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Xiangrui Meng edited comment on SPARK-38648 at 8/19/22 10:55 PM:
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I had
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Xiangrui Meng commented on SPARK-38648:
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I had an offline discussion with [~leewyang]. Summary:
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Xiangrui Meng commented on SPARK-38648:
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I think it is beneficial to both Spark and DL frameworks
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Xiangrui Meng updated SPARK-37004:
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Description:
Spark 3.2.0 turned on py4j pinned thread mode by default (SPARK-35303).
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Xiangrui Meng updated SPARK-37004:
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Issue Type: Bug (was: Improvement)
> Job cancellation causes py4j errors on Jupyter due to
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Xiangrui Meng updated SPARK-37004:
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Attachment: pinned.ipynb
> Job cancellation causes py4j errors on Jupyter due to pinned
Xiangrui Meng created SPARK-37004:
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Summary: Job cancellation causes py4j errors on Jupyter due to
pinned thread mode
Key: SPARK-37004
URL: https://issues.apache.org/jira/browse/SPARK-37004
Project:
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https://issues.apache.org/jira/browse/SPARK-36578?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng reassigned SPARK-36578:
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Assignee: Huaxin Gao
> Minor UnivariateFeatureSelector API doc improvement
>
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Xiangrui Meng commented on SPARK-36578:
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[~huaxingao] I saw some possible improvement to the API
Xiangrui Meng created SPARK-36578:
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Summary: Minor UnivariateFeatureSelector API doc improvement
Key: SPARK-36578
URL: https://issues.apache.org/jira/browse/SPARK-36578
Project: Spark
Issue
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Xiangrui Meng updated SPARK-36578:
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Affects Version/s: 3.1.1
> Minor UnivariateFeatureSelector API doc improvement
>
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Xiangrui Meng updated SPARK-36578:
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Target Version/s: (was: 3.2.0)
> Minor UnivariateFeatureSelector API doc improvement
>
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https://issues.apache.org/jira/browse/SPARK-34415?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17400644#comment-17400644
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Xiangrui Meng commented on SPARK-34415:
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[~phenry] [~srowen] The implementation doesn't do uniform
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Xiangrui Meng resolved SPARK-24374.
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Fix Version/s: 2.4.0
Target Version/s: 2.4.0
Resolution: Fixed
I'm marking
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Xiangrui Meng commented on SPARK-34080:
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Not sure if we have time for 3.1.1 release. But if there
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Xiangrui Meng updated SPARK-34080:
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Affects Version/s: 3.1.1
> Add UnivariateFeatureSelector to deprecate existing selectors
>
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Xiangrui Meng updated SPARK-34080:
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Description:
In SPARK-26111, we introduced a few univariate feature selectors, which share a
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Xiangrui Meng updated SPARK-34080:
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Description:
In SPARK-26111, we introduced a few univariate feature selectors, which share a
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Xiangrui Meng updated SPARK-34080:
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Description:
In SPARK-26111, we introduced a few univariate feature selectors, which share a
Xiangrui Meng created SPARK-34080:
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Summary: Add UnivariateFeatureSelector to deprecate existing
selectors
Key: SPARK-34080
URL: https://issues.apache.org/jira/browse/SPARK-34080
Project: Spark
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Xiangrui Meng commented on SPARK-32933:
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Why do we keep using the @keyword_only annotation after
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Xiangrui Meng edited comment on SPARK-32429 at 7/28/20, 4:30 PM:
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Xiangrui Meng commented on SPARK-32429:
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[~tgraves] Thanks for the clarification! It makes sense
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Xiangrui Meng commented on SPARK-32429:
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Couple questions:
1. Which GPU resource name do we use?
Xiangrui Meng created SPARK-31777:
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Summary: CrossValidator supports user-supplied folds
Key: SPARK-31777
URL: https://issues.apache.org/jira/browse/SPARK-31777
Project: Spark
Issue Type:
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Xiangrui Meng updated SPARK-31776:
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Issue Type: Improvement (was: New Feature)
> Literal lit() supports lists and numpy arrays
>
Xiangrui Meng created SPARK-31776:
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Summary: Literal lit() supports lists and numpy arrays
Key: SPARK-31776
URL: https://issues.apache.org/jira/browse/SPARK-31776
Project: Spark
Issue Type:
Xiangrui Meng created SPARK-31775:
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Summary: Support tensor type (TensorType) in Spark SQL/DataFrame
Key: SPARK-31775
URL: https://issues.apache.org/jira/browse/SPARK-31775
Project: Spark
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Xiangrui Meng updated SPARK-31610:
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Issue Type: Improvement (was: Bug)
> Expose hashFuncVersion property in HashingTF
>
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Xiangrui Meng updated SPARK-31610:
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Priority: Major (was: Critical)
> Expose hashFuncVersion property in HashingTF
>
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Xiangrui Meng updated SPARK-31610:
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Description:
Expose hashFuncVersion property in HashingTF
Some third-party library such as
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Xiangrui Meng reassigned SPARK-31610:
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Assignee: Weichen Xu
> Expose hashFunc property in HashingTF
>
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Xiangrui Meng resolved SPARK-31668.
