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build(deps-dev): bump torch-geometric from 2.3.1 to 2.5.2 #165

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merged 1 commit into from
Apr 9, 2024

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@dependabot dependabot bot commented on behalf of github Mar 21, 2024

Bumps torch-geometric from 2.3.1 to 2.5.2.

Release notes

Sourced from torch-geometric's releases.

PyG 2.5.2: Bugfixes

PyG 2.5.2 includes a bug fix for implementing MessagePassing layers in Google Colab.

Bug Fixes

  • Raise error in case inspect.get_source is not supported (#9068)

Full Changelog: pyg-team/pytorch_geometric@2.5.1...2.5.2

PyG 2.5.1: Bugfixes

PyG 2.5.1 includes a variety of bugfixes.

Bug Fixes

  • Ignore self.propagate appearances in comments when parsing MessagePassing implementation (#9044)
  • Fixed OSError on read-only file systems within MessagePassing (#9032)
  • Made MessagePassing interface thread-safe (#9001)
  • Fixed metaclass conflict in Dataset (#8999)
  • Fixed import errors on MessagePassing modules with nested inheritance (#8973)
  • Fix OSError when downloading datasets with simplecache (#8932)

Full Changelog: pyg-team/pytorch_geometric@2.5.0...2.5.1

PyG 2.5.0: Distributed training, graph tensor representation, RecSys support, native compilation

We are excited to announce the release of PyG 2.5 🎉🎉🎉

PyG 2.5 is the culmination of work from 38 contributors who have worked on features and bug-fixes for a total of over 360 commits since torch-geometric==2.4.0.

Highlights

torch_geometric.distributed

We are thrilled to announce the first in-house distributed training solution for PyG via the torch_geometric.distributed sub-package. Developers and researchers can now take full advantage of distributed training on large-scale datasets which cannot be fully loaded in memory of one machine at the same time. This implementation doesn't require any additional packages to be installed on top of the default PyG stack.

Key Advantages

  • Balanced graph partitioning via METIS ensures minimal communication overhead when sampling subgraphs across compute nodes.
  • Utilizing DDP for model training in conjunction with RPC for remote sampling and feature fetching routines (with TCP/IP protocol and gloo communication backend) allows for data parallelism with distinct data partitions at each node.

... (truncated)

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Bumps [torch-geometric](https://github.com/pyg-team/pytorch_geometric) from 2.3.1 to 2.5.2.
- [Release notes](https://github.com/pyg-team/pytorch_geometric/releases)
- [Changelog](https://github.com/pyg-team/pytorch_geometric/blob/master/CHANGELOG.md)
- [Commits](pyg-team/pytorch_geometric@2.3.1...2.5.2)

---
updated-dependencies:
- dependency-name: torch-geometric
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Mar 21, 2024
@laserkelvin laserkelvin merged commit f08eac7 into main Apr 9, 2024
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@laserkelvin laserkelvin deleted the dependabot/pip/torch-geometric-2.5.2 branch April 9, 2024 17:03
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