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5070Ti cuda error

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评估

难度
4/5
预计耗时
3-5 天
新手友好度
35/100
Issue 类型
缺陷
描述清晰度
需要澄清
活跃度
活跃
技术栈
python, pytorch

调研方向

Start with scripts/run_inference.py and the import chain through rfdiffusion/inference/utils.py, rfdiffusion/diffusion.py, and rfdiffusion/util_module.py. Reproduce the failure while recording the installed torch, DGL, and torchdata versions, then determine which compatible dependency set resolves the missing GraphBolt library. Done means documenting a reproducible installation and successful inference run.

由索引模型根据 Issue 内容生成。

描述

The current workstation GPU is a 5070ti, and the currently installed CUDA version is 12.8
Now I want to install a torch package compatible with CUDA 12.8; the command I use to install is:

pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128

After installation, I then built and installed RFdiffusion. After installation, when running computational tasks, I encountered an error with the torchdata package. Currently I have torchdata version 0.11.0 installed; in this version the datapipes module was removed or moved into the core library, but the DGL library I am using still imports from from torchdata.datapipes.iter, so I changed the torchdata version to 0.7.1 using the command

pip install torchdata==0.7.1 --no-deps

This ensures that my torch version won't be affected. Unfortunately, after the change a mismatch in the C++ underlying dynamic library versions appeared:

/home/kk203/Downloads/Soft/Test-RF/RFdiffusion/rfdiffusion/util.py:253: UserWarning: Using torch.cross without specifying the 
dim arg is deprecated.
Please either pass the dim explicitly or simply use torch.linalg.cross.
The default value of dim will change to agree with that of linalg.cross in a future release. (Triggered internally at /pytorch
/aten/src/ATen/native/Cross.cpp:63.)
Z = torch.cross(Xn, Yn)
Traceback (most recent call last):
File "/home/kk203/Downloads/Soft/Test-RF/RFdiffusion/scripts/run_inference.py", line 25, in
from rfdiffusion.inference import utils as iu
File "/home/kk203/Downloads/Soft/Test-RF/RFdiffusion/rfdiffusion/inference/utils.py", line 6, in
from rfdiffusion.diffusion import get_beta_schedule
File "/home/kk203/Downloads/Soft/Test-RF/RFdiffusion/rfdiffusion/diffusion.py", line 12, in
from rfdiffusion.util_module import ComputeAllAtomCoords
File "/home/kk203/Downloads/Soft/Test-RF/RFdiffusion/rfdiffusion/util_module.py", line 7, in
import dgl
File "/home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/dgl/init.py", line 16, in
from . import (
File "/home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/dgl/dataloading/init.py", line 13, in
from .dataloader import *
File "/home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/dgl/dataloading/dataloader.py", line 27, in
from ..distributed import DistGraph
File "/home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/dgl/distributed/init.py", line 5, in
from .dist_graph import DistGraph, DistGraphServer, edge_split, node_split
File "/home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/dgl/distributed/dist_graph.py", line 11, in
from .. import backend as F, graphbolt as gb, heterograph_index
File "/home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/dgl/graphbolt/init.py", line 55, in
load_graphbolt()
File "/home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/dgl/graphbolt/init.py", line 45, in load_graphbolt
raise FileNotFoundError(
FileNotFoundError: Cannot find DGL C++ graphbolt library at /home/kk203/.conda/envs/RFdiffusion/lib/python3.10/site-packages/d
gl/graphbolt/libgraphbolt_pytorch_2.10.0.so

I would like to ask if there are any good ways to resolve version conflicts between these libraries, or if anyone can recommend versions of torch, DGL, and other libraries to install for the 5070 Ti GPU so that GPU acceleration can be used.

主要语言
Python
星标
3.1k
派生
652
PR 合并指标
30 天内没有已合并 PR

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这个项目没有提供开发容器、Dockerfile 或贡献指南,环境需要你自己搭建:先看它的 README,通用步骤见我们的新手贡献指南。

从这里开始

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  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库,在一个分支上完成修改。
  4. 提交 Pull Request,并在描述里引用这个 Issue 编号。

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