release(0.1.19): pin torch<2.11 + GPU-compat self-check

.github/workflows/test.yml

@@ -7,11 +7,38 @@ on:
 
 jobs:
   test:
+    name: ${{ matrix.label }} / py${{ matrix.python-version }}
     runs-on: ubuntu-latest
+    continue-on-error: ${{ matrix.experimental }}
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
+        include:
+          # Lower bound — pinned torch 2.5.1 across all supported Python versions.
+          - python-version: "3.10"
+            label: "torch 2.5"
+            torch-pip-args: "torch==2.5.1+cpu torchvision==0.20.1+cpu torchaudio==2.5.1+cpu"
+            experimental: false
+          - python-version: "3.11"
+            label: "torch 2.5"
+            torch-pip-args: "torch==2.5.1+cpu torchvision==0.20.1+cpu torchaudio==2.5.1+cpu"
+            experimental: false
+          - python-version: "3.12"
+            label: "torch 2.5"
+            torch-pip-args: "torch==2.5.1+cpu torchvision==0.20.1+cpu torchaudio==2.5.1+cpu"
+            experimental: false
+          # Verified — torch 2.8 was validated end-to-end in the mosaic DDP path
+          # (1000-epoch run, UMAP and numerics consistent with single-GPU baseline).
+          - python-version: "3.12"
+            label: "torch 2.8"
+            torch-pip-args: "torch==2.8.* torchvision==0.23.* torchaudio==2.8.*"
+            experimental: false
+          # Upper bound — torch 2.10 is the latest within our <2.11 cap.
+          # 2.11 was excluded because its default CUDA wheels dropped Volta/Pascal.
+          - python-version: "3.12"
+            label: "torch 2.10"
+            torch-pip-args: "torch==2.10.* torchvision==0.25.* torchaudio==2.10.*"
+            experimental: false
 
     steps:
       - uses: actions/checkout@v4
@@ -25,15 +52,15 @@ jobs:
       - name: Install torch (CPU build)
         run: |
           python -m pip install --upgrade pip
-          pip install \
-            torch==2.5.1+cpu \
-            torchvision==0.20.1+cpu \
-            torchaudio==2.5.1+cpu \
+          pip install ${{ matrix.torch-pip-args }} \
             --index-url https://download.pytorch.org/whl/cpu
 
       - name: Install scmidas with dev extras
         run: pip install -e ".[dev]"
 
+      - name: Show installed torch
+        run: python -c "import torch; print('torch', torch.__version__)"
+
       - name: Lint (non-blocking)
         run: ruff check . || true
 

docs/source/release.md

@@ -6,6 +6,32 @@ All notable changes to this project will be documented in this file.
 
 ## Version 0.1.x
 
+### v0.1.19 (2026-05-03)
+*   **📦 Packaging — narrow torch upper bound to `<2.11`**
+    *   torch 2.11 dropped Volta (V100, CC 7.0) and Pascal (P100, GTX
+        10xx, CC 6.x) from its default `cu128` / `cu129` wheels (to
+        ship cuDNN 9.15.1, which is incompatible with those archs). On
+        those GPUs `pip install scmidas==0.1.18` would silently install
+        a torch that fails at the first CUDA op with
+        `no kernel image is available for execution on the device`.
+    *   The pin now reads `torch>=2.5,<2.11` (with matching
+        `torchvision<0.26` / `torchaudio<2.11`). Users on
+        Ampere/Hopper/Ada/Blackwell GPUs can manually upgrade past the
+        cap; users on Volta/Pascal stay on a working default install.
+    *   No source-code change — same scmidas as 0.1.18.
+*   **✨ Enhancements**
+    *   `import scmidas` now runs a one-time GPU self-check: if the
+        local torch wheel has no kernels for the local GPU, scmidas
+        emits a `UserWarning` with actionable guidance (downgrade torch
+        or use the cu126 wheel) instead of the user later seeing a raw
+        `no kernel image is available` error from somewhere deep in
+        their training loop. The check no-ops on CPU-only environments
+        and on working GPU setups.
+*   **⚙️ CI**
+    *   Test matrix gained a `torch 2.10` job (the new upper bound) and
+        dropped the previous experimental `torch latest` job. Lower
+        bound remains `torch 2.5.1` across Python 3.10 / 3.11 / 3.12.
+
 ### v0.1.18 (2026-05-02)
 *   **🐛 Bug Fixes (DDP + mosaic data)**
     *   Default `sampler_type='auto'` now picks the DDP sampler when a

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scmidas"
-version = '0.1.18'
+version = '0.1.19'
 description = "A torch-based integration method for single-cell multi-omic data."
 readme = "README.md"
 requires-python = ">=3.10"
@@ -26,14 +26,15 @@ classifiers = [
 # --- Runtime dependencies (minimal but complete for your current imports) ---
 dependencies = [
   # PyTorch.
-  # The previous <2.6 cap was a workaround for a suspected Lightning
-  # DDP incompatibility, but torch 2.8 has since been verified end-to-end
-  # in the mosaic DDP path (UMAP / numerics consistent with single-GPU).
-  # Keeping a major-version cap (<3) so a hypothetical breaking 3.x
-  # release does not silently land for users.
-  "torch>=2.5,<3",
-  "torchvision>=0.20,<1",
-  "torchaudio>=2.5,<3",
+  # Upper bound is <2.11 because torch 2.11 dropped Volta (V100, CC 7.0)
+  # and Pascal (P100, GTX 10xx, CC 6.x) from its CUDA 12.8/12.9 wheels
+  # to ship cuDNN 9.15.1 (which is incompatible with those archs).
+  # On those GPUs, `pip install scmidas` would silently install a torch
+  # that fails at the first CUDA op with "no kernel image is available".
+  # Newer-GPU users (Ampere+) can manually upgrade past this cap.
+  "torch>=2.5,<2.11",
+  "torchvision>=0.20,<0.26",
+  "torchaudio>=2.5,<2.11",
 
   # Lightning (uses lightning.pytorch.utilities.rank_zero_only)
   "lightning>=2.4,<2.7",

src/scmidas/__init__.py

@@ -10,3 +10,36 @@
 except PackageNotFoundError:
     __version__ = '0.0.0+unknown'
 del PackageNotFoundError, _pkg_version
+
+
+def _check_gpu_compat():
+    # torch 2.11 dropped Volta (V100, CC 7.0) and Pascal (P100/GTX 10xx,
+    # CC 6.x) from its default cu128/cu129 wheels. On those GPUs the first
+    # CUDA op raises "no kernel image is available" — translate that here.
+    try:
+        import warnings
+        import torch
+        if not torch.cuda.is_available():
+            return
+        try:
+            (torch.zeros(1, device='cuda') + 1).cpu()
+        except Exception as e:
+            if 'no kernel image is available' not in str(e):
+                return
+            major, minor = torch.cuda.get_device_capability()
+            warnings.warn(
+                f"torch {torch.__version__} has no CUDA kernels for your GPU "
+                f"({torch.cuda.get_device_name(0)}, compute capability "
+                f"{major}.{minor}). torch 2.11+ dropped Volta/Pascal from its "
+                f"default cu128/cu129 wheels. Fix: "
+                f"pip install 'torch<2.11' 'torchvision<0.26' 'torchaudio<2.11' "
+                f"or use the cu126 wheel via "
+                f"--index-url https://download.pytorch.org/whl/cu126",
+                UserWarning, stacklevel=2,
+            )
+    except Exception:
+        pass
+
+
+_check_gpu_compat()
+del _check_gpu_compat