[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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 --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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(sFitting 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(sInstances 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 ...
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? --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Github user asfgit closed the pull request at: https://github.com/apache/spark/pull/4593 --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Github user mengxr commented on the pull request: https://github.com/apache/spark/pull/4593#issuecomment-74621554 Merged into master and branch-1.3. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[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 commit ae3be842d42c563f351a83a9d016371b4b0008bb Author: Xiangrui Meng m...@databricks.com Date: 2015-02-13T18:12:45Z cache transformed data in glm --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
Github user mengxr commented on the pull request: https://github.com/apache/spark/pull/4593#issuecomment-74353784 test this please --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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[GitHub] spark pull request: [SPARK-5802][MLLIB] cache transformed data in ...
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