常用深度学习框架(keras,pytorch.cntk,theano)conda 安装--未整理

版本查询

cpu   
  
tensorflow  
conda  env  list 
source   activate   tensorflow

python  
import tensorflow as tf 和 tf.__version__   1.11.0



keras

conda  env  list
source   activate   keras 
import keras   2.2.2
print(keras.__version__)
import tensorflow as tf 
tf.__version__
1.11.0



pytorch  

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

1.2.0  


cntk 
/root/anaconda3/bin/conda  env  list
 source  activate  cntk-py35
需要添加变量
python  3.5.6
export  PATH=/root/anaconda3/bin:$PATH
 python -c "import cntk; print(cntk.__version__)"  
2.7



新的名字：conda-cntk-pass     cntk2.7


theano     



caffe2     直接使用
python  3.6.9 
import   caffe2  




gpu  

tensorflow-gpu:1.11.0     python 3.5  

export  PATH=/root/anaconda3/bin:$PATH
source  activate  tensorflow
 
1.11.0   新的名字 docker  commit  ba9743bcfc7d   gpu-tensflow-1.11:1.11.0


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

tensorflow 1.11.0
keras 2.2.2



nvidia-docker  run  -it --rm    pytorch-gpu:1.1.0  /bin/bash
pytorch   直接使用 
[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 


ValueError: You are trying to use the old GPU back-end. It was removed from Theano. Use device=cuda* now. See https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29 for more information
—————————
vim ~/.bashrc 
2：添加如下命令：

export THEANO_FLAGS='mode=FAST_RUN,device=cpu,floatX=float32'
1
3：使修改的theano设置生效：

source ~/.bashrc
1
4：编辑theano对于gpu的配置文件：

vim ~/.theanorc
1
5：添加内容如下：

[global]
device = cuda
floatX=float32
[nvcc]
flags=--machine=64
[lib]
cnmem=100
 

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 　之后跟随提示步骤，安装过程中可以自动添加路径到配置文件，也可以之后进行配置。在这期间输入 yes　 no　　　（在这里我是之后配置的所以执行３）
将其添加到大环境变量中去
－vim ~/.bashrc
－export PATH=~/anaconda3/bin:$PATH
－source ~/.bashrc
创建虚拟环境并安装theano (主要参考官网教程http://deeplearning.net/software/theano/install_ubuntu.html)
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
Install and configure the GPU drivers (这一步我没有尝试，因为本来就安装好了)
配置theanoGPU环境
vim ~/.theanorc
在空白文件中添加
[global]
floatX = float32
device = gpu3
[lib]
cnmem = 0.6 意味着有百分之60的显存分给当前终端
也可以不用5，直接在运行的时候使用命令：THEANO_FLAGS='device=cuda,floatX=float32'
默认为cuda0）
测试
test.py 文件：
from theano import function, config, shared, tensor
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], tensor.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in range(iters):
    r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (r,))
if numpy.any([isinstance(x.op, tensor.Elemwise) and
              ('Gpu' not in type(x.op).__name__)
              for x in f.maker.fgraph.toposort()]):
    print('Used the cpu')
else:
    print('Used the gpu')


caffe2
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())'

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

1,

cpu   
  
conda  create   -n  xx   --clone   nn(已经存在的虚拟环境)

tensorflow  


conda  env  list 
source   activate   tensorflow
 pip  install   tensorflow==1.11.0

python  
import tensorflow as tf 和 tf.__version__   1.11.0



keras
 pip  install   tensorflow==1.11.0
 pip  install   keras==2.2.2

conda  env  list
source   activate   keras 
import keras   2.2.2
print(keras.__version__)
import tensorflow as tf 
tf.__version__
1.11.0



pytorch  

https://pytorch.org/get-started/locally/   安装
pip3 install torch==1.2.0+cpu torchvision==0.4.0+cpu -f https://download.pytorch.org/whl/torch_stable.html  不行

conda install pytorch torchvision cpuonly -c pytorch  -n pytorch 


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

1.2.0  


cntk 

pip  install  https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp35-cp35m-linux_x86_64.whl
/root/anaconda3/bin/conda  env  list
 source  activate  cntk-py35
需要添加变量
python  3.5.6
export  PATH=/root/anaconda3/bin:$PATH
 python -c "import cntk; print(cntk.__version__)"  
2.7



