Auto-detect usable GPU (skip sm_120 RTX 5060, fallback to RTX 4060 Ti)

Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.4.0-devel-ubuntu22.04
+FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
 
 ENV DEBIAN_FRONTEND=noninteractive
 ENV TZ=Europe/Paris
@@ -45,15 +45,14 @@ RUN pip3 install --no-cache-dir \
     pillow-avif-plugin \
     cmcrameri
 
-# Build CuPy from source with nvcc, targeting sm_120 (RTX 5060) and nearby archs.
-# Pre-built wheels (cupy-cuda12x 14.x) don't include sm_120, so we compile.
-# This step takes ~30 min the first time; the image is cached after that.
-RUN apt-get update && apt-get install -y --no-install-recommends git && \
-    git clone --depth 1 --branch v14.0.0 https://github.com/cupy/cupy.git /tmp/cupy-src && \
-    cd /tmp/cupy-src && \
-    CUPY_NVCC_GENERATE_CODE='sm_89;sm_90;sm_120' \
-    pip3 install --no-cache-dir -e . && \
-    rm -rf /tmp/cupy-src
+# CuPy 13.4 on CUDA 11.8 with JIT compilation (CUPY_CUDA_COMPILE_WITH_CACHE=1).
+# JIT allows CuPy to compile kernels at runtime for GPU architectures not in
+# the pre-built wheel (sm_89 = RTX 4060 Ti).  nvcc must be in PATH at runtime.
+# NOTE: RTX 5060 (sm_120) is NOT yet supported by any CuPy version.
+# The pipeline auto-detects the best usable GPU (falls back to 4060 Ti).
+ENV CUPY_CUDA_COMPILE_WITH_CACHE=1
+ENV PATH=/usr/local/cuda/bin:${PATH}
+RUN pip3 install --no-cache-dir cupy-cuda11x==13.4.0
 
 # Copy and install the pipeline package
 COPY setup.py .