Created on 07-31-201708:10 PM - edited 08-17-201911:49 AM
In preparing for my talk at DataWorksSummit in Australia, I wanted to try yet another way to integrate Apache NiFi with TensorFlow. The common ways being: calling a Python TensorFlow script from Execute Command, calling a TensorFlow Serving server via gRPC, calling a C++ TensorFlow executable via Execute Command, running TensorFlow on the edge and having MiniFi send it to NiFi or calling a TensorFlowOnSpark job via Kafka, Site-to-Site.
Once you restart NiFi, you can add the TensorFlow Processor.
An example flow is to the use the very smart ListFile which will iterate through a list of files and keep track of which the last timestamp of files it accessed. So I point to a directory of files and the NiFi processor gets fed a ton of images to very quickly process. This is much faster than my calling out to a script.
The result of the run is a new attribute, probabilities, which is a string description of what it could be a confidence percentage as text.