def listen_for_results(self, output_h5_file, total_particles):
logger.info("Waiting for %i particle results" % total_particles)
particles = []
retrieved = 0
timeout = 200
while retrieved < total_particles:
try:
# self.result is an iterator that can timeout on next()
particle = self.result.next(timeout)
retrieved += 1
particles.append(particle)
except StopIteration:
assert retrieved >= total_particles
break
except:
logger.exception("Particle has FAILED!!")
continue
# We multiply by 90 here to save 10% for the exporting
logger.progress((round((float(retrieved) / total_particles) * 90., 1), "%s Particle(s) complete" % retrieved))
logger.info(particles)
results = ex.ResultsPyTable(output_h5_file)
for p in particles:
for x in range(len(p.locations)):
results.write(p.timestep_index_dump(x))
results.compute()
results.close()
return
def start_tasks(self, **kwargs):
try:
logger.info('Adding %i particles as tasks' % self.total_particle_count())
tasks = []
for part in self.particles:
forcer = BaseForcer(self.hydrodataset,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
shoreline_index_buffer=self.shoreline_index_buffer)
tasks.append(forcer)
logger.progress((5, 'Running model'))
return self.pool.map_async(Runner(), tasks)
except Exception:
logger.exception("Something didn't start correctly!")
raise
def listen_for_results(self, output_h5_file, total_particles):
logger.info("Waiting for %i particle results" % total_particles)
particles = []
retrieved = 0
timeout = 200
while retrieved < total_particles:
try:
# self.result is an iterator that can timeout on next()
particle = self.result.next(timeout)
retrieved += 1
particles.append(particle)
except StopIteration:
assert retrieved >= total_particles
break
except:
logger.exception("Particle has FAILED!!")
continue
# We multiply by 90 here to save 10% for the exporting
logger.progress((round((float(retrieved) / total_particles) * 90.,
1), "%s Particle(s) complete" % retrieved))
logger.info(particles)
results = ex.ResultsPyTable(output_h5_file)
for p in particles:
for x in range(len(p.locations)):
results.write(p.timestep_index_dump(x))
results.compute()
results.close()
return
def start_tasks(self, **kwargs):
try:
logger.info('Starting CachingDataController')
# Add data controller to the queue first so that it
# can get the initial data and is not blocked
data_controller = CachingDataController(self.hydrodataset, self.common_variables, self.n_run, self.get_data, self.write_lock, self.has_write_lock, self.read_lock, self.read_count,
self.time_chunk, self.horiz_chunk, self.times, self.start, self.point_get, self.reference_location, cache_path=self.cache_path)
self.tasks.put(data_controller)
# Create CachingDataController worker
self.data_controller_process = Consumer(self.tasks, self.results, self.n_run, self.nproc_lock, self.active, self.get_data, name="CachingDataController")
self.data_controller_process.start()
logger.info('Adding %i particles as tasks' % self.total_particle_count())
for part in self.particles:
forcer = CachingForcer(self.cache_path,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
shoreline_index_buffer=self.shoreline_index_buffer,
get_data=self.get_data,
read_lock=self.read_lock,
has_read_lock=self.has_read_lock,
read_count=self.read_count,
point_get=self.point_get,
data_request_lock=self.data_request_lock,
has_data_request_lock=self.has_data_request_lock
)
self.tasks.put(forcer)
# Create workers for the particles.
self.procs = [Consumer(self.tasks, self.results, self.n_run, self.nproc_lock, self.active, self.get_data, name="CachingForcer-%d" % i)
for i in range(self.nproc - 1) ]
logger.progress((5, 'Running model'))
for w in self.procs:
w.start()
logger.info('Started %s' % w.name)
return True
except Exception:
logger.exception("Something didn't start correctly!")
