The CUDA base image is pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime, so it
ships cuDNN 9. CTranslate2 — WhisperX and faster-whisper — links cuDNN 8, and
its absence aborts the backend process outright rather than raising (#1371).
scripts/setup.py side-loads the cuDNN 8 libraries for source installs, but the
Dockerfile never did, so every CTranslate2 ASR engine was unavailable in Docker
and the demo synthesis timed out with libcudnn_ops_infer.so.8 missing.
Install the same nvidia-cudnn-cu12==8.9.7.29 shim during the image build,
deriving the target from sys.prefix so it matches where backend/core/cudnn8.py
searches rather than hardcoding the conda path — sys.prefix differs between the
conda-based CUDA image and the ROCm venv. Guarded to GPU_FLAVOR=cuda, since
ROCm does not use cuDNN, and --no-deps keeps the base image's torch stack
untouched. A post-install assert fails the build if no .so.8 libraries landed,
rather than letting it resurface as the same runtime warning.
Fixes#2050
basil-k-aji-dev
a628ba161e