Pipeline

Pipelines allow one to connect flows of inference data transformed by Model components. A directed acyclic graph (DAG) of steps can be defined to join Models together. Each Model will need to be capable of receiving a V2 inference request and respond with a V2 inference response. An example Pipeline is shown below:

:language: yaml

The steps list shows three models: tfsimple1, tfsimple2 and tfsimple3. These three models each take two tensors called INPUT0 and INPUT1 of integers. The models produce two outputs OUTPUT0 (the sum of the inputs) and OUTPUT1 (subtraction of the second input from the first).

tfsimple1 and tfsimple2 take as inputs the input to the Pipeline: the default assumption when no explicit inputs are defined. tfsimple3 takes one V2 tensor input from each of the outputs of tfsimple1 and tfsimple2. As the outputs of tfsimple1 and tfsimple2 have tensors named OUTPUT0 and OUTPUT1 their names need to be changed to respect the expected input tensors and this is done with a tensorMap component providing this tensor renaming. This is only required if your models can not be directly chained together.

The output of the Pipeline is the output from the tfsimple3 model.

Detailed Specification

The full GoLang specification for a Pipeline is shown below:

:language: golang
:start-after: // PipelineSpec
:end-before: // PipelineStatus