[jira] [Commented] (SPARK-1486) Support multi-model training in MLlib
[ https://issues.apache.org/jira/browse/SPARK-1486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14139549#comment-14139549 ] Apache Spark commented on SPARK-1486: - User 'brkyvz' has created a pull request for this issue: https://github.com/apache/spark/pull/2451 Support multi-model training in MLlib - Key: SPARK-1486 URL: https://issues.apache.org/jira/browse/SPARK-1486 Project: Spark Issue Type: Improvement Components: MLlib Reporter: Xiangrui Meng Assignee: Burak Yavuz Priority: Critical It is rare in practice to train just one model with a given set of parameters. Usually, this is done by training multiple models with different sets of parameters and then select the best based on their performance on the validation set. MLlib should provide native support for multi-model training/scoring. It requires decoupling of concepts like problem, formulation, algorithm, parameter set, and model, which are missing in MLlib now. MLI implements similar concepts, which we can borrow. There are different approaches for multi-model training: 0) Keep one copy of the data, and train models one after another (or maybe in parallel, depending on the scheduler). 1) Keep one copy of the data, and train multiple models at the same time (similar to `runs` in KMeans). 2) Make multiple copies of the data (still stored distributively), and use more cores to distribute the work. 3) Collect the data, make the entire dataset available on workers, and train one or more models on each worker. Users should be able to choose which execution mode they want to use. Note that 3) could cover many use cases in practice when the training data is not huge, e.g., 1GB. This task will be divided into sub-tasks and this JIRA is created to discuss the design and track the overall progress. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-1486) Support multi-model training in MLlib
[ https://issues.apache.org/jira/browse/SPARK-1486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14136696#comment-14136696 ] Anant Daksh Asthana commented on SPARK-1486: That sounds very true and relevant. I am completely with you on this one. On Tue, Sep 16, 2014 at 5:50 PM, Xiangrui Meng (JIRA) j...@apache.org Support multi-model training in MLlib - Key: SPARK-1486 URL: https://issues.apache.org/jira/browse/SPARK-1486 Project: Spark Issue Type: Improvement Components: MLlib Reporter: Xiangrui Meng Assignee: Burak Yavuz Priority: Critical It is rare in practice to train just one model with a given set of parameters. Usually, this is done by training multiple models with different sets of parameters and then select the best based on their performance on the validation set. MLlib should provide native support for multi-model training/scoring. It requires decoupling of concepts like problem, formulation, algorithm, parameter set, and model, which are missing in MLlib now. MLI implements similar concepts, which we can borrow. There are different approaches for multi-model training: 0) Keep one copy of the data, and train models one after another (or maybe in parallel, depending on the scheduler). 1) Keep one copy of the data, and train multiple models at the same time (similar to `runs` in KMeans). 2) Make multiple copies of the data (still stored distributively), and use more cores to distribute the work. 3) Collect the data, make the entire dataset available on workers, and train one or more models on each worker. Users should be able to choose which execution mode they want to use. Note that 3) could cover many use cases in practice when the training data is not huge, e.g., 1GB. This task will be divided into sub-tasks and this JIRA is created to discuss the design and track the overall progress. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-1486) Support multi-model training in MLlib
[ https://issues.apache.org/jira/browse/SPARK-1486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14094673#comment-14094673 ] Kyle Ellrott commented on SPARK-1486: - It would be helpful to get some feedback if the work being done for SPARK-2372 would help with this issue. Support multi-model training in MLlib - Key: SPARK-1486 URL: https://issues.apache.org/jira/browse/SPARK-1486 Project: Spark Issue Type: Improvement Components: MLlib Reporter: Xiangrui Meng Assignee: Xiangrui Meng Priority: Critical It is rare in practice to train just one model with a given set of parameters. Usually, this is done by training multiple models with different sets of parameters and then select the best based on their performance on the validation set. MLlib should provide native support for multi-model training/scoring. It requires decoupling of concepts like problem, formulation, algorithm, parameter set, and model, which are missing in MLlib now. MLI implements similar concepts, which we can borrow. There are different approaches for multi-model training: 0) Keep one copy of the data, and train models one after another (or maybe in parallel, depending on the scheduler). 