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https://issues.apache.org/jira/browse/NIFI-7411?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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James Medel resolved NIFI-7411.
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Resolution: Resolved
NiFi + H2O Driverless AI MOJO Processor will not be merged into the project.
Instead it will be managed, maintained by H2O repo:
[https://github.com/h2oai/dai-deployment-examples/pull/18]
> Integrates NiFi with H2O Driverless AI MOJO Scoring Pipeline (Java Runtime)
> To Do ML Inference
> ----------------------------------------------------------------------------------------------
>
> Key: NIFI-7411
> URL: https://issues.apache.org/jira/browse/NIFI-7411
> Project: Apache NiFi
> Issue Type: New Feature
> Components: Extensions
> Affects Versions: 1.12.0
> Environment: Mac OS X Mojave 10.14.6
> Reporter: James Medel
> Priority: Major
> Time Spent: 2h 10m
> Remaining Estimate: 0h
>
> *NiFi and H2O Driverless AI Integration* via Custom NiFi Processor:
> Integrates NiFi with H2O Driverless AI by using Driverless AI's MOJO Scoring
> Pipeline (in Java Runtime) and NiFi's Custom Processor. This processor
> executes the MOJO Scoring Pipeline to do batch scoring or real-time scoring
> for one or more predicted labels on tabular data in the incoming flow file
> content. If the tabular data is one row, then the MOJO does real-time
> scoring. If the tabular data is multiple rows, then the MOJO does batch
> scoring. I would like to contribute my processor to NiFi as a new feature.
> *1 Custom Processor* created for NiFi:
> *ExecuteMojoScoringRecord* - Executes H2O Driverless AI's MOJO Scoring
> Pipeline in Java Runtime to do batch scoring or real-time scoring on a frame
> of data within each incoming flow file. It requires the user to add
> *mojo2-runtime.jar* filepath into *MOJO2 Runtime JAR Directory* ** property
> to dynamically modify the classpath. It also requires the user to add the
> *pipeline.mojo* filepath into the *Pipeline MOJO Filepath* property. This
> property is used in the onTrigger() method to get the pipeline.mojo filepath,
> so we can pass it into the
> MojoPipeline.loadFrom(pipelineMojoPath) to instantiate our MojoPipeline
> model. Then the record read in with Record Reader and the model are passed
> into a predict() method to make predictions on the test data within the
> record. Inside the predict() method, I use MojoFrameBuilder and
> MojoRowBuilder with the recordMap to build an input MojoFrame. Then I use the
> model's transform(input MojoFrame) method to make the predictions on the
> input and store them into an output MojoFrame. I iterate through the
> MojoFrame by row and column to store each key value pair prediction into the
> predictedRecordMap. I then convert the predictedRecordMap to predictedRecord
> and return the record back to onTrigger to write the record to the flow file
> content using RecordSetWriter. We keep writing predicted Records to the flow
> file content until there are no more records to write. Then we reach near end
> of onTrigger() and the flow file is either transferred on relationship
> failure, success or original to the next connection.
>
> *Hydraulic System Condition Monitoring* Data used in NiFi Flow:
>
> The sensor test data I used in this integration comes from UCI ML Repo:
> Condition Monitoring for Hydraulic Systems. I was able to predict the
> hydraulic cooling condition through NiFi and H2O Integration described above.
> This use case is hydraulic system predictive maintenance.
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