ubuntu22.04+pytorch2.3安装PyG图神经网络库

ubuntu下安装torch-geometric库，图神经网络

开发环境
ubuntu22.04
conda 24.5.0
python 3.9
pytorch 2.0.1
cuda 11.8

pyg的安装网上教程流传着许多安装方式，这些安装方式主要是：预先安装好pyg的依赖库，这些依赖库需要对应上python、pytorch、cuda的版本，需要小心对应，很容易出错；而且这些依赖库的安装，推荐采用的是预先编译好的库安装。

一、采用已编译好的包进行安装

即，先按python、pytorch、cuda版本，选择对应的pyg_lib、torch_cluster、torch_scatter、torch_sparse、torch_spline_conv 版本下载到本地，然后pip安装，最后安装pip install torch-geometric

1、首先我们安装pyg的

https://github.com/pyg-team/pytorch_geometric
在这里插入图片描述
点击here，进入https://data.pyg.org/whl/
在这里插入图片描述
点击你对应的torch版本及cuda版本，这里选择的是torch 2.01 及 cuda 11.8

然后，进入https://data.pyg.org/whl/torch-2.0.1%2Bcu118.html 如下页面

pyg_lib-0.2.0+pt20cu118-cp310-cp310-linux_x86_64.whl
pyg_lib-0.2.0+pt20cu118-cp311-cp311-linux_x86_64.whl
pyg_lib-0.2.0+pt20cu118-cp38-cp38-linux_x86_64.whl
pyg_lib-0.2.0+pt20cu118-cp39-cp39-linux_x86_64.whl
pyg_lib-0.3.0+pt20cu118-cp310-cp310-linux_x86_64.whl
pyg_lib-0.3.0+pt20cu118-cp311-cp311-linux_x86_64.whl
pyg_lib-0.3.0+pt20cu118-cp38-cp38-linux_x86_64.whl
pyg_lib-0.3.0+pt20cu118-cp39-cp39-linux_x86_64.whl
pyg_lib-0.3.1+pt20cu118-cp310-cp310-linux_x86_64.whl
pyg_lib-0.3.1+pt20cu118-cp311-cp311-linux_x86_64.whl
pyg_lib-0.3.1+pt20cu118-cp38-cp38-linux_x86_64.whl
pyg_lib-0.3.1+pt20cu118-cp39-cp39-linux_x86_64.whl
pyg_lib-0.4.0+pt20cu118-cp310-cp310-linux_x86_64.whl
pyg_lib-0.4.0+pt20cu118-cp311-cp311-linux_x86_64.whl
pyg_lib-0.4.0+pt20cu118-cp38-cp38-linux_x86_64.whl
pyg_lib-0.4.0+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_cluster-1.6.1+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_cluster-1.6.1+pt20cu118-cp310-cp310-win_amd64.whl
torch_cluster-1.6.1+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_cluster-1.6.1+pt20cu118-cp311-cp311-win_amd64.whl
torch_cluster-1.6.1+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_cluster-1.6.1+pt20cu118-cp38-cp38-win_amd64.whl
torch_cluster-1.6.1+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_cluster-1.6.1+pt20cu118-cp39-cp39-win_amd64.whl
torch_cluster-1.6.2+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_cluster-1.6.2+pt20cu118-cp310-cp310-win_amd64.whl
torch_cluster-1.6.2+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_cluster-1.6.2+pt20cu118-cp311-cp311-win_amd64.whl
torch_cluster-1.6.2+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_cluster-1.6.2+pt20cu118-cp38-cp38-win_amd64.whl
torch_cluster-1.6.2+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_cluster-1.6.2+pt20cu118-cp39-cp39-win_amd64.whl
torch_cluster-1.6.3+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_cluster-1.6.3+pt20cu118-cp310-cp310-win_amd64.whl
torch_cluster-1.6.3+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_cluster-1.6.3+pt20cu118-cp311-cp311-win_amd64.whl
torch_cluster-1.6.3+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_cluster-1.6.3+pt20cu118-cp38-cp38-win_amd64.whl
torch_cluster-1.6.3+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_cluster-1.6.3+pt20cu118-cp39-cp39-win_amd64.whl
torch_scatter-2.1.1+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_scatter-2.1.1+pt20cu118-cp310-cp310-win_amd64.whl
torch_scatter-2.1.1+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_scatter-2.1.1+pt20cu118-cp311-cp311-win_amd64.whl
