I am working on pysparkling in Databricks. I have built a model with pyspark transformers and h2o pysparkling algorithm. When I log the model on to mlflow and deploy in from a job cluster, I get the following error in the job cluster logs. I have mentioned pysparkling as a dependancy in _mlflow_conda_env. Any ideas about the workaround for this?! Thanks in advance!
ERROR Instrumentation: java.lang.ClassNotFoundException: ai.h2o.sparkling.ml.models.H2OGBMMOJOModel
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:419)
at java.lang.ClassLoader.loadClass(ClassLoader.java:352)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:227)
at org.apache.spark.ml.util.DefaultParamsReader$.loadParamsInstanceReader(ReadWrite.scala:630)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$load$4(Pipeline.scala:276)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:198)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:198)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$load$3(Pipeline.scala:274)
at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:284)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:284)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.load(Pipeline.scala:268)
at org.apache.spark.ml.PipelineModel$PipelineModelReader.$anonfun$load$7(Pipeline.scala:356)
at org.apache.spark.ml.MLEvents.withLoadInstanceEvent(events.scala:161)
at org.apache.spark.ml.MLEvents.withLoadInstanceEvent$(events.scala:156)
at org.apache.spark.ml.util.Instrumentation.withLoadInstanceEvent(Instrumentation.scala:43)
at org.apache.spark.ml.PipelineModel$PipelineModelReader.$anonfun$load$6(Pipeline.scala:355)
at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:284)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:284)
at org.apache.spark.ml.PipelineModel$PipelineModelReader.load(Pipeline.scala:355)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
at py4j.Gateway.invoke(Gateway.java:295)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:251)
at java.lang.Thread.run(Thread.java:748)enter code here