return False
def start_tasks(self, **kwargs):
logger.progress((5, 'Running model'))
rc = redis.from_url(self.redis_url)
if kwargs.get('task_queue_call'):
for p in self.particles:
try:
kwargs.get('task_queue_call')(func=particle_runner,
args=(
p,
self,
))
except Exception:
logger.exception(traceback.format_exc())
rc.publish(self.redis_results_channel,
json.dumps({
"status": "FAILED",
"uid": p.uid
}))
return True
else:
tasks = []
for p in self.particles:
f = BaseForcer(
self.hydrodataset,
particle=p,
common_variables=self.common_variables,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
redis_url=self.redis_url,
redis_results_channel=self.redis_results_channel,
shoreline_index_buffer=self.shoreline_index_buffer)
tasks.append(f)
self.pool = multiprocessing.Pool()
return self.pool.imap(Runner(), tasks)
return False
def listen_for_results(self):
logger.info("Waiting for %i particle results" % len(self.particles))
logger.progress((5, "Running model"))
particles = []
retrieved = 0
timeout = 200
while retrieved < len(self.particles):
try:
# @TODO: better mechanism than switching on type
if isinstance(self.pool, multiprocessing.pool.Pool):
# self.result is an iterator that can timeout on next()
particle = self.result.next(timeout)
retrieved += 1
particles.append(particle)
else:
# IPython parallel View
# self.result is an AsyncMapResult
from IPython.parallel import TimeoutError
try:
new_particles = self.result.get(timeout=1)
except TimeoutError:
pass # this is fine, get incremental progress below
else:
particles = new_particles
# progress is absolute, not incremental
retrieved = self.result.progress
except StopIteration:
assert retrieved >= len(self.particles)
break
except:
logger.exception("Particle has FAILED!!")
#logger.warn("Particle %s has FAILED!!" % particle.uid)
continue
# We multiply by 95 here to save 5% for the exporting
logger.progress((round((float(retrieved) / self.number_of_tasks) * 90., 1), "%s Particle(s) complete" % retrieved))
return particles
def run(self, **kwargs):
logger.progress((4, "Starting tasks"))
self.result = self.start_tasks(**kwargs)
if self.result is None:
raise BaseDataControllerError("Not all tasks started! Exiting.")
# Store results in hdf5 file for processing later
output_h5_file = None
if kwargs.get('output_path') is not None:
output_h5_file = os.path.join(kwargs.get('output_path'),
'results.h5')
if self.thread_result_listener is True:
rl = threading.Thread(name="ResultListener",
target=self.listen_for_results,
args=(output_h5_file,
self.total_particle_count()))
rl.daemon = True
rl.start()
rl.join() # This blocks until the tasks are all done.
else:
self.listen_for_results(output_h5_file, self.total_particle_count(
)) # This blocks until the tasks are all done.
logger.info('Tasks are all finished... Cleaning up!!')
self.cleanup()
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
logger.progress((96, "Exporting results"))
# Make sure output_path is also included
if kwargs.get("output_path", None) is not None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for fmt in formats:
logger.info("Exporting to: %s" % fmt)
try:
# Calls the export function
fmt.export(output_path, output_h5_file)
except:
logger.exception("Failed to export to: %s" % fmt)
else:
logger.warn(
'The output_formats parameter should be a list, not saving any output!'
)
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn(
'No output_formats parameter was defined, not saving any output!'
)
logger.progress((97, "Model Run Complete"))
return
def listen_for_results(self, output_h5_file, total_particles):
logger.info("Waiting for %i particle results" % total_particles)
particles = []
retrieved = 0
while retrieved < total_particles:
try:
# IPython parallel View
# self.result is an AsyncMapResult
from IPython.parallel import TimeoutError
try:
new_particles = self.result.get(timeout=1)
except TimeoutError:
pass # this is fine, get incremental progress below
else:
particles = new_particles
# progress is absolute, not incremental
retrieved = self.result.progress
except StopIteration:
assert retrieved >= total_particles
break
except:
logger.exception("Particle has FAILED!!")
continue
# We multiply by 90 here to save 10% for the exporting
logger.progress((round((float(retrieved) / total_particles) * 90., 1), "%s Particle(s) complete" % retrieved))
results = ex.ResultsPyTable(output_h5_file)
for p in particles:
for x in range(len(p.locations)):
results.write(p.timestep_index_dump(x))
results.compute()
results.close()
return
def run(self, hydrodataset, **kwargs):
self.hydrodataset = hydrodataset
self.setup_run(**kwargs)
logger.progress((4, "Starting tasks"))
self.result = self.start_tasks()
if self.result is None:
raise BaseDataControllerError("Not all tasks started! Exiting.")
# This blocks until the tasks are all done.
self.particles = self.listen_for_results()
logger.info('Consumers are all finished!')
logger.info('Cleaning up')
self.cleanup()
if len(self.particles) > 0:
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
logger.progress((96, "Exporting results"))
# Make sure output_path is also included
if kwargs.get("output_path", None) is not None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for format in formats:
logger.info("Exporting to: %s" % format)
try:
self.export(output_path, format=format)
except:
logger.exception("Failed to export to: %s" % format)
else:
logger.warn('The output_formats parameter should be a list, not saving any output!')