新的名字：conda-cntk-pass     cntk2.7


theano     



caffe2     直接使用
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"

pip  install   protobuf
pip  install  future




gpu  

tensorflow-gpu:1.11.0     python 3.5  

export  PATH=/root/anaconda3/bin:$PATH
source  activate  tensorflow
 
1.11.0   新的名字 docker  commit  ba9743bcfc7d   gpu-tensflow-1.11:1.11.0


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

tensorflow 1.11.0
keras 2.2.2



nvidia-docker  run  -it --rm    pytorch-gpu:1.1.0  /bin/bash
pytorch   直接使用 

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

conda install pytorch torchvision   -c pytorch  -n  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 


ValueError: You are trying to use the old GPU back-end. It was removed from Theano. Use device=cuda* now. See https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29 for more information
—————————
vim ~/.bashrc 
2：添加如下命令：

export THEANO_FLAGS='mode=FAST_RUN,device=cpu,floatX=float32'
1
3：使修改的theano设置生效：

source ~/.bashrc
1
4：编辑theano对于gpu的配置文件：

vim ~/.theanorc
1
5：添加内容如下：

[global]
device = cuda
floatX=float32
[nvcc]
flags=--machine=64
[lib]
cnmem=100
 

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 　之后跟随提示步骤，安装过程中可以自动添加路径到配置文件，也可以之后进行配置。在这期间输入 yes　 no　　　（在这里我是之后配置的所以执行３）
将其添加到大环境变量中去
－vim ~/.bashrc
－export PATH=~/anaconda3/bin:$PATH
－source ~/.bashrc
创建虚拟环境并安装theano (主要参考官网教程http://deeplearning.net/software/theano/install_ubuntu.html)
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
Install and configure the GPU drivers (这一步我没有尝试，因为本来就安装好了)
配置theanoGPU环境
vim ~/.theanorc
在空白文件中添加
[global]
floatX = float32
device = gpu3
[lib]
cnmem = 0.6 意味着有百分之60的显存分给当前终端
也可以不用5，直接在运行的时候使用命令：THEANO_FLAGS='device=cuda,floatX=float32'
（默认为cuda0）
测试
test.py 文件：
from theano import function, config, shared, tensor
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], tensor.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in range(iters):
    r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (r,))
if numpy.any([isinstance(x.op, tensor.Elemwise) and
              ('Gpu' not in type(x.op).__name__)
              for x in f.maker.fgraph.toposort()]):
    print('Used the cpu')
else:
    print('Used the gpu')






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())'







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








python -m pip install --user numpy scipy matplotlib  pandas  



 nltk  scikit-learn 

nltk安装
Install NLTK: run pip install --user -U nltk

Install Numpy (optional): run pip install --user -U numpy

Test installation: run python then type import nltk




Installing scikit-learn，require：
Python (>= 3.5)
NumPy (>= 1.11.0)
SciPy (>= 0.17.0)
joblib (>= 0.11)

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip

pip install -U scikit-learn
or conda:

conda install scikit-learn

2安装

anaconda  
https://repo.anaconda.com/archive/


conda create -n caffe_gpu -c defaults python=3.6 caffe-gpu
conda create -n caffe -c defaults python=3.6 caffe


import caffe
python -c "import caffe; print dir(caffe)"


https://blog.csdn.net/weixin_37251044/article/details/79763858


一、编译Caffe、PyCaffe

URL : https://github.com/BVLC/caffe.git
1
1.下载Caffe

git clone https://github.com/BVLC/caffe.git 
cd caffe

注意：如果想在anaconda下使用，就先 
source activate caffe_env 
然后在这个环境下安装 
利用anaconda2随意切换proto的版本，多proto并存，protobuf，libprotobuf

2.编译caffe

用cmake默认配置：
1
[注意]：一般需要修改config文件。

进入caffe根目录

mkdir build
cd build
cmake ..
make all -j8
make install 
make runtest -j8
3.安装pycaffe需要的依赖包，并编译pycaffe

cd ../python
conda install cython scikit-image ipython h5py nose pandas protobuf pyyaml jupyter
for req in $(cat requirements.txt); do pip install $req; done
cd ../build
make pycaffe -j8

4.添加pycaffe的环境变量

终端输入如下指令：
vim ~/.bashrc
在最后一行添加caffe的python路径(到达vim最后一行快捷键：Shift+G)：
export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
注意： /path/to/caffe是下载的Caffe的根目录，例如我的路径为：/home/Jack-Cui/caffe-master/python

Source环境变量，在终端执行如下命令：
source ~/.bashrc
注意： Source完环境变量，会退出testcaffe这个conda环境，再次使用命令进入即可。

四、测试

执行如下命令：
python -c "import caffe; print dir(caffe)"
fatal error: pyconfig.h: No such file or directory

