H2O.ai Blog
By: h2oai
Hi Makers!
We are happy to announce that Sparkling Water now fully supports Spark 2.3 and is available from our download page.
If you are using an older version of Spark, that’s no problem. Even though we suggest upgrading to the latest version possible, we keep the Sparkling Water releases for Spark 2.2 and 2.1 up-to-date with the latest version if we are not limited by Spark.
The last release of Sparkling Water contained several important bug fixes. The 3 major bug fixes are:
- Handle nulls properly in H2OMojoModel. In the previous versions, running predictions on the H2OMojoModel with null values would fail. We now handle the null values as missing values and it no longer fails.
- We marked the Spark dependencies in our maven packages as provided. This means that we assume that Spark dependencies are always provided by the run-time, which should always be true. This ensures a cleaner and more transparent Sparkling Water environment.
-
In PySparkling, the method as_h2o_frame didn’t issue an alert when we passed in a wrong input type. This method accepts only Spark DataFrames and RDDs, however, some users tried to pass different types and this method ended silently. Now we fail if the user passes a wrong data type to this method.
It is also important to mention that Spark 2.3 removed support for Scala 2.10. We’ve done the same in the release for Spark 2.3. Scala 2.10 is still supported in the older Spark versions.
The latest Sparkling Water versions also integrated with H2O 3.18.0.5 which brings several important fixes. The full change log for H2O 3.18.0.5 is available here and the full Sparkling Water change log can be viewed here.
Thank you!
Kuba
Senior Software Engineer, Sparkling Water Team
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
