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Posted to reviews@spark.apache.org by zhengruifeng <gi...@git.apache.org> on 2018/11/08 09:02:45 UTC
[GitHub] spark pull request #22974: [SPARK-22450][Core][MLLib][FollowUp] Safely regis...
GitHub user zhengruifeng opened a pull request:
https://github.com/apache/spark/pull/22974
[SPARK-22450][Core][MLLib][FollowUp] Safely register MultivariateGaussian
## What changes were proposed in this pull request?
register following classes in Kryo:
"org.apache.spark.ml.stat.distribution.MultivariateGaussian",
"org.apache.spark.mllib.stat.distribution.MultivariateGaussian"
## How was this patch tested?
added tests
Due to existing module dependency, I can not import spark-core in mllib-local's testsuits, so I do not add testsuite in `org.apache.spark.ml.stat.distribution.MultivariateGaussianSuite`.
And I notice that class `ClusterStats` in `ClusteringEvaluator` is registered in a different way, should it be modified to keep in line with others in ML? @srowen
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/zhengruifeng/spark kryo_MultivariateGaussian
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/22974.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 #22974
----
commit 0b9ed17837414b4cd0a788d48be9817723084da2
Author: zhengruifeng <ru...@...>
Date: 2018-11-08T08:16:57Z
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[GitHub] spark issue #22974: [SPARK-22450][WIP][Core][MLLib][FollowUp] Safely registe...
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[GitHub] spark issue #22974: [SPARK-22450][WIP][Core][MLLib][FollowUp] Safely registe...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/22974
@srowen Yes, this is the problem. I have to register `Param*` before any prediction model, but there are too many anonymous classes in `ParamValidators` and other places, and I have not found an efficient way to list those anonymous ones.
So I wonder whether we could continue to register all of them. Or only list simple classes like `MultivariateGaussian `?
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[GitHub] spark issue #22974: [SPARK-22450][Core][MLLib][FollowUp] Safely register Mul...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
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OK, that's the issue, yeah. Registration is an optimization. I wonder, what other classes should we add if we're going to add this one? I don't know if it needs a test. But if there are 10 other somewhat commonly-used classes that are serialized during Spark ML operations, they should be registered.
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[GitHub] spark issue #22974: [SPARK-22450][WIP][Core][MLLib][FollowUp] Safely registe...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/22974
retest this please
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[GitHub] spark pull request #22974: [SPARK-22450][WIP][Core][MLLib][FollowUp] Safely ...
Posted by asfgit <gi...@git.apache.org>.
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[GitHub] spark issue #22974: [SPARK-22450][Core][MLLib][FollowUp] Safely register Mul...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/22974
Do you mean fail in this pr? It was caused by a non-registered filed `BDM[Double]`.
`MultivariateGaussian` is used in GMM, kryo-registration should help performance.
As to mllib-local's dependency, it is another thing: current kryo-regiestered classes, like 'ml.linalg.Vector', 'ml.linalg.Matrix', do not have kryo test in their testsuites.
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Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/22974
I am not sure, but maybe all serializable classes need to be registered.
Since `MultivariateGaussian` is a public class, so I think we need to add it.
I also wonder whether a test is needed. If no longer needed, I can list all other public ones in ML in this PR.
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retest this please
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Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
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Can you explain the issue here? Spark's tests work fine right now.
mllib-local is not supposed to depend on core.
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[GitHub] spark issue #22974: [SPARK-22450][Core][MLLib][FollowUp] Safely register Mul...
Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/22974
@srowen Existing kryo-register testsuite need to import spark-core:
```
import org.apache.spark.SparkConf
import org.apache.spark.serializer.KryoSerializer
val conf = new SparkConf(false)
conf.set("spark.kryo.registrationRequired", "true")
val ser = new KryoSerializer(conf).newInstance()
```
Since mllib-local is not dependent on spark-core, current classes in mllib-local do not test kryo-serialization at all. E.g. `mllib.linalg.VectorsSuite` contains test `test("kryo class register")`, while `ml.linalg.VectorsSuite` do not have it.
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Posted by zhengruifeng <gi...@git.apache.org>.
Github user zhengruifeng commented on the issue:
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not all public serializable classes are needed to registered. Only those one which needed ser-deser should be registered, one important groups should be transformers and prediction models.
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You're requiring registration, which is what makes this fail, right? why do that? I think I'm missing something.
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OK, I wonder if you have time to take a quick pass over the "Serializable" classes in MLlib to identify a few more that are likely important to register with Kryo? there are probably a few more. If so then that would help improve this all at once rather than class by class.
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retest this please
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Hm. On the one hand, I don't actually think we need to _test_ that certain classes are registered except for some performance critical ones, maybe. However if a class can't be registered except by registering every class it touches (like internal anonymous ones), it may not have any effect to register many of these; Kryo won't work. Further, it may then break usages that have kryo registration required otherwise. I guess back out any registration of classes that seems to require a bunch of anonymous classes be registered too?
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retest this please
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@srowen I have some spare time, and will work on it.
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[GitHub] spark issue #22974: [SPARK-22450][WIP][Core][MLLib][FollowUp] Safely registe...
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[GitHub] spark issue #22974: [SPARK-22450][Core][MLLib][FollowUp] Safely register Mul...
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[GitHub] spark issue #22974: [SPARK-22450][WIP][Core][MLLib][FollowUp] Safely registe...
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**[Test build #98719 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/98719/testReport)** for PR 22974 at commit [`0c529fb`](https://github.com/apache/spark/commit/0c529fb7830b78c45b3f2a98046da9fa3061185f).
* This patch **fails due to an unknown error code, -9**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #22974: [SPARK-22450][Core][MLLib][FollowUp] Safely register Mul...
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[GitHub] spark issue #22974: [SPARK-22450][WIP][Core][MLLib][FollowUp] Safely registe...
Posted by srowen <gi...@git.apache.org>.
Github user srowen commented on the issue:
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@zhengruifeng you are right, let's stay conservative. Thanks for investigating. Are there any simple, commonly-used classes that should be serializable by themselves without registering other classes, anything else obvious to add? it may just be MultivariateGaussian
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