Error caught was: No module named 'triton'
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Description
Current Python: "O:\Projectorz\StableProjectorz\gen3d\trellis-stable-projectorz-101\trellis-spz\code\venv\Scripts\python.exe"
Virtual Env: O:\Projectorz\StableProjectorz\gen3d\trellis-stable-projectorz-101\trellis-spz\code\venv
_
Current Python: "O:\Projectorz\StableProjectorz\gen3d\trellis-stable-projectorz-101\trellis-spz\code\venv\Scripts\python.exe"
Virtual Env: O:\Projectorz\StableProjectorz\gen3d\trellis-stable-projectorz-101\trellis-spz\code\venv
Starting the server, please wait...
[SPARSE] Backend: spconv, Attention: xformers
Warp 1.6.0 initialized:
CUDA Toolkit 12.8, Driver 12.7
Devices:
"cpu" : "Intel64 Family 6 Model 183 Stepping 1, GenuineIntel"
"cuda:0" : "NVIDIA GeForce RTX 4070" (12 GiB, sm_89, mempool enabled)
Kernel cache:
C:\Users\jor\AppData\Local\NVIDIA\warp\Cache\1.6.0
O:\Projectorz\StableProjectorz\gen3d\trellis-stable-projectorz-101\trellis-spz\code\venv\Lib\site-packages\gradio_client\utils.py:1097: UserWarning: file() is deprecated and will be removed in a future version. Use handle_file() instead.
warnings.warn(
[SPARSE][CONV] spconv algo: native
[ATTENTION] Using backend: xformers
Please wait...
A matching Triton is not available, some optimizations will not be enabled.
Error caught was: No module named 'triton'
Using cache found in C:\Users\jor/.cache\torch\hub\facebookresearch_dinov2_main
C:\Users\jor/.cache\torch\hub\facebookresearch_dinov2_main\dinov2\layers\swiglu_ffn.py:43: UserWarning: xFormers is available (SwiGLU)
warnings.warn("xFormers is available (SwiGLU)")
C:\Users\jor/.cache\torch\hub\facebookresearch_dinov2_main\dinov2\layers\attention.py:27: UserWarning: xFormers is available (Attention)
warnings.warn("xFormers is available (Attention)")
C:\Users\jor/.cache\torch\hub\facebookresearch_dinov2_main\dinov2\layers\block.py:33: UserWarning: xFormers is available (Block)
warnings.warn("xFormers is available (Block)")
so how can i install that Triton ?
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The report provides only Python virtual-environment paths and server startup output, with no project file or test named. Start by reproducing startup in that environment and checking the dependency setup around the Triton warning. Done means determining whether Triton is required for functionality or only optional optimizations, then documenting the supported resolution.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100