Executor

# MXNet.mx.Executor — Type.

Executor

An executor is a realization of a symbolic architecture defined by a SymbolicNode. The actual forward and backward computation specified by the network architecture can be carried out with an executor.

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# Base.bind — Method.

bind(sym, ctx, args; args_grad=Dict(), aux_states=Dict(), grad_req=GRAD_WRITE)

Create an Executor by binding a SymbolicNode to concrete NDArray.

Arguments

• sym::SymbolicNode: the network architecture describing the computation graph.
• ctx::Context: the context on which the computation should run.
• args: either a list of NDArray or a dictionary of name-array pairs. Concrete arrays for all the inputs in the network architecture. The inputs typically include network parameters (weights, bias, filters, etc.), data and labels. See list_arguments and infer_shape.
• args_grad: a Vector of NDArray or a Dict contains NDArray
• aux_states: a Vector of NDArray or a Dict contains NDArray
• grad_req: single value, a Vector of GRAD_REQ or a Dict{Symbol,GRAD_REQ}

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# Base.print — Method.

print([io::IO], x::Executor)

Get a debug string about internal execution plan.

Can be used to get an estimated about the memory cost.

julia> x = mx.Variable(:x)
MXNet.mx.SymbolicNode x

julia> exec = mx.bind(x + 1, mx.cpu(), Dict(:x => mx.ones(2,3)))
mx.Executor Ptr{Void} @0x000055c3dee9eb30

julia> print(exec)
Symbol Outputs:
        output[0]=_plus_scalar0(0)
Variable:x
--------------------
Op:_plus_scalar, Name=_plus_scalar0
Inputs:
        arg[0]=x(0) version=0
Attrs:
        scalar=1.00000000e+00
Total 0 MB allocated
Total 11 TempSpace resource requested

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