【anaconda 环境搭建】环境搭建python快速30分钟_conda 安装python3.7

1、下载anaconda
https://repo.anaconda.com/archive/index.html

选择下载 Anaconda3-2019.10-Linux-x86_64.sh

2、安装linux 工具4个，上传，下载，解压，打包
yum install zip
yum install unzip
yum install lrzsz
Yum install wget

3、rz Anaconda3-2019.10-Linux-x86_64.sh
4、安装 bash Anaconda3-2019.10-Linux-x86_64.sh
一直按回车，直到出现yes,按yes,然后继续按回车，默认安装路径，初始化提升 也按yes.然后就安装成功了。

修改~/.bashrc
export PATH=/root/anaconda3/bin:$PATH
source ~/.bashrc

5、接下来配置清华镜像源。
cd ~
mkdir ~/.pip
cd ~/.pip
vim pip.conf
写入如下内容：
[global]
timeout = 6000
index-url = https://pypi.tuna.tsinghua.edu.cn/simple
trusted-host = pypi.tuna.tsinghua.edu.cn

换源成功。重新进入AI算法服务器

6、接下来 安装 python3.7.5环境以及安装依赖包，默认是3.7.4版本 ，这里我们需要安装python3.7.5

创建环境 conda create -n model python==3.7.5
激活环境 conda activate model
退出环境 conda deactivate

安装成功！

7、接下来进入python3.7.5环境，安装所需依赖包
先激活环境 conda activate model

进入model 环境之后，安装依赖包。用pip 命令安装即可。

pip install requests
pip install gunicorn==20.0.4
pip install uvicorn==0.11.3           
pip install uvloop==0.14.0
pip install fastapi==0.52.0
pip install xgboost==0.90
pip install xlrd==1.2.0
pip install scikit-learn==0.22.1
pip install joblib==0.14.1
pip install pandas==0.25.3
pip install tensorflow==2.0.0
pip install keras==2.3.1
pip install lightgbm==2.3.1
pip install pillow==8.0.1
pip install opencv-python==4.4.0.46
pip install uuid
pip install kafka
pip install kafka_python
pip install pymysql
pip install psycopg2-binary
pip install apscheduler

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8.也可以直接克隆 之前的虚拟环境。

conda env create -f model.yml
1

model.yml 内容如下：

name: model
channels:
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - ca-certificates=2022.3.29
  - certifi=2021.10.8
  - libedit=3.1.20210910
  - libffi=3.2.1
  - libgcc-ng=9.1.0
  - libstdcxx-ng=9.1.0
  - ncurses=6.3
  - openssl=1.1.1n
  - pip=21.2.2
  - python=3.7.5
  - readline=7.0
  - setuptools=61.2.0
  - sqlite=3.33.0
  - tk=8.6.11
  - wheel=0.37.1
  - xz=5.2.5
  - zlib=1.2.12
  - pip:
    - absl-py==1.0.0
    - apscheduler==3.9.1
    - astor==0.8.1
    - backports-zoneinfo==0.2.1
    - cached-property==1.5.2
    - cachetools==4.2.4
    - charset-normalizer==2.0.12
    - click==7.1.2
    - cython==0.29.32
    - dbutils==3.0.2
    - fastapi==0.52.0
    - gast==0.2.2
    - google-auth==1.35.0
    - google-auth-oauthlib==0.4.6
    - google-pasta==0.2.0
    - greenlet==1.1.2
    - grpcio==1.45.0
    - gunicorn==20.0.4
    - h11==0.9.0
    - h2==2.6.2
    - h5py==3.6.0
    - hpack==3.0.0
    - httptools==0.1.2
    - hyper==0.7.0
    - hyperframe==3.2.0
    - idna==3.3
    - importlib-metadata==4.11.3
    - joblib==0.14.1
    - kafka==1.3.5
    - kafka-python==2.0.2
    - keras==2.3.1
    - keras-applications==1.0.8
    - keras-preprocessing==1.1.2
    - ksql==0.10.2
    - lightgbm==2.3.1
    - markdown==3.3.6
    - numpy==1.21.6
    - oauthlib==3.2.0
    - opencv-python==4.4.0.46
    - opt-einsum==3.3.0
    - pandas==0.25.3
    - pillow==8.0.1
    - protobuf==3.20.1
    - psycopg2-binary==2.9.3
    - pyasn1==0.4.8
    - pyasn1-modules==0.2.8
    - pydantic==1.9.0
    - pymysql==1.0.2
    - python-dateutil==2.8.2
    - pytz==2022.1
    - pytz-deprecation-shim==0.1.0.post0
    - pyyaml==6.0
    - requests==2.27.1
    - requests-oauthlib==1.3.1
    - rsa==4.8
    - scikit-learn==0.22.1
    - scipy==1.7.3
    - six==1.16.0
    - sqlalchemy==1.4.39
    - starlette==0.13.2
    - tensorboard==2.0.2
    - tensorflow==2.0.0
    - tensorflow-estimator==2.0.1
    - termcolor==1.1.0
    - typing-extensions==4.2.0
    - tzdata==2022.1
    - tzlocal==4.2
    - urllib3==1.26.9
    - uuid==1.30
    - uvicorn==0.11.3
    - uvloop==0.14.0
    - websockets==8.1
    - werkzeug==2.1.1
    - wrapt==1.14.0
    - xgboost==0.90
    - xlrd==1.2.0
    - zipp==3.8.0
prefix: /root/anaconda3/envs/model


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