2. 程式人生 > >Ubuntu16.04使用Anaconda5搭建TensorFlow使用環境 圖文詳細教程

Ubuntu16.04使用Anaconda5搭建TensorFlow使用環境 圖文詳細教程

阿新 • • 發佈：2019-01-23

說明


- Ubuntu版本16.04 LTS
- Anaconda版本 5.0.1 (對應Python 3.6.3)
- Tensorflow 1.3.0(由Anaconda提供，你也可以選擇其他版本，包括GPU的)

Anaconda指的是一個開源的Python發行版本，其包含了conda、Python等180多個科學包及其依賴項，也提供了tensorflow的安裝。
GPU版本和CPU版本各有優劣，CPU版本IO處理高效，GPU版本矩陣運算高效，處理線上資料建議使用CPU版，處理下載好的資料建議使用GPU版。
安裝GPU版需要先安裝CUDA和cuDNN，其他相同。

本部落格撰寫於2018年1月2日，前面已經更新了一系列Python博文，之後還會繼續更新，與此同時開啟TensorFlow新篇章，祝大家新年快樂。
你在其他部落格上看到的安裝教程可能要比本篇繁瑣的多，由於使用了Anaconda，並在虛擬環境中安裝，要簡便很多。
（歡迎點選瀏覽器的星星按鈕收藏本部落格，也歡迎關注博主微博@從流域到海域，私信必回。）

安裝Anaconda5.0.1

這裡寫圖片描述
下載地址：
https://www.anaconda.com/download/#linux
依照系統位數選擇你需要的版本，本部落格選擇的是：64-Bit (x86) Installer (525 MB),然後安裝。

sudo bash Anaconda3-5.0.1-Linux-x86_64.sh

這裡寫圖片描述
ctrl + c跳到license agreement最底，輸入yes回車。
安裝過程遇到問題需要重新安裝，先執行下面的命令刪掉之前的資料夾。

sudo rm -rf anaconda3

安裝過程需要注意一點：

[/home/steve/anaconda3] >>> 
PREFIX=/home/steve/anaconda3
installing: python-3.6
 
.3-hc9025b9_1 ...
......（省略一系列安裝）
Do you wish the installer to prepend the Anaconda3 install location
to PATH in your /home/steve/.bashrc ? [yes|no]
[no] >>> yes   
#一定要在此處選擇yes 選擇yes之後直接進入下個步驟。
#如果它自動選no跳過了 按照提示在.bashrc裡新增提示給出的export的語句 其他教程如下：
vi ~/.bashrc
然後按o，把終端給出的Export語句貼上進去
然後按i，輸入:wq 儲存退出

完成安裝後，重啟terminal，輸入：

source ~/.bashrc   #不重啟電腦的情況下啟用設定

再輸入python。看到原來的python2.7被替換成python3.6.3 | Anaconda，證明安裝成功。

steve@steve-Lenovo-V2000:~$ python
Python 3.6.3 |Anaconda, Inc.| (default, Oct 13 2017, 12:02:49) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

