零、背景:


现有基于 Node.js 的项目,但需要整合 Data Science 同事的基于 python(jupyter) 的代码部分,以实现额外的数据分析功能。于是设想实现一个 microservices。下面介绍一些库的使用方法、自己写的 demo和遇到的坑,方便以后查阅。

一、jupyter_kernel_gateway


第一步,是想办法把 jupyter 文件当成一个 http server 启动,以便可以接受来自任何异构项目的调用。这里可以用到jupyter_kernel_gatewaynotebook-http 功能。

官方文档:https://jupyter-kernel-gateway.readthedocs.io/en/latest/http-mode.html

1、安装

pip install jupyter_kernel_gateway

2、启动

jupyter kernelgateway --KernelGatewayApp.api='kernel_gateway.notebook_http' --KernelGatewayApp.seed_uri='/Users/xjnotxj/Program/PythonProject/main.ipynb'

3、使用

import json
# imitate REQUEST args (调试时候用,平时请忽略)
# REQUEST = json.dumps({'body': {'age': ['181']}, 'args': {'sex': ['male'], 'location': ['shanghai']}, 'path': {'name': 'colin'}, 'headers': {'Content-Type': 'multipart/form-data; boundary=--------------------------149817035181009685206727', 'Cache-Control': 'no-cache', 'Postman-Token': '96c484cb-8709-4a42-9e12-3aaf18392c92', 'User-Agent': 'PostmanRuntime/7.6.0', 'Accept': '*/*', 'Host': 'localhost:8888', 'Accept-Encoding': 'gzip, deflate', 'Content-Length': '161', 'Connection': 'keep-alive'}})

注释定义路由:# POST /post/:name(可以多个 cell 一起用),请求体自动绑定在 req 对象上:

# POST /post/:name

req = json.loads(REQUEST)

# defined return vars
return_status = 200
return_code = 0
return_message = ''
return_data = {}

这里定义了一个检查 req 参数的 function,因为 jupyter_kernel_gateway 不支持 return 或者 exit 退出当前 request,还是会继续往后执行,导致多个输出干扰最终 response 结果。所以我这边代码逻辑写的不简洁,如果有知道改进的朋友可以告诉我。

# POST /post/:name

def checkReqValid(req):

    global return_code
global return_message # age
if 100 <= req["age"] or req["age"] < 0:
return_code = -2
return_message = "'age' is out of range"
return True return False

实现 controller 部分:

# POST /post/:name

try :

    name = req['path']['name']
age = int(req['body']['age'][0])
sex = req['args']['sex'][0]
location = req['args']['location'][0] if checkReqValid({"name":name,
"age":age,
"sex":sex,
"location":location}) == True:
pass
else :
# dosomething……
return_data = {
"name":name,
"age":age,
"sex":sex,
"location":location,
"req":req
} except KeyError: # check has field is empty
return_code = -1
return_message = "some field is empty" finally: # return data
print(json.dumps({
"code":return_code,
"message":return_message,
"data":return_data
}))

# ResponseInfo POST /post/:name 定义输出响应头,用 print 写入stdout 的方式来响应请求:

# ResponseInfo POST /post/:name

print(json.dumps({
"headers" : {
"Content-Type" : "application/json"
},
"status" : return_status
}))

当我访问localhost:8888/post/colin?sex=male&location=shanghai且body体为 age:18时,返回值为:

{
"code": 0,
"message": "",
"data": {
"name": "colin",
"age": 18,
"sex": "male",
"location": "shanghai",
"req": {
"body": {
"age": [
"18"
]
},
"args": {
"sex": [
"male"
],
"location": [
"shanghai"
]
},
"path": {
"name": "colin"
},
"headers": {
"Content-Type": "multipart/form-data; boundary=--------------------------981201125716045634129372",
"Cache-Control": "no-cache",
"Postman-Token": "ec0f5364-b0ea-4828-b987-c12f15573296",
"User-Agent": "PostmanRuntime/7.6.0",
"Accept": "*/*",
"Host": "localhost:8888",
"Accept-Encoding": "gzip, deflate",
"Content-Length": "160",
"Connection": "keep-alive"
}
}
}
}

