centos6.7上tensorflow源码安装

本文详细介绍了在Linux环境下安装TensorFlow所需的依赖环境,包括JDK 1.8以上版本、Python包numpy、CUDA 8.0及cuDNN v5等,并提供了安装步骤和验证方法。

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安装tensorflow依赖项:

1jdk-1.8以上版本

$ java -version 

java version "1.7.0_45"

Java(TM) SE Runtime Environment (build1.7.0_45-b18)

Java HotSpot(TM) 64-Bit Server VM (build24.45-b08, mixed mode)

如何低于1.8的JDK就需要重新安装一个1.8版本的jdk,否则会报如下错误,

ERROR: JDK version (1.7) is lower than 1.8,please set $JAVA_HOME.

解决方法:

JDK官方下载链接:

http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html

$ rpm -ivh jdk-8u121-linux-x64.rpm

$ rpm -qa | grep jdk 

jdk1.8.0_121-1.8.0_121-fcs.x86_64

java-1.6.0-openjdk-1.6.0.35-1.13.7.1.el6_6.x86_64

java-1.7.0-openjdk-devel-1.7.0.121-2.6.8.1.el6_8.x86_64

java-1.7.0-openjdk-1.7.0.121-2.6.8.1.el6_8.x86_64

$ rpm -ql jdk1.8.0_121-1.8.0_121-fcs.x86_64

/usr/java/jdk1.8.0_121/release

/usr/java/jdk1.8.0_121/src.zip

添加环境变量,

$ export JAVA_HOME=/usr/java/jdk1.8.0_121/

测试添加成功,

$ echo $JAVA_HOME 

/usr/java/jdk1.8.0_121/

证明环境变量配置成功。

2Pythonnumpydev

$ yum install python-numpy swig python-dev

3cuda8.0cudnnv5

cuda8.0下载地址,https://developer.nvidia.com/cuda-downloads

cudnn v5.0下载地址,https://developer.nvidia.com/rdp/cudnn-download

$ sh cuda_8.0.61_375.26_linux.run.26_linux-run

$ vim /etc/profile

加入下面2行环境路径

exportLD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64/:/usr/local/cuda/lib64:$LD_LIBRARY_PATH

exportPATH=/usr/local/cuda-8.0/bin:/usr/local/cuda/bin:$PATH

$ source /etc/profile

$ tar xvf cudnn-8.0-linux-x64-v5.0-ga.tgz

$ cd cuda

$ cp lib64/lib* /usr/local/cuda-8.0/lib64/

$ cp include/*.h /usr/local/cuda-8.0/include

 

安装bazel

$ git clone https://github.com/bazelbuild/bazel.git

$ cd bazel

$ git checkout tags/0.1.0

$ ./compile.sh

 

安装tensorflow

$ ./configure
$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer

$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu

 

测试tensorflow

$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print sess.run(hello)
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print sess.run(a+b)
42

 

 ps:

1.程序员们在接触一门新语言的时候,通常做的第一件事就是编写一个Hello World程序,这一惯例源自最初一批大神们对计算机程序的希冀,希望它们就像一个新生儿一样,能友善的对这个世界宣告它的到来。

2.数字42的含义非常深刻。据著名一本道科幻小说“银河系漫游指南”所说,42是一个关于“生命、宇宙以及一切”的问题的答案(哈?你问我问题是什么?)。谷歌的创始人很可能也是这本小说的狂热粉丝,因为谷歌把这个彩蛋也嵌入了它们的搜索引擎之中,而且谷歌的总部外面就有一个“42”的模型。

3.Tensor一词是张量的意思,张量是一种表示物理量的方式,这个方式就是用基向量与分量组合表示物理量(Combinationof basis vector and component)。由于基向量可以有丰富的组合,张量可以表示非常丰富的物理量。此外,张量所描述的物理量是不随观察者或者说参考系而变化的,当参考系变化时(其实就是基向量变化),其分量也会相应变化,最后结果就是基向量与分量的组合(也就是张量)保持不变。考虑到张量有如此强大的表示能力,又不随观察者不同而变化,能够有效的表示宇宙间的万物,LillianR. Lieber给了张量一个形象的称呼the fact of the universe.