安裝TensorFlow

建立一個tensorflow 虛擬環境：

conda create -n tensorflow python=3.6

啟用tensorflow虛擬環境(之後的使用每次也都要先啟用虛擬環境才可用)：

source activate tensorflow

anaconda search -t conda tensorflow #查詢當前可用的tensorflow包 下面是結果

[email protected]:~$ source activate tensorflow
(tensorflow) [email protected]:~$ 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        | py27_1, py34_1, py34_0, py36_0, py27_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       |    1.3.0 | 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.
     anaconda/tensorflow-base  |    1.3.0 | conda           | linux-64        | py27_0, py36h5293eaa_1, py36_0, py35h79a3156_1, py35_0, py27h0dbb4d0_1
                                          : TensorFlow is a machine learning library, base package contains only tensorflow.
     anaconda/tensorflow-gpu   |    1.3.0 | conda           | linux-ppc64le, linux-64, win-64 | py36_4, np111py27_0, py35cuda8.0cudnn6.0_0, py27_4, py35cuda7.5cudnn6.0_0, 0, py35cuda8.0cudnn5.1_0, py36cuda7.5cudnn5.1_0, py27cuda7.5cudnn5.1_0, py27cuda7.5cudnn6.0_0, np112py35_0, np112py27_0, py27cuda8.0cudnn5.1_0, np111py35_0, py27cuda8.0cudnn6.0_0, py36cuda7.5cudnn6.0_0, np112py36_0, np111py36_0, py36cuda8.0cudnn5.1_0, py36cuda8.0cudnn6.0_0, py35_4, py35cuda7.5cudnn5.1_0
                                          : TensorFlow is a machine learning library.
     anaconda/tensorflow-gpu-base |    1.3.0 | conda           | linux-64        | py27cuda8.0cudnn6.0_1, py27cuda8.0cudnn6.0_0, py35cuda8.0cudnn6.0_0, py36cuda8.0cudnn6.0_0, py36cuda8.0cudnn6.0_1, py35cuda8.0cudnn6.0_1
                                          : TensorFlow is a machine learning library, base GPU package, tensorflow only.
     anaconda/tensorflow-tensorboard |    0.1.5 | conda           | linux-64        | py36_0, py35_0, py27_0
                                          : TensorBoard lets you watch Tensors Flow
     aroth85/tensorflow        |    1.3.0 | conda           | linux-64        | py27_0    
                                          : TensorFlow helps the tensors flow
     conda-forge/r-tensorflow  |      0.7 | conda           | linux-64, osx-64, win-64 | r3.3.2_0, r3.4.1_0
     conda-forge/tensorflow    |    1.4.0 | conda           | linux-64, win-64, osx-64 | py36_2, py27_1, py34_1, py34_0, py36_0, py27_0, py27_2, py35_2, py35_0, py35_1
                                          : TensorFlow helps the tensors flow
     creditx/tensorflow        |    0.9.0 | conda           | linux-64        | py35_0, py27_0
                                          : TensorFlow helps the tensors flow
     derickl/tensorflow        |    1.0.1 | conda           | osx-64          | py27h5185c07_0
                                          : TensorFlow helps the tensors flow
     dhirschfeld/tensorflow    |    1.2.0 | conda           | win-64          | py36_0, py35_0
                                          : Computation using data flow graphs for scalable machine learning 
     dseuss/tensorflow         |          | conda           | osx-64          | py35_0    
     guyanhua/tensorflow       |    1.0.0 | conda           | linux-64        | py27_0    
     ijstokes/tensorflow       | 2017.03.03.1349 | conda, ipynb    | linux-64        | py35_0    
     intel/tensorflow          |    1.4.0 | conda, pypi     | linux-64        | np113py36_1, np113py27_1
     jjh_cio_testing/tensorflow |    1.3.0 | conda           | linux-64        | np111py27_0, np111py35_0, 0, py27_0, py36_0, np112py36_0, np113py35_0, np112py35_0, np111py36_0, np113py27_0, np112py27_0, np113py36_0, py35_0
                                          : TensorFlow is a machine learning library
     jjh_cio_testing/tensorflow-base |    1.3.0 | conda           | linux-64        | py27_0, py36h5293eaa_1, py36_0, py35h79a3156_1, py35_0, py27h0dbb4d0_1
                                          : TensorFlow is a machine learning library, base package contains only tensorflow.
     jjh_cio_testing/tensorflow-gpu |    1.3.0 | conda           | linux-64        | py35cuda7.5cudnn5.1_0, py36_0, py36_3, py36_2, py36_4, py36cuda7.5cudnn5.1_0, py35cuda8.0cudnn6.0_0, py27cuda8.0cudnn5.1_0, py27_4, py27_3, py27_2, py27_1, py27_0, py35cuda7.5cudnn6.0_0, np113py35_0, 0, py35cuda8.0cudnn5.1_0, py36cuda8.0cudnn5.1_0, np111py27_0, py27cuda7.5cudnn5.1_0, py27cuda7.5cudnn6.0_0, np112py35_0, np112py27_0, np113py36_0, np111py35_0, py27cuda8.0cudnn6.0_0, py36cuda7.5cudnn6.0_0, np112py36_0, np111py36_0, np113py27_0, py36cuda8.0cudnn6.0_0, py35_4, py35_2, py35_3, py35_0
                                          : TensorFlow is a machine learning library.
