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This document defines a proposal to use a new implementation of the operator graph for MXNet which will supersede the current implementation using NNVM. It will provide a more powerful and extensible implementation that will be easier to maintain and extend, cutting an external dependency to the project, (which we are thankful and have made contributions to). The operator graph for a deep learning framework is akin to the abstract syntax tree for a compiler, is the main data structure holding the computations that need to be performed and scheduled. 

Current status:

MXNet, as other deep learning frameworks uses a variable and operator graph to schedule computations, track variables and data flow and perform back-propagation and calculate gradients.

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