至此有没有感觉你已经深深的爱上了tensorflow!


reference:

http://www.tensorfly.cn/tfdoc/get_started/os_setup.html

https://www.youtube.com/watch?v=f5liqUk0ZTw

(nanodet) C:\Users\gyf>pip install tensorboard==2.10 Looking in indexes: http://mirrors.aliyun.com/pypi/simple/ Collecting tensorboard==2.10 Downloading http://mirrors.aliyun.com/pypi/packages/6b/42/e271c40c84c250b52fa460fda970899407c837a2049c53969f37e404b1f6/tensorboard-2.10.0-py3-none-any.whl (5.9 MB) ---------------------------------------- 5.9/5.9 MB 5.1 MB/s eta 0:00:00 Collecting absl-py>=0.4 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl (135 kB) Collecting grpcio>=1.24.3 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/87/7d/36009c38093e62969c708f20b86ab6761c2ba974b12ff10def6f397f24fa/grpcio-1.70.0-cp38-cp38-win_amd64.whl (4.3 MB) ---------------------------------------- 4.3/4.3 MB 5.8 MB/s eta 0:00:00 Collecting google-auth<3,>=1.6.3 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/17/63/b19553b658a1692443c62bd07e5868adaa0ad746a0751ba62c59568cd45b/google_auth-2.40.3-py2.py3-none-any.whl (216 kB) Collecting google-auth-oauthlib<0.5,>=0.4.1 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/b1/0e/0636cc1448a7abc444fb1b3a63655e294e0d2d49092dc3de05241be6d43c/google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB) Collecting markdown>=2.6.8 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/3f/08/83871f3c50fc983b88547c196d11cf8c3340e37c32d2e9d6152abe2c61f7/Markdown-3.7-py3-none-any.whl (106 kB) Requirement already satisfied: numpy>=1.12.0 in g:\.conda\envs\nanodet\lib\site-packages (from tensorboard==2.10) (1.24.3) Collecting protobuf<3.20,>=3.9.2 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/fd/38/cb53f28950a386c8d7e17fc305c97a158cf85d51d7e6caffe4f37336c138/protobuf-3.19.6-cp38-cp38-win_amd64.whl (896 kB) ---------------------------------------- 896.1/896.1 kB 6.7 MB/s eta 0:00:00 Requirement already satisfied: requests<3,>=2.21.0 in g:\.conda\envs\nanodet\lib\site-packages (from tensorboard==2.10) (2.32.3) Requirement already satisfied: setuptools>=41.0.0 in g:\.conda\envs\nanodet\lib\site-packages (from tensorboard==2.10) (75.1.0) Collecting tensorboard-data-server<0.7.0,>=0.6.0 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/74/69/5747a957f95e2e1d252ca41476ae40ce79d70d38151d2e494feb7722860c/tensorboard_data_server-0.6.1-py3-none-any.whl (2.4 kB) Collecting tensorboard-plugin-wit>=1.6.0 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/e0/68/e8ecfac5dd594b676c23a7f07ea34c197d7d69b3313afdf8ac1b0a9905a2/tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB) ---------------------------------------- 781.3/781.3 kB 4.8 MB/s eta 0:00:00 Collecting werkzeug>=1.0.1 (from tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/6c/69/05837f91dfe42109203ffa3e488214ff86a6d68b2ed6c167da6cdc42349b/werkzeug-3.0.6-py3-none-any.whl (227 kB) Requirement already satisfied: wheel>=0.26 in g:\.conda\envs\nanodet\lib\site-packages (from tensorboard==2.10) (0.44.0) Collecting cachetools<6.0,>=2.0.0 (from google-auth<3,>=1.6.3->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/72/76/20fa66124dbe6be5cafeb312ece67de6b61dd91a0247d1ea13db4ebb33c2/cachetools-5.5.2-py3-none-any.whl (10 kB) Collecting pyasn1-modules>=0.2.1 (from google-auth<3,>=1.6.3->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/47/8d/d529b5d697919ba8c11ad626e835d4039be708a35b0d22de83a269a6682c/pyasn1_modules-0.4.2-py3-none-any.whl (181 kB) Collecting rsa<5,>=3.1.4 (from google-auth<3,>=1.6.3->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/64/8d/0133e4eb4beed9e425d9a98ed6e081a55d195481b7632472be1af08d2f6b/rsa-4.9.1-py3-none-any.whl (34 kB) Collecting requests-oauthlib>=0.7.0 (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/3b/5d/63d4ae3b9daea098d5d6f5da83984853c1bbacd5dc826764b249fe119d24/requests_oauthlib-2.0.0-py2.py3-none-any.whl (24 kB) Collecting importlib-metadata>=4.4 (from markdown>=2.6.8->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/a0/d9/a1e041c5e7caa9a05c925f4bdbdfb7f006d1f74996af53467bc394c97be7/importlib_metadata-8.5.0-py3-none-any.whl (26 kB) Requirement already satisfied: charset-normalizer<4,>=2 in g:\.conda\envs\nanodet\lib\site-packages (from requests<3,>=2.21.0->tensorboard==2.10) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in g:\.conda\envs\nanodet\lib\site-packages (from requests<3,>=2.21.0->tensorboard==2.10) (3.7) Requirement already satisfied: urllib3<3,>=1.21.1 in g:\.conda\envs\nanodet\lib\site-packages (from requests<3,>=2.21.0->tensorboard==2.10) (2.2.3) Requirement already satisfied: certifi>=2017.4.17 in g:\.conda\envs\nanodet\lib\site-packages (from requests<3,>=2.21.0->tensorboard==2.10) (2024.8.30) Collecting MarkupSafe>=2.1.1 (from werkzeug>=1.0.1->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/92/21/357205f03514a49b293e214ac39de01fadd0970a6e05e4bf1ddd0ffd0881/MarkupSafe-2.1.5-cp38-cp38-win_amd64.whl (17 kB) Collecting zipp>=3.20 (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/62/8b/5ba542fa83c90e09eac972fc9baca7a88e7e7ca4b221a89251954019308b/zipp-3.20.2-py3-none-any.whl (9.2 kB) Collecting pyasn1<0.7.0,>=0.6.1 (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/c8/f1/d6a797abb14f6283c0ddff96bbdd46937f64122b8c925cab503dd37f8214/pyasn1-0.6.1-py3-none-any.whl (83 kB) Collecting oauthlib>=3.0.0 (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.10) Downloading http://mirrors.aliyun.com/pypi/packages/be/9c/92789c596b8df838baa98fa71844d84283302f7604ed565dafe5a6b5041a/oauthlib-3.3.1-py3-none-any.whl (160 kB) Installing collected packages: tensorboard-plugin-wit, zipp, tensorboard-data-server, pyasn1, protobuf, oauthlib, MarkupSafe, grpcio, cachetools, absl-py, werkzeug, rsa, requests-oauthlib, pyasn1-modules, importlib-metadata, markdown, google-auth, google-auth-oauthlib, tensorboard Successfully installed MarkupSafe-2.1.5 absl-py-2.3.1 cachetools-5.5.2 google-auth-2.40.3 google-auth-oauthlib-0.4.6 grpcio-1.70.0 importlib-metadata-8.5.0 markdown-3.7 oauthlib-3.3.1 protobuf-3.19.6 pyasn1-0.6.1 pyasn1-modules-0.4.2 requests-oauthlib-2.0.0 rsa-4.9.1 tensorboard-2.10.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 werkzeug-3.0.6 zipp-3.20.2
最新发布
07-26
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