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Updates the requirements on torch, torchvision, matplotlib, scikit-learn, xgboost and numpy to permit the latest version.
Updates torch to 2.6.0

Release notes

Sourced from torch's releases.

PyTorch 2.6.0 Release

  • Highlights
  • Tracked Regressions
  • Backwards Incompatible Change
  • Deprecations
  • New Features
  • Improvements
  • Bug fixes
  • Performance
  • Documentation
  • Developers

Highlights

We are excited to announce the release of PyTorch® 2.6 (release notes)! This release features multiple improvements for PT2: torch.compile can now be used with Python 3.13; new performance-related knob torch.compiler.set_stance; several AOTInductor enhancements. Besides the PT2 improvements, another highlight is FP16 support on X86 CPUs.

NOTE: Starting with this release we are not going to publish on Conda, please see [Announcement] Deprecating PyTorch’s official Anaconda channel for the details.

For this release the experimental Linux binaries shipped with CUDA 12.6.3 (as well as Linux Aarch64, Linux ROCm 6.2.4, and Linux XPU binaries) are built with CXX11_ABI=1 and are using the Manylinux 2.28 build platform. If you build PyTorch extensions with custom C++ or CUDA extensions, please update these builds to use CXX_ABI=1 as well and report any issues you are seeing. For the next PyTorch 2.7 release we plan to switch all Linux builds to Manylinux 2.28 and CXX11_ABI=1, please see [RFC] PyTorch next wheel build platform: manylinux-2.28 for the details and discussion.

Also in this release as an important security improvement measure we have changed the default value for weights_only parameter of torch.load. This is a backward compatibility-breaking change, please see this forum post for more details.

This release is composed of 3892 commits from 520 contributors since PyTorch 2.5. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve PyTorch. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits

Updates torchvision to 0.21.0

Release notes

Sourced from torchvision's releases.

Torchvision 0.21 release

Highlights

Detailed changes

Image decoding

Torchvision continues to improve its image decoding capabilities. For this version, we added support for HEIC and AVIF image formats. Things are a bit different this time: to enable it, you'll need to pip install torchvision-extra-decoders, and the decoders are available in torchvision as torchvision.io.decode_heic() and torchvision.io.decode_avif(). This is still experimental / BETA, so let us know if you encounter any issue.

Read more in our docs!

New Features

[io] Add support for decoding AVIF and HEIC image formats (#8671)

Improvements

[datasets] Don't error when dataset is already downloaded (#8691) [datasets] Don't print when dataset is already downloaded (#8681) [datasets] remove printing info in datasets (#8683) [utils] Add label_colors argument to draw_bounding_boxes (#8578) [models] Add __deepcopy__ support for DualGraphModule (#8708) [Docs] Various documentation improvements (#8798, #8709, #8576, #8620, #8846, #8758) [Code quality] Various code quality improvements (#8757, #8755, #8754, #8689, #8719, #8772, #8774, #8791, #8705)

Bug Fixes

[io] Fix memory leak in decode_webp (#8712) [io] Fix pyav 14 compatibility error (#8776) [models] Fix order of auxiliary networks in googlenet.py (#8743) [transforms] Fix adjust_hue on ARM (#8618) [reference scripts] Fix error when loading the cached dataset in video classification reference(#8727) [build] fix CUDA build with NVCC_FLAGS in env (#8692)

Tracked Regressions

[build] aarch64 builds are build with manylinux_2_34_aarch64 tag according to auditwheel check (#8883)

Contributors

We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

amdfaa Andreas Floros, Andrey Talman , Beh Chuen Yang, David Miguel Susano Pinto, GdoongMathew, Jason Chou, Li-Huai (Allan) Lin, Maohua Li, Nicolas Hug , pblwk, R. Yao, sclarkson, vfdev, Ștefan Talpalaru

Commits

Updates matplotlib to 3.10.1

Release notes

Sourced from matplotlib's releases.

REL: v3.10.1

This is the first bugfix release of the 3.10.x series.

