Local scoring (aka Sparkless) using Aardpfark #41
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| }.head | ||
| val vector = r(inputName).asInstanceOf[Vector].toArray | ||
| val input = s"""{"$inputName":${vector.mkString("[", ",", "]")}}""" | ||
| val res = e.action(e.jsonInput(input)).toString |
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@MLnick is using json is the most efficient way to call engine action?
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Have you looked at https://github.com/combust/mleap ? |
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@manuzhang last time I checked spark support wast that great, but it seems better now. Whats your experience with it? |
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@manuzhang let's chat over https://gitter.im ? |
| * | ||
| * @return a [[collection.mutable.Map]] with row contents | ||
| */ | ||
| def toMutableMap: collection.mutable.Map[String, Any] = { |
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so you are saying that row.getValuesMap[Any] should work as well? let me try.
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Oook, so my function is faster, because getValuesMap calls def getAs[T](fieldName: String): T = getAs[T](fieldIndex(fieldName)) for each value, while my function operates on indices.
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| def score(params: OpParams): ScoreFunction = { | ||
| require(params.modelLocation.isDefined, "Model location must be set in params") | ||
| val model = workflow.loadModel(params.modelLocation.get) |
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will the standard load method work on spark models that use parquet storage without a spark context?
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None of the spark ml readers require the context explicitly, but I will need to verify, cause they might get/create spark context inside. Do you have a model in mind that I can check against?
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oh snap, they simply create a spark context internally when loading models 🤦♂️ https://github.com/apache/spark/blob/5264164a67df498b73facae207eda12ee133be7d/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala#L212
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well, we also use spark context when reading the model & stages - https://github.com/salesforce/TransmogrifAI/blob/master/core/src/main/scala/com/salesforce/op/OpWorkflowModelReader.scala#L61
and
https://github.com/salesforce/TransmogrifAI/blob/master/features/src/main/scala/com/salesforce/op/stages/OpPipelineStageReader.scala#L63
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| // TODO: remove .map[Text] once Aardpfark supports null inputs for StringIndexer | ||
| val indexed = description.map[Text](v => if (v.isEmpty) Text("") else v) | ||
| .indexed(handleInvalid = StringIndexerHandleInvalid.Skip) |
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We should add a spark model that uses complicated serialization - maybe PCA since that uses parquet
| @@ -88,6 +88,8 @@ configure(allProjs) { | |||
| commonsValidatorVersion = '1.6' | |||
| commonsIOVersion = '2.6' | |||
| scoveragePluginVersion = '1.3.1' | |||
| hadrianVersion = '0.8.5' | |||
| aardpfarkVersion = '0.1.0-SNAPSHOT' | |||
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why are we pulling in a shapshot?
Describe the proposed solution
Added a subproject that enables loading and scoring models without Spark context but locally using Aardpfark (PFA for Spark) and Hadrian libraries instead. This allows orders of magnitude faster scoring times compared to Spark.
Describe alternatives you've considered
dbml-local, ml-local and a custom runtime.
Additional Context
Sample usage:
Test results (single thread running on MacBook Pro i7 3.5Ghz):
TODO: