January 5th, 2017

What is new in Sparkling Water 2.0.3 Release?

RSS icon RSS Category: Community, H2O Release, Sparkling Water
Fallback Featured Image

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

Leave a Reply

+
H2O LLM DataStudio Part II: Convert Documents to QA Pairs for fine tuning of LLMs

Convert unstructured datasets to Question-answer pairs required for LLM fine-tuning and other downstream tasks with

September 22, 2023 - by Genevieve Richards, Tarique Hussain and Shivam Bansal
+
Building a Fraud Detection Model with H2O AI Cloud

In a previous article[1], we discussed how machine learning could be harnessed to mitigate fraud.

July 28, 2023 - by Asghar Ghorbani
+
A Look at the UniformRobust Method for Histogram Type

Tree-based algorithms, especially Gradient Boosting Machines (GBM's), are one of the most popular algorithms used.

July 25, 2023 - by Hannah Tillman and Megan Kurka
+
H2O LLM EvalGPT: A Comprehensive Tool for Evaluating Large Language Models

In an era where Large Language Models (LLMs) are rapidly gaining traction for diverse applications,

July 19, 2023 - by Srinivas Neppalli, Abhay Singhal and Michal Malohlava
+
Testing Large Language Model (LLM) Vulnerabilities Using Adversarial Attacks

Adversarial analysis seeks to explain a machine learning model by understanding locally what changes need

July 19, 2023 - by Kim Montgomery, Pramit Choudhary and Michal Malohlava
+
Reducing False Positives in Financial Transactions with AutoML

In an increasingly digital world, combating financial fraud is a high-stakes game. However, the systems

July 14, 2023 - by Asghar Ghorbani

Ready to see the H2O.ai platform in action?

Make data and AI deliver meaningful and significant value to your organization with our state-of-the-art AI platform.