def evaluate(self, params, ii, jj):
print(params)
new_outpath = self.ouputdir + "/bbo_out_" + str(ii) + "_" + str(jj)
if os.path.exists(self.ouputdir) != True:
os.makedirs(self.ouputdir)
if os.path.exists(new_outpath) != True:
os.makedirs(new_outpath)
command = "cd " + self.algpath[
0:-8] + ";PYTHONPATH=./ python " + self.algpath[
-8:] + " --lr " + str(
params["lr"]) + " --outputdir " + str(new_outpath)
print(new_outpath, command)
#os.system(command)
info = API_tools.creat_mission(str("bbo_" + str(ii) + "_" + str(jj)),
command, "qwer", "wudch", "woodchen")
return 1
def post(self,request):
# AutoML 新建任务
# res 字典数据格式详见 @/Frontend/src/pages/AutoML/CreateMission/data.d.ts
# export interface Former {
# //Base set
# type:string;
# name:string;
# description?:string;
# //Dataset set
# dataName?:string; //新建数据集的名称
# dataOutput?:string; //新建数据集的输出路径
# dataInput?:string; //新建数据集的输入路径
# dataSelection?:string; // 已有数据集的id
# //Model set
# modelsize:number;
# }
# @指项目文件夹路径
user = auth.get_user(request)
form_dict=request.data
# 创建任务
#{'type': 'Image_Classification', 'name': 'dsad', 'modelsize': 12321, 'dataSelection': 3}
FRONT_DEBUG=True
if(not FRONT_DEBUG):
datasetname = None
algtype = form_dict["type"]
jobname = form_dict['name']
maxflops = int(form_dict['modelsize'])
datasetid = form_dict['dataSelection']
if form_dict['dataSelection'] != None:
datasetname = models.Dataset.objects.filter(id = int(datasetid))[0]
# print(datasetname.name)
if algtype == 'Image_Classification':
algdict = ["efficientnet_b3a","mobilenetv2_120d","efficientnet_lite0","mobilenetv2_100","mobilenetv3_large_100"]
if maxflops > 900:
algname = 'efficientnet_b3a'
elif maxflops > 600:
algname = 'mobilenetv2_120d'
elif maxflops > 400:
algname = 'efficientnet_lite0'
elif maxflops > 300:
algname = 'mobilenetv2_100'
else:
algname = 'mobilenetv3_large_100'
#-------挂载CP算法操作----------
#back_untils.alg_cp(r'./../../algorithm/classification/pytorch_automodel/image_classification',"")
#-----------------------------
#command = "cd ../userhome/fakejobspace/algorithm/classification/pytorch_automodel/image_classification/;"
command = "cd ../userhome;mkdir jobspace;cd jobspace;rm -r algorithm;mkdir algorithm;cd algorithm;" \
"git clone https://github.com/MAC-AutoML/PCL_AutoML_System.git;cd ..;" \
"mkdir image_classification;cd ..;"
# 测试时使用fakejobspace中的算法运行
command = command+"cd jobspace/algorithm/PCL_AutoML_System/algorithm/classification/pytorch_automodel/image_classification;"
command = command + "PYTHONPATH=./ python Timm.py "
expdirname = str(jobname) + "_" + str(datasetname) + "_" + str(maxflops) + "_exp_" + str(time.time())
outputdir = "/userhome/jobspace/image_classification/"+expdirname
command = command + " --outputdir " + outputdir
command = command + " --dataset " + str(datasetname)
command = command + " --algname " + str(algname)
print(command)
info = API_tools.creat_mission(str(jobname), command, user.tocken, user, user.first_name)
if not info["payload"]:
print("error~!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
return Response(data=errParser(errcode=404))
timeArray = time.localtime()
otherStyleTime = time.strftime("%Y-%m-%d %H:%M:%S", timeArray)
jobid = API_tools.get_keyword(str(info["payload"]["jobId"]))
name = API_tools.get_keyword(str(jobname))
username = API_tools.get_keyword(str(user.username))
user_id = str(user.id)
state = "WAITTING"
createdTime = API_tools.get_keyword(str(otherStyleTime))
completedTime = str(0)
_path = API_tools.get_keyword(str(outputdir))
Da = models.User_algorithm.objects.filter(user_id=user.id).filter(name=algname)[0]
algorithm_id = Da.id
dataset_id = form_dict['dataSelection']
with connection.cursor() as cursor:
sqltext = "INSERT INTO `automl_web`.`_app_user_job`(`jobid`, `name`, `username`, `user_id`, `state`, `createdTime`, `completedTime`,`_path`, `algorithm_id`, `dataset_id`) " \
"VALUES('{0}', '{1}', '{2}', '{3}', '{4}', '{5}', '{6}', '{7}', '{8}','{9}');".format(
jobid, name, username, user_id, state, createdTime, completedTime, _path, algorithm_id, dataset_id
)
print("$$$$$$$$$$$", sqltext)
cursor.execute(sqltext)
# 创建完成
# 【】前端 后端 需要添加判断任务是否创建成功
res=[]
res=Parser(res)
return Response(data=res)