https://github.com/dciborow/machinelearningsamples-h2ospark

MachineLearningSamples-H2OSpark
https://github.com/dciborow/machinelearningsamples-h2ospark

Last synced: about 2 months ago
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MachineLearningSamples-H2OSpark

• Host: GitHub
• URL: https://github.com/dciborow/machinelearningsamples-h2ospark
• Owner: dciborow
• License: mit
• Created: 2017-11-01T15:23:06.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-03-21T23:48:44.000Z (almost 3 years ago)
• Last Synced: 2024-10-18T07:17:03.470Z (4 months ago)
• Language: Python
• Homepage:
• Size: 819 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.TXT

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README

        
# H2O Spark using Azure Machine Learning Workbench

## Link to the Microsoft DOCS site

The detailed documentation for this real world scenario includes the step-by-step walkthrough:

.

## Link to the Gallery GitHub repository

## Overview

This scenario shows how to use Azure Machine Learning Workbench to scale out machine learning algorithms that implement h2o API. We show how to configure and use a remote Docker container and Spark cluster as an execution backend for h2o on Spark.

## Key components needed to run this scenario

* Ubuntu Data Science Virtual Machine. We recommend using a virtual machine with at least 8 cores and 28 Gb of memory.

* Spark HDInsight cluster. We recommend having a cluster with at least 4 worker nodes and at least 28 Gb of memory in each node. 

* Azure storage account. 

# Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a

Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us

the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide

a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions

provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

For more information, see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or

contact [[email protected]](mailto:[email protected]) with any additional questions or comments.