版本查詢
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
需要添加變量
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:$PATH
conda env list
source activate keras
python3.5
tensorflow 1.11.0
keras 2.2.2
nvidia-docker run -it --rm pytorch-gpu:1.1.0 /bin/bash
pytorch 直接使用
[root@191ddd30d4ae /]# python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.__version__)
1.1.0
>>> print(torch.cuda.device_count())
1
>>> print(torch.cuda.is_available())
True
>>>
cntk
source activate cntk-py35 python3.5
python -c "import cntk; print(cntk.__version__)"
2.4
theano
ValueError: You are trying to use the old GPU back-end. It was removed from Theano. Use device=cuda* now. See https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29 for more information
—————————
vim ~/.bashrc
2：添加如下命令：
export THEANO_FLAGS='mode=FAST_RUN,device=cpu,floatX=float32'
1
3：使修改的theano設置生效：
source ~/.bashrc
1
4：編輯theano對於gpu的配置文件：
vim ~/.theanorc
1
5：添加內容如下：
[global]
device = cuda
floatX=float32
[nvcc]
flags=--machine=64
[lib]
cnmem=100
gpu-theano-in-use:1.0.4 python2.7
source activate theano
python test.py
>>> import theano
/root/anaconda3/envs/theano/lib/python2.7/site-packages/theano/gpuarray/dnn.py:184: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to a version >= v5 and <= v7.
warnings.warn("Your cuDNN version is more recent than "
Using cuDNN version 7603 on context None
Mapped name None to device cuda: GeForce GTX 960M (0000:01:00.0)
>>> theano.__version__
u'1.0.4'
>>>
https://www.jianshu.com/p/4cc75a79dce9
Linux下安裝miniconda
在官網下載miniconda3
執行：bash Miniconda3-latest-Linux-x86_64.sh 之后跟隨提示步驟，安裝過程中可以自動添加路徑到配置文件，也可以之后進行配置。在這期間輸入 yes no （在這里我是之后配置的所以執行３）
將其添加到大環境變量中去
－vim ~/.bashrc
－export PATH=~/anaconda3/bin:$PATH
－source ~/.bashrc
創建虛擬環境並安裝theano (主要參考官網教程http://deeplearning.net/software/theano/install_ubuntu.html)
基於python2.7創建一個名為theano的環境： conda create --name theano python=2.7
進入虛擬環境： source activate theano
－使用conda安裝：conda install numpy scipy mkl
pip install parameterized
conda install theano pygpu
－使用pip安裝：pip install Theano
Install and configure the GPU drivers (這一步我沒有嘗試，因為本來就安裝好了)
配置theanoGPU環境
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的顯存分給當前終端
也可以不用5，直接在運行的時候使用命令：THEANO_FLAGS='device=cuda,floatX=float32'
默認為cuda0）
測試
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(已經存在的虛擬環境)
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/ 安裝
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
需要添加變量
python 3.5.6
export PATH=/root/anaconda3/bin:$PATH
python -c "import cntk; print(cntk.__version__)"
2.7
新的名字：conda-cntk-pass cntk2.7
theano
caffe2 直接使用
python 3.6.9
import caffe2
安裝
conda create -n caffe2 python=3.6
conda activate caffe2
conda install pytorch-nightly-cpu -c pytorch -n caffe2
python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"
pip install protobuf
pip install future
gpu
tensorflow-gpu:1.11.0 python 3.5
export PATH=/root/anaconda3/bin:$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:$PATH
conda env list
source activate keras
python3.5
tensorflow 1.11.0
keras 2.2.2
nvidia-docker run -it --rm pytorch-gpu:1.1.0 /bin/bash
pytorch 直接使用
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
conda install pytorch torchvision -c pytorch -n pytorch
[root@191ddd30d4ae /]# python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.__version__)
1.1.0
>>> print(torch.cuda.device_count())
1
>>> print(torch.cuda.is_available())
True
>>>
cntk
source activate cntk-py35 python3.5
python -c "import cntk; print(cntk.__version__)"
2.4
theano
ValueError: You are trying to use the old GPU back-end. It was removed from Theano. Use device=cuda* now. See https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29 for more information
—————————
vim ~/.bashrc
2：添加如下命令：
export THEANO_FLAGS='mode=FAST_RUN,device=cpu,floatX=float32'
1
3：使修改的theano設置生效：
source ~/.bashrc
1
4：編輯theano對於gpu的配置文件：
vim ~/.theanorc
1
5：添加內容如下：
[global]
device = cuda
floatX=float32
[nvcc]
flags=--machine=64
[lib]
cnmem=100
gpu-theano-in-use:1.0.4 python2.7
source activate theano
python test.py
>>> import theano
/root/anaconda3/envs/theano/lib/python2.7/site-packages/theano/gpuarray/dnn.py:184: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to a version >= v5 and <= v7.