如果使用的是系统的python路径，解决方法如下：

make clean
export CPLUS_INCLUDE_PATH=/usr/include/python2.7
make all -j8
如果使用的是anaconda Python，路径如下：

export CPLUS_INCLUDE_PATH=/home/gpf/anaconda3/include/python3.6m

http://blog.csdn.net/GPFYCF521/article/details/80387869


        cd /usr/local/src/caffe-master/
    2  ll
    3  make  pycaffe 
    4  find   /  -name  "Python.h"
    5  export CPLUS_INCLUDE_PATH=/usr/local/src/Python-3.6.4/Include/Python.h:$CPLUS_INCLUDE_PATH
    6  make  clean 
    7  make  pycaffe
    8  export CPLUS_INCLUDE_PATH=/usr/local/src/Python-3.6.4/Include/:$CPLUS_INCLUDE_PATH
    9  make  clean 
   10  make  pycaffe
   11  export CPLUS_INCLUDE_PATH=
   12  export CPLUS_INCLUDE_PATH=/usr/local/src/Python-3.6.4/Include/:$CPLUS_INCLUDE_PATH
   13  make  clean 
   14  make  pycaffe
   15  find   /   -name  "pyconfig.h"
   16   yum install python-devel.x86_64
   17  make   clean 
   18  make  pycaffe
   19  find python3.6
   20  locate python3.6
   21  make clean
   22  export CPLUS_INCLUDE_PATH=/usr/include/python2.7
   23  export CPLUS_INCLUDE_PATH=
   24  export CPLUS_INCLUDE_PATH=/root/anaconda3/include/python3.5m
   25  make  all 
   26  find   /   -name  "pycaffe"
   27  history 





装的是python3.6，项目中用到boost相关代码，编译时找不到pyconfig.h。看了一下/usr/include/python3.6和/usr/include/python3.6m，都只有一个pyconfig-64.h文件。
网上查了一圈，找了各种方法都搞不定，其中一种方法可以安装一堆.h进/usr/include/python2.7，3.6文件夹中还是没有。方法如下：

1. 可以先查看一下含python-devel的包

    yum search python | grep python-devel

2. 64位安装python-devel.x86_64，32位安装python-devel.i686，我这里安装:

    sudo yum install python-devel.x86_64

受此启发，输入命令查找3.6版本相关的python包
yum search python | grep python36
发现下面这个应该是我们想要的
python36u-devel.x86_64 : Libraries and header files needed for Python
 
yum install python36u-devel.x86_64


conda create -n caffe_gpu -c defaults python=3.5 caffe-gpu
conda create -n caffe -c defaults python=3.5 caffe





CONDA  安裝caffe 
一、编译Caffe、PyCaffe

URL : https://github.com/BVLC/caffe.git
1
1.下载Caffe

git clone https://github.com/BVLC/caffe.git 
cd caffe

注意：如果想在anaconda下使用，就先 
source activate caffe_env 
然后在这个环境下安装 
利用anaconda2随意切换proto的版本，多proto并存，protobuf，libprotobuf

2.编译caffe

用cmake默认配置：
1
[注意]：一般需要修改config文件。

进入caffe根目录

mkdir build
cd build
cmake ..
make all -j8
make install 
make runtest -j8
 
3.安装pycaffe需要的依赖包，并编译pycaffe

cd ../python
conda install cython scikit-image ipython h5py nose pandas protobuf pyyaml jupyter
for req in $(cat requirements.txt); do pip install $req; done
cd ../build
make pycaffe -j8
 
4.添加pycaffe的环境变量

终端输入如下指令：
1
vim ~/.bashrc
1
在最后一行添加caffe的python路径(到达vim最后一行快捷键：Shift+G)：
1
export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
1
2
注意： /path/to/caffe是下载的Caffe的根目录，例如我的路径为：/home/Jack-Cui/caffe-master/python

Source环境变量，在终端执行如下命令：
1
source ~/.bashrc
1
注意： Source完环境变量，会退出testcaffe这个conda环境，再次使用命令进入即可。

四、测试

执行如下命令：
python -c "import caffe; print dir(caffe)"

输出结果如下：


 注意： 如果创建了conda环境，每次想要使用caffe，需要先进入这个创建的conda环境。


export   PATH=/root/anaconda3/bin:$PATH


conda create -n caffe  -c defaults python=3.5

conda  install  caffe-gpu

conda  install  tensorflow-gpu==1.11.0   


conda create --name  tensorflow    python=3.5

source activate tensorflow

source deactivate

conda    remove  -n   tensorflow   --all

import tensorflow as tf 和 tf.__version__

您正在使用GPU版本。您可以列出可用的tensorflow设备
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())



conda 安装pytorch  
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/


添加清华源
命令行中直接使用以下命令

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge 
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/

