- A `DeviceOrd` struct is implemented to indicate the device. It will eventually replace the `gpu_id` parameter.
- The `predictor` parameter is removed.
- Fallback to `DMatrix` when `inplace_predict` is not available.
- The heuristic for choosing a predictor is only used during training.
- Pass context from booster to DMatrix.
- Use context instead of integer for `n_threads`.
- Check the consistency configuration for `max_bin`.
- Test for all combinations of initialization options.
- Fix prediction range.
- Support prediction cache in mt-hist.
- Support model slicing.
- Make the booster a Python iterable by defining `__iter__`.
- Cleanup removed/deprecated parameters.
- A new field in the output model `iteration_indptr` for pointing to the ranges of trees for each iteration.
* Prepare for improving Windows networking compatibility.
* Include dmlc filesystem indirectly as dmlc/filesystem.h includes windows.h, which
conflicts with winsock2.h
* Define `NOMINMAX` conditionally.
* Link the winsock library when mysys32 is used.
* Add config file for read the doc.
* Implement ubjson.
This is a partial implementation of UBJSON with support for typed arrays. Some missing
features are `f64`, typed object, and the no-op.
* Add a new ctor to tensor for `initilizer_list`.
* Change labels from host device vector to tensor.
* Rename the field from `labels_` to `labels` since it's a public member.
* [CI] Drop CUDA 10.1; Require 11.0
* Change NCCL version
* Use CUDA 10.1 for clang-tidy, for now
* Remove JDK 11 and 12
* Fix NCCL version
* Don't require 11.0 just yet, until clang-tidy is fixed
* Skip MultiClassesSerializationTest.GpuHist
* Disable JSON serialization for now.
* Multi-class classification is checkpointing for each iteration.
This brings significant overhead.
Revert: 90355b4f007ae
* Set R tests to use binary.
* Use pre-rounding based method to obtain reproducible floating point
summation.
* GPU Hist for regression and classification are bit-by-bit reproducible.
* Add doc.
* Switch to thrust reduce for `node_sum_gradient`.
* Disable parameter validation for now.
Scikit-Learn passes all parameters down to XGBoost, whether they are used or
not.
* Add option `validate_parameters`.