torch_scatter-2.1.1+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_scatter-2.1.1+pt20cu118-cp38-cp38-win_amd64.whl
torch_scatter-2.1.1+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_scatter-2.1.1+pt20cu118-cp39-cp39-win_amd64.whl
torch_scatter-2.1.2+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_scatter-2.1.2+pt20cu118-cp310-cp310-win_amd64.whl
torch_scatter-2.1.2+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_scatter-2.1.2+pt20cu118-cp311-cp311-win_amd64.whl
torch_scatter-2.1.2+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_scatter-2.1.2+pt20cu118-cp38-cp38-win_amd64.whl
torch_scatter-2.1.2+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_scatter-2.1.2+pt20cu118-cp39-cp39-win_amd64.whl
torch_sparse-0.6.17+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_sparse-0.6.17+pt20cu118-cp310-cp310-win_amd64.whl
torch_sparse-0.6.17+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_sparse-0.6.17+pt20cu118-cp311-cp311-win_amd64.whl
torch_sparse-0.6.17+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_sparse-0.6.17+pt20cu118-cp38-cp38-win_amd64.whl
torch_sparse-0.6.17+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_sparse-0.6.17+pt20cu118-cp39-cp39-win_amd64.whl
torch_sparse-0.6.18+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_sparse-0.6.18+pt20cu118-cp310-cp310-win_amd64.whl
torch_sparse-0.6.18+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_sparse-0.6.18+pt20cu118-cp311-cp311-win_amd64.whl
torch_sparse-0.6.18+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_sparse-0.6.18+pt20cu118-cp38-cp38-win_amd64.whl
torch_sparse-0.6.18+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_sparse-0.6.18+pt20cu118-cp39-cp39-win_amd64.whl
torch_spline_conv-1.2.2+pt20cu118-cp310-cp310-linux_x86_64.whl
torch_spline_conv-1.2.2+pt20cu118-cp310-cp310-win_amd64.whl
torch_spline_conv-1.2.2+pt20cu118-cp311-cp311-linux_x86_64.whl
torch_spline_conv-1.2.2+pt20cu118-cp311-cp311-win_amd64.whl
torch_spline_conv-1.2.2+pt20cu118-cp38-cp38-linux_x86_64.whl
torch_spline_conv-1.2.2+pt20cu118-cp38-cp38-win_amd64.whl
torch_spline_conv-1.2.2+pt20cu118-cp39-cp39-linux_x86_64.whl
torch_spline_conv-1.2.2+pt20cu118-cp39-cp39-win_amd64.whl

pyg_lib、torch_cluster、torch_scatter、torch_sparse、torch_spline_conv 都逐一选择一个版本下载

注意选择对python的版本(cp310即python 3.10版本)即操作系统(linux or win)

下载完成如下所示
在这里插入图片描述
开始本地安装依赖库，如下

# 激活对应的conda环境
$ conda acitvate pyt2.0
# pip 安装上面5个库
$ pip install pyg_lib-0.4.0+pt20cu118-cp39-cp39-linux_x86_64.whl 
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing ./pyg_lib-0.4.0+pt20cu118-cp39-cp39-linux_x86_64.whl
Installing collected packages: pyg-lib
Successfully installed pyg-lib-0.4.0+pt20cu118

$ pip install torch_cluster-1.6.3+pt20cu118-cp39-cp39-linux_x86_64.whl 
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing ./torch_cluster-1.6.3+pt20cu118-cp39-cp39-linux_x86_64.whl
Requirement already satisfied: scipy in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-cluster==1.6.3+pt20cu118) (1.13.1)
Requirement already satisfied: numpy<2.3,>=1.22.4 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from scipy->torch-cluster==1.6.3+pt20cu118) (1.23.5)
Installing collected packages: torch-cluster
Successfully installed torch-cluster-1.6.3+pt20cu118

