This is an interesting question. As we make more mature full featured engines they will begin to employ hyper parameter search techniques or reinforcement params. This means that there is a new stage in the workflow or a feedback loop not already accounted for.
Short answer is no, unless you want to re-write your engine.json after every train and probably keep the old one for safety. You must re-train to get the new params put into the metastore and therefor available to your engine. What we do for the Universal Recommender is have a special new workflow phase, call it a self-tuning phase, where we search for the right tuning of parameters. This it done with code that runs outside of pio and creates parameters that go into the engine.json. This can be done periodically to make sure the tuning is still optimal. Not sure whether feedback or hyper parameter search is the best architecture for you. From: Tihomir Lolić <tihomir.lo...@gmail.com> <tihomir.lo...@gmail.com> Reply: user@predictionio.apache.org <user@predictionio.apache.org> <user@predictionio.apache.org> Date: February 12, 2018 at 2:02:48 PM To: user@predictionio.apache.org <user@predictionio.apache.org> <user@predictionio.apache.org> Subject: Dynamically change parameter list Hi, I am trying to figure out how to dynamically update algorithm parameter list. After the train is finished only model is updated. The reason why I need this data to be updated is that I am creating data mapping based on the training data. Is there a way to update this data after the train is done? Here is the code that I am using. The variable that and should be updated after the train is marked *bold red.* import io.prediction.controller.{EmptyParams, EngineParams} import io.prediction.data.storage.EngineInstance import io.prediction.workflow.CreateWorkflow.WorkflowConfig import io.prediction.workflow._ import org.apache.spark.ml.linalg.SparseVector import org.joda.time.DateTime import org.json4s.JsonAST._ import scala.collection.mutable object TrainApp extends App { val envs = Map("FOO" -> "BAR") val sparkEnv = Map("spark.master" -> "local") val sparkConf = Map("spark.executor.extraClassPath" -> ".") val engineFactoryName = "LogisticRegressionEngine" val workflowConfig = WorkflowConfig( engineId = EngineConfig.engineId, engineVersion = EngineConfig.engineVersion, engineVariant = EngineConfig.engineVariantId, engineFactory = engineFactoryName ) val workflowParams = WorkflowParams( verbose = workflowConfig.verbosity, skipSanityCheck = workflowConfig.skipSanityCheck, stopAfterRead = workflowConfig.stopAfterRead, stopAfterPrepare = workflowConfig.stopAfterPrepare, sparkEnv = WorkflowParams().sparkEnv ++ sparkEnv ) WorkflowUtils.modifyLogging(workflowConfig.verbose) val dataSourceParams = DataSourceParams(sys.env.get("APP_NAME").get) val preparatorParams = EmptyParams() *val algorithmParamsList = Seq("Logistic" -> LogisticParams(columns = Array[String](),* * dataMapping = Map[String, Map[String, SparseVector]]()))* val servingParams = EmptyParams() val engineInstance = EngineInstance( id = "", status = "INIT", startTime = DateTime.now, endTime = DateTime.now, engineId = workflowConfig.engineId, engineVersion = workflowConfig.engineVersion, engineVariant = workflowConfig.engineVariant, engineFactory = workflowConfig.engineFactory, batch = workflowConfig.batch, env = envs, sparkConf = sparkConf, dataSourceParams = JsonExtractor.paramToJson(workflowConfig.jsonExtractor, workflowConfig.engineParamsKey -> dataSourceParams), preparatorParams = JsonExtractor.paramToJson(workflowConfig.jsonExtractor, workflowConfig.engineParamsKey -> preparatorParams), algorithmsParams = JsonExtractor.paramsToJson(workflowConfig.jsonExtractor, algorithmParamsList), servingParams = JsonExtractor.paramToJson(workflowConfig.jsonExtractor, workflowConfig.engineParamsKey -> servingParams) ) val (engineLanguage, engineFactory) = WorkflowUtils.getEngine(engineInstance.engineFactory, getClass.getClassLoader) val engine = engineFactory() val engineParams = EngineParams( dataSourceParams = dataSourceParams, preparatorParams = preparatorParams, algorithmParamsList = algorithmParamsList, servingParams = servingParams ) val engineInstanceId = CreateServer.engineInstances.insert(engineInstance) CoreWorkflow.runTrain( env = envs, params = workflowParams, engine = engine, engineParams = engineParams, engineInstance = engineInstance.copy(id = engineInstanceId) ) CreateServer.actorSystem.shutdown() } Thank you, Tihomir