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Python 3.14 support #26

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jono merged 6 commits from python-3.14-support into main 2026-09-26 15:02:03 +00:00
Conversation 1 Commits 6 Files Changed 5
+9 -7
5 changed files with 9 additions and 7 deletions
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.gitea/workflows/publish-python.yml
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@@ -16,7 +16,7 @@ jobs:
- name: Set up Python - name: Set up Python
uses: actions/setup-python@v5 uses: actions/setup-python@v5
with: with:
python-version: '3.12' python-version: '3.14'
- name: Install dependencies - name: Install dependencies
run: | run: |
.gitea/workflows/test.yml
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@@ -6,7 +6,7 @@ jobs:
runs-on: nvidia-cuda runs-on: nvidia-cuda
strategy: strategy:
matrix: matrix:
python-version: ["3.8", "3.10", "3.12"] python-version: ["3.8", "3.10", "3.12", "3.14"]
steps: steps:
- name: Checkout repository - name: Checkout repository
CONTRIBUTING.md
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@@ -81,7 +81,7 @@ You can replicate our CI environment exactly to minimize "it works on my machine
* **CUDA Toolkit:** 12.5.1 * **CUDA Toolkit:** 12.5.1
* **Base Docker Image:** `nvidia/cuda:12.5.1-devel-ubuntu24.04` * **Base Docker Image:** `nvidia/cuda:12.5.1-devel-ubuntu24.04`
* **Node.js Version:** 20.x * **Node.js Version:** 20.x
* **Python Versions Tested:** 3.8, 3.10, 3.12 * **Python Versions Tested:** 3.8, 3.10, 3.12, 3.14
* **CI Docker Image:** You can pull the exact image used by our runners from Docker Hub: * **CI Docker Image:** You can pull the exact image used by our runners from Docker Hub:
```bash ```bash
pyproject.toml
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@@ -28,6 +28,8 @@ classifiers = [
"Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Mathematics",
] ]
tests/test_polysolve.py
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@@ -139,7 +139,7 @@ def test_get_real_roots_numpy(quadratic_func):
Tests that the NumPy-based genetic algorithm approximates the roots correctly. Tests that the NumPy-based genetic algorithm approximates the roots correctly.
""" """
# Using more generations for higher accuracy in testing # Using more generations for higher accuracy in testing
ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3) ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
roots = quadratic_func.get_real_roots(ga_opts, use_cuda=False) roots = quadratic_func.get_real_roots(ga_opts, use_cuda=False)
@@ -158,7 +158,7 @@ def test_get_real_roots_cuda(quadratic_func):
It will be skipped automatically if CuPy is not available. It will be skipped automatically if CuPy is not available.
""" """
ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3) ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
roots = quadratic_func.get_real_roots(ga_opts, use_cuda=True) roots = quadratic_func.get_real_roots(ga_opts, use_cuda=True)
@@ -172,7 +172,7 @@ def test_get_roots_numpy(complex_func):
Tests that the NumPy-based genetic algorithm approximates the roots correctly. Tests that the NumPy-based genetic algorithm approximates the roots correctly.
""" """
# Using more generations for higher accuracy in testing # Using more generations for higher accuracy in testing
ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3) ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
roots = complex_func.get_roots(ga_opts, use_cuda=False) roots = complex_func.get_roots(ga_opts, use_cuda=False)
@@ -191,7 +191,7 @@ def test_get_roots_cuda(complex_func):
It will be skipped automatically if CuPy is not available. It will be skipped automatically if CuPy is not available.
""" """
ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3) ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
roots = complex_func.get_roots(ga_opts, use_cuda=True) roots = complex_func.get_roots(ga_opts, use_cuda=True)