$ pip install torch_scatter-2.1.2+pt20cu118-cp39-cp39-linux_x86_64.whl 
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing ./torch_scatter-2.1.2+pt20cu118-cp39-cp39-linux_x86_64.whl
Installing collected packages: torch-scatter
Successfully installed torch-scatter-2.1.2+pt20cu118

$ pip install torch_sparse-0.6.18+pt20cu118-cp39-cp39-linux_x86_64.whl 
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing ./torch_sparse-0.6.18+pt20cu118-cp39-cp39-linux_x86_64.whl
Requirement already satisfied: scipy in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-sparse==0.6.18+pt20cu118) (1.13.1)
Requirement already satisfied: numpy<2.3,>=1.22.4 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from scipy->torch-sparse==0.6.18+pt20cu118) (1.23.5)
Installing collected packages: torch-sparse
Successfully installed torch-sparse-0.6.18+pt20cu118

$ pip install torch_spline_conv-1.2.2+pt20cu118-cp39-cp39-linux_x86_64.whl 
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing ./torch_spline_conv-1.2.2+pt20cu118-cp39-cp39-linux_x86_64.whl
Installing collected packages: torch-spline-conv
Successfully installed torch-spline-conv-1.2.2+pt20cu118

然后安装pyg

pip install torch-geometric

$ pip install torch-geometric
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting torch-geometric
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/97/f0/66ad3a5263aa16efb534aaf4e7da23ffc28c84efbbd720b0c5ec174f6242/torch_geometric-2.5.3-py3-none-any.whl (1.1 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 1.3 MB/s eta 0:00:00
Collecting tqdm (from torch-geometric)
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/18/eb/fdb7eb9e48b7b02554e1664afd3bd3f117f6b6d6c5881438a0b055554f9b/tqdm-4.66.4-py3-none-any.whl (78 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 78.3/78.3 kB 5.5 MB/s eta 0:00:00
Requirement already satisfied: numpy in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (1.23.5)
Requirement already satisfied: scipy in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (1.13.1)
Collecting fsspec (from torch-geometric)
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5e/44/73bea497ac69bafde2ee4269292fa3b41f1198f4bb7bbaaabde30ad29d4a/fsspec-2024.6.1-py3-none-any.whl (177 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 177.6/177.6 kB 1.8 MB/s eta 0:00:00
Requirement already satisfied: jinja2 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (3.1.3)
Requirement already satisfied: aiohttp in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (3.9.5)
Requirement already satisfied: requests in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (2.31.0)
Requirement already satisfied: pyparsing in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (3.0.9)
Requirement already satisfied: scikit-learn in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (1.4.2)
Requirement already satisfied: psutil>=5.8.0 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from torch-geometric) (5.9.0)
Requirement already satisfied: aiosignal>=1.1.2 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from aiohttp->torch-geometric) (1.2.0)
Requirement already satisfied: attrs>=17.3.0 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from aiohttp->torch-geometric) (23.1.0)
Requirement already satisfied: frozenlist>=1.1.1 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from aiohttp->torch-geometric) (1.4.0)
Requirement already satisfied: multidict<7.0,>=4.5 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from aiohttp->torch-geometric) (6.0.4)
Requirement already satisfied: yarl<2.0,>=1.0 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from aiohttp->torch-geometric) (1.9.3)
Requirement already satisfied: async-timeout<5.0,>=4.0 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from aiohttp->torch-geometric) (4.0.3)
Requirement already satisfied: MarkupSafe>=2.0 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from jinja2->torch-geometric) (2.1.3)
Requirement already satisfied: charset-normalizer<4,>=2 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from requests->torch-geometric) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from requests->torch-geometric) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from requests->torch-geometric) (2.1.0)
Requirement already satisfied: certifi>=2017.4.17 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from requests->torch-geometric) (2024.6.2)
Requirement already satisfied: joblib>=1.2.0 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from scikit-learn->torch-geometric) (1.4.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages (from scikit-learn->torch-geometric) (2.2.0)
Installing collected packages: tqdm, fsspec, torch-geometric
Successfully installed fsspec-2024.6.1 torch-geometric-2.5.3 tqdm-4.66.4

