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Using DJL.AI For Deep Learning BERT Q&A in NiFi DataFlows

 

Using DJL.AI For Deep Learning BERT Q&A in NiFi DataFlows


Introduction:

I will be talking about this processor at Apache Con @ Home 2020 in my "Apache Deep Learning 301" talk with Dr. Ian Brooks.

Sometimes you want your Deep Learning Easy and in Java, so let's do that with DJL in a custom Apache NiFi processor running in CDP Data Hubs.   This one does BERT QA.


To use the processor feed in a paragraph to analyze via the paragraph parameter in the NiFi processor.   Also feed in a question, like Why? or something very specific like asking the date or an event.


The pretrained model is BERT QA model using PyTorch. the NiFi Processor Source:

https://github.com/tspannhw/nifi-djlqa-processor


Grab the Recent Release NAR to install to your NiFi lib directories:

https://github.com/tspannhw/nifi-djlqa-processor/releases/tag/1.2


Example Run





Demo Data Source

https://newsapi.org/v2/everything?q=cloudera&apiKey=REGISTERFORAKEY



Reference:



Deep Learning Note:   

BERT QA Model


Tip


Make sure you have 1-2 GB of RAM extra for your NiFi instance for running each DJL processor.   If you have a lot of text, run more nodes and/or RAM.   Make sure you have at least 8 cores per Deep Learning process.   I prefer JDK 11 for this.


See Also:   https://www.datainmotion.dev/2019/12/easy-deep-learning-in-apache-nifi-with.html



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