# 设置搜索时显示通道地址
conda config --set show_channel_urls yes


————————————————————————————————————————————————————————————————————————————————
设置搜索时显示通道地址                                                           |
 conda config --set show_channel_urls yes
conda GPU的命令如图所示：
conda install pytorch torchvision -c pytorch
conda CPU的命令如图所示：
conda install pytorch-cpu -c pytorch 

pip3 install torchvision

pytorch-gpu
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
 
import torch
print(torch.__version__)   
print(torch.cuda.device_count())
print(torch.cuda.is_available())


--------------------------------------------------------------------------------|


conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/


 conda config --set show_channel_urls yes 
查看已经添加的channels

conda config --get channels
已添加的channel在哪里查看

vim ~/.condarc

conda search gatk
安装完成后，可以用“which 软件名”来查看该软件安装的位置：

 which gatk
如需要安装特定的版本:
conda install 软件名=版本号
conda install gatk=3.7


查看已安装软件:

conda list
更新指定软件:

conda update gatk
卸载指定软件:

conda remove gatk





cntk  

https://blog.csdn.net/Jonms/article/details/79550512
ubuntu1604   cuda -cudnn
接着，运行下面的命令安装anaconda

$ sh Anaconda3-5.1.0-Linux-x86_64.sh
anaconda的安装很简单，这里就不多描述。

CNTK需要你的系统安装有OpenMPI。在Ubuntu中可以通过以下命令安装

$ sudo apt install openmpi-bin
然后，创建名为cntk-py35的虚拟环境

$ conda create --name cntk-py35 python=3.5 numpy scipy h5py jupyter
激活cntk虚拟环境

$ source activate cntk-py35
关闭cntk虚拟环境

$ source deactivate
激活虚拟环境后，用pip安装CNTK(GPU)即可

$ pip install https://cntk.ai/PythonWheel/GPU/cntk-2.4-cp35-cp35m-linux_x86_64.whl
测试CNTK是否安装成功并输出CNTK版本

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





cpu  
pip  install  https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp35-cp35m-linux_x86_64.whl

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



报错：
ImportError: No module named 'cntk._cntk_py'
ImportError: libpython3.5m.so.1.0: cannot open shared object file: No such file or directory

处理：
 find     /  -name  "libpython3.5m.so.1.0"   找到路径  使用conda安装的

/root/anaconda3/envs/cntk-py35/lib/   加入环境变量
#cd /etc/ld.so.conf.d

#vim python3.conf

将编译后的python/lib地址加入conf文件

#ldconfig


容器环境变量会丢失，使用dockerfile重新赋值。  export   PATH=/root/anaconda3/bin:$PATH     上面的链接库配置

pip  https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp36-cp36m-linux_x86_64.whl





python3.7环境下

theano  

apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev
 pip install Theano


NumPy (~30s): python -c "import numpy; numpy.test()"
SciPy (~1m): python -c "import scipy; scipy.test()"
Theano (~30m): python -c "import theano; theano.test()"

已安装cuda
export PATH=/usr/local/cuda-5.5/bin:$PATH
 
export LD_LIBRARY_PATH=/usr/local/cuda-5.5/lib64:$LD_LIBRARY_PATH





安装Caffe2
docker pull caffe2ai/caffe2
 
# to test
nvidia-docker run -it caffe2ai/caffe2:latest python -m caffe2.python.operator_test.relu_op_test
 
# to interact
nvidia-docker run -it caffe2ai/caffe2:latest /bin/bash
 

python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"
#返回Success就OK
python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'
#返回1就OK
#进入python输入
from caffe2.python import workspace

错误:
ModuleNotFoundError: No module named 'google'
pip  install   protobuf
ModuleNotFoundError: No module named 'past'

 pip  install  future 


安装后检测
python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"


gpu检测
python -m caffe2.python.operator_test.relu_op_test


Python2.7和Python3.6下都可以，不过只是cpu版本，只限于Mac和Ubuntu平台下：

conda install -c caffe2 caffe2


参考网址：
https://blog.csdn.net/qq_35451572/article/details/79428167


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


https://blog.csdn.net/zmm__/article/details/90285887

https://blog.csdn.net/u013842516/article/details/80604409




使用Docker安装GPU版本caffe2

https://blog.csdn.net/Andrwin/article/details/94736930
caffe安装
https://blog.csdn.net/jacky_ponder/article/details/53129355



cntk