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