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# <div align="center"><strong>torch-scatter-2.0.9</strong></div>
**编译安装步骤** ## 简介
torch-scatter是一个在PyTorch库中使用的Python库,它用于从张量中随机选择元素并返回一个新的张量。这个库提供了一种简单的方法来创建具有随机标签的数据集,这对于许多机器学习任务非常有用,例如数据增强或生成对抗网络(GANs)。
- 1. 安装与dtk版本对应的pytorch torchvision whl(可在光合社区下载https://developer.hpccube.com/tool/)以及其他依赖库 ## 依赖安装
+ pytorch1.10或者pytorch1.13 以及对应的torchvision(建议dtk-22.04.2、dtk-23.04与dtk-23.10)
+ python 3.7-3.10
例如:
### 1、使用源码编译方式安装
pip install https://cancon.hpccube.com:65024/directlink/4/pytorch/dtk22.10/torch-1.10.0a0+git2040069.dtk2210-cp38-cp38-manylinux2014_x86_64.whl
#### 编译环境准备
pip install https://cancon.hpccube.com:65024/directlink/4/vision/dtk22.10/torchvision-0.10.0a0+e04d001.dtk2210-cp38-cp38-manylinux2014_x86_64.whl 提供2种环境准备方式:
pip install -r requirements.txt 1. 基于光源pytorch基础镜像环境:镜像下载地址:[https://sourcefind.cn/#/image/dcu/pytorch](https://sourcefind.cn/#/image/dcu/pytorch),根据pytorch、python、dtk及系统下载对应的镜像版本。
- 2. 添加编译时的conda环境及部分库的环境变量 2. 基于现有python环境:安装pytorch和torchvision,whl包下载目录:[https://cancon.hpccube.com:65024/4/main/pytorch](https://cancon.hpccube.com:65024/4/main/pytorch)[https://cancon.hpccube.com:65024/4/main/vision](https://cancon.hpccube.com:65024/4/main/vision),根据python、dtk版本,下载对应pytorch和torchvision的whl包。安装命令如下:
```shell
- 2.1 激活对应的conda环境: pip install torch* (下载的torch的whl包)
pip install torchvision* (下载的torchvision的whl包)
pip install setuptools==59.5.0 wheel
source ~/miniconda3/etc/profile.d/conda.sh ```
conda activate torch1.10_py39_dtk22.10 #### 源码编译安装
- 代码下载
- 2.2 加载对应的module,包括dtk: ```shell
git clone http://developer.hpccube.com/codes/aicomponent/torch-scatter # 根据编译需要切换分支
```
module purge - 源码编译(进入torch-scatter目录):
```
module load compiler/devtoolset/7.3.1 mpi/hpcx/gcc-7.3.1 compiler/dtk/22.10.1 export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$C_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$CPLUS_INCLUDE_PATH
module list export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$C_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$CPLUS_INCLUDE_PATH
- 2.3 加载所需的依赖库的环境变量(根据各集群实际路径调整): export C_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$C_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$CPLUS_INCLUDE_PATH
export LD_LIBRARY_PATH=$ROCM_PATH/rocrand/lib:$LD_LIBRARY_PATH
export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$C_INCLUDE_PATH export FORCE_ONLY_HIP=1
export CC=hipcc
export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$CPLUS_INCLUDE_PATH export CXX=hipcc
export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$C_INCLUDE_PATH python setup.py install
```
export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$CPLUS_INCLUDE_PATH #### 注意事项
+ 若使用pip install下载安装过慢,可添加pypi清华源:-i https://pypi.tuna.tsinghua.edu.cn/simple/
export C_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$C_INCLUDE_PATH + ROCM_PATH为dtk的路径,默认为/opt/dtk
export CPLUS_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$CPLUS_INCLUDE_PATH ## 验证
export LD_LIBRARY_PATH=$ROCM_PATH/rocrand/lib:$LD_LIBRARY_PATH ```python
import torch
- 2.4 修改编译器环境变量: from torch_scatter import scatter_max
src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
export FORCE_ONLY_HIP=1 index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
export CC=hipcc out, argmax = scatter_max(src, index, dim=-1)
```
export CXX=hipcc
```
- 3. 编译安装 print(out)
tensor([[0, 0, 4, 3, 2, 0],
[2, 4, 3, 0, 0, 0]])
python setup.py install
print(argmax)
tensor([[5, 5, 3, 4, 0, 1]
[1, 4, 3, 5, 5, 5]])
```
## Known Issue
- 该库没有基于cpu环境修改,仅支持dcu,请在有dcu卡的环境运行。
- 如需完整使用所有pyg功能,请pip install torch-geometric
## 参考资料
- [README_ORIGIN](README_ORIGIN.md)
- [https://pypi.org/project/torch-scatter/2.0.9/](https://pypi.org/project/torch-scatter/2.0.9/)
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