ci: support macOS core development

.github/workflows/ci.yml

@@ -34,6 +34,18 @@ jobs:
       - run: uv sync --dev
       - run: uv run pytest
 
+  test-macos:
+    name: test (macOS core)
+    runs-on: macos-14
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.x"
+          enable-cache: true
+      - run: uv sync --dev
+      - run: uv run pytest tests/unit tests/integration -m "not gpu"
+
   package:
     name: package
     runs-on: ubuntu-latest

README.md

@@ -19,20 +19,24 @@ historical checkpoint investigation, not a supported runtime backend.
 
 ## Installation
 
-The supported development environment is Python 3.12 (`>=3.12,<3.13`), with
-PyTorch 2.2.2 and the optional ROOT-GNN stack DGL 2.4.0. The canonical setup
-is:
+The supported development environment is Python 3.12 (`>=3.12,<3.13`). Core
+development is supported on macOS and Linux:
 
 ```bash
+# macOS (Apple Silicon): core package development and CPU tests
+uv sync --dev
+
+# Linux x86_64 with an NVIDIA GPU: validated ROOT-GNN development
 uv sync --dev --extra root-gnn
 ```
 
 The core package can be installed without DGL when only shared data or task
 code is needed. ROOT-GNN models, graph construction, and ROOT-GNN parity tests
-require the `root-gnn` extra. The DGL extra uses the validated CUDA 12.1 wheel
-source configured in `pyproject.toml`; a compatible NVIDIA driver is still
-required. Do not add site-specific CUDA, Slurm, or filesystem paths to model
-or task configuration.
+require the `root-gnn` extra on Linux x86_64. That extra uses the validated
+CUDA 12.1 wheel sources configured in `pyproject.toml`; a compatible NVIDIA
+driver is still required. On macOS, use the core environment for shared code,
+configuration, linting, and tests that do not require DGL or CUDA. Do not add
+site-specific CUDA, Slurm, or filesystem paths to model or task configuration.
 
 ## Quick start
 

docs/testing.md

@@ -19,6 +19,10 @@ uv run pytest -m gpu -v
 GNN4COLLIDERS_ROOT_FIXTURE=/path/to/reduced.root uv run pytest -m real_data -v
 ```
 
+On macOS, use `uv sync --dev` and run the shared-package tests. ROOT-GNN,
+CUDA, and the required ROOT-GNN parity gate are validated on Linux x86_64 with
+the `root-gnn` extra installed.
+
 Unit tests use deterministic, small tensors and generated ROOT files. DGL,
 ONNX, CUDA, distributed execution, and reduced real-data fixtures remain
 optional layers. Tests should assert public contracts and scientific

pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
 
 [project.optional-dependencies]
 root-gnn = [
-    "dgl==2.4.0+cu121",
+    "dgl==2.4.0+cu121; sys_platform == 'linux' and platform_machine == 'x86_64'",
 ]
 onnx = [
     "onnx>=1.16,<2",
@@ -50,12 +50,11 @@ version = { attr = "gnn4colliders.__version__" }
 "gnn4colliders.configs" = ["*.yaml", "*/*.yaml", "*/*/*.yaml"]
 
 [tool.uv.sources]
-torch = { index = "pytorch-cu121" }
-dgl = { index = "dgl" }
-
-[tool.uv]
-environments = [
-    "sys_platform == 'linux' and platform_machine == 'x86_64' and python_full_version >= '3.12' and python_full_version < '3.13'",
+torch = [
+    { index = "pytorch-cu121", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
+]
+dgl = [
+    { index = "dgl", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
 ]
 
 [[tool.uv.index]]

tests/unit/graphs/test_dgl_graph.py

@@ -1,7 +1,10 @@
+import pytest
 import torch
 
 from gnn4colliders.graphs import build_dgl_graph
 
+pytest.importorskip("dgl")
+
 
 def test_build_dgl_graph_attaches_compatible_features():
     node_features = torch.tensor(