ubuntu14.04 + anaconda3-5 + ipython notebook + tensorflow安装
2018-01-28 00:08
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1.安装 anaconda3-5
在清华大学开源的镜像网站上下载 anaconda3-5,比从官网上下载快很多。
清华大学开源镜像
安装完成后,将 {HOME}/anaconda3/bin添加到PATH中; {HOME}/anaconda3/lib
添加到LD_LIBRARY_PATH中。
2. 安装 ipython notebook
现在系统中共存着python2.7和python3.5两套pyhton版本,并且都添加到的系统的搜索路径中。若使用pip install jupyter 的命令安装,系统会默认使用python2.7的pip进行安装,会报错 --- 提示需要升级pip(pyhton2.7不兼容ipython notebook)。
我的做法是改变pyhton2.7文件夹的名字(将/usr/local/lib/python2.7 改为 /usr/local/lib/python2.7_),系统找不到python2.7便会调用python3.5的pip进行安装。
完成 ipython notebook的安装,在终端输入ipython notebook便可以愉快的使用了。
3. 安装tensorflow
在Anaconda的仓库中搜索可用的tensorflow的版本:
选择一个版本“jjhelmus/tensorflow-gpu” 查询版本信息:
运行安装命令:
最后输入:
查看是否安装成功
在清华大学开源的镜像网站上下载 anaconda3-5,比从官网上下载快很多。
清华大学开源镜像
安装完成后,将 {HOME}/anaconda3/bin添加到PATH中; {HOME}/anaconda3/lib
添加到LD_LIBRARY_PATH中。
2. 安装 ipython notebook
现在系统中共存着python2.7和python3.5两套pyhton版本,并且都添加到的系统的搜索路径中。若使用pip install jupyter 的命令安装,系统会默认使用python2.7的pip进行安装,会报错 --- 提示需要升级pip(pyhton2.7不兼容ipython notebook)。
我的做法是改变pyhton2.7文件夹的名字(将/usr/local/lib/python2.7 改为 /usr/local/lib/python2.7_),系统找不到python2.7便会调用python3.5的pip进行安装。
pip install jupyter
完成 ipython notebook的安装,在终端输入ipython notebook便可以愉快的使用了。
3. 安装tensorflow
在Anaconda的仓库中搜索可用的tensorflow的版本:
anaconda search -t conda tensorflow
Using Anaconda API: https://api.anaconda.org Packages: Name | Version | Package Types | Platforms | Builds ------------------------- | ------ | --------------- | --------------- | ---------- GlaxoSmithKline/tensorflow | 0.12.0 | conda | linux-64 | py27hb0d0e74_0 : TensorFlow is a machine learning library HCC/tensorflow | 1.4.0 | conda | linux-64 | py34_1, py27_1, py27_0, py36_0, py35_0, py35_1 : Computation using data flow graphs for scalable machine learning. HCC/tensorflow-cpucompat | 1.4.0 | conda | linux-64 | py36_0, py27_0, py35_0, py34_0 : Computation using data flow graphs for scalable machine learning. HCC/tensorflow-fma | 1.4.0 | conda | linux-64 | py27_1, py34_1, py27_0, py36_0, py34_0, py35_0, py35_1 : Computation using data flow graphs for scalable machine learning. SentientPrime/tensorflow | 0.6.0 | conda | osx-64 | py27_0 : TensorFlow helps the tensors flow SmartAg/tensorflow_gpu | 1.0.1 | conda | linux-aarch64 | 0 aaronzs/tensorflow | 1.4.0 | conda | linux-64, win-64, osx-64 | py36h39705f4_0, py36h8a03e48_0, py35hc784f49_0, py36h6db853c_0, py35h2d7a08b_0, py35h1150644_0, py35h5a8cc8b_0, py35hc0f5839_0, py36hebc11a6_0, py35ha700c16_0, py36hf8f6b73_0, py36heb185b1_0, py35hf9a0815_0, py36h2003710_0, py36h4df9c7b_0, py35h6467dd0_0, py36hd42d972_0, py36he4e0f4f_0, py35h89e3332_0 : TensorFlow helps the tensors flow aaronzs/tensorflow-gpu | 1.4.0 | conda | linux-64, win-64 | py35h95763ad_0, py36h03e8729_0, py35h8ac8084_0, py35hb2e3085_0, py35hc6fb95a_0, py36ha20c466_0, py35h3b8745f_0, py36hbec5d8f_0, py36h74c31d8_0, py36h6bf4e57_0, py36h7b11560_0, py35h14e71af_0, py36h559dc3e_0 : TensorFlow helps the tensors flow aaronzs/tensorflow-tensorboard | 0.4.0rc3 | conda | linux-64, osx-64, win-64 | py35h30a7cae_0, py36h1eb756b_0, py35h8792995_0, py35h98b1d99_0, py36hbb25e9c_0, py35h0e1fd4a_0, py36h7c6d2df_0, py35h6181586_0, py36h1ee23b2_0, py36hffc986b_0, py35h85b20a5_0, py35h93bdf65_0, py36h4568b58_0, py36h5698cb7_0, py35h985ceb1_0, py35h83d8c28_0, py36hf2576c0_0, py36h52f5384_0, py36h9271151_0, py36ha443a3c_0, py35hbab8bba_0, py35h14ff132_0, py36h9a29024_0, py35h9958e77_0, py36h662c838_0, py36hd60226d_0 : TensorBoard lets you watch Tensors Flow acellera/tensorflow-cuda | 0.12.1 | conda | linux-64 | 1 anaconda/tensorflow | 1.4.1 | conda | linux-ppc64le, linux-64, osx-64, win-64 | np111py27_0, 0, np111py34_0, py36_0, np112py36_0, py27_0, np112py35_0, np111py36_0, py35_0, np112py27_0, np111py35_0 : TensorFlow is a machine learning library.
选择一个版本“jjhelmus/tensorflow-gpu” 查询版本信息:
anaconda show jjh_cio_testing/tensorflow-gpu
Using Anaconda API: https://api.anaconda.org Name: tensorflow-gpu Summary: TensorFlow is a machine learning library. Access: public Package Types: conda Versions: + 1.0.1 + 1.1.0 + 1.2.1 + 1.3.0 To install this package with conda run: conda install --channel https://conda.anaconda.org/jjh_cio_testing tensorflow-gpu
运行安装命令:
conda install --channel https://conda.anaconda.org/jjh_cio_testing tensorflow-gpu
最后输入:
import tensorflow as tf
查看是否安装成功
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