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2020-04-22 22:14:21 +01:00
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@@ -72,218 +72,13 @@ The platform follows the idea of "separate development frontend and backend". Th
![泰斯特平台结构图_V1.0](https://github.com/amazingTest/Taisite-Platform/blob/master/images/泰斯特平台结构图_V1.0.png)
## IV . Deploy
## deploy
### Deploy under windows
[click me](https://mp.weixin.qq.com/s/bLyDWHCAPCshF8vmbSHtWw)
#### 0. Clone
## how to use
git clone https://github.com/amazingTest/Taisite-Platform.git
#### 1. Install python 3 env
#### 2. deploy NLP model
[Download model](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip)
2.2 Extract the compression package
2.3 Install python dependent-packages
pip install tensorflow==1.14.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install bert-serving-server==1.9.1 -i https://pypi.tuna.tsinghua.edu.cn/simple
2.4 Start the model
// Execute after the current directory is switched to the model folder directory
bert-serving-start -model_dir ./chinese_L-12_H-768_A-12/ -num_worker=1
After the startup is successful, the output is as follows:
![NLP模型启动成功输出](https://github.com/amazingTest/Taisite-Platform/blob/master/images/NLP模型启动成功输出.png)
#### 3. Deploy Mongodb database
#### 4. Set system environment variables
AUTOTEST_PLATFORM_ENV=production
AUTOTEST_PLATFORM_NLP_SERVER_HOST=127.0.0.1
AUTOTEST_PLATFORM_MONGO_HOST=${MONGO_HOST}
AUTOTEST_PLATFORM_MONGO_PORT=${MONGO_PORT}
AUTOTEST_PLATFORM_MONGO_USERNAME=${USERNAME}
AUTOTEST_PLATFORM_MONGO_PASSWORD=${PASSWORD}
AUTOTEST_PLATFORM_MONGO_DEFAULT_DBNAME=taisite
Where AUTOTEST_PLATFORM_ENV defaults to production (required)
AUTOTEST_PLATFORM_MONGO_HOST and AUTOTEST_PLATFORM_MONGO_PORT indicate the address and port of the database (required)
AUTOTEST_PLATFORM_MONGO_USERNAME and AUTOTEST_PLATFORM_MONGO_PASSWORD represent the account password of the database (if not required)
AUTOTEST_PLATFORM_NLP_SERVER_HOST (Natural Language Model Service) defaults to native boot (not required)
AUTOTEST_PLATFORM_MONGO_DEFAULT_DBNAME is the default data table name (required)
After the setting is completed, you can test it with the following commands (CMD switches to the project root directory)
python ./backend/config.py
If the configuration is successful, you can see the input configuration data.
#### 5. Package the front-end dist file (I have done this for you, skip it if you don't need secondary development)
5.1 Install the Vue environment, download node.js and configure the environment, download the npm package manager
5.2 Cmd into the frontend directory, configure cnpm:
npm install -g cnpm --registry=https://registry.npm.taobao.org
5.3 Execute the install dependency package command:
cnpm install
5.4 Execute the package command:
cnpm run build
If successfully packaged, the dist folder will be generated in the project root directory.
#### 6. Start backend
// Switch to the project root directory to execute
pip install -r ./backend/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
// Start backend (default 5050 port)
python ./backend/run.py
// Create a platform administrator account password
python ./backend/createAdminUser.py
#### 7. Access project
You can now log in using http://127.0.0.1:5050/#/login using the created administrator account password.
![平台登录界面2.png](https://github.com/amazingTest/Taisite-Platform/blob/master/images/平台登录界面2.png)
### Docker containerized deployment in Linux environment
#### 0. Clone
git clone https://github.com/amazingTest/Taisite-Platform.git
#### 1. Natural language model deployment
sudo -i
docker pull shaoyuyishiwo/bertserver
docker run --name autotest-platform-bertserver -d shaoyuyishiwo/bertserver
#### 2. Mongo database deployment (skip this step if an existing database is available)
2.1 Start database & data mount to host
sudo -i
docker pull mongo
docker run --name autotest-platform-mongo -p 27017:27017 -v /data/db:/data/db -v /data/configdb:/data/configdb ``-d mongo
2.2 Create a database account
docker exec -it autotest-platform-mongo /bin/bash
mongo
> use admin
switched to db admin
> db.createUser({user:"${USERNAME}",pwd:"${PASSWORD}",roles:["root"]})
Successfully added user: { "user" : "admin", "roles" : [ "root" ] }
2.3 Database memory expansion (recommended)
> db.adminCommand({setParameter:1, internalQueryExecMaxBlockingSortBytes:335544320})
{ "was" : 33554432, "ok" : 1 }
#### 3. Environment variable configuration
// Edit /etc/profile file
sudo -i
vi /etc/profile
If there is a warning, select (E)dit anyway (enter E)
3.1 Insert the following data at the end of the text (enter i to get into insert status)
export AUTOTEST_PLATFORM_ENV=production
export AUTOTEST_PLATFORM_NLP_SERVER_HOST=${BERT_IPADRESS}
export AUTOTEST_PLATFORM_MONGO_HOST=${MONGO_HOST}
export AUTOTEST_PLATFORM_MONGO_PORT=${MONGO_PORT}
export AUTOTEST_PLATFORM_MONGO_USERNAME=${USERNAME}
export AUTOTEST_PLATFORM_MONGO_PASSWORD=${PASSWORD}
export AUTOTEST_PLATFORM_MONGO_DEFAULT_DBNAME=${DBNAME}
The variable is a dynamic value. The deployer can input it according to the actual situation.
The DBNAME value can be arbitrarily customized (database table name). The BERT_IPADRESS and
MONGO_HOST values can be queried by the following commands:
docker inspect autotest-platform-bertserver
docker inspect autotest-platform-mongo // If you used the above steps to deploy the database
The output is shown below:
![控制台输出1.png](https://github.com/amazingTest/Taisite-Platform/blob/master/images/控制台输出1.png)
3.2 After inserting, click the ESC button, type :wq and click Enter to save.
3.3 Environment variables take effect immediately after executing the following command
source /etc/profile
#### 4. Start the project
Before you start the project, you need to change the timezone info by modifying the RUN script in **Dockerfile.backend** which stay
in first-level directory of the project. The default timezone is Asia/Shanghai.
// Execute the deployment file in the project root directory
sh deploy ${PORT}
The ${PORT} variable fills in the project access port, and the administrator account password is also created when the
project starts, as shown in the following figure:
![控制台输出2.png](https://github.com/amazingTest/Taisite-Platform/blob/master/images/控制台输出2.png)
#### 5. Access project
The browser can access the ${PORT} port of the deployment server address.
![平台登录界面.png](https://github.com/amazingTest/Taisite-Platform/blob/master/images/平台登录界面.png)
#### EXTRA. FQA
The following output represents the NLP model startup failure
![NLP部署失败.png](https://github.com/amazingTest/Taisite-Platform/blob/master/images/NLP部署失败.png)
Solution steps:
1. Remove the code from ./backend/app/init.py:
![不使用NLP模型方法指南1.png](https://github.com/amazingTest/Taisite-Platform/blob/master/images/不使用NLP模型方法指南1.png)
2. Modify the following code in ./backend/testframe/interfaceTest/tester.py to pass:
![不使用NLP模型方法指南2.png](https://github.com/amazingTest/Taisite-Platform/blob/master/images/不使用NLP模型方法指南2.png)
When you start the project after you finish, you will not depend on the natural language model~
[click me](https://shimo.im/docs/8TqxG3Ttjvj9yT8T)
## V . Contact me