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn('No output format defined, not saving any output!')
else:
logger.warn("Model didn't actually do anything, check the log.")
if self.error_code == -2:
raise BaseDataControllerError("Error in the BaseDataController")
else:
raise ModelError("Error in the model")
logger.progress((97, "Model Run Complete"))
return self.particles
def run(self, **kwargs):
logger.progress((4, "Starting tasks"))
self.result = self.start_tasks(**kwargs)
if self.result is None:
raise BaseDataControllerError("Not all tasks started! Exiting.")
# Store results in hdf5 file for processing later
output_h5_file = None
if kwargs.get('output_path') is not None:
output_h5_file = os.path.join(kwargs.get('output_path'), 'results.h5')
if self.thread_result_listener is True:
rl = threading.Thread(name="ResultListener", target=self.listen_for_results, args=(output_h5_file, self.total_particle_count()))
rl.daemon = True
rl.start()
rl.join() # This blocks until the tasks are all done.
else:
self.listen_for_results(output_h5_file, self.total_particle_count()) # This blocks until the tasks are all done.
logger.info('Tasks are all finished... Cleaning up!!')
self.cleanup()
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
logger.progress((96, "Exporting results"))
# Make sure output_path is also included
if kwargs.get("output_path", None) is not None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for fmt in formats:
logger.info("Exporting to: %s" % fmt)
try:
# Calls the export function
fmt.export(output_path, output_h5_file)
except:
logger.exception("Failed to export to: %s" % fmt)
else:
logger.warn('The output_formats parameter should be a list, not saving any output!')
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn('No output_formats parameter was defined, not saving any output!')
logger.progress((97, "Model Run Complete"))
return
def run(run_id):
# Sleep to give the Run object enough time to save
time.sleep(10)
with app.app_context():
from paegan.logger import logger
job = get_current_job()
output_path = os.path.join(current_app.config['OUTPUT_PATH'], run_id)
shutil.rmtree(output_path, ignore_errors=True)
os.makedirs(output_path)
cache_path = os.path.join(current_app.config['CACHE_PATH'], run_id)
shutil.rmtree(cache_path, ignore_errors=True)
os.makedirs(cache_path)
temp_animation_path = os.path.join(current_app.config['OUTPUT_PATH'], "temp_images_" + run_id)
shutil.rmtree(temp_animation_path, ignore_errors=True)
os.makedirs(temp_animation_path)
# Set up Logger
queue = multiprocessing.Queue(-1)
f, log_file = tempfile.mkstemp(dir=cache_path, prefix=run_id, suffix=".log")
os.close(f)
# Close any existing handlers
(hand.close() for hand in logger.handlers)
# Remove any existing handlers
logger.handlers = []
logger.setLevel(logging.PROGRESS)
handler = MultiProcessingLogHandler(log_file, queue)
handler.setLevel(logging.PROGRESS)
formatter = logging.Formatter('[%(asctime)s] - %(levelname)s - %(name)s - %(processName)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
