] # ntn variables dataName = 'sbWT' caseLst = list() wqData = waterQuality.DataModelWQ(dataName) codeLst = varNtnUsgsLst label = 'ntnSq' for code in codeLst: varX = ['00060'] + gridMET.varLst + \ [varNtnLst[varNtnUsgsLst.index(code)], 'distNTN'] varY = [code] varYC = None subsetLst = ['{}-Y{}'.format(code, x) for x in [1, 2]] # wrap up # for subset in subsetLst: subset = subsetLst[0] saveName = '{}-{}-{}-{}'.format(dataName, code, label, subset) caseName = basins.wrapMaster(dataName=dataName, trainName=subset, batchSize=[None, 100], outName=saveName, varX=varX, varY=varY, varYC=varYC) caseLst.append(caseName) cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/cmdTrain.py -M {}' for caseName in caseLst: slurm.submitJobGPU(caseName, cmdP.format(caseName), nH=24)
from hydroDL.master import slurm from hydroDL import kPath from hydroDL.app import waterQuality from hydroDL.data import gageII, usgs, gridMET from hydroDL.master import basins import pandas as pd import numpy as np import os import time caseLst = list() dataName = 'basinAll' subsetLst = ['Y8090', 'Y0010'] for subset in subsetLst: saveName = '{}-{}-opt1'.format(dataName, subset) caseName = basins.wrapMaster(dataName=dataName, trainName=subset, saveEpoch=50, batchSize=[None, 1000], outName=saveName) caseLst.append(caseName) saveName = '{}-{}-opt2'.format(dataName, subset) caseName = basins.wrapMaster(dataName=dataName, trainName=subset, saveEpoch=50, batchSize=[None, 1000], varY=None, varX=usgs.varQ+gridMET.varLst, outName=saveName) caseLst.append(caseName) cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/cmdTrain.py -M {}' for caseName in caseLst: slurm.submitJobGPU(caseName, cmdP.format(caseName), nH=48, nM=64)
from hydroDL import kPath from hydroDL.data import usgs, gageII, gridMET, ntn from hydroDL.master import slurm from hydroDL.master import basinFull varX = gridMET.varLst varY = ['runoff'] + usgs.newC varXC = gageII.lstWaterQuality varYC = None dataName = 'sbY30N5' sd = '1979-01-01' ed = '2010-01-01' outName = '{}-B10'.format(dataName) master = basinFull.wrapMaster(outName=outName, dataName=dataName, varX=varX, varY=varY, varXC=varXC, varYC=varYC, sd=sd, ed=ed) cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/hydroDL/master/cmd/basinFull.py -M {}' slurm.submitJobGPU(outName, cmdP.format(outName), nH=24, nM=32)
from hydroDL.master import slurm from hydroDL import kPath import os # slurm.submitJobGPU('modelA','python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/modelA.py',nH=20) # slurm.submitJobGPU('modelC','python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/modelC.py',nH=20) # wrap up data # codePath = os.path.join(kPath.dirCode, 'app', # 'waterQual', 'model', 'wrapData.py') # jobName = 'wrapUpData' # cmdLine = 'python {}'.format(codePath) # slurm.submitJob(jobName, cmdLine, nH=1, nM=64) # TRAIN MODEL # slurm.submitJobGPU('basinRef','python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/trainModel1.py',nH=24) # slurm.submitJobGPU( # 'basinAll', 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/trainModel2.py', nH=48, nM=64) cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/runCmd.py -D {} -O {}' nameP = '{}-opt{}' dLst = ['HBN', 'HBN-30d'] optLst = [1, 2, 3, 4] for d in dLst: for opt in optLst: print(cmdP.format(d, opt)) slurm.submitJobGPU(nameP.format(d, opt), cmdP.format(d, opt), nH=8)