.. frameworks/getting_started.rst Getting Started ############### No matter what your level of experience with :abbr:`Deep Learning (DL)` systems may be, nGraph provides a path to start working with the DL stack. Let's begin with the easiest and most straightforward options. .. figure:: ../graphics/translation-flow-to-ng-fofx.png :width: 725px :alt: Translation flow to nGraph function graph The easiest way to get started is to use the latest PyPI `ngraph-tensorflow-bridge`_, which has instructions for Linux* systems, and tips for users of Mac OS X. You can install TensorFlow\* and nGraph to a virtual environment; otherwise, the code will install to a system location. .. code-block:: console pip install tensorflow pip install ngraph-tensorflow-bridge .. note:: You may need to use the latest versions of ```tensorflow``` and the bridge to get pip installs to work. See the :doc:`tensorflow_connect` file for more detail about working with TensorFlow\*. That's it! Now you can test the installation by running the following command: .. code-block:: console python -c "import tensorflow as tf; print('TensorFlow version: ',tf.__version__);import ngraph_bridge; print(ngraph_bridge.__version__)" Output will look something like: :: TensorFlow version: [version] nGraph bridge version: b'[version]' nGraph version used for this build: b'[version-rc-hash]' TensorFlow version used for this build: v[version-hash] CXX11_ABI flag used for this build: boolean More detail in the `ngraph_bridge examples`_ directory. ONNX ==== Another easy way to get started working with the :abbr:`DL (Deep Learning)` stack is to try the examples available via `nGraph ONNX`_. Installation ------------ To prepare your environment to use nGraph and ONNX, install the Python packages for nGraph, ONNX and NumPy: :: $ pip install ngraph-core onnx numpy Now you can start exploring some of the :doc:`onnx_integ` examples. See also nGraph's :doc:`../python_api/index`. PlaidML ======= See the :ref:`ngraph_plaidml_backend` section on how to build the nGraph-PlaidML. Other integration paths ======================= If you are considering incorporating components from the nGraph Compiler stack in your framework or neural network design, another useful doc is the section on :doc:`generic-configs`. Contents here are also useful if you are working on something built-from-scratch, or on an existing framework that is less widely-supported than the popular frameworks like TensorFlow and PyTorch. .. _ngraph-tensorflow-bridge: https://pypi.org/project/ngraph-tensorflow-bridge .. _ngraph ONNX: https://github.com/NervanaSystems/ngraph-onnx .. _ngraph_bridge examples: https://github.com/tensorflow/ngraph-bridge/blob/master/examples/README.md