* [jvm-packages] add gpu_hist tree method
* change updater hist to grow_quantile_histmaker
* add gpu scheduling
* pass correct parameters to xgboost library
* remove debug info
* add use.cuda for pom
* add CI for gpu_hist for jvm
* add gpu unit tests
* use gpu node to build jvm
* use nvidia-docker
* Add CLI interface to create_jni.py using argparse
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Remove f-string, since it's not supported by Python 3.5 (#5330)
* Remove f-string, since it's not supported by Python 3.5
* Add Python 3.5 to CI, to ensure compatibility
* Remove duplicated matplotlib
* Show deprecation notice for Python 3.5
* Fix lint
* Fix lint
* Fix a unit test that mistook MINOR ver for PATCH ver
* Enforce only major version in JSON model schema
* Bump version to 1.1.0-SNAPSHOT
* bump scala to 2.12 which requires java 8 and also newer flink and akka
* put scala version in artifactId
* fix appveyor
* fix for scaladoc issue that looks like https://github.com/scala/bug/issues/10509
* fix ci_build
* update versions in generate_pom.py
* fix generate_pom.py
* apache does not have a download for spark 2.4.3 distro using scala 2.12 yet, so for now i use a tgz i put on s3
* Upload spark-2.4.3-bin-scala2.12-hadoop2.7.tgz to our own S3
* Update Dockerfile.jvm_cross
* Update Dockerfile.jvm_cross
* All Linux tests are now in Jenkins CI
* Tests are now de-coupled from builds. We can now build XGBoost with one version of CUDA/JDK and test it with another version of CUDA/JDK
* Builds (compilation) are significantly faster because 1) They use C5 instances with faster CPU cores; and 2) build environment setup is cached using Docker containers
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* wrap iterators
* enable copartition training and validationset
* add parameters
* converge code path and have init unit test
* enable multi evals for ranking
* unit test and doc
* update example
* fix early stopping
* address the offline comments
* udpate doc
* test eval metrics
* fix compilation issue
* fix example
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* update version
* 0.82
* add back train method but mark as deprecated
* fix scalastyle error
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* instrumentation
* use log console
* better measurement
* fix erros in example
* update histmaker
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* add new
* update doc
* finish Gang Scheduling
* more
* intro
* Add sections: Prediction, Model persistence and ML pipeline.
* Add XGBoost4j-Spark MLlib pipeline example
* partial finished version
* finish the doc
* adjust code
* fix the doc
* use rst
* Convert XGBoost4J-Spark tutorial to reST
* Bring XGBoost4J up to date
* add note about using hdfs
* remove duplicate file
* fix descriptions
* update doc
* Wrap HDFS/S3 export support as a note
* update
* wrap indexing_mode example in code block
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* [jvm-packages] XGBoost Spark integration refactor. (#3313)
* XGBoost Spark integration refactor.
* Make corresponding update for xgboost4j-example
* Address comments.
* [jvm-packages] Refactor XGBoost-Spark params to make it compatible with both XGBoost and Spark MLLib (#3326)
* Refactor XGBoost-Spark params to make it compatible with both XGBoost and Spark MLLib
* Fix extra space.
* [jvm-packages] XGBoost Spark supports ranking with group data. (#3369)
* XGBoost Spark supports ranking with group data.
* Use Iterator.duplicate to prevent OOM.
* Update CheckpointManagerSuite.scala
* Resolve conflicts
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* update 0.80
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* change version of jvm to keep consistent with other pkgs
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* add dev script to update version and update versions
* Allowed subsampling test from the training data frame/RDD
The implementation requires storing 1 - trainTestRatio points in memory
to make the sampling work.
An alternative approach would be to construct the full DMatrix and then
slice it deterministically into train/test. The peak memory consumption
of such scenario, however, is twice the dataset size.
* Removed duplication from 'XGBoost.train'
Scala callers can (and should) use names to supply a subset of
parameters. Method overloading is not required.
* Reuse XGBoost seed parameter to stabilize train/test splitting
* Added early stopping support to non-distributed XGBoost
Closes#1544
* Added early-stopping to distributed XGBoost
* Moved construction of 'watches' into a separate method
This commit also fixes the handling of 'baseMargin' which previously
was not added to the validation matrix.
* Addressed review comments
* Converted ml.dmlc.xgboost4j.LabeledPoint to Scala
This allows to easily integrate LabeledPoint with Spark DataFrame APIs,
which support encoding/decoding case classes out of the box. Alternative
solution would be to keep LabeledPoint in Java and make it a Bean by
generating boilerplate getters/setters. I have decided against that, even
thought the conversion in this PR implies a public API change.
I also had to remove the factory methods fromSparseVector and
fromDenseVector because a) they would need to be duplicated to support
overloaded calls with extra data (e.g. weight); and b) Scala would expose
them via mangled $.MODULE$ which looks ugly in Java.
Additionally, this commit makes it possible to switch to LabeledPoint in
all public APIs and effectively to pass initial margin/group as part of
the point. This seems to be the only reliable way of implementing distributed
learning with these data. Note that group size format used by single-node
XGBoost is not compatible with that scenario, since the partition split
could divide a group into two chunks.
* Switched to ml.dmlc.xgboost4j.LabeledPoint in RDD-based public APIs
Note that DataFrame-based and Flink APIs are not affected by this change.
* Removed baseMargin argument in favour of the LabeledPoint field
* Do a single pass over the partition in buildDistributedBoosters
Note that there is no formal guarantee that
val repartitioned = rdd.repartition(42)
repartitioned.zipPartitions(repartitioned.map(_ + 1)) { it1, it2, => ... }
would do a single shuffle, but in practice it seems to be always the case.
