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  • 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
    
    
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  • 原文地址:https://www.cnblogs.com/g2thend/p/11698811.html
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