+++ /dev/null
-## nGraph Compiler stack
-
-[nGraph][ngraph_github] is an open-source graph compiler for Artificial
-Neural Networks (ANNs). The nGraph Compiler stack provides an inherently
-efficient graph-based compilation infrastructure designed to be compatible
-with the many upcoming processors, like the Intel Nervana™ Neural Network
-Processor (Intel® Nervana™ NNP), while also unlocking a massive performance
-boost on any existing hardware targets in your neural network: both GPUs
-and CPUs. Using its flexible infrastructure, you will find it becomes
-much easier to create Deep Learning (DL) models that can adhere to the
-"write once, run anywhere" mantra that enables your AI solutions to easily
-go from concept to production to scale.
-
-Frameworks using nGraph to execute workloads have shown [up to 45X] performance
-boost compared to native implementations.
-
-### Using the Python API
-
-nGraph can be used directly with the [Python API][api_python] described here, or
-with the [C++ API][api_cpp] described in the [core documentation]. Alternatively,
-its performance benefits can be realized through frontends such as
-[TensorFlow][frontend_tf], [PaddlePaddle][paddle_paddle] and [ONNX][frontend_onnx].
-You can also create your own custom framework to integrate directly with the
-[nGraph Ops] for highly-targeted graph execution.
-
-## Installation
-
-nGraph is available as binary wheels you can install from PyPI. nGraph binary
-wheels are currently tested on Ubuntu 16.04. To build and test on other
-systems, you may want to try [building][ngraph_building] from sources.
-
-Installing nGraph Python API from PyPI is easy:
-
- pip install ngraph-core
-
-## Usage example
-
-Using nGraph's Python API to construct a computation graph and execute a
-computation is simple. The following example shows how to create a minimal
-`(A + B) * C` computation graph and calculate a result using 3 numpy arrays
-as input.
-
-
-```python
-import numpy as np
-import ngraph as ng
-
-A = ng.parameter(shape=[2, 2], name='A', dtype=np.float32)
-B = ng.parameter(shape=[2, 2], name='B', dtype=np.float32)
-C = ng.parameter(shape=[2, 2], name='C', dtype=np.float32)
-# >>> print(A)
-# <Parameter: 'A' ([2, 2], float)>
-
-model = (A + B) * C
-# >>> print(model)
-# <Multiply: 'Multiply_14' ([2, 2])>
-
-runtime = ng.runtime(backend_name='CPU')
-# >>> print(runtime)
-# <Runtime: Backend='CPU'>
-
-computation = runtime.computation(model, A, B, C)
-# >>> print(computation)
-# <Computation: Multiply_14(A, B, C)>
-
-value_a = np.array([[1, 2], [3, 4]], dtype=np.float32)
-value_b = np.array([[5, 6], [7, 8]], dtype=np.float32)
-value_c = np.array([[9, 10], [11, 12]], dtype=np.float32)
-
-result = computation(value_a, value_b, value_c)
-# >>> print(result)
-# [[ 54. 80.]
-# [110. 144.]]
-
-print('Result = ', result)
-```
-
-[up to 45X]: https://ai.intel.com/ngraph-compiler-stack-beta-release/
-[frontend_onnx]: https://pypi.org/project/ngraph-onnx/
-[paddle_paddle]: https://ngraph.nervanasys.com/docs/latest/frameworks/paddle_integ.html
-[frontend_tf]: https://pypi.org/project/ngraph-tensorflow-bridge/
-[ngraph_github]: https://github.com/NervanaSystems/ngraph "nGraph on GitHub"
-[ngraph_building]: https://github.com/NervanaSystems/ngraph/blob/master/python/BUILDING.md "Building nGraph"
-[api_python]: https://ngraph.nervanasys.com/docs/latest/python_api/ "nGraph's Python API documentation"
-[api_cpp]: https://ngraph.nervanasys.com/docs/latest/backend-support/cpp-api.html
-[core documentation]: https://ngraph.nervanasys.com/docs/latest/core/overview.html
-[nGraph Ops]: http://ngraph.nervanasys.com/docs/latest/ops/index.html
-
-