* Exposed baseMargin in DataFrame-based API
* Addressed review comments
* Pass baseMargin to XGBoost.trainWithDataFrame via params
* Reverted MLLabeledPoint in Spark APIs
As discussed, baseMargin would only be supported for DataFrame-based APIs.
* Cleaned up baseMargin tests
- Removed RDD-based test, since the option is no longer exposed via
public APIs
- Changed DataFrame-based one to check that adding a margin actually
affects the prediction
* Pleased Scalastyle
* Addressed more review comments
* Pleased scalastyle again
* Fixed XGBoost.fromBaseMarginsToArray
which always returned an array of NaNs even if base margin was not
specified. Surprisingly this only failed a few tests.
* Disabled excessive Spark logging in tests
* Fixed a singature of XGBoostModel.predict
Prior to this commit XGBoostModel.predict produced an RDD with
an array of predictions for each partition, effectively changing
the shape wrt the input RDD. A more natural contract for prediction
API is that given an RDD it returns a new RDD with the same number
of elements. This allows the users to easily match inputs with
predictions.
This commit removes one layer of nesting in XGBoostModel.predict output.
Even though the change is clearly non-backward compatible, I still
think it is well justified.
* Removed boxing in XGBoost.fromDenseToSparseLabeledPoints
* Inlined XGBoost.repartitionData
An if is more explicit than an opaque method name.
* Moved XGBoost.convertBoosterToXGBoostModel to XGBoostModel
* Check the input dimension in DMatrix.setBaseMargin
Prior to this commit providing an array of incorrect dimensions would
have resulted in memory corruption. Maybe backport this to C++?
* Reduced nesting in XGBoost.buildDistributedBoosters
* Ensured consistent naming of the params map
* Cleaned up DataBatch to make it easier to comprehend
* Made scalastyle happy
* Added baseMargin to XGBoost.train and trainWithRDD
* Deprecated XGBoost.train
It is ambiguous and work only for RDDs.
* Addressed review comments
* Revert "Fixed a singature of XGBoostModel.predict"
This reverts commit 06bd5dcae7780265dd57e93ed7d4135f4e78f9b4.
* Addressed more review comments
* Fixed NullPointerException in buildDistributedBoosters
* [jvm-packages] Scala implementation of the Rabit tracker.
A Scala implementation of RabitTracker that is interface-interchangable with the
Java implementation, ported from `tracker.py` in the
[dmlc-core project](https://github.com/dmlc/dmlc-core).
* [jvm-packages] Updated Akka dependency in pom.xml.
* Refactored the RabitTracker directory structure.
* Fixed premature stopping of connection handler.
Added a new finite state "AwaitingPortNumber" to explicitly wait for the
worker to send the port, and close the connection. Stopping the actor
prematurely sends a TCP RST to the worker, causing the worker to crash
on AssertionError.
* Added interface IRabitTracker so that user can switch implementations.
* Default timeout duration changes.
* Dependency for Akka tests.
* Removed the main function of RabitTracker.
* A skeleton for testing Akka-based Rabit tracker.
* waitFor() in RabitTracker no longer throws exceptions.
* Completed unit test for the 'start' command of Rabit tracker.
* Preliminary support for Rabit Allreduce via JNI (no prepare function support yet.)
* Fixed the default timeout duration.
* Use Java container to avoid serialization issues due to intermediate wrappers.
* Added tests for Allreduce/model training using Scala Rabit tracker.
* Added spill-over unit test for the Scala Rabit tracker.
* Fixed a typo.
* Overhaul of RabitTracker interface per code review.
- Removed methods start() waitFor() (no arguments) from IRabitTracker.
- The timeout in start(timeout) is now worker connection timeout, as tcp
socket binding timeout is less intuitive.
- Dropped time unit from start(...) and waitFor(...) methods; the default
time unit is millisecond.
- Moved random port number generation into the RabitTrackerHandler.
- Moved all Rabit-related classes to package ml.dmlc.xgboost4j.scala.rabit.
* More code refactoring and comments.
* Unified timeout constants. Readable tracker status code.
* Add comments to indicate that allReduce is for tests only. Removed all other variants.
* Removed unused imports.
* Simplified signatures of training methods.
- Moved TrackerConf into parameter map.
- Changed GeneralParams so that TrackerConf becomes a standalone parameter.
- Updated test cases accordingly.
* Changed monitoring strategies.
* Reverted monitoring changes.
* Update test case for Rabit AllReduce.
* Mix in UncaughtExceptionHandler into IRabitTracker to prevent tracker from hanging due to exceptions thrown by workers.
* More comprehensive test cases for exception handling and worker connection timeout.
* Handle executor loss due to unknown cause: the newly spawned executor will attempt to connect to the tracker. Interrupt tracker in such case.
* Per code-review, removed training timeout from TrackerConf. Timeout logic must be implemented explicitly and externally in the driver code.
* Reverted scalastyle-config changes.
* Visibility scope change. Interface tweaks.
* Use match pattern to handle tracker_conf parameter.
* Minor clarification in JNI code.
* Clearer intent in match pattern to suppress warnings.
* Removed Future from constructor. Block in start() and waitFor() instead.
* Revert inadvertent comment changes.
* Removed debugging information.
* Updated test cases that are a bit finicky.
* Added comments on the reasoning behind the unit tests for testing Rabit tracker robustness.
* bump up to scala 2.11
* framework of data frame integration
* test consistency between RDD and DataFrame
* order preservation
* test order preservation
* example code and fix makefile
* improve type checking
* improve APIs
* user docs
* work around travis CI's limitation on log length
* adjust test structure
* integrate with Spark -1 .x
* spark 2.x integration
* remove spark 1.x implementation but provide instructions on how to downgrade