4、坑

(1)cell 里涉及到注释实现的路由功能时,首行不能是空行,不然报错:
✘ xjnotxj@jiangchengzhideMacBook-Pro  ~/Program/PythonProject  jupyter kernelgateway --KernelGatewayApp.api='kernel_gateway.notebook_http' --KernelGatewayApp.seed_uri='/Users/xjnotxj/Program/PythonProject/tuo.ipynb'
[KernelGatewayApp] Kernel started: bb13bcd6-514f-4682-b627-e6809cbb13ac
Traceback (most recent call last):
File "/anaconda3/bin/jupyter-kernelgateway", line 11, in <module>
sys.exit(launch_instance())
File "/anaconda3/lib/python3.7/site-packages/jupyter_core/application.py", line 266, in launch_instance
return super(JupyterApp, cls).launch_instance(argv=argv, **kwargs)
File "/anaconda3/lib/python3.7/site-packages/traitlets/config/application.py", line 657, in launch_instance
app.initialize(argv)
File "/anaconda3/lib/python3.7/site-packages/kernel_gateway/gatewayapp.py", line 382, in initialize
self.init_webapp()
File "/anaconda3/lib/python3.7/site-packages/kernel_gateway/gatewayapp.py", line 449, in init_webapp
handlers = self.personality.create_request_handlers()
File "/anaconda3/lib/python3.7/site-packages/kernel_gateway/notebook_http/__init__.py", line 112, in create_request_handlers
raise RuntimeError('No endpoints were discovered. Check your notebook to make sure your cells are annotated correctly.')
RuntimeError: No endpoints were discovered. Check your notebook to make sure your cells are annotated correctly.
✘ xjnotxj@jiangchengzhideMacBook-Pro  ~/Program/PythonProject  [IPKernelApp] WARNING | Parent appears to have exited, shutting down.
(2)response 里argsbody体里的参数值是一个长度为1的数组
# 注意取法
sex = req['args']['sex'][0]

二、papermill


第二步,就是用类似胶水的东西,把不同的 Data Science 处理脚本,粘连起来,依次调用。

为什么要使用papermill,而不是直接调用脚本?

(1)规范了调用jurpyter文件和传参的模式

(2)执行jurpyter文件后可以生成 out 文件,方便回溯

(3)上下文变量按照每一个jurpyter文件划分区域去存储,互不干扰

1、安装

https://github.com/nteract/papermill

pip install papermill

2、使用

(1)a.ipynb
import papermill as pm

for i, item in enumerate(data):
data[i] = item * multiple pm.record("data", data)
print(data)
(2)main.ipynb
data=[1,2,3]
data
# 也可以通过命令行运行,详细看文档
pm.execute_notebook(
'a.ipynb',
'a_out.ipynb',
parameters = dict(data=data,multiple=3)
)

执行main.ipynb后:

1、会生成a_out.ipynb新文件(见下文的(3))

2、有绑定在a_out.ipynb上的上下文变量:

re = pm.read_notebook('a_out.ipynb').dataframe
re
0data[1, 2, 3]parametera_out.ipynb
1multiple3parametera_out.ipynb
2data[3, 6, 9]recorda_out.ipynb

获取参数稍微有一些繁琐,我这里封装了个 function:

# getNotebookData args
# [filename] .ipynb的文件路径
# [field] 取值变量
# [default_value] 默认返回值(default:None)
# [_type] 'parameter'|'record'(default) def getPMNotebookData(filename, field ,default_value = None,_type='record'):
result = default_value
try:
re = pm.read_notebook(filename).dataframe
result = re[re['name']==field][re['type']==_type]["value"].values[0]
except:
pass
finally:
return result
data = getPMNotebookData('a_out.ipynb', 'data', 0)
data
# [3, 6, 9]
(3)a_out.ipynb

生成的这个新文件,会多出两块内容:

1、在所有 cell 的最开头,会自动插入新的 cell,里面有我们传入的参数

# Parameters
data = [1, 2, 3]
multiple = 3

2、cell 对应的 out 信息

[3, 6, 9]

3、坑

(1)参数不能传 pd.Dataframe 类型

会报错:

TypeError: Object of type DataFrame is not JSON serializable

解决办法:

1、序列化 Dataframe

Dataframe提供了两种序列化的方式,df.to_json()df.to_csv(),解析或者详细的用法请看:https://github.com/nteract/papermill/issues/215

缺点:

在序列化的过程中,Dataframe 每列的数据类型会发生丢失,重新读取后需重新指定。

2、不通过 papermill 的传参机制去传输 Dataframe,而是通过 csv 中间文件承接 【推荐】

三、docker 封装


第三步,就是用 docker ,封装设计好的 microservices,以便部署。

待写……

04-20 12:26
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