[
https://issues.apache.org/jira/browse/SPARK-10324?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiangrui Meng updated SPARK-10324:
----------------------------------
Description:
Following SPARK-8445, we created this master list for MLlib features we plan to
have in Spark 1.6. Please view this list as a wish list rather than a concrete
plan, because we don't have an accurate estimate of available resources. Due to
limited review bandwidth, features appearing on this list will get higher
priority during code review. But feel free to suggest new items to the list in
comments. We are experimenting with this process. Your feedback would be
greatly appreciated.
h1. Instructions
h2. For contributors:
* Please read
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
carefully. Code style, documentation, and unit tests are important.
* If you are a first-time Spark contributor, please always start with a
[starter
task|https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20%22In%20Progress%22%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20labels%20%3D%20starter%20AND%20%22Target%20Version%2Fs%22%20%3D%201.6.0]
rather than a medium/big feature. Based on our experience, mixing the
development process with a big feature usually causes long delay in code review.
* Never work silently. Let everyone know on the corresponding JIRA page when
you start working on some features. This is to avoid duplicate work. For small
features, you don't need to wait to get JIRA assigned.
* For medium/big features or features with dependencies, please get assigned
first before coding and keep the ETA updated on the JIRA. If there exist no
activity on the JIRA page for a certain amount of time, the JIRA should be
released for other contributors.
* Do not claim multiple (>3) JIRAs at the same time. Try to finish them one
after another.
* Remember to add `@Since("1.6.0")` annotation to new public APIs.
* Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code review
greatly helps improve others' code as well as yours.
h2. For committers:
* Try to break down big features into small and specific JIRA tasks and link
them properly.
* Add "starter" label to starter tasks.
* Put a rough estimate for medium/big features and track the progress.
* If you start reviewing a PR, please add yourself to the Shepherd field on
JIRA.
* If the code looks good to you, please comment "LGTM". For non-trivial PRs,
please ping a maintainer to make a final pass.
* After merging a PR, create and link JIRAs for Python, example code, and
documentation if necessary.
h1. Roadmap (WIP)
This is NOT [a complete list of MLlib JIRAs for
1.6|https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20%22In%20Progress%22%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20%22Target%20Version%2Fs%22%20%3D%201.6.0%20ORDER%20BY%20priority%20DESC].
We only include umbrella JIRAs and high-level tasks.
(TBA)
was:
Following SPARK-8445, we created this master list for MLlib features we plan to
have in Spark 1.6. Please view this list as a wish list rather than a concrete
plan, because we don't have an accurate estimate of available resources. Due to
limited review bandwidth, features appearing on this list will get higher
priority during code review. But feel free to suggest new items to the list in
comments. We are experimenting with this process. Your feedback would be
greatly appreciated.
h1. Instructions
h2. For contributors:
* Please read
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
carefully. Code style, documentation, and unit tests are important.
* If you are a first-time Spark contributor, please always start with a
[starter
task|https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20%22In%20Progress%22%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20labels%20%3D%20starter%20AND%20%22Target%20Version%2Fs%22%20%3D%201.5.0]
rather than a medium/big feature. Based on our experience, mixing the
development process with a big feature usually causes long delay in code review.
* Never work silently. Let everyone know on the corresponding JIRA page when
you start working on some features. This is to avoid duplicate work. For small
features, you don't need to wait to get JIRA assigned.
* For medium/big features or features with dependencies, please get assigned
first before coding and keep the ETA updated on the JIRA. If there exist no
activity on the JIRA page for a certain amount of time, the JIRA should be
released for other contributors.
* Do not claim multiple (>3) JIRAs at the same time. Try to finish them one
after another.
* Remember to add `@Since("1.6.0")` annotation to new public APIs.
* Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code review
greatly helps improve others' code as well as yours.
h2. For committers:
* Try to break down big features into small and specific JIRA tasks and link
them properly.
* Add "starter" label to starter tasks.
* Put a rough estimate for medium/big features and track the progress.
* If you start reviewing a PR, please add yourself to the Shepherd field on
JIRA.
* If the code looks good to you, please comment "LGTM". For non-trivial PRs,
please ping a maintainer to make a final pass.
* After merging a PR, create and link JIRAs for Python, example code, and
documentation if necessary.
h1. Roadmap (WIP)
This is NOT [a complete list of MLlib JIRAs for
1.6|https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20%22In%20Progress%22%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20%22Target%20Version%2Fs%22%20%3D%201.6.0%20ORDER%20BY%20priority%20DESC].
We only include umbrella JIRAs and high-level tasks.
(TBA)
> MLlib 1.6 Roadmap
> -----------------
>
> Key: SPARK-10324
> URL: https://issues.apache.org/jira/browse/SPARK-10324
> Project: Spark
> Issue Type: Umbrella
> Components: ML, MLlib
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Priority: Critical
>
> Following SPARK-8445, we created this master list for MLlib features we plan
> to have in Spark 1.6. Please view this list as a wish list rather than a
> concrete plan, because we don't have an accurate estimate of available
> resources. Due to limited review bandwidth, features appearing on this list
> will get higher priority during code review. But feel free to suggest new
> items to the list in comments. We are experimenting with this process. Your
> feedback would be greatly appreciated.
> h1. Instructions
> h2. For contributors:
> * Please read
> https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
> carefully. Code style, documentation, and unit tests are important.
> * If you are a first-time Spark contributor, please always start with a
> [starter
> task|https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20%22In%20Progress%22%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20labels%20%3D%20starter%20AND%20%22Target%20Version%2Fs%22%20%3D%201.6.0]
> rather than a medium/big feature. Based on our experience, mixing the
> development process with a big feature usually causes long delay in code
> review.
> * Never work silently. Let everyone know on the corresponding JIRA page when
> you start working on some features. This is to avoid duplicate work. For
> small features, you don't need to wait to get JIRA assigned.
> * For medium/big features or features with dependencies, please get assigned
> first before coding and keep the ETA updated on the JIRA. If there exist no
> activity on the JIRA page for a certain amount of time, the JIRA should be
> released for other contributors.
> * Do not claim multiple (>3) JIRAs at the same time. Try to finish them one
> after another.
> * Remember to add `@Since("1.6.0")` annotation to new public APIs.
> * Please review others' PRs (https://spark-prs.appspot.com/#mllib). Code
> review greatly helps improve others' code as well as yours.
> h2. For committers:
> * Try to break down big features into small and specific JIRA tasks and link
> them properly.
> * Add "starter" label to starter tasks.
> * Put a rough estimate for medium/big features and track the progress.
> * If you start reviewing a PR, please add yourself to the Shepherd field on
> JIRA.
> * If the code looks good to you, please comment "LGTM". For non-trivial PRs,
> please ping a maintainer to make a final pass.
> * After merging a PR, create and link JIRAs for Python, example code, and
> documentation if necessary.
> h1. Roadmap (WIP)
> This is NOT [a complete list of MLlib JIRAs for
> 1.6|https://issues.apache.org/jira/issues/?jql=project%20%3D%20SPARK%20AND%20status%20in%20(Open%2C%20%22In%20Progress%22%2C%20Reopened)%20AND%20component%20in%20(ML%2C%20MLlib)%20AND%20%22Target%20Version%2Fs%22%20%3D%201.6.0%20ORDER%20BY%20priority%20DESC].
> We only include umbrella JIRAs and high-level tasks.
> (TBA)
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