# Progress stuff. Hokey!
progress_deque = collections.deque(maxlen=1)
progress_handler = ProgressHandler(progress_deque)
progress_handler.setLevel(logging.PROGRESS)
logger.addHandler(progress_handler)
e = threading.Event()
def save_progress():
while e.wait(5) is not True:
try:
record = progress_deque.pop()
if record == StopIteration:
break
job.meta["updated"] = record[0]
if record is not None and record[1] >= 0:
job.meta["progress"] = record[1]
if isinstance(record[2], unicode) or isinstance(record[2], str):
job.meta["message"] = record[2]
job.save()
except IndexError:
pass
except Exception:
raise
return
t = threading.Thread(name="ProgressUpdater", target=save_progress)
t.daemon = True
t.start()
model = None
try:
logger.progress((0, "Configuring model"))
run = db.Run.find_one( { '_id' : ObjectId(run_id) } )
if run is None:
return "Failed to locate run %s. May have been deleted while task was in the queue?" % run_id
geometry = loads(run['geometry'])
start_depth = run['release_depth']
num_particles = run['particles']
time_step = run['timestep']
num_steps = int(math.ceil((run['duration'] * 24 * 60 * 60) / time_step))
start_time = run['start'].replace(tzinfo = pytz.utc)
shoreline_path = run['shoreline_path'] or app.config.get("SHORE_PATH")
shoreline_feat = run['shoreline_feature']
# Set up output directory/bucket for run
output_formats = ['Shapefile', 'NetCDF', 'Trackline']
# Setup Models
models = []
if run['cached_behavior'] is not None and run['cached_behavior'].get('results', None) is not None:
behavior_data = run['cached_behavior']['results'][0]
l = LarvaBehavior(data=behavior_data)
models.append(l)
models.append(Transport(horizDisp=run['horiz_dispersion'], vertDisp=run['vert_dispersion']))
# Setup ModelController
model = ModelController(geometry=geometry, depth=start_depth, start=start_time, step=time_step, nstep=num_steps, npart=num_particles, models=models, use_bathymetry=True, use_shoreline=True,
time_chunk=run['time_chunk'], horiz_chunk=run['horiz_chunk'], time_method=run['time_method'], shoreline_path=shoreline_path, shoreline_feature=shoreline_feat, reverse_distance=1500)
# Run the model
cache_file = os.path.join(cache_path, run_id + ".nc.cache")
bathy_file = current_app.config['BATHY_PATH']
model.run(run['hydro_path'], output_path=output_path, bathy=bathy_file, output_formats=output_formats, cache=cache_file, remove_cache=False, caching=run['caching'])
# Skip creating movie output_path
"""
from paegan.viz.trajectory import CFTrajectory
logger.info("Creating animation...")
for filename in os.listdir(output_path):
if os.path.splitext(filename)[1][1:] == "nc":
# Found netCDF file
netcdf_file = os.path.join(output_path,filename)
traj = CFTrajectory(netcdf_file)
success = traj.plot_animate(os.path.join(output_path,'animation.avi'), temp_folder=temp_animation_path, bathy=app.config['BATHY_PATH'])
if not success:
logger.info("Could not create animation")
else:
logger.info("Animation saved")
"""
job.meta["outcome"] = "success"
job.save()
return "Successfully ran %s" % run_id
except Exception as exception:
logger.warn("Run FAILED, cleaning up and uploading log.")
logger.warn(exception.message)
job.meta["outcome"] = "failed"
job.save()
raise
finally:
logger.progress((99, "Processing output files"))
# Close the handler so we can upload the log file without a file lock
(hand.close() for hand in logger.handlers)
queue.put(StopIteration)
# Break out of the progress loop
e.set()
t.join()
# Move logfile to output directory
shutil.move(log_file, os.path.join(output_path, 'model.log'))
# Move cachefile to output directory if we made one
if run['caching']:
shutil.move(cache_file, output_path)
output_files = []
for filename in os.listdir(output_path):
outfile = os.path.join(output_path, filename)
output_files.append(outfile)
result_files = []
base_access_url = current_app.config.get('NON_S3_OUTPUT_URL', None)
# Handle results and cleanup
if current_app.config['USE_S3'] is True:
base_access_url = urljoin("http://%s.s3.amazonaws.com/output/" % current_app.config['S3_BUCKET'], run_id)
# Upload results to S3 and remove the local copies
conn = S3Connection()
bucket = conn.get_bucket(current_app.config['S3_BUCKET'])
for outfile in output_files:
# Don't upload the cache file
if os.path.basename(outfile) == os.path.basename(cache_file):
continue
# Upload the outfile with the same as the run name
_, ext = os.path.splitext(outfile)
new_filename = slugify(unicode(run['name'])) + ext
k = Key(bucket)
k.key = "output/%s/%s" % (run_id, new_filename)
k.set_contents_from_filename(outfile)
k.set_acl('public-read')
result_files.append(base_access_url + "/" + new_filename)
os.remove(outfile)
shutil.rmtree(output_path, ignore_errors=True)
else:
for outfile in output_files:
result_files.append(urljoin(base_access_url, run_id) + "/" + os.path.basename(outfile))
shutil.rmtree(temp_animation_path, ignore_errors=True)
# Set output fields
run.output = result_files
run.ended = datetime.utcnow()
run.compute()
run.save()
# Cleanup
logger.removeHandler(handler)
del formatter
del handler
del logger
del model
queue.close()
job.meta["message"] = "Complete"
job.save()
def run(self, hydrodataset, **kwargs):
# Add ModelController description to logfile
logger.info(self)
# Add the model descriptions to logfile
for m in self._models:
logger.info(m)
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
times = range(0, (self._step * self._nstep) + 1, self._step)
# Calculate a datetime object for each model timestep
# This method is duplicated in DataController and ForceParticle
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(
times, start=self.start)
time_chunk = self._time_chunk
horiz_chunk = self._horiz_chunk
low_memory = kwargs.get("low_memory", False)