This release contains several bug-fixes and adjustments:

  • Respect array alpha with interpolation_stage='rgba' in _Imagebase::_make_image
  • Remove md5 usage to prevent issues on FIPS enabled systems
  • Fix pyplot.matshow figure handling
  • Fix modifying Axes' position also alters the original Bbox object used for initialization
  • Fix title position for polar plots
  • Add version gate to GTK4 calls when necessary
  • Raise warning if both c and facecolors are used in scatter plot

As well as several documentation improvements and corrections.

Commits
  • 6fc8169 REL 3.10.1
  • 33361fb Release notes v3.10.1
  • 2495bbc Fix toctrees for 3.10 release notes
  • 526785e Github stats v3.10.1
  • d23b173 Merge v3.10.0-doc into v3.10.x
  • 9fe0dad Merge pull request #29682 from meeseeksmachine/auto-backport-of-pr-29680-on-v...
  • d3cf53d Merge pull request #29683 from meeseeksmachine/auto-backport-of-pr-29670-on-v...
  • 2944994 Backport PR #29670: DOC: change marginal scatter plot to subplot_mosaic
  • b4f94a4 Backport PR #29680: DOC: fix the bug of examples\event_handling
  • 4f3d478 Merge pull request #29676 from meeseeksmachine/auto-backport-of-pr-29666-on-v...
  • Additional commits viewable in compare view

Updates scikit-learn to 1.6.1

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.6.1

We're happy to announce the 1.6.1 release.

This release contains fixes for a few regressions introduced in 1.6.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.6.html#version-1-6-1

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Thanks to everyone who contributed to this release !

Commits
  • f159b78 trigger wheel builder [cd build]
  • 73cca70 generate changelog
  • afaa070 bump version
  • 1f43fd2 DOC: Updates to Macro vs micro-averaging in plot_roc.py (#29845)
  • ea8a725 🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 (#30593)
  • bc291f1 🔒 🤖 CI Update lock files for scipy-dev CI build(s) 🔒 🤖 ...
  • f5f2b9c 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
  • acbb862 TST Fix doctest due to GradientBoostingClassifier difference with scipy 1.15 ...
  • 42831e5 FIX warn if an estimator does have a concrete sklearn_tags implementation...
  • 0d2ce43 FIX change FutureWarnings to DeprecationWarnings for the tags (#30573)
  • Additional commits viewable in compare view

Updates xgboost to 2.1.4

Release notes

Sourced from xgboost's releases.

2.1.4 Patch Release

The 2.1.4 patch release incorporates the following fixes on top of the 2.1.3 release:

  • XGBoost is now compatible with scikit-learn 1.6 (#11021, #11162)
  • Build wheels with CUDA 12.8 and enable Blackwell support (#11187, #11202)
  • Adapt to RMM 25.02 logger changes (#11153)

Full Changelog: dmlc/xgboost@v2.1.3...v2.1.4

Additional artifacts:

You can verify the downloaded packages by running the following command on your Unix shell:

echo "<hash> <artifact>" | shasum -a 256 --check
b6ce5870d03cc1233cad5ff8460f670a2aff78625adfb578c0b9eec3b8b88406  xgboost-2.1.4.tar.gz
9780ba8314824eac7b8565cc2af8ea692fd4898712052a49132ac3fdf7c0ab2b  xgboost_r_gpu_linux_2.1.4.tar.gz

Experimental binary packages for R with CUDA enabled

  • xgboost_r_gpu_linux_2.1.4.tar.gz: Download

Source tarball

Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

Starting from 2.1.0, release note is recorded in the documentation.

This file records the changes in xgboost library in reverse chronological order.

2.0.0 (2023 Aug 16)

We are excited to announce the release of XGBoost 2.0. This note will begin by covering some overall changes and then highlight specific updates to the package.