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Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 28413
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Xiangrui Meng updated SPARK-31610:
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Summary: Expose hashFuncVersion property in HashingTF (was: Expose
hashFunc property in
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Xiangrui Meng resolved SPARK-31610.
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Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 28413
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Xiangrui Meng updated SPARK-31610:
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Issue Type: Bug (was: Improvement)
> Expose hashFunc property in HashingTF
>
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Xiangrui Meng updated SPARK-31610:
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Priority: Critical (was: Major)
> Expose hashFunc property in HashingTF
>
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https://issues.apache.org/jira/browse/SPARK-31549?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-31549:
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Target Version/s: 3.0.0
> Pyspark SparkContext.cancelJobGroup do not work correctly
>
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Xiangrui Meng resolved SPARK-31497.
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Resolution: Fixed
Issue resolved by pull request 28279
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Xiangrui Meng updated SPARK-31497:
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Fix Version/s: 3.0.0
> Pyspark CrossValidator/TrainValidationSplit with pipeline estimator
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Xiangrui Meng updated SPARK-31497:
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Target Version/s: 3.0.0
> Pyspark CrossValidator/TrainValidationSplit with pipeline estimator
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https://issues.apache.org/jira/browse/SPARK-30969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17046795#comment-17046795
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Xiangrui Meng commented on SPARK-30969:
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[~Ngone51] [~jiangxb1987] Is there a JIRA to deprecate
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Xiangrui Meng updated SPARK-30969:
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Environment: (was: Resource coordination is used for the case where
multiple workers
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Xiangrui Meng updated SPARK-30969:
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Priority: Critical (was: Major)
> Remove resource coordination support from Standalone
>
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Xiangrui Meng reassigned SPARK-30969:
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Assignee: wuyi
> Remove resource coordination support from Standalone
>
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Xiangrui Meng updated SPARK-30969:
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Description: Resource coordination is used for the case where multiple
workers running on the
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Xiangrui Meng updated SPARK-30667:
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Fix Version/s: (was: 3.0.0)
> Support simple all gather in barrier task context
>
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Xiangrui Meng reopened SPARK-30667:
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> Support simple all gather in barrier task context
>
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Xiangrui Meng resolved SPARK-30667.
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Resolution: Fixed
> Support simple all gather in barrier task context
>
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Xiangrui Meng reassigned SPARK-30667:
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Assignee: Sarth Frey
> Support simple all gather in barrier task context
>
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Xiangrui Meng updated SPARK-30667:
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Target Version/s: 3.0.0 (was: 3.1.0)
> Support simple all gather in barrier task context
>
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Xiangrui Meng updated SPARK-30667:
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Fix Version/s: 3.0.0
> Support simple all gather in barrier task context
>
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https://issues.apache.org/jira/browse/SPARK-30762?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng reassigned SPARK-30762:
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Assignee: Liang Zhang
> Add dtype="float32" support to vector_to_array UDF
>
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Xiangrui Meng updated SPARK-30762:
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Component/s: PySpark
> Add dtype="float32" support to vector_to_array UDF
>
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Xiangrui Meng updated SPARK-30667:
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Description:
Currently we offer task.barrier() to coordinate tasks in barrier mode. Tasks
Xiangrui Meng created SPARK-30667:
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Summary: Support simple all gather in barrier task context
Key: SPARK-30667
URL: https://issues.apache.org/jira/browse/SPARK-30667
Project: Spark
Issue
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https://issues.apache.org/jira/browse/SPARK-30154?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-30154.
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Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 26910
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Xiangrui Meng updated SPARK-30154:
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Description:
If a PySpark user wants to convert MLlib sparse/dense vectors in a DataFrame
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Xiangrui Meng updated SPARK-30154:
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Summary: PySpark UDF to convert MLlib vectors to dense arrays (was: Allow
PySpark code
Xiangrui Meng created SPARK-30154:
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Summary: Allow PySpark code efficiently convert MLlib vectors to
dense arrays
Key: SPARK-30154
URL: https://issues.apache.org/jira/browse/SPARK-30154
Project:
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https://issues.apache.org/jira/browse/SPARK-28978?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-28978.
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Fix Version/s: 3.0.0
Assignee: Bago Amirbekian
Resolution: Fixed
> PySpark:
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Xiangrui Meng resolved SPARK-29417.
