ci: try MacOS ci with faiss-cpu>=1.12

Signed-off-by: Élie Goudout <[email protected]>

Author: eliegoudout

.github/workflows/ci.yml

@@ -6,6 +6,10 @@ on:
   pull_request:
     branches: [develop, release]
 
+env:
+  GITHUB_TOKEN: ${{ secrets.GH_TOKEN_TORCHHUB }}
+  KMP_DUPLICATE_LIB_OK: TRUE
+
 jobs:
   build:
     name: continuous-integration
@@ -36,7 +40,7 @@ jobs:
           uv -v run mypy
 
       - name: Build docs (Posix)
-        if: matrix.os != 'windows-latest' && matrix.os != 'macos-latest'  # Temporary (github.com/ThalesGroup/scio/issues/2)
+        if: matrix.os != 'windows-latest'
         env:
           SPHINXOPTS: --fail-on-warning
         run: |
@@ -55,7 +59,6 @@ jobs:
           uv -v pip install dist/scio_pypi-1.0.0rc3-py3-none-any.whl
 
       - name: Run pytest
-        if: matrix.os != 'macos-latest'  # Temporary (github.com/ThalesGroup/scio/issues/2)
         id: run-pytest
         continue-on-error: true
         run: |
@@ -82,7 +85,6 @@ jobs:
 
       # TEMPORARILY DISABLED (https://github.com/ThalesGroup/scio/issues/9)
       # - name: Run pytest with lowest resolution
-      #   if: matrix.os != 'macos-latest'  # Temporary (github.com/ThalesGroup/scio/issues/2)
       #   id: run-pytest-lowest
       #   continue-on-error: true
       #   run: |

docs/src/tutorials/inferring_with_confidence.py

@@ -23,7 +23,13 @@
 sample_shape = (3, 32, 32)
 calib_data = torch.rand(10, *sample_shape)
 calib_labels = torch.randint(0, 10, calib_data.shape)
-net = torch.hub.load("ThalesGroup/scio:hub", "tiniest", trust_repo=True, verbose=False)
+net = torch.hub.load(
+    "ThalesGroup/scio:hub",
+    "tiniest",
+    trust_repo=True,
+    verbose=False,
+    skip_validation=True,
+)
 net = net.to(calib_data)
 
 # %%

docs/src/tutorials/visualizing_and_evaluating_ood_detection_algorithms.py

@@ -31,7 +31,13 @@
 calib_data = calib_data / 255  # Convert [0, 255] uint8 from HuggingFace to [0, 1] float
 
 sample_shape = calib_data.shape[1:]
-net = torch.hub.load("ThalesGroup/scio:hub", "tiniest", trust_repo=True, verbose=False)
+net = torch.hub.load(
+    "ThalesGroup/scio:hub",
+    "tiniest",
+    trust_repo=True,
+    verbose=False,
+    skip_validation=True,
+)
 net = net.to(calib_data)
 
 # %%

docs/src/user_guide/installation_compatibility.rst

@@ -18,15 +18,15 @@ If you wish to install from source or wheels manually, you can download `release
 
 OS Compatibility
 ----------------
-The library is available and functional on **Ubuntu**, **Windows** and *MacOS* (minus ``faiss``-related features ─ feel free to show your interest `here <https://github.com/ThalesGroup/scio/issues/2>`_).
+The library is available and functional on **Ubuntu**, **Windows** and *MacOS* (minus ``faiss``-related features ─ feel free to show your interest `here <https://github.com/ThalesGroup/scio/issues/2>`__).
 
 Framework Compatiblity
 ----------------------
 Although confidence scores are not a particularity of Neural Networks, ``scio`` is precisely developed to handle such models. More precisely, only *PyTorch* models (inheriting from ``torch.nn.Module``) are currently supported.
 
 GPU Compatibility
 -----------------
-Our library is **fully compatible** and tested with CUDA devices for native use of GPU acceleration! Note however that features using ``faiss`` currently use CPU-bound indexes, introducing a potential GPU > CPU > GPU bottleneck. This may be improved in future versions ─ feel free to show your interest `here <https://github.com/ThalesGroup/scio/issues/18>`_.
+Our library is **fully compatible** and tested with CUDA devices for native use of GPU acceleration! Note however that features using ``faiss`` currently use CPU-bound indexes, introducing a potential GPU > CPU > GPU bottleneck. This may be improved in future versions ─ feel free to show your interest `here <https://github.com/ThalesGroup/scio/issues/18>`__.
 
 Supported Data Types
 --------------------