# Should we remove the cache file at the end of the run?
remove_cache = kwargs.get("remove_cache", True)
self.bathy_path = kwargs.get("bathy", None)
self.cache_path = kwargs.get("cache", None)
if self.cache_path is None:
# Generate temp filename for dataset cache
default_cache_dir = os.path.join(os.path.dirname(__file__),
"_cache")
temp_name = AsaRandom.filename(prefix=str(
datetime.now().microsecond),
suffix=".nc")
self.cache_path = os.path.join(default_cache_dir, temp_name)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(
self.geometry, MultiPolygon):
point_locations = [
Location4D(latitude=loc.y,
longitude=loc.x,
depth=self._depth,
time=self.start)
for loc in AsaTransport.fill_polygon_with_points(
goal=self._npart, polygon=self.geometry)
]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in xrange(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
# This is where it makes sense to implement the multiprocessing
# looping for particles and models. Can handle each particle in
# parallel probably.
#
# Get the number of cores (may take some tuning) and create that
# many workers then pass particles into the queue for the workers
mgr = multiprocessing.Manager()
nproc = multiprocessing.cpu_count() - 1
if nproc <= 0:
raise ValueError(
"Model does not run using less than two CPU cores")
# Each particle is a task, plus the DataController
number_of_tasks = len(self.particles) + 1
# We need a process for each particle and one for the data controller
nproc = min(number_of_tasks, nproc)
# When a particle requests data
data_request_lock = mgr.Lock()
# PID of process with lock
has_data_request_lock = mgr.Value('int', -1)
nproc_lock = mgr.Lock()
# Create the task queue for all of the particles and the DataController
tasks = multiprocessing.JoinableQueue(number_of_tasks)
# Create the result queue for all of the particles and the DataController
results = mgr.Queue(number_of_tasks)
# Create the shared state objects
get_data = mgr.Value('bool', True)
# Number of tasks
n_run = mgr.Value('int', number_of_tasks)
updating = mgr.Value('bool', False)
# When something is reading from cache file
read_lock = mgr.Lock()
# list of PIDs that are reading
has_read_lock = mgr.list()
read_count = mgr.Value('int', 0)
# When something is writing to the cache file
write_lock = mgr.Lock()
# PID of process with lock
has_write_lock = mgr.Value('int', -1)
point_get = mgr.Value('list', [0, 0, 0])
active = mgr.Value('bool', True)
logger.progress((3, "Initializing and caching hydro model's grid"))
try:
ds = CommonDataset.open(hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
common_variables = self.get_common_variables_from_dataset(ds)
logger.debug("Pickling time variable to disk for particles")
timevar = ds.gettimevar(common_variables.get("u"))
f, timevar_pickle_path = tempfile.mkstemp()
os.close(f)
f = open(timevar_pickle_path, "wb")
pickle.dump(timevar, f)
f.close()
ds.closenc()
except:
logger.warn("Failed to access remote dataset %s" % hydrodataset)
raise DataControllerError("Inaccessible DAP endpoint: %s" %
hydrodataset)
# Add data controller to the queue first so that it
# can get the initial data and is not blocked
logger.debug('Starting DataController')
logger.progress((4, "Starting processes"))
data_controller = parallel.DataController(hydrodataset,
common_variables,
n_run,
get_data,
write_lock,
has_write_lock,
read_lock,
read_count,
time_chunk,
horiz_chunk,
times,
self.start,
point_get,
self.reference_location,
low_memory=low_memory,
cache=self.cache_path)
tasks.put(data_controller)
# Create DataController worker
data_controller_process = parallel.Consumer(tasks,
results,
n_run,
nproc_lock,
active,
get_data,
name="DataController")
data_controller_process.start()
logger.debug('Adding %i particles as tasks' % len(self.particles))
for part in self.particles:
forcing = parallel.ForceParticle(
part,
hydrodataset,
common_variables,
timevar_pickle_path,
times,
self.start,
self._models,
self.reference_location.point,
self._use_bathymetry,
self._use_shoreline,
self._use_seasurface,
get_data,
n_run,
read_lock,
has_read_lock,
read_count,
point_get,
data_request_lock,
has_data_request_lock,
reverse_distance=self.reverse_distance,
bathy=self.bathy_path,
shoreline_path=self.shoreline_path,
cache=self.cache_path,
time_method=self.time_method)
tasks.put(forcing)
