- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)
- static function in header. (which is marked as unused due to translation unit
visibility).
- Implicit copy operator is deprecated.
- Unused lambda capture.
- Moving a temporary variable prevents copy elision.
- Rewrite GPU demos. notebook is converted to script to avoid committing additional png plots.
- Add GPU demos into the sphinx gallery.
- Add RMM demos into the sphinx gallery.
- Test for firing threads with different device ordinals.
- Save the updater sequence as an array instead of object.
- Warn only once.
The compatibility is kept, but we should be able to break it as the config is not loaded
in pickle model and it's declared to be not stable.
- 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.
* Use ptr from mmap for `GHistIndexMatrix` and `ColumnMatrix`.
- Define a resource for holding various types of memory pointers.
- Define ref vector for holding resources.
- Swap the underlying resources for GHist and ColumnM.
- Add documentation for current status.
- s390x support is removed. It should work if you can compile XGBoost, all the old workaround code does is to get GCC to compile.
- Update SparseDMatrix comment.
- Use a pointer in the bitfield. We will replace the `std::vector<bool>` in `ColumnMatrix` with bitfield.
- Clean up the page source. The timer is removed as it's inaccurate once we swap the mmap pointer into the page.
- Rework the precision metric for both CPU and GPU.
- Mention it in the document.
- Cleanup old support code for GPU ranking metric.
- Deterministic GPU implementation.
* Drop support for classification.
* type.
* use batch shape.
* lint.
* cpu build.
* cpu build.
* lint.
* Tests.
* Fix.
* Cleanup error message.
- Implement a simple `IterSpan` for passing iterators with size.
- Use shared memory for column size counts.
- Use one thread for each sample in row count to reduce atomic operations.
Thrust implementation of `thrust::all_of/any_of/none_of` adopts an early stopping strategy
to bailout early by dividing the input into small batches. This is not ideal for data
validation as we expect all data to be valid. The strategy leads to excessive kernel
launches and stream synchronization.
* Use reduce from dh instead.
