Using DJL.AI For Deep Learning Based Sentiment Analysis in NiFi DataFlow
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.
Grab the Source:
https://github.com/tspannhw/nifi-djlsentimentanalysis-processor
Grab the Recent Release NAR to install to your NiFi lib directories:
https://github.com/tspannhw/nifi-djlsentimentanalysis-processor/releases/tag/1.2
Example Run
Demo Data Source
Reference:
- Deep Learning Sentiment Analysis with DJL.ai
- https://github.com/awslabs/djl/blob/master/mxnet/mxnet-engine/README.md
- https://github.com/aws-samples/djl-demo/tree/master/flink/sentiment-analysis
- https://github.com/awslabs/djl/releases
Deep Learning Note:
The pretrained model is DistilBERT model trained by HuggingFace using PyTorch.
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