# Create workers for the particles.
procs = [
parallel.Consumer(tasks,
results,
n_run,
nproc_lock,
active,
get_data,
name="ForceParticle-%d" % i)
for i in xrange(nproc - 1)
]
for w in procs:
w.start()
logger.debug('Started %s' % w.name)
# Get results back from queue, test for failed particles
return_particles = []
retrieved = 0.
error_code = 0
logger.info("Waiting for %i particle results" % len(self.particles))
logger.progress((5, "Running model"))
while retrieved < number_of_tasks:
try:
# Returns a tuple of code, result
code, tempres = results.get(timeout=240)
except Queue.Empty:
# Poll the active processes to make sure they are all alive and then continue with loop
if not data_controller_process.is_alive(
) and data_controller_process.exitcode != 0:
# Data controller is zombied, kill off other processes.
get_data.value == False
results.put((-2, "DataController"))
new_procs = []
old_procs = []
for p in procs:
if not p.is_alive() and p.exitcode != 0:
# Do what the Consumer would do if something finished.
# Add something to results queue
results.put((-3, "ZombieParticle"))
# Decrement nproc (DataController exits when this is 0)
with nproc_lock:
n_run.value = n_run.value - 1
# Remove task from queue (so they can be joined later on)
tasks.task_done()
# Start a new Consumer. It will exit if there are no tasks available.
np = parallel.Consumer(tasks,
results,
n_run,
nproc_lock,
active,
get_data,
name=p.name)
new_procs.append(np)
old_procs.append(p)
# Release any locks the PID had
if p.pid in has_read_lock:
with read_lock:
read_count.value -= 1
has_read_lock.remove(p.pid)
if has_data_request_lock.value == p.pid:
has_data_request_lock.value = -1
try:
data_request_lock.release()
except:
pass
if has_write_lock.value == p.pid:
has_write_lock.value = -1
try:
write_lock.release()
except:
pass
for p in old_procs:
try:
procs.remove(p)
except ValueError:
logger.warn(
"Did not find %s in the list of processes. Continuing on."
% p.name)
for p in new_procs:
procs.append(p)
logger.warn(
"Started a new consumer (%s) to replace a zombie consumer"
% p.name)
p.start()
else:
# We got one.
retrieved += 1
if code == None:
logger.warn("Got an unrecognized response from a task.")
elif code == -1:
logger.warn("Particle %s has FAILED!!" % tempres.uid)
elif code == -2:
error_code = code
logger.warn(
"DataController has FAILED!! Removing cache file so the particles fail."
)
try:
os.remove(self.cache_path)
except OSError:
logger.debug(
"Could not remove cache file, it probably never existed"
)
pass
elif code == -3:
error_code = code
logger.info(
"A zombie process was caught and task was removed from queue"
)
elif isinstance(tempres, Particle):
logger.info("Particle %d finished" % tempres.uid)
return_particles.append(tempres)
# We mulitply by 95 here to save 5% for the exporting
logger.progress(
(round((retrieved / number_of_tasks) * 90.,
1), "Particle %d finished" % tempres.uid))
elif tempres == "DataController":
logger.info("DataController finished")
logger.progress((round((retrieved / number_of_tasks) * 90.,
1), "DataController finished"))
else:
logger.info("Got a strange result on results queue")
logger.info(str(tempres))
logger.info("Retrieved %i/%i results" %
(int(retrieved), number_of_tasks))
if len(return_particles) != len(self.particles):
logger.warn(
"Some particles failed and are not included in the output")
# The results queue should be empty at this point
assert results.empty() is True
# Should be good to join on the tasks now that the queue is empty
logger.info("Joining the task queue")
tasks.join()
# Join all processes
logger.info("Joining the processes")
for w in procs + [data_controller_process]:
# Wait 10 seconds
w.join(10.)
if w.is_alive():
# Process is hanging, kill it.
logger.info(
"Terminating %s forcefully. This should have exited itself."
