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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
/* 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?
The Conda environment is shown below.
name: trellis2
channels:
dependencies:
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?