warnings.warn("Your cuDNN version is more recent than "
Using cuDNN version 7603 on context None
Mapped name None to device cuda: GeForce GTX 960M (0000:01:00.0)
>>> theano.__version__
u'1.0.4'
>>>
https://www.jianshu.com/p/4cc75a79dce9
Linux下安裝miniconda
在官網下載miniconda3
執行：bash Miniconda3-latest-Linux-x86_64.sh 之后跟隨提示步驟，安裝過程中可以自動添加路徑到配置文件，也可以之后進行配置。在這期間輸入 yes no （在這里我是之后配置的所以執行３）
將其添加到大環境變量中去
－vim ~/.bashrc
－export PATH=~/anaconda3/bin:$PATH
－source ~/.bashrc
創建虛擬環境並安裝theano (主要參考官網教程http://deeplearning.net/software/theano/install_ubuntu.html)
基於python2.7創建一個名為theano的環境： conda create --name theano python=2.7
進入虛擬環境： source activate theano
－使用conda安裝：conda install numpy scipy mkl
pip install parameterized
conda install theano pygpu
－使用pip安裝：pip install Theano
Install and configure the GPU drivers (這一步我沒有嘗試，因為本來就安裝好了)
配置theanoGPU環境
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的顯存分給當前終端
也可以不用5，直接在運行的時候使用命令：THEANO_FLAGS='device=cuda,floatX=float32'
（默認為cuda0）
測試
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
看官網文檔安裝
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安裝
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安裝
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、編譯Caffe、PyCaffe
URL : https://github.com/BVLC/caffe.git
1
1.下載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
然后在這個環境下安裝
利用anaconda2隨意切換proto的版本，多proto並存，protobuf，libprotobuf
2.編譯caffe
用cmake默認配置：
1
[注意]：一般需要修改config文件。
進入caffe根目錄
mkdir build
cd build
cmake ..
make all -j8
make install
make runtest -j8
3.安裝pycaffe需要的依賴包，並編譯pycaffe
cd ../python
conda install cython scikit-image ipython h5py nose pandas protobuf pyyaml jupyter
for req in $(cat requirements.txt); do pip install $req; done
cd ../build
make pycaffe -j8
4.添加pycaffe的環境變量
終端輸入如下指令：
vim ~/.bashrc
在最后一行添加caffe的python路徑(到達vim最后一行快捷鍵：Shift+G)：
export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
注意： /path/to/caffe是下載的Caffe的根目錄，例如我的路徑為：/home/Jack-Cui/caffe-master/python
Source環境變量，在終端執行如下命令：
source ~/.bashrc
注意： Source完環境變量，會退出testcaffe這個conda環境，再次使用命令進入即可。
四、測試
執行如下命令：
python -c "import caffe; print dir(caffe)"
fatal error: pyconfig.h: No such file or directory
如果使用的是系統的python路徑，解決方法如下：
make clean
export CPLUS_INCLUDE_PATH=/usr/include/python2.7
make all -j8
如果使用的是anaconda Python，路徑如下：
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
裝的是python3.6，項目中用到boost相關代碼，編譯時找不到pyconfig.h。看了一下/usr/include/python3.6和/usr/include/python3.6m，都只有一個pyconfig-64.h文件。
網上查了一圈，找了各種方法都搞不定，其中一種方法可以安裝一堆.h進/usr/include/python2.7，3.6文件夾中還是沒有。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位安裝python-devel.x86_64，32位安裝python-devel.i686，我這里安裝:
sudo yum install python-devel.x86_64
受此啟發，輸入命令查找3.6版本相關的python包
yum search python | grep python36
發現下面這個應該是我們想要的
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、編譯Caffe、PyCaffe
URL : https://github.com/BVLC/caffe.git
1
1.下載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
然后在這個環境下安裝
利用anaconda2隨意切換proto的版本，多proto並存，protobuf，libprotobuf
2.編譯caffe
用cmake默認配置：
1
[注意]：一般需要修改config文件。
進入caffe根目錄
mkdir build
cd build
cmake ..