安装完成后，查看一下版本

$ conda list torch
# packages in environment at /home/myPC/miniconda3/envs/pyt-gpu-2.0:
#
# Name                    Version                   Build  Channel
pytorch                   2.0.1           gpu_cuda118py39he342708_0    defaults
torch-cluster             1.6.3+pt20cu118          pypi_0    pypi
torch-geometric           2.5.3                    pypi_0    pypi
torch-scatter             2.1.2+pt20cu118          pypi_0    pypi
torch-sparse              0.6.18+pt20cu118          pypi_0    pypi
torch-spline-conv         1.2.2+pt20cu118          pypi_0    pypi

$ conda list pyg-lib
# packages in environment at /home/myPC/miniconda3/envs/pyt-gpu-2.0:
#
# Name                    Version                   Build  Channel
pyg-lib                   0.4.0+pt20cu118          pypi_0    pypi

下载的几个离线包已正常安装！

，导入一下，验证一下，出现如下报错

OSError: /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_cluster/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs

$ ipython
Python 3.9.18 (main, Sep 11 2023, 13:41:44) 
Type 'copyright', 'credits' or 'license' for more information
IPython 8.15.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import torch_geometric.datasets
/home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/typing.py:54: UserWarning: An issue occurred while importing 'pyg-lib'. Disabling its usage. Stacktrace: /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/libpyg.so: undefined symbol: _ZNK5torch8autograd4Node4nameEv
  warnings.warn(f"An issue occurred while importing 'pyg-lib'. "
/home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/typing.py:72: UserWarning: An issue occurred while importing 'torch-scatter'. Disabling its usage. Stacktrace: /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_scatter/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
  warnings.warn(f"An issue occurred while importing 'torch-scatter'. "
/home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/typing.py:83: UserWarning: An issue occurred while importing 'torch-cluster'. Disabling its usage. Stacktrace: /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_cluster/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
  warnings.warn(f"An issue occurred while importing 'torch-cluster'. "
/home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/typing.py:99: UserWarning: An issue occurred while importing 'torch-spline-conv'. Disabling its usage. Stacktrace: /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_spline_conv/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
  warnings.warn(
/home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/typing.py:110: UserWarning: An issue occurred while importing 'torch-sparse'. Disabling its usage. Stacktrace: /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_sparse/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs
  warnings.warn(f"An issue occurred while importing 'torch-sparse'. "
---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
Cell In[1], line 1
----> 1 import torch_geometric.datasets

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/__init__.py:13
     11 import torch_geometric.loader
     12 import torch_geometric.transforms
---> 13 import torch_geometric.datasets
     14 import torch_geometric.nn
     15 import torch_geometric.explain

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/datasets/__init__.py:101
     99 from .sbm_dataset import RandomPartitionGraphDataset
    100 from .mixhop_synthetic_dataset import MixHopSyntheticDataset
--> 101 from .explainer_dataset import ExplainerDataset
    102 from .infection_dataset import InfectionDataset
    103 from .ba2motif_dataset import BA2MotifDataset

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/datasets/explainer_dataset.py:9
      7 from torch_geometric.datasets.graph_generator import GraphGenerator
      8 from torch_geometric.datasets.motif_generator import MotifGenerator
----> 9 from torch_geometric.explain import Explanation
     12 class ExplainerDataset(InMemoryDataset):
     13     r"""Generates a synthetic dataset for evaluating explainabilty algorithms,
     14     as described in the `"GNNExplainer: Generating Explanations for Graph
     15     Neural Networks" <https://arxiv.org/abs/1903.03894>`__ paper.
   (...)
     66             (default: :obj:`None`)
     67     """