     jjh_cio_testing/tensorflow-gpu-base |    1.3.0 | conda           | linux-64        | py27cuda8.0cudnn6.0_1, py27cuda8.0cudnn6.0_0, py35cuda8.0cudnn6.0_0, py36cuda8.0cudnn6.0_0, py36cuda8.0cudnn6.0_1, py35cuda8.0cudnn6.0_1
                                          : TensorFlow is a machine learning library, base GPU package, tensorflow only.
     jjh_cio_testing/tensorflow-tensorboard |    0.1.5 | conda           | linux-64        | py36_0, py35_0, py27_0
                                          : TensorBoard lets you watch Tensors Flow
     jjh_ppc64le/tensorflow    |    1.2.1 | conda           | linux-ppc64le   | py27_0, py36_0, np112py36_0, np112py35_0, np112py27_0, py35_0
                                          : TensorFlow is a machine learning library
     jjh_ppc64le/tensorflow-gpu |    1.2.1 | conda           | linux-ppc64le   | py27cuda8.0cudnn6.0_0, np112py36_0, np112py35_0, py35cuda8.0cudnn6.0_0, py36cuda8.0cudnn6.0_0, np112py27_0
                                          : TensorFlow is a machine learning library
     jjhelmus/tensorflow       | 0.12.0rc0 | conda, pypi     | linux-64, osx-64 | py27_1, py34_1, py27_0, py34_0, py27_2, py35_0, py35_1
                                          : TensorFlow helps the tensors flow
     jjhelmus/tensorflow-gpu   |    1.0.1 | conda           | linux-64        | np112py35_5, np112py36_5, py27_2, np112py27_5
                                          : TensorFlow is a machine learning library.
     jjhelmus/tensorflow-gpu-base |    1.3.0 | conda           | linux-64        | py27cuda8.0cudnn6.0_1, py35cuda8.0cudnn6.0_1
                                          : TensorFlow is a machine learning library, base GPU package, tensorflow only.
     kevin-keraudren/tensorflow |    0.9.0 | conda           | linux-64        | py35_12   
     loopbio/tensorflow        |    1.3.0 | conda           | linux-64        | cuda8_cudnn6_mkl_xla_1
                                          : TensorFlow is a machine learning library
     marta-sd/tensorflow       |    1.2.0 | conda           | linux-64        | py27_2, py35hbaace4d_3, py27he497762_3, py36_2, py36hb9c984a_3, py35_2
     marta-sd/tensorflow-gpu   |    1.2.0 | conda           | linux-64        | py27_1, py36h1323ef4_2, py36_1, py27_0, py35hddb9974_2, py27h4f63904_2, py35_0, py35_1
     memex/tensorflow          |    0.5.0 | conda           | linux-64, osx-64 | py27_2    
                                          : TensorFlow helps the tensors flow
     mhworth/tensorflow        |    0.7.1 | conda           | osx-64          | py27_1    
                                          : TensorFlow helps the tensors flow
     miovision/tensorflow      | 0.10.0.gpu | conda           | linux-64, osx-64 | py35_1    
     msarahan/tensorflow       | 1.0.0rc2 | conda           | linux-64        | np111py36_0, np111py27_0, np111py35_0, np111py34_0
     mutirri/tensorflow        | 0.10.0rc0 | conda           | linux-64        | np111py27_0, np111py35_0, np111py34_0
     mwojcikowski/tensorflow   |    1.0.1 | conda           | linux-64        | py36_0, py35_0, py35_1
     nehaljwani/tensorflow     |    1.2.1 | conda           | osx-64, win-64  | np112py27_0, py36_0, np112py36_0, np112py35_0, py35_0
                                          : TensorFlow is a machine learning library
     nehaljwani/tensorflow-gpu |    1.1.0 | conda           | win-64          | np112py36_0, np112py35_0
                                          : TensorFlow is a machine learning library
     r/r-tensorflow            |      1.4 | conda           | linux-64, win-32, win-64, linux-32, osx-64 | py36r3.4.1_0, r342h38ebd79_0, r342h0e1eca8_0, r342hd3d5cfb_0, r342h0bf44f9_0, r3.4.1_0, r342h935e3b1_0
                                          : Interface to 'TensorFlow' <https://www.tensorflow.org/>,  an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations,  while the graph edges represent the multidimensional data arrays  (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop,  server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team  within Google's Machine Intelligence research organization for the  purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
     rdonnelly/tensorflow      |    0.9.0 | conda           | linux-64        | py27_0, py35_0, py34_0
     sdvillal/tensorflow       |    1.3.0 | conda           | linux-64        | cuda8_cudnn6_mkl_xla_1, cuda8_cudnn6_mkl_xla_0, py27_1, py27_0
                                          : TensorFlow is a machine learning library
     test_org_002/tensorflow   | 0.10.0rc0 | conda           |                 | np111py27_0, np111py35_0, np111py34_0
     thomasantony/tensorflow_gpu |    1.0.1 | conda           | linux-aarch64   | 0         
Found 52 packages

