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h2o-pysparkling-3.0 3.40.0.4.post1

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Sparkling Water integrates H2O's Fast Scalable Machine Learning with Spark

Sparkling Water integrates H2O's Fast Scalable Machine Learning with Spark

Stars: 950, Watchers: 950, Forks: 372, Open Issues: 28

The h2oai/sparkling-water repo was created 8 years ago and the last code push was 4 days ago.
The project is popular with 950 github stars!

How to Install h2o-pysparkling-3-0

You can install h2o-pysparkling-3-0 using pip

pip install h2o-pysparkling-3-0

or add it to a project with poetry

poetry add h2o-pysparkling-3-0

Package Details

Author
H2O.ai
License
Apache v2
Homepage
https://github.com/h2oai/sparkling-water
PyPi
https://pypi.org/project/h2o-pysparkling-3.0/
GitHub Repo
https://github.com/h2oai/sparkling-water

Classifiers

  • Software Development/Build Tools
No  h2o-pysparkling-3-0  pypi packages just yet.

Errors

A list of common h2o-pysparkling-3-0 errors.

Code Examples

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GitHub Issues

The h2o-pysparkling-3-0 package has 28 open issues on GitHub

  • [SW-2684] Make io.fabric8.kubernetes-client just a complileOnly dependency to minimize size of uber jar
  • [SW-2680] Expose SHAP values for H2OMOJOPipeline
  • [SW-2681] Add Comment to Documentation about Contributions Support only in Binomial and Regression Models
  • [SW-2651] Add info about overriding mojo2 library
  • Missing provider for modeling steps XGBoost when training AutoML
  • [SW-2674] ChicagoCrimeApp refactor
  • [SW-2646] Calculate Metrics on Arbitrary Dataset
  • [SW-2622] Avoid Multiple Materialization of Spark DataFrame During Conversion to H2OFrame

See more issues on GitHub

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