Sparkling Water integrates H2O's Fast Scalable Machine Learning with Spark
Project description
This package contains just functionality for scoring with Sparkling Water, H2O-3 and Driverless AI MOJO models.
Documentation describing scoring with H2O-3 MOJO models is located at:
For Spark 3.2 - https://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/deployment/load_mojo.html
For Spark 3.1 - https://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/deployment/load_mojo.html
For Spark 3.0 - https://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/deployment/load_mojo.html
For Spark 2.4 - https://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/deployment/load_mojo.html
For Spark 2.3 - https://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/deployment/load_mojo.html
For Spark 2.2 - https://docs.h2o.ai/sparkling-water/2.2/latest-stable/doc/deployment/load_mojo.html
Documentation describing scoring with Driverless AI MOJO models is located at:
For Spark 3.2 - https://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/deployment/load_mojo_pipeline.html
For Spark 3.1 - https://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/deployment/load_mojo_pipeline.html
For Spark 3.0 - https://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/deployment/load_mojo_pipeline.html
For Spark 2.4 - https://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/deployment/load_mojo_pipeline.html
For Spark 2.3 - https://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/deployment/load_mojo_pipeline.html
For Spark 2.2 - https://docs.h2o.ai/sparkling-water/2.2/latest-stable/doc/deployment/load_mojo_pipeline.html
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Hashes for h2o_pysparkling_scoring_2.2-3.36.1.1-1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | c609b981111c3f97bf9703385a412db60ad1d9a8e791ceb07ccd26a138ea93ad |
|
MD5 | 31cce56ccbabdbf6302565c75598dc77 |
|
BLAKE2b-256 | 5c435387ef58b5730e49a29fca2bccc9f621fef79913dc92f85fecd9dcf53014 |