from flask_appbuilder import Model from sqlalchemy import Column, Integer, String, ForeignKey,Float from sqlalchemy.orm import relationship import datetime,time,json from sqlalchemy import ( Boolean, Column, create_engine, DateTime, ForeignKey, Integer, MetaData, String, Table, Text, Enum, ) from myapp.models.base import MyappModelBase from myapp.models.helpers import AuditMixinNullable, ImportMixin from flask import escape, g, Markup, request from myapp import app,db from myapp.models.helpers import ImportMixin # 添加自定义model from sqlalchemy import Column, Integer, String, ForeignKey ,Date,DateTime from flask_appbuilder.models.decorators import renders from flask import Markup import datetime metadata = Model.metadata conf = app.config # 定义model class NNI(Model,AuditMixinNullable,MyappModelBase): __tablename__ = 'nni' id = Column(Integer, primary_key=True) job_type = Column(Enum('Job'),nullable=False,default='Job') project_id = Column(Integer, ForeignKey('project.id'), nullable=False) # 定义外键 project = relationship( "Project", foreign_keys=[project_id] ) name = Column(String(200), unique = True, nullable=False) namespace = Column(String(200), nullable=False,default='katib') describe = Column(Text) parallel_trial_count = Column(Integer,default=3) maxExecDuration = Column(Integer,default=3600) max_trial_count = Column(Integer,default=12) max_failed_trial_count = Column(Integer,default=3) objective_type = Column(Enum('maximize','minimize'),nullable=False,default='maximize') objective_goal = Column(Float, nullable=False,default=0.99) objective_metric_name = Column(String(200), nullable=False,default='accuracy') objective_additional_metric_names = Column(String(200),default='') # 逗号分隔 algorithm_name = Column(String(200),nullable=False,default='Random') algorithm_setting = Column(Text,default='') # 搜索算法的配置 parameters=Column(Text,default='{}') # 搜索超参的配置 job_json = Column(Text, default='{}') # 根据不同算法和参数写入的task模板 trial_spec=Column(Text,default='') # 根据不同算法和参数写入的task模板 # code_dir = Column(String(200), default='') # 代码挂载 working_dir = Column(String(200), default='') # 挂载 volume_mount = Column(String(100), default='kubeflow-user-workspace(pvc):/mnt,kubeflow-archives(pvc):/archives') # 挂载 node_selector = Column(String(100), default='cpu=true,train=true') # 挂载 image_pull_policy = Column(Enum('Always', 'IfNotPresent'), nullable=False, default='Always') resource_memory = Column(String(100), default='1G') resource_cpu = Column(String(100), default='1') resource_gpu = Column(String(100), default='') experiment=Column(Text,default='') # 构建出来的实验体 alert_status = Column(String(100), default='Pending,Running,Succeeded,Failed,Terminated') # 哪些状态会报警Pending,Running,Succeeded,Failed,Unknown,Waiting,Terminated def __repr__(self): return self.name @property def run(self): return Markup(f'运行') @renders('parameters') def parameters_html(self): return Markup('
' + self.parameters + '
') # ''' # "\"单反斜杠 %5C # "|" %7C # 回车 %0D%0A # 空格 %20 # 双引号 %22 # "&" %26 # ''' @property def name_url(self): return Markup(f'{self.name}') @property def describe_url(self): return Markup(f'{self.describe}') @renders('trial_spec') def trial_spec_html(self): return Markup('
' + self.trial_spec + '
') @renders('experiment') def experiment_html(self): return Markup('
' + self.experiment + '
') @property def log(self): return Markup(f'log') def clone(self): return NNI( name=self.name.replace('_','-'), job_type = self.job_type, describe=self.describe, namespace=self.namespace, project_id=self.project_id, parallel_trial_count=self.parallel_trial_count, max_trial_count=self.max_trial_count, max_failed_trial_count=self.max_failed_trial_count, objective_type=self.objective_type, objective_goal=self.objective_goal, objective_metric_name=self.objective_metric_name, objective_additional_metric_names=self.objective_additional_metric_names, algorithm_name=self.algorithm_name, algorithm_setting=self.algorithm_setting, parameters=self.parameters, job_json = self.job_json, trial_spec=self.trial_spec, volume_mount=self.volume_mount, node_selector=self.node_selector, image_pull_policy=self.image_pull_policy, resource_memory=self.resource_memory, resource_cpu=self.resource_cpu, experiment=self.experiment, alert_status=self.alert_status )