As of version ``0.15``, there is a new backend API to work with functions that
can be compiled as a runtime ``Executable``. Where previously ``Backend`` used a
``shared_ptr<Function>`` as the handle passed to the ``call`` method to execute
a compiled object, the addition of the ``shared_ptr<Executable>`` object has
more direct methods to actions such as ``validate``, ``call``, ``get_performance_data``, and so on. This new API permits any executable to be saved or loaded *into* or
more direct methods to actions such as ``validate``, ``call``, ``get_performance_data``,
and so on. This new API permits any executable to be saved or loaded *into* or
*out of* storage and makes it easier to distinguish when a Function is compiled,
thus making the internals of the ``Backend`` and ``Executable`` easier to
@@ -15,8 +15,9 @@ each of these briefly to get started.
A framework bridge interfaces with the "frontend" Core API. A framework bridge
is a component that sits between a framework like TensorFlow or MXNet, and the
nGraph Core frontend API. A framework bridge does two things: first, it
translates a framework's operations into graphs in nGraph’s in-memory :abbr:`Intermediary Representation (IR)`. Second, it executes the nGraph IR graphs via
the backend execution interface.
translates a framework's operations into graphs in nGraph’s in-memory
:abbr:`Intermediary Representation (IR)`. Second, it executes the nGraph IR
graphs via the backend execution interface.
The details of bridge implementation vary from framework to framework, but there
are some common patterns: a fairly typical example for a graph-based framework
...
...
@@ -59,16 +60,14 @@ descriptions:
.. csv-table::
:header: "Namespace", "Description", "Location in Repo", "Docs"
:widths: 23, 53, 13, 23
:escape: ~
``ngraph``, The Intel nGraph C++ API, `ngraph`_, Implicit namespace omitted from most API documentation
``ngraph``, The Intel nGraph C++ API, ngraph, Implicit namespace omitted from most API documentation
``builder``, "Convenience functions that create additional graph nodes to implement commonly-used recipes; for example, auto-broadcast", `builder`_, Coming Soon
``descriptor``, Descriptors are compile-time representations of objects that will appear at run-time, `descriptor`_, Coming Soon
``op``, Ops used in graph construction, `op`_, :doc:`../ops/index`
``runtime``, The objects and methods used for executing the graph, `runtime`_, :doc:`../backend-support/cpp-api`
``runtime``, The objects and methods used for executing the graph, `runtime`_, :doc:`../backends/cpp-api`
[nGraph's build instructions][ngraph_build] give detailed instructions on building nGraph on different operating systems. Please make sure you specify the options `-DNGRAPH_PYTHON_BUILD_ENABLE=ON` and `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` when building nGraph. Use the `make python_wheel` command to build nGraph and create Python packages.
If you want to try a newer version of nGraph's Python API than is available from
PyPI, you can build your own latest version from the source code. This
process is very similar to what is outlined in our [ngraph_build] instructions
with two important differences:
Basic build procedure on an Ubuntu system:
1. You must specify: `-DNGRAPH_PYTHON_BUILD_ENABLE=ON` and `-DNGRAPH_ONNX_IMPORT_ENABLE=ON`
