This release has H2O core – 3.10.1.2

Important Feature:

This architectural change allows to connect to existing h2o cluster from sparkling water. This has a benefit that we are no longer affected by Spark killing it’s executors thus we should have more stable solution in environment with lots of h2o/spark node. We are working on article on how to use this very important feature in Sparkling Water 2.0.3.
Release notes: https://0xdata.atlassian.net/secure/ReleaseNote.jspa?projectId=12000&version=16601 

2.0.3 (2017-01-04)

• Bug
  • SW-152 – ClassNotFound with spark-submit
  • SW-266 – H2OContext shouldn’t be Serializable
  • SW-276 – ClassLoading issue when running code using SparkSubmit
  • SW-281 – Update sparkling water tests so they use correct frame locking
  • SW-283 – Set spark.sql.warehouse.dir explicitly in tests because of SPARK-17810
  • SW-284 – Fix CraigsListJobTitlesApp to use local file instead of trying to get one from hdfs
  • SW-285 – Disable timeline service also in python integration tests
  • SW-286 – Add missing test in pysparkling for conversion RDD[Double] -> H2OFrame
  • SW-287 – Fix bug in SparkDataFrame converter where key wasn’t random if not specified
  • SW-288 – Improve performance of Dataset tests and call super.afterAll
  • SW-289 – Fix PySparkling numeric handling during conversions
  • SW-290 – Fixes and improvements of task used to extended h2o jars by sparkling-water classes
  • SW-292 – Fix ScalaCodeHandlerTestSuite
• New Feature
  • SW-178 – Allow external h2o cluster to act as h2o backend in Sparkling Water
• Improvement
  • SW-282 – Integrate SW with H2O 3.10.1.2 ( Support for external cluster )
  • SW-291 – Use absolute value for random number in sparkling-water in internal backend
  • SW-295 – H2OConf should be parameterized by SparkConf and not by SparkContext

Please visit https://community.h2o.ai to learn more about it, provide feedback and ask for assistance as needed.
@avkashchauhan | @h2oai