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CHANGELOG.md

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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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## \[2.5.0\] - 2023-MM-DD
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## \[2.6.0\] - 2024-MM-DD
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### Added
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### Changed
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### Deprecated
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### Fixed
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### Removed
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## \[2.5.0\] - 2024-02-16
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### Added
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conda/pyg/meta.yaml

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package:
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name: pyg
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version: 2.4.0
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version: 2.5.0
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source:
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url: https://files.pythonhosted.org/packages/e6/6e/a596e2ddecc3b13a0d576495369a30309fb54c74fadf0bbca645bfbcaa2f/torch_geometric-2.4.0.tar.gz

conda/pytorch-geometric/meta.yaml

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package:
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name: pytorch-geometric
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version: 2.4.0
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version: 2.5.0
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source:
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url: https://files.pythonhosted.org/packages/e6/6e/a596e2ddecc3b13a0d576495369a30309fb54c74fadf0bbca645bfbcaa2f/torch_geometric-2.4.0.tar.gz

docs/source/tutorial/distributed_pyg.rst

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By default, METIS tries to balance the number of nodes of each type in each partition while minimizing the number of edges between partitions.
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This ensures that the resulting partitions provide maximal local access of neighbors, enabling samplers to perform local computations without the need for communication between different compute nodes.
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Through this partitioning approach, every edge receives a distinct assignment, while "halo nodes" (1-hop neighbors that fall into a different partition) are replicated.
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Through this partitioning approach, every node receives a distinct assignment, while "halo nodes" (1-hop neighbors that fall into a different partition) are replicated.
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Halo nodes ensure that neighbor sampling for a single node in a single layer stays purely local.
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.. figure:: ../_figures/dist_part.png

pyproject.toml

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[project]
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name="torch_geometric"
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version="2.4.0"
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version="2.5.0"
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authors=[
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{name="Matthias Fey", email="[email protected]"},
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]

torch_geometric/__init__.py

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contrib = LazyLoader('contrib', globals(), 'torch_geometric.contrib')
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graphgym = LazyLoader('graphgym', globals(), 'torch_geometric.graphgym')
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__version__ = '2.4.0'
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__version__ = '2.5.0'
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__all__ = [
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'EdgeIndex',

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