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/explain/__init__.py:3
      1 from .config import ExplainerConfig, ModelConfig, ThresholdConfig
      2 from .explanation import Explanation, HeteroExplanation
----> 3 from .algorithm import *  # noqa
      4 from .explainer import Explainer
      5 from .metric import *  # noqa

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/explain/algorithm/__init__.py:1
----> 1 from .base import ExplainerAlgorithm
      2 from .dummy_explainer import DummyExplainer
      3 from .gnn_explainer import GNNExplainer

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/explain/algorithm/base.py:14
      8 from torch_geometric.explain import Explanation, HeteroExplanation
      9 from torch_geometric.explain.config import (
     10     ExplainerConfig,
     11     ModelConfig,
     12     ModelReturnType,
     13 )
---> 14 from torch_geometric.nn import MessagePassing
     15 from torch_geometric.typing import EdgeType, NodeType
     16 from torch_geometric.utils import k_hop_subgraph

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/nn/__init__.py:5
      3 from .data_parallel import DataParallel
      4 from .to_hetero_transformer import to_hetero
----> 5 from .to_hetero_with_bases_transformer import to_hetero_with_bases
      6 from .to_fixed_size_transformer import to_fixed_size
      7 from .encoding import PositionalEncoding, TemporalEncoding

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/nn/to_hetero_with_bases_transformer.py:9
      6 from torch import Tensor
      7 from torch.nn import Module, Parameter
----> 9 from torch_geometric.nn.conv import MessagePassing
     10 from torch_geometric.nn.dense import Linear
     11 from torch_geometric.nn.fx import Transformer

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/nn/conv/__init__.py:8
      6 from .cugraph.sage_conv import CuGraphSAGEConv
      7 from .graph_conv import GraphConv
----> 8 from .gravnet_conv import GravNetConv
      9 from .gated_graph_conv import GatedGraphConv
     10 from .res_gated_graph_conv import ResGatedGraphConv

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_geometric/nn/conv/gravnet_conv.py:13
     10 from torch_geometric.typing import OptTensor, PairOptTensor, PairTensor
     12 try:
---> 13     from torch_cluster import knn
     14 except ImportError:
     15     knn = None

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_cluster/__init__.py:18
     16 spec = cuda_spec or cpu_spec
     17 if spec is not None:
---> 18     torch.ops.load_library(spec.origin)
     19 else:  # pragma: no cover
     20     raise ImportError(f"Could not find module '{library}_cpu' in "
     21                       f"{osp.dirname(__file__)}")

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch/_ops.py:643, in _Ops.load_library(self, path)
    638 path = _utils_internal.resolve_library_path(path)
    639 with dl_open_guard():
    640     # Import the shared library into the process, thus running its
    641     # static (global) initialization code in order to register custom
    642     # operators with the JIT.
--> 643     ctypes.CDLL(path)
    644 self.loaded_libraries.add(path)

File ~/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/ctypes/__init__.py:382, in CDLL.__init__(self, name, mode, handle, use_errno, use_last_error, winmode)
    379 self._FuncPtr = _FuncPtr
    381 if handle is None:
--> 382     self._handle = _dlopen(self._name, mode)
    383 else:
    384     self._handle = handle

OSError: /home/myPC/miniconda3/envs/pyt-gpu-2.0/lib/python3.9/site-packages/torch_cluster/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs

上面的问题经过各种尝试，又是切换pytroch的版本，又是切换cuda的版本、python的版本，重复下载pyg_lib、torch_cluster、torch_scatter、torch_sparse、torch_spline_conv 的其他版本，还是失败！逐一import torch_cluster或者import torch_scatter等，发现没一个库可以用，猜测可能是在conda下，使用pip安装的原因，燃鹅，conda环境下pip安装的包又能正常使用conda list查看到，pip安装的包，也确实安装到了conda对应的环境目录下；

各种尝试验证下，都失败了，几乎绝望放弃了，官网上的conda install -c pyg pyg又无法使用，pip逐一安装的方式又无法使用，绝望!