1) Keep one copy of the data, and train multiple models at the same time (similar to `runs` in KMeans). 2) Make multiple copies of the data (still stored distributively), and use more cores to distribute the work. 3) Collect the data, make the entire dataset available on workers, and train one or more models on each worker. Users should be able to choose which execution mode they want to use. Note that 3) could cover many use cases in practice when the training data is not huge, e.g., 1GB. This task will be divided into sub-tasks and this JIRA is created to discuss the design and track the overall progress. -- This message was sent by Atlassian JIRA (v6.2#6252) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-1486) Support multi-model training in MLlib
[ https://issues.apache.org/jira/browse/SPARK-1486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14062339#comment-14062339 ] Erik Erlandson commented on SPARK-1486: --- Does the dev on this issue effectively subsume SPARK-1457 and/or SPARK-1856 ? Support multi-model training in MLlib - Key: SPARK-1486 URL: https://issues.apache.org/jira/browse/SPARK-1486 Project: Spark Issue Type: Improvement Components: MLlib Reporter: Xiangrui Meng Assignee: Xiangrui Meng Priority: Critical Fix For: 1.1.0 It is rare in practice to train just one model with a given set of parameters. Usually, this is done by training multiple models with different sets of parameters and then select the best based on their performance on the validation set. MLlib should provide native support for multi-model training/scoring. It requires decoupling of concepts like problem, formulation, algorithm, parameter set, and model, which are missing in MLlib now. MLI implements similar concepts, which we can borrow. There are different approaches for multi-model training: 0) Keep one copy of the data, and train models one after another (or maybe in parallel, depending on the scheduler). 1) Keep one copy of the data, and train multiple models at the same time (similar to `runs` in KMeans). 2) Make multiple copies of the data (still stored distributively), and use more cores to distribute the work. 3) Collect the data, make the entire dataset available on workers, and train one or more models on each worker. Users should be able to choose which execution mode they want to use. Note that 3) could cover many use cases in practice when the training data is not huge, e.g., 1GB. This task will be divided into sub-tasks and this JIRA is created to discuss the design and track the overall progress. -- This message was sent by Atlassian JIRA (v6.2#6252)
[jira] [Commented] (SPARK-1486) Support multi-model training in MLlib
[ https://issues.apache.org/jira/browse/SPARK-1486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14052783#comment-14052783 ] Kyle Ellrott commented on SPARK-1486: - In the case where you are using the same training/optimization procedure, you can group together the sets of samples using a key. There is an example of this in the pull request linked to SPARK-2372. This could be a very effective way to deal with simultaneously training all of the different folds from a kFolds split. Support multi-model training in MLlib - Key: SPARK-1486 URL: https://issues.apache.org/jira/browse/SPARK-1486 Project: Spark Issue Type: Improvement Components: MLlib Reporter: Xiangrui Meng Assignee: Xiangrui Meng Priority: Critical Fix For: 1.1.0 It is rare in practice to train just one model with a given set of parameters. Usually, this is done by training multiple models with different sets of parameters and then select the best based on their performance on the validation set. MLlib should provide native support for multi-model training/scoring. It requires decoupling of concepts like problem, formulation, algorithm, parameter set, and model, which are missing in MLlib now. MLI implements similar concepts, which we can borrow. There are different approaches for multi-model training: 0) Keep one copy of the data, and train models one after another (or maybe in parallel, depending on the scheduler). 1) Keep one copy of the data, and train multiple models at the same time (similar to `runs` in KMeans). 2) Make multiple copies of the data (still stored distributively), and use more cores to distribute the work. 3) Collect the data, make the entire dataset available on workers, and train one or more models on each worker. Users should be able to choose which execution mode they want to use. Note that 3) could cover many use cases in practice when the training data is not huge, e.g., 1GB. This task will be divided into sub-tasks and this JIRA is created to discuss the design and track the overall progress. -- This message was sent by Atlassian JIRA (v6.2#6252)
