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https://issues.apache.org/jira/browse/SPARK-19071?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15834232#comment-15834232
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Nick Pentreath commented on SPARK-19071:
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Thanks [~bryanc] for the design and working on the PoCs. I believe many Spark
use cases will involve fairly large amounts of data, but not necessarily huge
models, making parallel model evaluation very useful. Agree it makes sense to
start with a fairly naive approach, try to make it transparent to users, and if
necessarily expose a simple param to control parallelism. Selecting a default
will indeed be tricky, but perhaps it's best to set it to {{1}} and let users
decide.
We will need to consider the caching element. Currently there are existing
issues with individual models deciding when to cache (see SPARK-18608). I think
we need to also think about how this fits in with that issue, and try to come
up with a more unified approach on what gets cached by which component, and how
that is decided. It may require adding an argument {{handlePersistence}} to
{{fit}} (probably private). Default will be {{true}} but for pipelines it could
be {{false}} to allow the pipeline or cross-validator to make the decisions.
Let's focus on step 1 for Spark 2.2 timeframe. Could we create the some linked
JIRAs to represent the tasks (you can link them as being contained by this
umbrella JIRA). I should have cycles to shepherd this.
> Optimizations for ML Pipeline Tuning
> ------------------------------------
>
> Key: SPARK-19071
> URL: https://issues.apache.org/jira/browse/SPARK-19071
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Bryan Cutler
>
> This is a parent task to plan the addition of optimizations in ML tuning for
> parallel model evaluation and more efficiency with pipelines. They will
> benefit Crossvalidator and TrainValidationSplit when performing a parameter
> grid search. The proposal can be broken into 3 steps in order of simplicity:
> 1. Add ability to evaluate models in parallel.
> 2. Optimize param grid for pipelines, as described in SPARK-5844
> 3. Add parallel model evaluation to the optimized pipelines from step 2
> See the linked design document for details on the proposed implementation.
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