二、pip一步安装

正确的姿势，只需要一步就能安装了上，我们看看git官网以及pyg的官网的原文

https://github.com/pyg-team/pytorch_geometric
在这里插入图片描述
https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html#
在这里插入图片描述
原来PyG 2.3版本以后，不需要任何其他库即可安装

赶紧把其他之前安装的依赖卸载

# 之前未安装过这些依赖的，可跳过这步
pip uninstall torch-geometric torch-scatter torch-sparse torch-spline-conv pyg-lib torch_cluster

我们再看看当前的环境

运行环境如下：
ubuntu 22.04
python 3.10
pytorch 2.3.0
cuda 11.8

执行安装

pip install torch_geometric

查看一下版本

conda list torch-geometric
# packages in environment at /home/myPC/miniconda3/envs/pyg:
#
# Name                    Version                   Build  Channel
torch-geometric           2.5.3                    pypi_0    pypi

验证一下，无限报错

$ ipython
Python 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.25.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import torch_geometric

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/home/myPC/miniconda3/envs/pyg/bin/ipython", line 11, in <module>
    sys.exit(start_ipython())
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/__init__.py", line 130, in start_ipython
    return launch_new_instance(argv=argv, **kwargs)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/traitlets/config/application.py", line 1075, in launch_instance
    app.start()
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/terminal/ipapp.py", line 317, in start
    self.shell.mainloop()
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/terminal/interactiveshell.py", line 917, in mainloop
    self.interact()
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/terminal/interactiveshell.py", line 910, in interact
    self.run_cell(code, store_history=True)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3075, in run_cell
    result = self._run_cell(
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3130, in _run_cell
    result = runner(coro)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
    coro.send(None)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3334, in run_cell_async
    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3517, in run_ast_nodes
    if await self.run_code(code, result, async_=asy):
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3577, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-1-c36e13293883>", line 1, in <module>
    import torch_geometric
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch_geometric/__init__.py", line 5, in <module>
    from .isinstance import is_torch_instance
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch_geometric/isinstance.py", line 8, in <module>
    import torch._dynamo
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/__init__.py", line 64, in <module>
    torch.manual_seed = disable(torch.manual_seed)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/decorators.py", line 50, in disable
    return DisableContext()(fn)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 410, in __call__
    (filename is None or trace_rules.check(fn))
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/trace_rules.py", line 3378, in check
    return check_verbose(obj, is_inlined_call).skipped
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/trace_rules.py", line 3361, in check_verbose
    rule = torch._dynamo.trace_rules.lookup_inner(
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/trace_rules.py", line 3442, in lookup_inner
    rule = get_torch_obj_rule_map().get(obj, None)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/trace_rules.py", line 2782, in get_torch_obj_rule_map
    obj = load_object(k)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/trace_rules.py", line 2811, in load_object
    val = _load_obj_from_str(x[0])
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/_dynamo/trace_rules.py", line 2795, in _load_obj_from_str
    return getattr(importlib.import_module(module), obj_name)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/nested/_internal/nested_tensor.py", line 417, in <module>
    values=torch.randn(3, 3, device="meta"),
/home/myPC/miniconda3/envs/pyg/lib/python3.10/site-packages/torch/nested/_internal/nested_tensor.py:417: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at /home/conda/feedstock_root/build_artifacts/libtorch_1715556200933/work/torch/csrc/utils/tensor_numpy.cpp:84.)
  values=torch.randn(3, 3, device="meta"),

numpy库又有问题，不对了；尝试更新一下numpy到2.0版本

conda install -c conda-forge numpy==2.0

再次测试

ipython
Python 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.25.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import torch_geometric

这次没报任何错误，完美

总结torch-geometric版本组合

可行的组合版本(亲测)：python 3.10 + pytroch2.3 + cuda11.8 + torch-geometric 2.5.3 + numpy 2.0

另外一种版本组合(亲测)：python3.12 + pytroch2.3 + cuda11.8 + torch-geometric 2.5.3 + numpy 1.26

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