Run 'anaconda show <USER/PACKAGE>' to get installation details
#你可以看到1.4.0的也有 博主求穩選了Anaconda的官方包 你也可安裝1.4.0版本的
#conda-forge/tensorflow 這個是1.4.0版本的

檢視一個包的詳情資訊

(tensorflow) steve@steve-Lenovo-V2000:~$ anaconda show anaconda/tensorflow
Using Anaconda API: https://api.anaconda.org
Name:    tensorflow
Summary: TensorFlow is a machine learning library.
Access:  public
Package Types:  conda
Versions:
   + 0.10.0rc0
   + 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/anaconda tensorflow

安裝tensorflow(直接copy結果給出的命令)

 conda install --channel https://conda.anaconda.org/anaconda tensorflow
 #過程如下 有點慢
(tensorflow) steve@steve-Lenovo-V2000:~$ conda install --channel https://conda.anaconda.org/anaconda tensorflow
Fetching package metadata .............
Solving package specifications: .

Package plan for installation in environment /home/steve/.conda/envs/tensorflow:

The following NEW packages will be INSTALLED:

    backports:              1.0-py36hfa02d7e_1    anaconda
    backports.weakref:      1.0rc1-py36_0         anaconda
    bleach:                 1.5.0-py36_0          anaconda
    html5lib:               0.9999999-py36_0      anaconda
    intel-openmp:           2018.0.0-hc7b2577_8   anaconda
    libprotobuf:            3.4.1-h5b8497f_0      anaconda
    markdown:               2.6.9-py36_0          anaconda
    mkl:                    2018.0.1-h19d6760_4   anaconda
    numpy:                  1.13.3-py36ha12f23b_0 anaconda
    protobuf:               3.4.1-py36h306e679_0  anaconda
    six:                    1.11.0-py36h372c433_1 anaconda
    tensorflow:             1.3.0-0               anaconda
    tensorflow-base:        1.3.0-py36h5293eaa_1  anaconda
    tensorflow-tensorboard: 0.1.5-py36_0          anaconda
    werkzeug:               0.12.2-py36hc703753_0 anaconda

Proceed ([y]/n)? y

intel-openmp-2 100% |################################| Time: 0:00:07  81.54 kB/s
mkl-2018.0.1-h 100% |################################| Time: 0:34:04  94.72 kB/s
libprotobuf-3. 100% |################################| Time: 0:01:56  36.22 kB/s
libprotobuf-3. 100% |################################| Time: 0:03:10  22.22 kB/s
backports-1.0- 100% |################################| Time: 0:00:00   7.37 MB/s
markdown-2.6.9 100% |################################| Time: 0:00:04  23.35 kB/s
numpy-1.13.3-p 100% |################################| Time: 0:01:37  41.65 kB/s
six-1.11.0-py3 100% |################################| Time: 0:00:00  26.59 kB/s
werkzeug-0.12. 100% |################################| Time: 0:00:06  61.63 kB/s
backports.weak 100% |################################| Time: 0:00:00   6.29 MB/s
html5lib-0.999 100% |################################| Time: 0:00:03  47.86 kB/s
protobuf-3.4.1 100% |################################| Time: 0:00:12  47.52 kB/s
bleach-1.5.0-p 100% |################################| Time: 0:00:00  54.13 kB/s
tensorflow-bas 100% |################################| Time: 0:01:39 376.32 kB/s
tensorflow-ten 100% |################################| Time: 0:00:02 667.15 kB/s
tensorflow-1.3 100% |################################| Time: 0:00:00   6.24 MB/s

