentation](https://mxfusion.readthedocs.io/en/latest/index.html)
 
 [Contributions](CONTRIBUTING.md)
 
-[Tutorials](Tutorials.md)
 
 ## Community
 We welcome your contributions and questions and are working to build a responsive community around MXFusion. Feel free to file an Github issue if you find a bug or want to request a new feature.

conftest.py

@@ -1,7 +1,10 @@
-import pytest
+import mxnet as mx
 import numpy as np
-
+import pytest
+import random
 
 @pytest.fixture(scope='session')
 def set_seed():
+    random.seed(0)
     np.random.seed(0)
+    mx.random.seed(0)