* Make train in xgboost4j respect print params
Previously no setting in params argument of Booster::train would prevent
the Rabit.trackerPrint call. This can fill up a lot of screen space in
the case that many folds are being trained.
* Setting "silent" in this map to "true", "True", a non-zero integer, or
a string that can be parsed to such an int will prevent printing.
* Setting "verbose_eval" to "False" or "false" will prevent printing.
* Setting "verbose_eval" to an int (or a String parseable to an int) n
will result in printing every n steps, or no printing is n is zero.
This is to match the python behaviour described here:
https://www.kaggle.com/c/rossmann-store-sales/discussion/17499
* Fixed 'slient' typo in xgboost4j test
* private access on two methods
* Optimisations for gpu_hist.
* Use streams to overlap operations.
* ColumnSampler now uses HostDeviceVector to prevent repeatedly copying feature vectors to the device.
* Brought the silent parameter for the SKLearn-like API back, marked it deprecated.
- added deprecation notice and warning
- removed silent from the tests for the SKLearn-like API
* Improved multi-node multi-GPU random forests.
- removed rabit::Broadcast() from each invocation of column sampling
- instead, syncing the PRNG seed when a ColumnSampler() object is constructed
- this makes non-trivial column sampling significantly faster in the distributed case
- refactored distributed GPU tests
- added distributed random forests tests
* Upgrade gtest for clang-tidy.
* Use CMake to install GTest instead of mv.
* Don't enforce clang-tidy to return 0 due to errors in thrust.
* Add a small test for tidy itself.
* Reformat.
* Added SKLearn-like random forest Python API.
- added XGBRFClassifier and XGBRFRegressor classes to SKL-like xgboost API
- also added n_gpus and gpu_id parameters to SKL classes
- added documentation describing how to use xgboost for random forests,
as well as existing caveats
* Initial commit to support multi-node multi-gpu xgboost using dask
* Fixed NCCL initialization by not ignoring the opg parameter.
- it now crashes on NCCL initialization, but at least we're attempting it properly
* At the root node, perform a rabit::Allreduce to get initial sum_gradient across workers
* Synchronizing in a couple of more places.
- now the workers don't go down, but just hang
- no more "wild" values of gradients
- probably needs syncing in more places
* Added another missing max-allreduce operation inside BuildHistLeftRight
* Removed unnecessary collective operations.
* Simplified rabit::Allreduce() sync of gradient sums.
* Removed unnecessary rabit syncs around ncclAllReduce.
- this improves performance _significantly_ (7x faster for overall training,
20x faster for xgboost proper)
* pulling in latest xgboost
* removing changes to updater_quantile_hist.cc
* changing use_nccl_opg initialization, removing unnecessary if statements
* added definition for opaque ncclUniqueId struct to properly encapsulate GetUniqueId
* placing struct defintion in guard to avoid duplicate code errors
* addressing linting errors
* removing
* removing additional arguments to AllReduer initialization
* removing distributed flag
* making comm init symmetric
* removing distributed flag
* changing ncclCommInit to support multiple modalities
* fix indenting
* updating ncclCommInitRank block with necessary group calls
* fix indenting
* adding print statement, and updating accessor in vector
* improving print statement to end-line
* generalizing nccl_rank construction using rabit
* assume device_ordinals is the same for every node
* test, assume device_ordinals is identical for all nodes
* test, assume device_ordinals is unique for all nodes
* changing names of offset variable to be more descriptive, editing indenting
* wrapping ncclUniqueId GetUniqueId() and aesthetic changes
* adding synchronization, and tests for distributed
* adding to tests
* fixing broken #endif
* fixing initialization of gpu histograms, correcting errors in tests
* adding to contributors list
* adding distributed tests to jenkins
* fixing bad path in distributed test
* debugging
* adding kubernetes for distributed tests
* adding proper import for OrderedDict
* adding urllib3==1.22 to address ordered_dict import error
* added sleep to allow workers to save their models for comparison
* adding name to GPU contributors under docs
* Fix early stop with xgboost4j-spark
* Update XGBoost.java
* Update XGBoost.java
* Update XGBoost.java
To use -Float.MAX_VALUE as the lower bound, in case there is positive metric.
* Only update best score if the current score is better (no update when equal)
* Update xgboost-spark tutorial to fix early stopping docs.
* Fix test_gpu_coordinate.
* Use `gpu_coord_descent` in test.
* Reduce number of running rounds.
* Remove nthread.
* Use githubusercontent for r-appveyor.
* Use githubusercontent in travis r tests.