Initial work on multi-target trees with vector-leaf outputs

We have been working on vector-leaf tree models for multi-target regression, multi-label classification, and multi-class classification in version 2.0. Previously, XGBoost would build a separate model for each target. However, with this new feature that's still being developed, XGBoost can build one tree for all targets. The feature has multiple benefits and trade-offs compared to the existing approach. It can help prevent overfitting, produce smaller models, and build trees that consider the correlation between targets. In addition, users can combine vector leaf and scalar leaf trees during a training session using a callback. Please note that the feature is still a working in progress, and many parts are not yet available. See #9043 for the current status. Related PRs: (#8538, #8697, #8902, #8884, #8895, #8898, #8612, #8652, #8698, #8908, #8928, #8968, #8616, #8922, #8890, #8872, #8889, #9509) Please note that, only the hist (default) tree method on CPU can be used for building vector leaf trees at the moment.

New device parameter.

A new device parameter is set to replace the existing gpu_id, gpu_hist, gpu_predictor, cpu_predictor, gpu_coord_descent, and the PySpark specific parameter use_gpu. Onward, users need only the device parameter to select which device to run along with the ordinal of the device. For more information, please see our document page (https://xgboost.readthedocs.io/en/stable/parameter.html#general-parameters) . For example, with device="cuda", tree_method="hist", XGBoost will run the hist tree method on GPU. (#9363, #8528, #8604, #9354, #9274, #9243, #8896, #9129, #9362, #9402, #9385, #9398, #9390, #9386, #9412, #9507, #9536). The old behavior of gpu_hist is preserved but deprecated. In addition, the predictor parameter is removed.

hist is now the default tree method

Starting from 2.0, the hist tree method will be the default. In previous versions, XGBoost chooses approx or exact depending on the input data and training environment. The new default can help XGBoost train models more efficiently and consistently. (#9320, #9353)

GPU-based approx tree method

There's initial support for using the approx tree method on GPU. The performance of the approx is not yet well optimized but is feature complete except for the JVM packages. It can be accessed through the use of the parameter combination device="cuda", tree_method="approx". (#9414, #9399, #9478). Please note that the Scala-based Spark interface is not yet supported.

Optimize and bound the size of the histogram on CPU, to control memory footprint

XGBoost has a new parameter max_cached_hist_node for users to limit the CPU cache size for histograms. It can help prevent XGBoost from caching histograms too aggressively. Without the cache, performance is likely to decrease. However, the size of the cache grows exponentially with the depth of the tree. The limit can be crucial when growing deep trees. In most cases, users need not configure this parameter as it does not affect the model's accuracy. (#9455, #9441, #9440, #9427, #9400).

Along with the cache limit, XGBoost also reduces the memory usage of the hist and approx tree method on distributed systems by cutting the size of the cache by half. (#9433)

Improved external memory support

There is some exciting development around external memory support in XGBoost. It's still an experimental feature, but the performance has been significantly improved with the default hist tree method. We replaced the old file IO logic with memory map. In addition to performance, we have reduced CPU memory usage and added extensive documentation. Beginning from 2.0.0, we encourage users to try it with the hist tree method when the memory saving by QuantileDMatrix is not sufficient. (#9361, #9317, #9282, #9315, #8457)

Learning to rank

We created a brand-new implementation for the learning-to-rank task. With the latest version, XGBoost gained a set of new features for ranking task including:

  • A new parameter lambdarank_pair_method for choosing the pair construction strategy.
  • A new parameter lambdarank_num_pair_per_sample for controlling the number of samples for each group.
  • An experimental implementation of unbiased learning-to-rank, which can be accessed using the lambdarank_unbiased parameter.
  • Support for custom gain function with NDCG using the ndcg_exp_gain parameter.
  • Deterministic GPU computation for all objectives and metrics.
  • NDCG is now the default objective function.
  • Improved performance of metrics using caches.
  • Support scikit-learn utilities for XGBRanker.
  • Extensive documentation on how learning-to-rank works with XGBoost.

For more information, please see the tutorial. Related PRs: (#8771, #8692, #8783, #8789, #8790, #8859, #8887, #8893, #8906, #8931, #9075, #9015, #9381, #9336, #8822, #9222, #8984, #8785, #8786, #8768)

Automatically estimated intercept

... (truncated)

Commits

Updates torch from 2.5.1 to 2.6.0

Release notes

Sourced from torch's releases.