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Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 26083
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Xiangrui Meng reassigned SPARK-28206:
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Assignee: Hyukjin Kwon
> "@pandas_udf" in doctest is rendered as ":pandas_udf" in html
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Xiangrui Meng resolved SPARK-28206.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 25060
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Xiangrui Meng updated SPARK-28206:
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Attachment: Screen Shot 2019-06-28 at 9.55.13 AM.png
> "@pandas_udf" in doctest is rendered
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Xiangrui Meng updated SPARK-28206:
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Issue Type: Bug (was: Documentation)
> "@pandas_udf" in doctest is rendered as ":pandas_udf"
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Xiangrui Meng updated SPARK-28206:
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Summary: "@pandas_udf" in doctest is rendered as ":pandas_udf" in html API
doc (was:
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Xiangrui Meng updated SPARK-28206:
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Summary: "@pandas_udf" in doctest is rendered as ":pandas_udf" in html
(was: "@" is rendered
Xiangrui Meng created SPARK-28206:
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Summary: "@" is rendered as ":" in doctest
Key: SPARK-28206
URL: https://issues.apache.org/jira/browse/SPARK-28206
Project: Spark
Issue Type:
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https://issues.apache.org/jira/browse/SPARK-28115?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-28115.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 24917
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https://issues.apache.org/jira/browse/SPARK-28056?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-28056.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 24897
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Xiangrui Meng reassigned SPARK-28056:
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Assignee: Xiangrui Meng
> Document SCALAR_ITER Pandas UDF
>
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https://issues.apache.org/jira/browse/SPARK-26412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-26412.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 24643
Xiangrui Meng created SPARK-28056:
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Summary: Document SCALAR_ITER Pandas UDF
Key: SPARK-28056
URL: https://issues.apache.org/jira/browse/SPARK-28056
Project: Spark
Issue Type: Documentation
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Xiangrui Meng resolved SPARK-28030.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 24855
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https://issues.apache.org/jira/browse/SPARK-27823?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng reassigned SPARK-27823:
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Assignee: Xiangrui Meng (was: Thomas Graves)
> Add an abstraction layer for
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Xiangrui Meng updated SPARK-28030:
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Description:
{code}
echo 123 > "/tmp/test space.txt"
Xiangrui Meng created SPARK-28030:
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Summary: Binary file data source doesn't support space in file
names
Key: SPARK-28030
URL: https://issues.apache.org/jira/browse/SPARK-28030
Project: Spark
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Xiangrui Meng commented on SPARK-27360:
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Done. Thanks for taking the task!
> Standalone cluster
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Xiangrui Meng deleted SPARK-27999:
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> setup resources when Standalone Worker starts up
>
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Xiangrui Meng updated SPARK-27372:
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Issue Type: Story (was: Sub-task)
Parent: (was: SPARK-27360)
> Standalone
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Xiangrui Meng updated SPARK-27371:
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Summary: Standalone master receives resource info from worker and allocate
driver/executor
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Xiangrui Meng updated SPARK-27371:
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Summary: Master receives resource info from worker and allocate
driver/executor properly
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Xiangrui Meng deleted SPARK-27370:
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> spark-submit requests GPUs in standalone mode
> -
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Xiangrui Meng updated SPARK-27369:
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Summary: Standalone worker can load resource conf and discover resources
(was: Standalone
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Xiangrui Meng updated SPARK-27368:
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Description:
Design draft:
Scenarios:
* client-mode, worker might create one or more
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https://issues.apache.org/jira/browse/SPARK-27368?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-27368:
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Description:
Design draft:
Scenarios:
* client-mode, worker might create one or more
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https://issues.apache.org/jira/browse/SPARK-27368?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-27368:
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Description:
Design draft:
Scenarios:
* client-mode, worker might create one or more
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https://issues.apache.org/jira/browse/SPARK-27968?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-27968.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 24816
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https://issues.apache.org/jira/browse/SPARK-26412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-26412:
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Description:
Pandas UDF is the ideal connection between PySpark and DL model inference
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https://issues.apache.org/jira/browse/SPARK-26412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-26412:
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Description:
Pandas UDF is the ideal connection between PySpark and DL model inference
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https://issues.apache.org/jira/browse/SPARK-26412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-26412:
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Description:
Pandas UDF is the ideal connection between PySpark and DL model inference
Xiangrui Meng created SPARK-27968:
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Summary: ArrowEvalPythonExec.evaluate shouldn't eagerly read the
first row
Key: SPARK-27968
URL: https://issues.apache.org/jira/browse/SPARK-27968
Project: Spark
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https://issues.apache.org/jira/browse/SPARK-27368?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-27368:
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Description:
Design draft:
Scenarios:
* client-mode, worker might create one or more
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https://issues.apache.org/jira/browse/SPARK-27368?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-27368:
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Description:
Design draft:
Scenarios:
* client-mode, worker might create one or more
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https://issues.apache.org/jira/browse/SPARK-27366?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-27366.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 24374
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https://issues.apache.org/jira/browse/SPARK-27888?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16855838#comment-16855838
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Xiangrui Meng commented on SPARK-27888:
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It would be nice if we can find some PySpark users who
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https://issues.apache.org/jira/browse/SPARK-27884?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng reassigned SPARK-27884:
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Assignee: Xiangrui Meng
> Deprecate Python 2 support in Spark 3.0
>
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https://issues.apache.org/jira/browse/SPARK-27888?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16855832#comment-16855832
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Xiangrui Meng commented on SPARK-27888:
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This JIRA is not to inform users that Python 2 is
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https://issues.apache.org/jira/browse/SPARK-27886?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-27886.
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Resolution: Done
> Add Apache Spark project to https://python3statement.org/
>
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