def submit_ca():
runfile = (
"FILEPATH/"
"analysis.py")
ijs = incomplete_jobs()
for loc in locs:
any_submitted = False
for a in ages:
if "%s/%s_%s.h5" % (outdir, loc, a) in ijs:
any_submitted = True
sge.qsub(
runfile,
'anem_%s' % loc,
parameters=[loc, a, ndraws, max_iters, sex_id_list,
year_id_list, consecutive_small_changes,
small_change, scale_factor, data_dir,
h5_dir, outdir],
conda_env='anemia_17_v5',
slots=7,
memory=14,
project='proj_anemia',
stdout="FILEPATH",
stderr="FILEPATH")
if any_submitted:
sleep(5)
def submit_upload_prep():
runfile = (
"{FILEPATH}/"
"uploads/write_standard_files.py")
for loc in locs:
sge.qsub(
runfile,
'anem_wsf_%s' % loc,
parameters=[loc],
conda_env='anemia',
project='proj_anemia',
slots=2,
memory=4)
def launch_failed():
with open("strCodeDir/miss_squeeze.csv") as f:
relaunch = f.readlines()
relaunch = [r.split(".")[0].split("_") for r in relaunch]
for r in relaunch:
runfile = "strCodeDir/squeeze_em_all.py"
params = ['--location_id', r[0], '--year_id', r[1], '--sex_id', r[2]]
sge.qsub(runfile,
'sqz_%s_%s_%s' % (r[0], r[1], r[2]),
parameters=params,
slots=30,
memory=60,
project='proj_como',
conda_env="isqueeze")
def submit_upload_prep():
runfile = (
"FILEPATH/"
"uploads/write_standard_files_uc.py")
for loc in locs:
sge.qsub(
runfile,
'anem_wsf_%s' % loc,
parameters=[loc],
conda_env='anemia_17_v5',
project='proj_anemia',
slots=4,
memory=8,
stdout="FILEPATH",
stderr="FILEPATH")
def dependent_submit(location_id, hold_ids):
node = lt.get_node_by_id(location_id)
nodelvl = lt.get_nodelvl_by_id(location_id)
num_children = len(node.children)
if num_children == 0:
return 0
else:
jids = []
for y in range(1990, 2016, 5):
job_name = "dm_%s_%s_%s_%s_%s" % (
mvid, location_id, sex[0], str(y)[2:], cv_iter)
if location_id == 1:
num_slots = 20
else:
num_slots = min(20, num_children * 2)
if (location_id, sex, y, cv_iter) in rrs:
params = [mvid, location_id, sex, y, cv_iter]
jid = sge.qsub(runfile,
job_name,
project=project,
holds=hold_ids,
slots=num_slots,
memory=int(math.ceil(num_slots * 2.5)),
parameters=params,
conda_env='cascade_ode',
prepend_to_path=
'/ihme/code/central_comp/anaconda/bin',
stderr='%s/%s.error' %
(logdir, job_name))
jids.append(jid)
all_jids.append(jid)
for c in node.children:
dependent_submit(c.id, jids)
def submit_rf():
runfile = (
"/{FILEPATH}/"
"uploads/nutrition_iron_exposure.py")
ijs = rf_incomplete_jobs()
for loc in locs:
if "{FILEPATH}/exp_%s.csv" % loc in ijs:
sge.qsub(
runfile,
'anem_rf_%s' % loc,
parameters=[loc],
conda_env='anemia',
project='proj_anemia',
jobtype='python',
slots=6,
memory=12,
stdout="{STD OUTPUT FILEPATH",
stderr="{STD ERROR FILEPATH}")
def submit_cf():
runfile = (
"FILEPATH"
"calculate_cf.py")
for y in year_id_list:
for s in sex_id_list:
for a in ages:
sge.qsub(
runfile,
'cf_%s_%s_%s' % (y, s, a),
parameters=[y, s, a],
conda_env='anemia_17_v5',
project='proj_anemia',
jobtype='python',
slots=1,
memory=2,
stdout="FILEPATH",
stderr="FILEPATH")
def launch_squeeze(autism_value=0.29, idiopathic=95.0, autism_resid=0.05):
locations = dbtrees.loctree(None, 35)
