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Posted to reviews@spark.apache.org by mengxr <gi...@git.apache.org> on 2015/02/13 19:15:28 UTC
[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/4593
[SPARK-5802][MLLIB] cache transformed data in glm
If we need to transform the input data, we should cache the output to avoid re-computing feature vectors every iteration. @dbtsai
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/mengxr/spark SPARK-5802
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/4593.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #4593
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commit ae3be842d42c563f351a83a9d016371b4b0008bb
Author: Xiangrui Meng <me...@databricks.com>
Date: 2015-02-13T18:12:45Z
cache transformed data in glm
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74621554
Merged into master and branch-1.3.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74353784
test this please
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74299914
[Test build #27445 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/27445/consoleFull) for PR 4593 at commit [`ae3be84`](https://github.com/apache/spark/commit/ae3be842d42c563f351a83a9d016371b4b0008bb).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74306433
[Test build #27445 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/27445/consoleFull) for PR 4593 at commit [`ae3be84`](https://github.com/apache/spark/commit/ae3be842d42c563f351a83a9d016371b4b0008bb).
* This patch **fails Spark unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74357608
Test PASSed.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74357607
[Test build #27466 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/27466/consoleFull) for PR 4593 at commit [`ae3be84`](https://github.com/apache/spark/commit/ae3be842d42c563f351a83a9d016371b4b0008bb).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/4593
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74353892
[Test build #27466 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/27466/consoleFull) for PR 4593 at commit [`ae3be84`](https://github.com/apache/spark/commit/ae3be842d42c563f351a83a9d016371b4b0008bb).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by petro-rudenko <gi...@git.apache.org>.
Github user petro-rudenko commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-75550855
@dbtsai, @joshdevins here's an issue i have. I'm using new ml pipeline with hyperparameter grid search. Because folds doesn't depend from hyperparameter, i've reimplemented a bit LogisticRegression to not unpersist data:
```scala
class CustomLogisticRegression extends LogisticRegression {
var oldInstances: RDD[LabeledPoint] = null
override def fit(dataset: SchemaRDD, paramMap: ParamMap): LogisticRegressionModel = {
println(s"Fitting dataset ${dataset.id} with ParamMap $paramMap.")
transformSchema(dataset.schema, paramMap, logging = true)
import dataset.sqlContext._
val map = this.paramMap ++ paramMap
val instances = dataset.select(map(labelCol).attr, map(featuresCol).attr)
.map {
case Row(label: Double, features: Vector) =>
LabeledPoint(label, features)
}
//For parallel grid search
this.synchronized({
if (oldInstances == null || oldInstances.id != instances.id) {
if (oldInstances != null) {
oldInstances.unpersist()
}
oldInstances = instances
instances.setName(s"Instances for LR with ParamMap $paramMap and RDD ${dataset.id}")
instances.persist(StorageLevel.MEMORY_AND_DISK)
}
})
val lr = (new LogisticRegressionWithLBFGS)
.setValidateData(false)
lr.optimizer
.setRegParam(map(regParam))
.setNumIterations(map(maxIter))
val lrOldModel = lr.run(instances)
val lrm = new LogisticRegressionModel(this, map, lr.run(instances).weights)
//instances.unpersist()
// copy model params
Params.inheritValues(map, this, lrm)
lrm
}
}
```
Then for 3 folds in crossvalidation and 3 hyperparameters to LogisticRegression i got something like this:
```
Fitting dataset 11 with ParamMap {
CustomLogisticRegression-f35ae4d3-regParam: 0.5
}
Fitting dataset 11 with ParamMap {
CustomLogisticRegression-f35ae4d3-regParam: 0.1
}
Fitting dataset 11 with ParamMap {
CustomLogisticRegression-f35ae4d3-regParam: 0.01
}
Fitting dataset 12 with ParamMap {
CustomLogisticRegression-f35ae4d3-regParam: 0.5
}
Fitting dataset 12 with ParamMap {
CustomLogisticRegression-f35ae4d3-regParam: 0.1
}
Fitting dataset 12 with ParamMap {
CustomLogisticRegression-f35ae4d3-regParam: 0.01
}
```
So persistence on the model level need to cache folds for hyperparameters grid search, but persistence on GLM level need to speed-up Standart scalar transformation etc. Don't know yet how to do this efficiently without double caching.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74306448
Test FAILed.
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by dbtsai <gi...@git.apache.org>.
Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-74805610
Sorry for the late reply since I'm traveling recently. My concern is that will this cause "caching twice" in the new ML api? For example, in ml/classification/LogisticRegression.scala, the data is persisted before any transformation, and then we cache the data again after the feature transformation. Should we remove the persist in ml package and just cache it after feature transformation here?
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Posted by joshdevins <gi...@git.apache.org>.
Github user joshdevins commented on the pull request:
https://github.com/apache/spark/pull/4593#issuecomment-75540678
I have the same concern as @dbtsai in his comment. Most consumers of this API will already be caching their dataset before the learning phase. Without user care, this will introduce effectively double caching (in terms of data size of cached RDDs) and will cause many jobs to fail after upgrading by exceeding available heap for RDD cache. Furthermore, we are making assumptions about how to cache -- in-memory only in this case. Should we parameterise this? Perhaps that will help send the message in the API that there is caching also done before learning. (FWIW, in-memory is definitely the right default choice here.)
See email thread on dev for my specific encountering of this bug:
http://mail-archives.apache.org/mod_mbox/spark-dev/201502.mbox/%3CCAH5MZvMBjqOST-9Nr9k1z1rUODfSiczr_fV9kwqDFqAMNLC2Zw%40mail.gmail.com%3E
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