build(deps): bump xgboost from 2.0.3 to 2.1.0

[dependabot]

Bumps xgboost from 2.0.3 to 2.1.0.

Release notes

Sourced from xgboost's releases.

Release 2.1.0 stable

2.1.0 (2024 Jun 20)

We are thrilled to announce the XGBoost 2.1 release. This note will start by summarizing some general changes and then highlighting specific package updates. As we are working on a new R interface, this release will not include the R package. We'll update the R package as soon as it's ready. Stay tuned!

Networking Improvements

An important ongoing work for XGBoost, which we've been collaborating on, is to support resilience for improved scaling and federated learning on various platforms. The existing networking library in XGBoost, adopted from the RABIT project, can no longer meet the feature demand. We've revamped the RABIT module in this release to pave the way for future development. The choice of using an in-house version instead of an existing library is due to the active development status with frequent new feature requests like loading extra plugins for federated learning. The new implementation features:

• Both CPU and GPU communication (based on NCCL).
• A reusable tracker for both the Python package and JVM packages. With the new release, the JVM packages no longer require Python as a runtime dependency.
• Supports federated communication patterns for both CPU and GPU.
• Supports timeout. The high-level interface parameter is currently hard-coded to 30 minutes, which we plan to improve.
• Supports significantly more data types.
• Supports thread-based workers.
• Improved handling for worker errors, including better error messages when one of the peers dies during training.
• Work with IPv6. Currently, this is only supported by the dask interface.
• Built-in support for various operations like broadcast, allgatherV, allreduce, etc.

Related PRs (#9597, #9576, #9523, #9524, #9593, #9596, #9661, #10319, #10152, #10125, #10332, #10306, #10208, #10203, #10199, #9784, #9777, #9773, #9772, #9759, #9745, #9695, #9738, #9732, #9726, #9688, #9681, #9679, #9659, #9650, #9644, #9649, #9917, #9990, #10313, #10315, #10112, #9531, #10075, #9805, #10198, #10414).

The existing option of using MPI in RABIT is removed in the release. (#9525)

NCCL is now fetched from PyPI.

In the previous version, XGBoost statically linked NCCL, which significantly increased the binary size and led to hitting the PyPI repository limit. With the new release, we have made a significant improvement. The new release can now dynamically load NCCL from an external source, reducing the binary size. For the PyPI package, the nvidia-nccl-cu12 package will be fetched during installation. With more downstream packages reusing NCCL, we expect the user environments to be slimmer in the future as well. (#9796, #9804, #10447)

Multi-output

We continue the work on multi-target and vector leaf in this release:

• Revise the support for custom objectives with a new API, XGBoosterTrainOneIter. This new function supports strided matrices and CUDA inputs. In addition, custom objectives now return the correct shape for prediction. (#9508)
• The hinge objective now supports multi-target regression (#9850)
• Fix the gain calculation with vector leaf (#9978)
• Support graphviz plot for multi-target tree. (#10093)
• Fix multi-output with alternating strategies. (#9933)

Please note that the feature is still in progress and not suitable for production use.

Federated Learning

Progress has been made on federated learning with improved support for column-split, including the following updates:

• Column split work for both CPU and GPU. In addition, categorical data is now compatible with column split. (#9562, #9609, #9611, #9628, #9539, #9578, #9685, #9623, #9613, #9511, #9384, #9595)
• The use of UBJson to serialize split entries for column split has been implemented, aiding vector-leaf with column-based data split. (#10059, #10055, #9702)
• Documentation and small fixes. (#9610, #9552, #9614, #9867)

Ongoing work for SYCL support.

XGBoost is developing a SYCL plugin for SYCL devices, starting with the hist tree method. (#10216, #9800, #10311, #9691, #10269, #10251, #10222, #10174, #10080, #10057, #10011, #10138, #10119, #10045, #9876, #9846, #9682) XGBoost now supports launchable inference on SYCL devices, and work on adding SYCL support for training is ongoing.

Looking ahead, we plan to complete the training in the coming releases and then focus on improving test coverage for SYCL, particularly for Python tests.

Optimizations

• Implement column sampler in CUDA for GPU-based tree methods. This helps us get faster training time when column sampling is employed (#9785)
• CMake LTO and CUDA arch (#9677)
• Small optimization to external memory with a thread pool. This reduces the number of threads launched during iteration. (#9605, #10288, #10374)

... (truncated)

Commits

• 213ebf7 Bump version to 2.1.0 from 2.1.0rc1. (#10451)
• d4bd5cf [backport] Link CMAKE_DL_LIBS when dlopen is used. (#10447) (#10450)
• e3bcedd [CI] Stop vendoring libomp.dylib in MacOS Python wheels (#10440) (#10448)
• ec2f56a [backport] Update rapids (#10435) (#10443)
• 13f96c4 [backport] Fix categorical data with external memory. (#10433) (#10442)
• f38ff20 [CI] Migrate to rockylinux8 / manylinux_2_28_x86_64 (#10399) (#10436)
• 63b49f3 [backport] Allow blocking launch of federated tracker. (#10414) (#10425)
• 6094106 [backport] Allow unaligned pointer if the array is empty. (#10418) (#10424)
• f789e50 [backport][dask] Workaround the tokenizer. (#10419) (#10423)
• b994f2a [backport] [CI] Fix JVM tests on Windows (#10404) (#10421)
• Additional commits viewable in compare view

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