RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED

The Conda environment is shown below.
name: trellis2
channels:

• defaults
  dependencies:
• _libgcc_mutex=0.1=main
• _openmp_mutex=5.1=1_gnu
• bzip2=1.0.8=h5eee18b_6
• ca-certificates=2026.3.19=h06a4308_0
• ld_impl_linux-64=2.44=h9e0c5a2_3
• libexpat=2.7.5=h7354ed3_0
• libffi=3.4.4=h6a678d5_1
• libgcc=15.2.0=h69a1729_7
• libgcc-ng=15.2.0=h166f726_7
• libgomp=15.2.0=h4751f2c_7
• libnsl=2.0.0=h5eee18b_0
• libstdcxx=15.2.0=h39759b7_7
• libstdcxx-ng=15.2.0=hc03a8fd_7
• libuuid=1.41.5=h5eee18b_0
• libxcb=1.17.0=h9b100fa_0
• libzlib=1.3.1=h47b2149_1
• ncurses=6.5=h7934f7d_0
• openssl=3.5.6=h1b28b03_0
• packaging=26.0=py310h06a4308_0
• pip=26.0.1=pyhc872135_1
• pthread-stubs=0.3=h0ce48e5_1
• python=3.10.20=h741d88c_0
• readline=8.3=hc2a1206_0
• sqlite=3.51.2=h3e8d24a_0
• tk=8.6.15=h54e0aa7_0
• wheel=0.46.3=py310h06a4308_0
• xorg-libx11=1.8.12=h9b100fa_1
• xorg-libxau=1.0.12=h9b100fa_0
• xorg-libxdmcp=1.1.5=h9b100fa_0
• xorg-xorgproto=2024.1=h5eee18b_1
• xz=5.8.2=h448239c_0
• zlib=1.3.1=h47b2149_1
• pip:
  • absl-py==2.4.0
  • aiofiles==24.1.0
  • annotated-doc==0.0.4
  • annotated-types==0.7.0
  • anyio==4.13.0
  • brotli==1.2.0
  • certifi==2026.4.22
  • click==8.3.3
  • cuda-bindings==13.2.0
  • cuda-pathfinder==1.5.3
  • cuda-toolkit==13.0.2
  • cumesh==0.0.1
  • easydict==1.13
  • einops==0.8.2
  • exceptiongroup==1.3.1
  • fastapi==0.136.1
  • ffmpy==1.0.0
  • filelock==3.29.0
  • flash-attn==2.7.3
  • flex-gemm==1.0.0
  • fsspec==2026.3.0
  • glcontext==3.0.0
  • gradio==6.0.1
  • gradio-client==2.0.0
  • groovy==0.1.2
  • grpcio==1.80.0
  • h11==0.16.0
  • hf-xet==1.4.3
  • httpcore==1.0.9
  • httpx==0.28.1
  • huggingface-hub==1.11.0
  • idna==3.13
  • imageio==2.37.3
  • imageio-ffmpeg==0.6.0
  • jinja2==3.1.6
  • kornia==0.8.2
  • kornia-rs==0.1.10
  • lpips==0.1.4
  • markdown==3.10.2
  • markdown-it-py==4.0.0
  • markupsafe==3.0.3
  • mdurl==0.1.2
  • moderngl==5.12.0
  • mpmath==1.3.0
  • networkx==3.4.2
  • ninja==1.13.0
  • numpy==2.2.6
  • nvdiffrast==0.4.0
  • nvdiffrec-render==0.0.0
  • nvidia-cublas==13.1.0.3
  • nvidia-cublas-cu12==12.4.5.8
  • nvidia-cuda-cupti==13.0.85
  • nvidia-cuda-cupti-cu12==12.4.127
  • nvidia-cuda-nvrtc==13.0.88
  • nvidia-cuda-nvrtc-cu12==12.4.127
  • nvidia-cuda-runtime==13.0.96
  • nvidia-cuda-runtime-cu12==12.4.127
  • nvidia-cudnn-cu12==9.1.0.70
  • nvidia-cudnn-cu13==9.21.1.3
  • nvidia-cufft==12.0.0.61
  • nvidia-cufft-cu12==11.2.1.3
  • nvidia-cufile==1.15.1.6
  • nvidia-curand==10.4.0.35
  • nvidia-curand-cu12==10.3.5.147
  • nvidia-cusolver==12.0.4.66
  • nvidia-cusolver-cu12==11.6.1.9
  • nvidia-cusparse==12.6.3.3
  • nvidia-cusparse-cu12==12.3.1.170
  • nvidia-cusparselt-cu12==0.6.2
  • nvidia-cusparselt-cu13==0.8.0
  • nvidia-nccl-cu12==2.21.5
  • nvidia-nccl-cu13==2.28.9
  • nvidia-nvjitlink==13.0.88
  • nvidia-nvjitlink-cu12==12.4.127
  • nvidia-nvshmem-cu13==3.4.5
  • nvidia-nvtx==13.0.85
  • nvidia-nvtx-cu12==12.4.127
  • o-voxel==0.0.1
  • opencv-python-headless==4.13.0.92
  • orjson==3.11.8
  • pandas==2.3.3
  • pillow==12.1.1
  • pillow-simd==9.5.0.post2
  • plyfile==1.1.3
  • protobuf==7.34.1
  • psutil==7.2.2
  • pydantic==2.12.4
  • pydantic-core==2.41.5
  • pydub==0.25.1
  • pygments==2.20.0
  • python-dateutil==2.9.0.post0
  • python-multipart==0.0.26
  • pytz==2026.1.post1
  • pyyaml==6.0.3
  • regex==2026.4.4
  • rich==15.0.0
  • safehttpx==0.1.7
  • safetensors==0.7.0
  • scipy==1.15.3
  • semantic-version==2.10.0
  • setuptools==81.0.0
  • shellingham==1.5.4
  • six==1.17.0
  • starlette==0.52.1
  • sympy==1.13.1
  • tensorboard==2.20.0
  • tensorboard-data-server==0.7.2
  • timm==1.0.26
  • tokenizers==0.22.2
  • tomlkit==0.13.3
  • torch==2.6.0+cu124
  • torchaudio==2.6.0+cu124
  • torchvision==0.21.0+cu124
  • tqdm==4.67.3
  • transformers==5.6.2
  • trimesh==4.12.0
  • triton==3.2.0
  • typer==0.24.2
  • typing-extensions==4.15.0
  • typing-inspection==0.4.2
  • tzdata==2026.1
  • utils3d==0.0.2
  • uvicorn==0.46.0
  • werkzeug==3.1.8
  • zstandard==0.25.0

Run the command: cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2
The following information is returned.
#define CUDNN_MAJOR 9
#define CUDNN_MINOR 0
#define CUDNN_PATCHLEVEL 0

#define CUDNN_VERSION (CUDNN_MAJOR * 10000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)

/* cannot use constexpr here since this is a C-only file */

After checking relevant information, this issue appears to be caused by a cuDNN version mismatch. Could you please advise on feasible solutions to resolve this problem?

Start by comparing the cuDNN version reported by /usr/include/cudnn_version.h with the installed nvidia-cudnn and torch==2.6.0+cu124 packages listed in the environment. Reproduce the CUDNN_STATUS_NOT_INITIALIZED error in this Conda environment and verify that a compatible dependency configuration resolves it.