make all -j8
make install
make runtest -j8
3.安裝pycaffe需要的依賴包，並編譯pycaffe
cd ../python
conda install cython scikit-image ipython h5py nose pandas protobuf pyyaml jupyter
for req in $(cat requirements.txt); do pip install $req; done
cd ../build
make pycaffe -j8
4.添加pycaffe的環境變量
終端輸入如下指令：
1
vim ~/.bashrc
1
在最后一行添加caffe的python路徑(到達vim最后一行快捷鍵：Shift+G)：
1
export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
1
2
注意： /path/to/caffe是下載的Caffe的根目錄，例如我的路徑為：/home/Jack-Cui/caffe-master/python
Source環境變量，在終端執行如下命令：
1
source ~/.bashrc
1
注意： Source完環境變量，會退出testcaffe這個conda環境，再次使用命令進入即可。
四、測試
執行如下命令：
python -c "import caffe; print dir(caffe)"
輸出結果如下：
注意： 如果創建了conda環境，每次想要使用caffe，需要先進入這個創建的conda環境。
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設備
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
conda 安裝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/
添加清華源
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/
# 設置搜索時顯示通道地址
conda config --set show_channel_urls yes
————————————————————————————————————————————————————————————————————————————————
設置搜索時顯示通道地址 |
conda config --set show_channel_urls yes
conda GPU的命令如圖所示：
conda install pytorch torchvision -c pytorch
conda CPU的命令如圖所示：
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
查看已經添加的channels
conda config --get channels
已添加的channel在哪里查看
vim ~/.condarc
conda search gatk
安裝完成后，可以用“which 軟件名”來查看該軟件安裝的位置：
which gatk
如需要安裝特定的版本:
conda install 軟件名=版本號
conda install gatk=3.7
查看已安裝軟件:
conda list
更新指定軟件:
conda update gatk
卸載指定軟件:
conda remove gatk
cntk
https://blog.csdn.net/Jonms/article/details/79550512
ubuntu1604 cuda -cudnn
接着，運行下面的命令安裝anaconda
$ sh Anaconda3-5.1.0-Linux-x86_64.sh
anaconda的安裝很簡單，這里就不多描述。
CNTK需要你的系統安裝有OpenMPI。在Ubuntu中可以通過以下命令安裝
$ sudo apt install openmpi-bin
然后，創建名為cntk-py35的虛擬環境
$ conda create --name cntk-py35 python=3.5 numpy scipy h5py jupyter
激活cntk虛擬環境
$ source activate cntk-py35
關閉cntk虛擬環境
$ source deactivate
激活虛擬環境后，用pip安裝CNTK(GPU)即可
$ pip install https://cntk.ai/PythonWheel/GPU/cntk-2.4-cp35-cp35m-linux_x86_64.whl
測試CNTK是否安裝成功並輸出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__)"
報錯：
ImportError: No module named 'cntk._cntk_py'
ImportError: libpython3.5m.so.1.0: cannot open shared object file: No such file or directory
處理：
find / -name "libpython3.5m.so.1.0" 找到路徑 使用conda安裝的
/root/anaconda3/envs/cntk-py35/lib/ 加入環境變量
#cd /etc/ld.so.conf.d
#vim python3.conf
將編譯后的python/lib地址加入conf文件
#ldconfig
容器環境變量會丟失，使用dockerfile重新賦值。 export PATH=/root/anaconda3/bin:$PATH 上面的鏈接庫配置
pip https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp36-cp36m-linux_x86_64.whl
python3.7環境下
theano
apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev
pip install Theano
NumPy (~30s): python -c "import numpy; numpy.test()"
SciPy (~1m): python -c "import scipy; scipy.test()"
Theano (~30m): python -c "import theano; theano.test()"
已安裝cuda
export PATH=/usr/local/cuda-5.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-5.5/lib64:$LD_LIBRARY_PATH
安裝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
#進入python輸入
from caffe2.python import workspace
錯誤:
ModuleNotFoundError: No module named 'google'
pip install protobuf
ModuleNotFoundError: No module named 'past'
pip install future
安裝后檢測
python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"
gpu檢測
python -m caffe2.python.operator_test.relu_op_test
Python2.7和Python3.6下都可以，不過只是cpu版本，只限於Mac和Ubuntu平台下：
conda install -c caffe2 caffe2
參考網址：
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安裝GPU版本caffe2
https://blog.csdn.net/Andrwin/article/details/94736930
caffe安裝
https://blog.csdn.net/jacky_ponder/article/details/53129355
cntk
