def call_analysis(analysis_func, sliceno_, q, preserve_result, parent_pid, **kw):
try:
status._start('analysis(%d)' % (sliceno_,), parent_pid, 't')
os.close(_prof_fd)
for stupid_inconsistent_name in ('sliceno', 'index'):
if stupid_inconsistent_name in kw:
kw[stupid_inconsistent_name] = sliceno_
setattr(g, stupid_inconsistent_name, sliceno_)
for dw in dataset._datasetwriters.values():
if dw._for_single_slice is None:
dw._set_slice(sliceno_)
res = analysis_func(**kw)
if preserve_result:
# Remove defaultdicts until we find one with a picklable default_factory.
# (This is what you end up doing manually anyway.)
def picklable(v):
try:
pickle.dumps(v, pickle.HIGHEST_PROTOCOL)
return True
except Exception:
return False
def fixup(d):
if isinstance(d, defaultdict) and not picklable(d.default_factory):
if not d:
return {}
v = next(iteritems(d))
if isinstance(v, defaultdict) and not picklable(v.default_factory):
return {k: fixup(v) for k, v in iteritems(d)}
else:
return dict(d)
else:
return d
def save(item, name):
blob.save(fixup(item), name, sliceno=sliceno_, temp=True)
if isinstance(res, tuple):
if sliceno_ == 0:
blob.save(len(res), "Analysis.tuple", temp=True)
for ix, item in enumerate(res):
save(item, "Analysis.%d." % (ix,))
else:
if sliceno_ == 0:
blob.save(False, "Analysis.tuple", temp=True)
save(res, "Analysis.")
from extras import saved_files
dw_lens = {}
dw_minmax = {}
for name, dw in dataset._datasetwriters.items():
if dw._for_single_slice in (None, sliceno_,):
dw.close()
dw_lens[name] = dw._lens
dw_minmax[name] = dw._minmax
status._end()
q.put((sliceno_, time(), saved_files, dw_lens, dw_minmax, None,))
except:
status._end()
q.put((sliceno_, time(), {}, {}, {}, fmt_tb(1),))
print_exc()
sleep(5) # give launcher time to report error (and kill us)
exitfunction()
def execute_process(workdir,
jobid,
slices,
result_directory,
common_directory,
source_directory,
index=None,
workspaces=None,
daemon_url=None,
subjob_cookie=None,
parent_pid=0):
g.JOBID = jobid
setproctitle('launch')
path = os.path.join(workdir, jobid)
try:
os.chdir(path)
except Exception:
print("Cannot cd to workdir", path)
exit(1)
g.params = params = job_params()
method_ref = import_module(params.package + '.a_' + params.method)
g.sliceno = -1
if workspaces:
jobid_module.put_workspaces(workspaces)
def maybe_dataset(v):
if isinstance(v, list):
return [maybe_dataset(e) for e in v]
if not v:
return ''
try:
return dataset.Dataset(v)
except IOError:
return v
datasets = DotDict(
{k: maybe_dataset(v)
for k, v in params.datasets.items()})
g.options = params.options
g.datasets = datasets
g.jobids = params.jobids
method_ref.options = params.options
method_ref.datasets = datasets
method_ref.jobids = params.jobids
# compatibility names
g.SLICES = slices
g.JOBID = jobid
g.jobid = jobid
g.METHOD = params.method
g.WORKSPACEPATH = workdir
g.CAPTION = params.caption
g.PACKAGE = params.package
g.RESULT_DIRECTORY = result_directory
g.COMMON_DIRECTORY = common_directory
g.SOURCE_DIRECTORY = source_directory
g.index = -1
g.daemon_url = daemon_url
g.running = 'launch'
status._start('%s %s' % (
jobid,
params.method,
), parent_pid)
def dummy():
pass
prepare_func = getattr(method_ref, 'prepare', dummy)
analysis_func = getattr(method_ref, 'analysis', dummy)
synthesis_func = getattr(method_ref, 'synthesis', dummy)
synthesis_needs_analysis = 'analysis_res' in getarglist(synthesis_func)
# A chain must be finished from the back, so sort on that.
sortnum_cache = {}
def dw_sortnum(name):
if name not in sortnum_cache:
dw = dataset._datasetwriters[name]
if dw.previous and dw.previous.startswith(jobid + '/'):
pname = dw.previous.split('/')[1]
num = dw_sortnum(pname) + 1
else:
num = 0
sortnum_cache[name] = num
return sortnum_cache[name]
prof = {}
if prepare_func is dummy:
prof['prepare'] = 0 # truthish!
else:
t = time()
g.running = 'prepare'
g.subjob_cookie = subjob_cookie
setproctitle(g.running)
with status.status(g.running):
g.prepare_res = method_ref.prepare(**args_for(method_ref.prepare))
to_finish = [
dw.name for dw in dataset._datasetwriters.values()
if dw._started
]
if to_finish:
with status.status("Finishing datasets"):
for name in sorted(to_finish, key=dw_sortnum):
dataset._datasetwriters[name].finish()
prof['prepare'] = time() - t
setproctitle('launch')
from extras import saved_files
if analysis_func is dummy:
prof['per_slice'] = []
prof['analysis'] = 0
else:
t = time()
g.running = 'analysis'
g.subjob_cookie = None # subjobs are not allowed from analysis
with status.status(
'Waiting for all slices to finish analysis') as update:
g.update_top_status = update
prof['per_slice'], files, g.analysis_res = fork_analysis(
slices, analysis_func, args_for(analysis_func),
synthesis_needs_analysis)
del g.update_top_status
prof['analysis'] = time() - t
saved_files.update(files)
t = time()
g.running = 'synthesis'
g.subjob_cookie = subjob_cookie
setproctitle(g.running)
with status.status(g.running):
synthesis_res = synthesis_func(**args_for(synthesis_func))
if synthesis_res is not None:
blob.save(synthesis_res, temp=False)
if dataset._datasetwriters:
with status.status("Finishing datasets"):
for name in sorted(dataset._datasetwriters, key=dw_sortnum):
dataset._datasetwriters[name].finish()
t = time() - t
prof['synthesis'] = t
from subjobs import _record
status._end()
return None, (prof, saved_files, _record)