測試一下：

(tensorflow) [email protected]:~$ python
Python 3.6.4 |Anaconda, Inc.| (default, Dec 21 2017, 21:42:08) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> session = tf.Session()
2018-01-03 13:39:26.170690: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2018-01-03 13:39:26.170746: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-01-03 13:39:26.170772: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-01-03 13:39:26.170792: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-01-03 13:39:26.170813: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
>>> print(session.run(hello))
b'Hello, TensorFlow!'
>>> exit()
(tensorflow) [email protected]:~$ source deactivate #退出虛擬環境
[email protected]:~$

可以通過在終端中輸入export TF_CPP_MIN_LOG_LEVEL=2解決 warnning，博主覺得這樣只是改了記錄方法而已，問題依然存在，警告提示的只是tensorflow沒有編譯成XX指令，但在你的機器上這些加速CPU執行的指令依然是可用的。因此可用忽略。

相關推薦

Ubuntu16.04使用Anaconda5搭建TensorFlow使用環境 圖文詳細教程

說明 - Ubuntu版本16.04 LTS - Anaconda版本 5.0.1 (對應Python 3.6.3) - Tensorflow 1.3.0(由Anaconda提供，你也可以選擇其他版本，包括GPU的) Anaconda指的是一個開

linux搭建node環境超詳細教程

rect 詳細步驟 命令 存在 rec figure 控制臺 source ins linux 環境搭建詳細步驟 1.訪問官方網址：https://nodejs.org/en/download/ 2.選擇和你服務器版本相關的壓縮包，復制下載鏈接 3.服務器登錄ssh

Win10搭建wamp環境超詳細教程（php7.1.4 + mysql5.7.18 + apache2.4）

前言 wamp整合軟體用了一年了，最近突然覺得應該自己來搭建環境，畢竟用別人的總是感覺不舒服，出了許多bug也不好找。 PHP安裝 首先到官網上面去下載適合自己的php版本。 1.php目前最新版

Windows 10 搭建 Django 環境（詳細教程）

目錄 1. Python 下載及安裝 （2）下載後，點選exe檔案安裝，下方的方框打鉤，新增 Python 到環境變數。 （3）我選擇預設安裝，然後等它安裝完就可以了。 （4）測試是否安裝成功，開啟 cmd ，輸

Android------Myeclipse10搭建android執行環境圖文詳細步驟--------SDK的安裝配置+ADT外掛線上安裝

下載Android SDK 可以直接用連結 http://dl.google.com/android/android-sdk_r24.4.1-windows.zip 在迅雷中直接新建下載 點選 SDK Manager.exe安裝(不同版本名稱有些差異,找準SDK就對了

linux（ubuntu16.04）如何開啟圖片圖文詳細教程

圖片工具linux（ubuntu16.04）如下：eog （eye of gmone，是linux下內建的圖片檢視器。ubuntu16.04）fbi （ubuntu16.04下需安裝：apt install fbi）fibda使用方法在圖片檔案目錄中使用eog，例如root目

【新手教程】手把手教你搭建騰訊雲伺服器，圖文詳細教程

· 背景     暑假期間，愁著無聊但也不能荒廢學業吧，畢竟以後想靠技術混口飯吃！為了實施自己的計劃，特地挑了一個便宜的雲伺服器來用作自己的後臺；這不是學生狗沒錢嘛，所以我就挑了一個騰訊雲伺服器。雖說配置很低，但夠我們玩就行。因為想寫一個電商App，資料總不能從本地資料

