* When building pull requests, use Docker cache for master branch
Docker build caches are per-branch, so new pull requests will initially
have no build cache, causing the Docker containers to be built from
scratch. New pull requests should use the cache associated with the
master branch. This makes sense, since most pull requests do not modify
the Dockerfile.
* Add comments
* make the assignments of HostDeviceVector exception safe.
* storing a dummy GPUDistribution instance in HDV for CPU based code.
* change testxgboost binary location to build directory.
* 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.
