机器学习需要linux环境吗,在linux上安装机器学习开发环境
2017-05-15
下载Anoconda
Anaconda 是一个用于科学计算的 Python 发行版，支持 Linux, Mac, Windows, 包含了众多流行的科学计算、数据分析的 Python 包。
Anaconda 具有比pip包管理更强大的能力,不仅管理python 包的依赖,也同时管理其他非python的依赖,所以有逐渐取代pip的趋势. 同时, Anaconda还有virtual envi等功能, 可以创建虚拟环境.
Anaconda 安装包可以到 https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/ 下载。
TUNA 还提供了 Anaconda 仓库的镜像，运行以下命令:
conda config –add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config –set show_channel_urls yes
即可添加 Anaconda Python 免费仓库。
Conda 三方源
当前tuna还维护了一些anaconda三方源。
配置
Conda Forge
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
msys2
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-4.3.1-Linux-x86_64.sh
[zhouhh@mainServer ~]$ bash Anaconda3-4.3.1-Linux-x86_64.sh
Welcome to Anaconda3 4.3.1 (by Continuum Analytics, Inc.)
[zhouhh@mainServer ~]$ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
[zhouhh@mainServer ~]$ conda config --set show_channel_urls yes
[zhouhh@mainServer ~]$ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
[zhouhh@mainServer ~]$ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
[zhouhh@mainServer ~]$ vi .condarc
channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- defaults
show_channel_urls: true
安装机器学习相关包
安装numpy scipy mkl
[zhouhh@mainServer ~]$ conda install numpy scipy mkl
Fetching package metadata ...
CondaHTTPError: HTTP None None for url
Elapsed: None
An HTTP error occurred when trying to retrieve this URL.
HTTP errors are often intermittent, and a simple retry will get you on your way.
SSLError(SSLError(SSLError("bad handshake: Error([('SSL routines', 'ssl3_get_server_certificate', 'certificate verify failed')],)",),),)
删除 .condarc的- defaults
[zhouhh@mainServer ~]$ conda config --show
add_anaconda_token: True
add_pip_as_python_dependency: True
allow_softlinks: True
always_copy: False
always_softlink: False
always_yes: False
auto_update_conda: True
binstar_upload: None
changeps1: True
channel_alias: https://conda.anaconda.org
channel_priority: True
channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
client_ssl_cert:
client_ssl_cert_key:
create_default_packages: []
debug: False
default_channels:
- https://repo.continuum.io/pkgs/free
- https://repo.continuum.io/pkgs/r
- https://repo.continuum.io/pkgs/pro
disallow: []
envs_dirs:
- /home/zhouhh/anaconda3/envs
- /home/zhouhh/.conda/envs
json: False
offline: False
proxy_servers: {}
quiet: False
shortcuts: True
show_channel_urls: True
ssl_verify: True
track_features: []
update_dependencies: True
use_pip: True
verbosity: 0
安装theano
[zhouhh@mainServer ~]$ conda intall theano
The following NEW packages will be INSTALLED:
libgpuarray: 0.6.4-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
mako: 1.0.6-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
pygpu: 0.6.4-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
theano: 0.9.0-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
安装keras
缺省会安装tensorflow 1.1
[zhouhh@mainServer ~]$ conda install keras
Fetching package metadata .....
Solving package specifications: .
Package plan for installation in environment /home/zhouhh/anaconda3:
The following NEW packages will be INSTALLED:
keras: 2.0.2-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
libprotobuf: 3.2.0-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
protobuf: 3.2.0-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tensorflow: 1.1.0-np112py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
测试机器学习环境
[zhouhh@mainServer ~]$ python3
Python 3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 12:22:00)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from keras.models import Sequential
Using TensorFlow backend.
>>> from keras.layers import Dense, Activation
>>> model = Sequential([
... Dense(32, input_shape=(784,)),
... Activation('relu'),
... Dense(10),
... Activation('softmax'),
... ])
修改.keras/keras.json
将”backend”: “tensorflow”改为
“backend”: “theano”
[zhouhh@mainServer ~]$ cat .keras/keras.json
{
"epsilon": 1e-07,
"floatx": "float32",
"image_data_format": "channels_last",
"backend": "theano"
}
[zhouhh@mainServer ~]$ python3
Python 3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 12:22:00)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from keras.models import Sequential
Using Theano backend.
>>> from keras.layers import Dense, Activation
>>> model = Sequential([
... Dense(32, input_shape=(784,)),
... Activation('relu'),
... Dense(10),
... Activation('softmax'),
... ])
>>>
至此,keras,tensorflow和theano安装成功
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