PyTorch 2.6.0 Release

  • Highlights
  • Tracked Regressions
  • Backwards Incompatible Change
  • Deprecations
  • New Features
  • Improvements
  • Bug fixes
  • Performance
  • Documentation
  • Developers

Highlights

We are excited to announce the release of PyTorch® 2.6 (release notes)! This release features multiple improvements for PT2: torch.compile can now be used with Python 3.13; new performance-related knob torch.compiler.set_stance; several AOTInductor enhancements. Besides the PT2 improvements, another highlight is FP16 support on X86 CPUs.

NOTE: Starting with this release we are not going to publish on Conda, please see [Announcement] Deprecating PyTorch’s official Anaconda channel for the details.

For this release the experimental Linux binaries shipped with CUDA 12.6.3 (as well as Linux Aarch64, Linux ROCm 6.2.4, and Linux XPU binaries) are built with CXX11_ABI=1 and are using the Manylinux 2.28 build platform. If you build PyTorch extensions with custom C++ or CUDA extensions, please update these builds to use CXX_ABI=1 as well and report any issues you are seeing. For the next PyTorch 2.7 release we plan to switch all Linux builds to Manylinux 2.28 and CXX11_ABI=1, please see [RFC] PyTorch next wheel build platform: manylinux-2.28 for the details and discussion.

Also in this release as an important security improvement measure we have changed the default value for weights_only parameter of torch.load. This is a backward compatibility-breaking change, please see this forum post for more details.

This release is composed of 3892 commits from 520 contributors since PyTorch 2.5. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve PyTorch. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits

Updates matplotlib from 3.9.2 to 3.10.1

Release notes

Sourced from matplotlib's releases.

REL: v3.10.1

This is the first bugfix release of the 3.10.x series.

This release contains several bug-fixes and adjustments:

  • Respect array alpha with interpolation_stage='rgba' in _Imagebase::_make_image
  • Remove md5 usage to prevent issues on FIPS enabled systems
  • Fix pyplot.matshow figure handling
  • Fix modifying Axes' position also alters the original Bbox object used for initialization
  • Fix title position for polar plots
  • Add version gate to GTK4 calls when necessary
  • Raise warning if both c and facecolors are used in scatter plot

As well as several documentation improvements and corrections.

Commits
  • 6fc8169 REL 3.10.1
  • 33361fb Release notes v3.10.1
  • 2495bbc Fix toctrees for 3.10 release notes
  • 526785e Github stats v3.10.1
  • d23b173 Merge v3.10.0-doc into v3.10.x
  • 9fe0dad Merge pull request #29682 from meeseeksmachine/auto-backport-of-pr-29680-on-v...
  • d3cf53d Merge pull request #29683 from meeseeksmachine/auto-backport-of-pr-29670-on-v...
  • 2944994 Backport PR #29670: DOC: change marginal scatter plot to subplot_mosaic
  • b4f94a4 Backport PR #29680: DOC: fix the bug of examples\event_handling
  • 4f3d478 Merge pull request #29676 from meeseeksmachine/auto-backport-of-pr-29666-on-v...
  • Additional commits viewable in compare view

Updates scikit-learn from 1.5.2 to 1.6.1

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.6.1

We're happy to announce the 1.6.1 release.

This release contains fixes for a few regressions introduced in 1.6.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.6.html#version-1-6-1

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Thanks to everyone who contributed to this release !

Commits
  • f159b78 trigger wheel builder [cd build]
  • 73cca70 generate changelog
  • afaa070 bump version
  • 1f43fd2 DOC: Updates to Macro vs micro-averaging in plot_roc.py (#29845)
  • ea8a725 🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 (#30593)
  • bc291f1 🔒 🤖 CI Update lock files for scipy-dev CI build(s) 🔒 🤖 ...
  • f5f2b9c 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
  • acbb862 TST Fix doctest due to GradientBoostingClassifier difference with scipy 1.15 ...
  • 42831e5 FIX warn if an estimator does have a concrete sklearn_tags implementation...
  • 0d2ce43 FIX change FutureWarnings to DeprecationWarnings for the tags (#30573)
  • Additional commits viewable in compare view

Updates numpy from 2.1.2 to 2.2.3

Release notes

Sourced from numpy's releases.

