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Fixes linter warning: numpydoc-validation flagged mismatched underline lengths in docstring section headers.
Fixes linter warning: numpydoc-validation flagged GL08 (missing docstring) on 4 modules.
Run `pre-commit run --all-files`.
Run `pre-commit run --all-files`.
After adding these, running `pre-commit run --all-files` indicates no existing issues.
Run `codespell --write-changes`, then manually correct.
Fix grammar using Claude Haiku 4.5, then manually correct. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
Use `return 1` instead of `exit 1` so that sourcing the script with an invalid platform prints the error without killing the shell. Fixes geometric-intelligence#305
Verify that `source uv_env_setup.sh INVALID` does not kill the user's shell (uses return instead of exit).
Replace if/elif chain with case statement and string concatenation. No functional changes.
torch >= 2.6 defaults to weights_only=True, which breaks OGB's torch.load calls that serialize PyG data classes. Register the needed classes as safe globals so datasets load without errors. Guarded with hasattr for compatibility with torch < 2.6.
Prevents PyG's fs.torch_load from falling back to weights_only=False when loading processed datasets that contain numpy scalars and dtypes. Includes numpy.dtypes.*DType subclasses for numpy >= 1.25.
torch.tensor(existing_tensor) is deprecated; use .detach().clone(). nx.from_numpy_matrix is removed in NetworkX 3.0; use from_numpy_array.
Replace hardcoded TORCH_VER="2.3.0" with auto-detection so the setup script works with any torch version resolved by uv. Loosen torch pin from ==2.3.0 to >=2.3.0 to allow newer versions.
Add no-build-package to pyproject.toml to prevent uv from building these packages from PyPI sdists. This forces resolution from the PyG find-links wheels, which are pre-built for the correct PyTorch + CUDA version. Applies to all uv commands, not just the setup script. Remove extra-build-dependencies section (no longer needed since we never build from source).
Add pytorch-cu128 index to pyproject.toml and cu128 option to the setup script. This is required for newer GPUs (e.g. Blackwell architecture) that need CUDA 12.8+.
This only affects users doing a manual install; the setup script installs them via --all-extras. Making them optional avoids install failures for users who don't need NSD, ED-GNN, or point cloud lifting backbones, as these packages require pre-built wheels matching the exact PyTorch + CUDA version. Move top-level imports of torch_sparse, torch_scatter, and torch_cluster to lazy imports inside the functions that use them, so that importing topobench doesn't crash without the [sparse] extra. Add [sparse] to the [all] extra group.
Verify that importing topobench and triggering backbone auto-discovery works without the [sparse] extra installed.
This was referenced Apr 11, 2026
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Checklist
PR series: #303 #307 #308 309
This PR is only for commits da969a2..9741e93. It is number 3 in a series of staged PRs, building upon #308 by adding commits da969a2..9741e93; if #308 is rejected, the commits in this PR need to be manually reviewed. In particular, 2953cfb is required so that the OGB dataset loader does not crash.
Description
Commits:
==2.3.0to>=2.3.0.The last two shouldn't affect users who do the automated install, but for users who are playing around with later CUDA versions, later torch versions, etc, having those dependencies as optional makes things easier as you have less dependencies to manage if you're not using those backbones.
If you don't want to support torch >2.3.0, we could add a CLI option to use a specific torch version and update the installation instructions to use that, noting that non-2.3.0 versions are experimental. However, it would be good to support >2.3.0 torch versions because GPUs that require them are only becoming more common.
Issue
Fixes #306.