Sparkling Water integrates H2O's Fast Scalable Machine Learning with Spark
Project description
This package contains complete functionality for training and scoring Sparkling Water/H2O-3 MOJO models. It’s also possible to use this package for scoring with Driverless AI MOJO models.
PySparkling Documentation is hosted at our documentation page:
For Spark 3.5 - http://docs.h2o.ai/sparkling-water/3.5/latest-stable/doc/pysparkling.html
For Spark 3.4 - http://docs.h2o.ai/sparkling-water/3.4/latest-stable/doc/pysparkling.html
For Spark 3.3 - http://docs.h2o.ai/sparkling-water/3.3/latest-stable/doc/pysparkling.html
For Spark 3.2 - http://docs.h2o.ai/sparkling-water/3.2/latest-stable/doc/pysparkling.html
For Spark 3.1 - http://docs.h2o.ai/sparkling-water/3.1/latest-stable/doc/pysparkling.html
For Spark 3.0 - http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/pysparkling.html
For Spark 2.4 - http://docs.h2o.ai/sparkling-water/2.4/latest-stable/doc/pysparkling.html
For Spark 2.3 - http://docs.h2o.ai/sparkling-water/2.3/latest-stable/doc/pysparkling.html
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
File details
Details for the file h2o_pysparkling_3.5-3.46.0.1.post1.tar.gz.
File metadata
- Download URL: h2o_pysparkling_3.5-3.46.0.1.post1.tar.gz
- Upload date:
- Size: 225.9 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/0.0.0 pkginfo/1.9.6 readme-renderer/27.0 requests/2.28.1 requests-toolbelt/1.0.0 urllib3/1.26.15 tqdm/4.65.0 importlib-metadata/4.8.1 keyring/23.2.1 rfc3986/2.0.0 colorama/0.4.5 CPython/3.6.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d3acd063ddaa1d0efb0c60fd16c465299f91518ad4adf16923d27974230b02a7 |
|
| MD5 | 64ab339b00144503a379a732ab13b429 |
|
| BLAKE2b-256 | 7f53f72e05e338a379addcc34addd28c785cc17d86622c140cdd14e8a8e49d61 |
