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From "Eric Yang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (CHUKWA-819) Add CaffeONSpark support to Chukwa for memory leak detection
Date Mon, 01 May 2017 23:13:04 GMT

     [ https://issues.apache.org/jira/browse/CHUKWA-819?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Eric Yang updated CHUKWA-819:
-----------------------------
    Description: 
Computer vision algorithm can be used to detect patterns in common problems.  This new feature
is design to automate some troubleshooting advise that can be identified by looking at metrics
chart patterns.

CaffeOnSpark is a computer vision library that can be used for enforced machine learning training
to classify common traits that might be detectable by visualization.

This feature is composed of:

1.  Metrics to graph generator
2.  Caffe on Spark configuration for categorizing problems.
3.  Docker scripts to show end to end integration
4.  Documentation on how to use the training framework.

  was:Add CaffeOnSpark to Chukwa docker image to support memory leak image machine training
using Caffe.  

        Summary: Add CaffeONSpark support to Chukwa for memory leak detection  (was: Add CaffeONSpark
support to Chukwa Docker creation)

> Add CaffeONSpark support to Chukwa for memory leak detection
> ------------------------------------------------------------
>
>                 Key: CHUKWA-819
>                 URL: https://issues.apache.org/jira/browse/CHUKWA-819
>             Project: Chukwa
>          Issue Type: New Feature
>    Affects Versions: 0.8.0
>            Reporter: Fay Wang
>            Assignee: Fay Wang
>             Fix For: 0.9.0
>
>
> Computer vision algorithm can be used to detect patterns in common problems.  This new
feature is design to automate some troubleshooting advise that can be identified by looking
at metrics chart patterns.
> CaffeOnSpark is a computer vision library that can be used for enforced machine learning
training to classify common traits that might be detectable by visualization.
> This feature is composed of:
> 1.  Metrics to graph generator
> 2.  Caffe on Spark configuration for categorizing problems.
> 3.  Docker scripts to show end to end integration
> 4.  Documentation on how to use the training framework.



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