搭建SVN服務器詳細教程

目錄 全連接 conn 選擇 如何 下載地址 建議 www. 一個 搭建SVN服務器詳細教程 本教程會從最基本的下載安裝到上傳代碼，下載代碼這條線來詳細講述如何完成SVN服務器的搭建 下載並安裝VisualSVN server 下載並安裝TortoiseSVN 導入項目

ubuntu16.04 搭建java 環境

rac ubuntu16 jdk jdk1.8 jdk8 port cal ash bashrc 環境：阿裏雲 ubuntu16.04jdk : 1.8 1、下載jdk http://www.oracle.com/technetwork/java/javase/downlo

新手入門篇：虛擬機搭建hadoop環境的詳細步驟

文檔 優勢 indent gic 地址 完成 align 頁面 一段 前兩天看到有人留言問在什麽情況下需要部署hadoop，我給的回答也很簡單，就是在需要處理海量數據的時候才需要考慮部署hadoop。關於這個問題在很早之前的一篇分享文檔也有說到這個問題，數據量少的完全發揮不

TensorFlow（1）：使用docker鏡像搭建TensorFlow環境

根據 free nts 安裝配置 wiki 永久 ebo 關於 exec 1，關於TensorFlow TensorFlow 隨著AlphaGo的勝利也火了起來。 google又一次成為大家膜拜的大神了。google大神在引導這機器學習的方向。 同時docker 也是一個

[step by step]利用docker搭建Tensorflow環境（tensorboard + tensorflow+gpu）

前言 本篇文章搭建環境的作業系統是ubuntu14，windows搭建docker的方式與ubuntu有所區別，win的使用者可以點選原文中的參考連線進行docker的搭建。掛載tensorboard的方法是一樣的，可供大家參考 搭建docker 官方文件 https://do

阿里雲Tesla P100GPU雲伺服器搭建TensorFlow環境

最近基於深度學習的影象識別專案需要用到GPU加速,申請了阿里雲的GPU伺服器,在搭建過程中遇到了一些問題,現在將搭建過程記錄 環境: 阿里雲GPU伺服器Tesla P100 作業系統: Ubuntu 16.04 準備安裝包(這個是我用來測試搭建的包,如果想用新版本,請自行下載,對應的包檔案會提

JDK的下載、安裝、配置及校驗 — 全程圖文詳細教程

JDK的下載、安裝、配置及校驗 — 全程圖文詳細教程 JAVA的學習和開發，必須安裝配置好JDK（java development kit java開發工具包）。Java的開發平臺主要分2類：Java SE是標準版，Java應用程式開發 Application；Java EE是企業版

Ubuntu16.04 搭建java環境

因為本人學的java，主要搭建的是java的環境。這裡搭建的java8+mysql環境。 1.java8安裝 ###安裝依賴庫 apt-get install python-software-properties apt install software-properties-commo

基於Anaconda在windows下搭建TensorFlow環境（cpu版本安裝）

                          安裝TensorFlow CPU版本過程 目錄： 一.定義：TensorFlow是谷歌基於DistBelief進行研發的第二代人工智慧學習系統 一.定義： TensorFlow是谷歌基於DistBelie

ubuntu16.04 搭建samba環境

ubuntu16.04 搭建samba環境 安裝 下載samba [email protected]:/home# apt-get install samba [email protected]:/home# service smb star

eos智慧合約開發-01 ubuntu16.04搭建eos環境

EOS三個元件： • nodeos：服務端區塊鏈節點元件 • cleos：命令列介面，與區塊鏈互動，管理錢包，管理賬戶，在區塊鏈上呼叫方法。（很重要，相當於以太坊web3） • keosd：管理EOSIO錢包的元件。 編譯過程遇到問題如下： 1： clone

hadoop2.x單機搭建分散式叢集超詳細教程

【前言】 1.個人PC機配置：戴爾，筆記本，記憶體8G，硬碟西數500G，CPU酷睿i5 2.由於工作中需要經常在叢集上做測試，另外我一直想學習大資料，因此結合百度+同事，有了此篇教程，初學者，不足之處，可在下方留言 【準備工作】 下載如下5個軟體： 1.VMw

阿里雲搭建SS代理超詳細教程

阿里雲配置shadowsocks需要特別注意的一點： 需要去控制檯去配置安全組規則，以允許shadowsocks對應的埠。否則shadowsocks服務依舊不可用。 如何允許埠？ Employ multiple ports on ssserve