cpugpu 安装框架pytorch，cntk,theano及测试

Posted 2022-10-23 g2thend

一，cpu 下安装

tensorflow
conda env list
source activate tensorflow
直接安装相应版本

python
import tensorflow as tf
tf.__version__ 1.11.0

keras 直接安装

conda env list
source activate keras
import keras 2.2.2
print(keras.__version__)
import tensorflow as tf
tf.__version__

pytorch

import torch
print(torch.__version__)
print(torch.cuda.device_count())
print(torch.cuda.is_available())

cntk
/root/anaconda3/bin/conda env list
source activate cntk-py35

python 3.5.6
export PATH=/root/anaconda3/bin:$PATH
python -c "import cntk; print(cntk.__version__)"

theano

caffe2
python 3.6.9
import caffe2

安装
conda create -n caffe2 python=3.6
conda activate caffe2
conda install pytorch-nightly-cpu -c pytorch -n caffe2

python -c ‘from caffe2.python import core‘ 2>/dev/null && echo "Success" || echo "Failure"
报错:
pip install protobuf
pip install future

参考官网安装即可

gpu

tensorflow-gpu:1.11.0 python 3.5

export PATH=/root/anaconda3/bin:$PATH
source activate tensorflow

keras
export PATH=/root/anaconda3/bin:$PATH
conda env list
source activate keras
python3.5

nvidia-docker run -it --rm pytorch-gpu:1.1.0 /bin/bash
pytorch
[root@191ddd30d4ae /]# python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

import torch
print(torch.__version__)
1.1.0
print(torch.cuda.device_count())
1
print(torch.cuda.is_available())
True

cntk

source activate cntk-py35 python3.5

python -c "import cntk; print(cntk.__version__)"
2.4

theano

gpu-theano-in-use:1.0.4 python2.7

source activate theano
python test.py

import theano
/root/anaconda3/envs/theano/lib/python2.7/site-packages/theano/gpuarray/dnn.py:184: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to a version >= v5 and <= v7.
warnings.warn("Your cuDNN version is more recent than "
Using cuDNN version 7603 on context None
Mapped name None to device cuda: GeForce GTX 960M (0000:01:00.0)
theano.__version__
u‘1.0.4‘

https://www.jianshu.com/p/4cc75a79dce9
Linux下安装miniconda
在官网下载miniconda3
执行：bash Miniconda3-latest-Linux-x86_64.sh 　
－vim ~/.bashrc
－export PATH=~/anaconda3/bin:$PATH
－source ~/.bashrc
创建虚拟环境并安装theano
基于python2.7创建一个名为theano的环境
conda create --name theano python=2.7
进入虚拟环境： source activate theano
－使用conda安装：conda install numpy scipy mkl
pip install parameterized
conda install theano pygpu

       －使用pip安装：pip install Theano

测试参考官网文档

caffe2
看官网文档安装
https://caffe2.ai/docs/getting-started.html?platform=ubuntu&configuration=compile

https://blog.csdn.net/qq_35451572/article/details/79428167

cmake -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-9.0 -DCUDNN_ROOT_DIR=/usr/local/cuda

To check if Caffe2 build was successful

python -c ‘from caffe2.python import core‘ 2>/dev/null && echo "Success" || echo "Failure"

To check if Caffe2 GPU build was successful

This must print a number > 0 in order to use Detectron

python -c ‘from caffe2.python import workspace; print(workspace.NumCudaDevices())‘

参考
https://blog.csdn.net/Yan_Joy/article/details/70241319

https://www.nvidia.com/en-gb/data-center/gpu-accelerated-applications/caffe2/
https://blog.csdn.net/qq_35451572/article/details/79428167
https://blog.csdn.net/qq_16525279/article/details/79724728
https://blog.csdn.net/y_f_raquelle/article/details/83278953
https://www.cnblogs.com/nanzhao/p/9596844.html

附:conda常用

1. conda env list 或 conda info -e 查看当前存在哪些虚拟环境

2. conda update conda 检查更新当前conda

3. conda update --all 更新本地已安装的包

4. conda create -n your_env_name python=X.X（2.7、3.6等） anaconda 命令创建python版本为X.X、名字为your_env_name的虚拟环境。your_env_name文件可以在Anaconda安装目录envs文件下找到。

5. Windows: activate your_env_name(虚拟环境名称) 激活虚拟环境

6. conda install -n your_env_name [package] 安装package到your_env_name中

7. linux: source deactivate Windows: deactivate 关闭虚拟环境

8. conda remove -n your_env_name(虚拟环境名称) --all 删除虚拟环境

9. conda remove --name your_env_name package_name 删除环境中的某个