2.2.3 (Feb 13, 2025)

NumPy 2.2.3 Release Notes

NumPy 2.2.3 is a patch release that fixes bugs found after the 2.2.2 release. The majority of the changes are typing improvements and fixes for free threaded Python. Both of those areas are still under development, so if you discover new problems, please report them.

This release supports Python versions 3.10-3.13.

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

  • !amotzop
  • Charles Harris
  • Chris Sidebottom
  • Joren Hammudoglu
  • Matthew Brett
  • Nathan Goldbaum
  • Raghuveer Devulapalli
  • Sebastian Berg
  • Yakov Danishevsky +

Pull requests merged

A total of 21 pull requests were merged for this release.

  • #28185: MAINT: Prepare 2.2.x for further development
  • #28201: BUG: fix data race in a more minimal way on stable branch
  • #28208: BUG: Fix from_float_positional errors for huge pads
  • #28209: BUG: fix data race in np.repeat
  • #28212: MAINT: Use VQSORT_COMPILER_COMPATIBLE to determine if we should...
  • #28224: MAINT: update highway to latest
  • #28236: BUG: Add cpp atomic support (#28234)
  • #28237: BLD: Compile fix for clang-cl on WoA
  • #28243: TYP: Avoid upcasting float64 in the set-ops
  • #28249: BLD: better fix for clang / ARM compiles
  • #28266: TYP: Fix timedelta64.__divmod__ and timedelta64.__mod__...
  • #28274: TYP: Fixed missing typing information of set_printoptions
  • #28278: BUG: backport resource cleanup bugfix from gh-28273
  • #28282: BUG: fix incorrect bytes to stringdtype coercion
  • #28283: TYP: Fix scalar constructors
  • #28284: TYP: stub numpy.matlib
  • #28285: TYP: stub the missing numpy.testing modules
  • #28286: CI: Fix the github label for TYP: PR's and issues
  • #28305: TYP: Backport typing updates from main
  • #28321: BUG: fix race initializing legacy dtype casts
  • #28324: CI: update test_moderately_small_alpha

... (truncated)

Commits

@dependabot
chore(deps): bump the py-dependencies group across 3 directories with…
d85610a 
… 6 updates

Updates the requirements on [torch](https://github.com/pytorch/pytorch), [torchvision](https://github.com/pytorch/vision), [matplotlib](https://github.com/matplotlib/matplotlib), [scikit-learn](https://github.com/scikit-learn/scikit-learn), [xgboost](https://github.com/dmlc/xgboost) and [numpy](https://github.com/numpy/numpy) to permit the latest version.

Updates `torch` to 2.6.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.5.1...v2.6.0)

Updates `torchvision` to 0.21.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.20.1...v0.21.0)

Updates `matplotlib` to 3.10.1
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](matplotlib/matplotlib@v3.9.2...v3.10.1)

Updates `scikit-learn` to 1.6.1
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.5.2...1.6.1)

Updates `xgboost` to 2.1.4
- [Release notes](https://github.com/dmlc/xgboost/releases)
- [Changelog](https://github.com/dmlc/xgboost/blob/master/NEWS.md)
- [Commits](dmlc/xgboost@v2.1.2...v2.1.4)

Updates `torch` from 2.5.1 to 2.6.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.5.1...v2.6.0)

Updates `matplotlib` from 3.9.2 to 3.10.1
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](matplotlib/matplotlib@v3.9.2...v3.10.1)

Updates `scikit-learn` from 1.5.2 to 1.6.1
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.5.2...1.6.1)

Updates `numpy` from 2.1.2 to 2.2.3
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v2.1.2...v2.2.3)

---
updated-dependencies:
- dependency-name: torch
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: torchvision
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: matplotlib
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: scikit-learn
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: xgboost
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: torch
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: scikit-learn
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Mar 1, 2025
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