runfile = "strCodeDir/squeeze_em_all.py"
for location_id in [l.id for l in locations.leaves()]:
for year_id in [1990, 1995, 2000, 2005, 2010, 2015]:
for sex_id in [1, 2]:
params = [
'--location_id', location_id, '--year_id', year_id,
'--sex_id', sex_id
]
sge.qsub(runfile,
'sqz_%s_%s_%s' % (location_id, year_id, sex_id),
parameters=params,
slots=30,
memory=60,
project='proj_como',
conda_env="isqueeze")
def submit_uncertainty():
runfile = (
"{FILEPATH}/"
"add_uncertainty.py")
meids = pd.read_excel("/{FILEPATH}/priors/in_out_meid_map.xlsx", "out_meids")
meids = meids.filter(like='modelable_entity').values.flatten()
for meid in meids:
sge.qsub(
runfile,
'anem_un_%s' % meid,
parameters = [meid, ndraws],
conda_env='anemia',
project='proj_anemia',
jobtype='python',
slots=40,
memory=25,
stdout="{STD OUTPUT FILEPATH}",
stderr="{STD ERROR FILEPATH}")
def submit_save_results():
runfile = (
"/{FILEPATH}/"
"save_custom.py")
meids = pd.read_excel("/{FILEPATH}/priors/in_out_meid_map.xlsx", "out_meids")
meids = meids.filter(like='modelable_entity').values.flatten()
for meid in meids:
sge.qsub(
runfile,
'anem_sr_%s' % meid,
parameters=[meid],
conda_env='anemia',
project='proj_anemia',
jobtype='python',
slots=30,
memory=20,
stdout="{STD OUTPUT FILEPATH}",
stderr="{STD ERROR FILEPATH}")
def submit_global(mvid, project):
""" Submit the global dismod_ode job """
dirs = get_directories(mvid)
logdir = dirs['logdir']
gfile = "%s/run_global.py" % settings.this_path
jobname = 'dm_%s_boot' % mvid
jid = sge.qsub(gfile,
jobname,
project=project,
slots=15,
memory=30,
parameters=[mvid],
conda_env='cascade_ode',
prepend_to_path='strDir',
stderr='%s/%s.error' % (logdir, jobname))
return jid
def submit_jobtree(self, cv_iter):
"""Submits a jobtree, which manages a given full/cross-validation
run from global on down through the cascade"""
jobname = 'dm_{}_G{}'.format(self.mvid, cv_iter)
jid = sge.qsub(gfile,
jobname,
project=self.project,
slots=20,
memory=40,
parameters=[
self.mvid, '--submit_stage', 'jt', '--cv_iter',
cv_iter
],
conda_env='cascade_ode',
prepend_to_path='strDir',
stderr='{}/{}.error'.format(self.logdir, jobname))
return jid
def resubmit_self_check(self, hold_jids):
"""Submits a job that checks that all child location-year-sex groups
have run succesfully. If any have failed, it resubmits the below-global
levels of the cascade (i.e. the submit_cascade function)"""
jobname = 'dm_{}_G{}'.format(self.mvid, self.cv_iter_id)
jid = sge.qsub(gfile,
jobname,
project=self.project,
slots=20,
memory=40,
holds=hold_jids,
parameters=[
self.mvid, '--submit_stage', 'jt', '--cv_iter',
self.cv_iter_id
],
conda_env='cascade_ode',
prepend_to_path='strDir',
stderr='{}/{}.error'.format(self.logdir, jobname))
return jid
def dependent_submit(location_id, hold_ids):
node = loctree.get_node_by_id(location_id)
num_children = len(node.children)
if num_children == 0:
return 0
else:
if (location_id, sex, y) in run_set:
job_name = "casc_%s_%s_%s" % (location_id,
sex[0],
str(y)[2:])
num_slots = min(8, num_children)
jid = sge.qsub(
runfile,
