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We are going to get the OAP MLlib performance gain on Databricks, but it seems OAP MLLib jar can not be loaded by Databricks runtime. The error log is as blow:
We use Kmeans Demo to test.
import org.apache.spark.ml.clustering.KMeans
import org.apache.spark.ml.evaluation.ClusteringEvaluator
spark.sparkContext.setLogLevel("INFO")
val dataset = spark.read.format("libsvm").load("/FileStore/mllib_data/sample_kmeans_data.txt")
// Trains a k-means model.
val kmeans = new KMeans().setK(2).setSeed(1L)
val model = kmeans.fit(dataset)
// Make predictions
val predictions = model.transform(dataset)
// Evaluate clustering by computing Silhouette score
val evaluator = new ClusteringEvaluator()
val silhouette = evaluator.evaluate(predictions)
println(s"Silhouette with squared euclidean distance = $silhouette")
// Shows the result.
println("Cluster Centers: ")
model.clusterCenters.foreach(println)
The text was updated successfully, but these errors were encountered:
xwu99
changed the title
[Cloud][Databricks]InvalidClassException: scala.Product$class on Dataproc 2.0 when running Hibench
[Cloud][Databricks] OAP MLLib jar can not be loaded by Databricks runtime
Nov 2, 2021
Could I know if Databricks runtime are using K8S as cluster manager?
I try to dig this information out, but I couldn't find any info about it from the official docs.
According to Databrick's slides page 23, I guess Databricks uses its own cluster manager.
We are going to get the OAP MLlib performance gain on Databricks, but it seems OAP MLLib jar can not be loaded by Databricks runtime. The error log is as blow:
We use Kmeans Demo to test.
The text was updated successfully, but these errors were encountered: