nGraph Basic Concepts¶

The nGraph represents neural networks in uniform format. User can create different operations and combined their to one ngraph::Function.

nGraph Function and Graph Representation¶

nGraph function is a very simple thing: it stores shared pointers to ngraph::op::Parameter, ngraph::op::Result and ngraph::op::Sink operations that are inputs, outputs and sinks of the graph. Sinks of the graph have no consumers and are not included in the results vector. All other operations hold each other via shared pointers: child operation holds its parent (hard link). If operation has no consumers and it’s not Result or Sink operation (shared pointer counter is zero) then it will be destructed and won’t be accessible anymore. Each operation in ngraph::Function has a std::shared_ptr<ngraph::Node> type.

For details on how to build an nGraph Function, see the Build nGraph Function page.

Operations¶

ngraph::Op represents any abstract operations in the nGraph representation. You need to use this class to create custom operations.

Operation Sets¶

Operation set represents the set of some nGraph operations. nGraph::Opset is a class which provide a functionality to work with operation sets. Custom operation set should be created to support custom operation. Please read Extensibility DevGuide for more details.

Static and Partial Shapes¶

nGraph has two types for shape representation:

ngraph::Shape - Represents static (fully defined) shapes.
ngraph::PartialShape - Represents dynamic shapes. That means that the rank or some of dimensions are dynamic (undefined). ngraph::PartialShape can be converted to ngraph::Shape using the get_shape() method if all dimensions are static; otherwise the conversion raises an exception.

For examples, see the Dynamic Shape and Rank section in the Overview of Transformations API.

Transformation API¶

nGraph transformation API allows you to manipulate the graph represented by nGraph::Function. For more details, see the Overview of Transformations API section.

Pattern Matcher¶

For more details, see the Pattern Matching section in the Overview of Transformations API.

Prev Next