This module enables local scoring with TransmogrifAI models without the need for Spark Session during scoring. Instead it implementes local inference by applying TransmogrifAI's transformers and MLeap runtime on JVM. It delivers unprecedented portability and performance of TransmogrifAI models allowing the serving of scores from any JVM process.

Add the transmogrifai-local dependency into your project.

For Gradle in build.gradle add:

dependencies {
    compile 'com.salesforce.transmogrifai:transmogrifai-local_2.11:0.5.3'
}

For SBT in build.sbt add:

libraryDependencies += "com.salesforce.transmogrifai" %% "transmogrifai-local" % "0.5.3"

Then in your code you may load and score models as follows:

import com.salesforce.op.local._

// Spark Session needed for model loading & score function creation
implicit val spark = SparkSession.builder().getOrCreate()

// Load the trained model
val model = OpWorkflowModel.load("/path/to/model")

// Create score function once and use it indefinitely
val scoreFn = model.scoreFunction

// Spark Session can be stopped now since it's not required during local scoring
spark.stop()

// Compute scores with score function
val rawData = Seq(Map("name" -> "Peter", "age" -> 18), Map("name" -> "John", "age" -> 23))
val scores = rawData.map(scoreFn)

Or using the local runner:

val scoreFn = new OpWorkflowRunnerLocal(workflow).scoreFunction(opParams)

Note: Spark Session is only required for loading the model & preparing the scoring function. Once scoring function is returned the Spark Session can be shutdown since it's not required during local scoring.

Below is an example of measured scoring performance on a sample with 10 fields and 12 transformations applied. Executed on MacBook Pro i7 3.5Ghz in a single thread.

Scored 6,000,000 records in 202s
Average time per record: 0.0336ms