job_name,
holds=hold_ids,
slots=num_slots,
memory=num_slots*2,
parameters=[mvid, location_id, sex, y])
jid = [jid]
else:
jid = []
for c in node.children:
dependent_submit(c.id, jid)
def submit_varnish(self, hold_jids):
"""Submits a job that 'varnishes' this run, meaning it:
1. Uploads fits
2. Uploads adjusted data
3. Computes fit statistics
4. Uploads fit statistics
5. Attempts to generate diagnostic plots
5. Computes finals
6. Uploads finals
7. Updates the status of the model to finished
"""
varn_jobname = 'dm_%s_varnish' % (self.mvid)
varn_jid = sge.qsub(finfile,
varn_jobname,
project=self.project,
slots=20,
memory=40,
parameters=[self.mvid],
holds=hold_jids,
conda_env='cascade_ode',
prepend_to_path='strDir',
stderr='%s/%s.error' % (self.logdir, varn_jobname))
return varn_jid
def dependent_submit(location_id, hold_ids):
node = self.cascade.loctree.get_node_by_id(location_id)
num_children = len(node.children)
if num_children == 0:
return 0
else:
jids = []
for y in [1990, 1995, 2000, 2005, 2010, 2016]:
job_name = "dm_%s_%s_%s_%s_%s" % (
self.mvid, location_id, sex[0], str(y)[2:],
self.cv_iter_id)
if location_id == 1:
num_slots = 20
else:
num_slots = min(20, num_children * 2)
path_to_conda_bin = ('strDir')
if ((location_id, sex, y, self.cv_iter_id)
in incomplete_jobs):
params = [
self.mvid, location_id, sex, y, self.cv_iter_id
]
jid = sge.qsub(
cfile,
job_name,
project=self.project,
holds=hold_ids,
slots=num_slots,
memory=int(math.ceil(num_slots * 2.5)),
parameters=params,
conda_env='cascade_ode',
prepend_to_path=path_to_conda_bin,
stderr='%s/%s.error' % (self.logdir, job_name))
jids.append(jid)
all_jids.append(jid)
for c in node.children:
dependent_submit(c.id, jids)
from jobmon import sge
from glob import glob
import os
dirs = glob("strOutDir/*")
runfile = "save_custom_results"
for d in dirs:
meid = os.path.basename(d)
sge.qsub(runfile,
"ss_save_%s" % meid,
parameters=[
meid, "super-squeeze result", d, '--env', 'prod',
'--file_pattern', '{location_id}_{year_id}_{sex_id}.h5',
'--h5_tablename', 'draws', '--best'
],
jobtype=None,
conda_env="como",
slots=20,
memory=40,
project='proj_como')
from hierarchies import dbtrees
from jobmon import sge
import sys
cvid = sys.argv[1]
lt = dbtrees.loctree(None, 35)
runfile = 'smaller_bins.py'
for l in lt.leaves():
sge.qsub(runfile,
'sb_%s' % l,
parameters=[cvid, l],
jobtype='python',
project='proj_como',
conda_env='como')
cv_run = mvm.cross_validate_id.values[0]
meid = mvm.modelable_entity_id.values[0]
if meid in [9422, 7695, 1175]:
project = "proj_tb"
else:
project = "proj_dismod"
if cv_run == 1:
cv_iters = range(11)
jids = []
for i in cv_iters:
jobname = 'dm_%s_diag%s' % (mvid, i),
jid = sge.qsub(
thisfile,
jobname,
project=project,
slots=15,
memory=30,
parameters=[mvid, 0, i],
conda_env='cascade_ode',
prepend_to_path='/ihme/code/central_comp/anaconda/bin',
stderr='%s/%s.error' % (logdir, jobname))
jids.append(jid)
# Submit finishing job
varn_jobname = 'dm_%s_varnish' % (mvid)
varn_jid = sge.qsub(
finfile,
varn_jobname,
project=project,
holds=jids,
slots=15,
memory=30,