% w.name)
w.terminate()
logger.info('Workers complete')
self.particles = return_particles
# Remove Manager so it shuts down
del mgr
# Remove pickled timevar
os.remove(timevar_pickle_path)
# Remove the cache file
if remove_cache is True:
try:
os.remove(self.cache_path)
except OSError:
logger.debug(
"Could not remove cache file, it probably never existed")
logger.progress((96, "Exporting results"))
if len(self.particles) > 0:
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
# Make sure output_path is also included
if kwargs.get("output_path", None) != None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for format in formats:
logger.info("Exporting to: %s" % format)
try:
self.export(output_path, format=format)
except:
logger.error("Failed to export to: %s" %
format)
else:
logger.warn(
'The output_formats parameter should be a list, not saving any output!'
)
else:
logger.warn(
'No output path defined, not saving any output!')
else:
logger.warn('No output format defined, not saving any output!')
else:
logger.warn("Model didn't actually do anything, check the log.")
if error_code == -2:
raise DataControllerError("Error in the DataController")
else:
raise ModelError("Error in the model")
logger.progress((99, "Model Run Complete"))
return
def listen_for_results(self):
try:
# Get results back from queue, test for failed particles
return_particles = []
retrieved = 0.
self.error_code = 0
logger.info("Waiting for %i particle results" % len(self.particles))
logger.progress((5, "Running model"))
while retrieved < self.number_of_tasks:
try:
# Returns a tuple of code, result
code, tempres = self.results.get(timeout=240)
except Queue.Empty:
# Poll the active processes to make sure they are all alive and then continue with loop
if not self.data_controller_process.is_alive() and self.data_controller_process.exitcode != 0:
# Data controller is zombied, kill off other processes.
self.get_data.value is False
self.results.put((-2, "CachingDataController"))
new_procs = []
old_procs = []
for p in self.procs:
if not p.is_alive() and p.exitcode != 0:
# Do what the Consumer would do if something finished.
# Add something to results queue
self.results.put((-3, "ZombieParticle"))
# Decrement nproc (CachingDataController exits when this is 0)
with self.nproc_lock:
self.n_run.value = self.n_run.value - 1
# Remove task from queue (so they can be joined later on)
self.tasks.task_done()
# Start a new Consumer. It will exit if there are no tasks available.
np = Consumer(self.tasks, self.results, self.n_run, self.nproc_lock, self.active, self.get_data, name=p.name)
new_procs.append(np)
old_procs.append(p)
# Release any locks the PID had
if p.pid in self.has_read_lock:
with self.read_lock:
self.read_count.value -= 1
self.has_read_lock.remove(p.pid)
if self.has_data_request_lock.value == p.pid:
self.has_data_request_lock.value = -1
try:
self.data_request_lock.release()
except:
pass
if self.has_write_lock.value == p.pid:
self.has_write_lock.value = -1
try:
self.write_lock.release()
except:
pass
for p in old_procs:
try:
self.procs.remove(p)
except ValueError:
logger.warn("Did not find %s in the list of processes. Continuing on." % p.name)
for p in new_procs:
self.procs.append(p)
logger.warn("Started a new consumer (%s) to replace a zombie consumer" % p.name)
p.start()
else:
# We got one.
retrieved += 1
if code is None:
logger.warn("Got an unrecognized response from a task.")
elif code == -1:
logger.warn("Particle %s has FAILED!!" % tempres.uid)
elif code == -2:
self.error_code = code
logger.warn("CachingDataController has FAILED!! Removing cache file so the particles fail.")
try:
os.remove(self.cache_path)
except OSError:
logger.debug("Could not remove cache file, it probably never existed")
pass
elif code == -3:
self.error_code = code
logger.info("A zombie process was caught and task was removed from queue")
elif isinstance(tempres, Particle):
logger.info("Particle %d finished" % tempres.uid)
return_particles.append(tempres)
# We mulitply by 95 here to save 5% for the exporting
logger.progress((round((retrieved / self.number_of_tasks) * 90., 1), "Particle %d finished" % tempres.uid))
elif tempres == "CachingDataController":
logger.info("CachingDataController finished")
logger.progress((round((retrieved / self.number_of_tasks) * 90., 1), "CachingDataController finished"))
else:
logger.info("Got a strange result on results queue")
logger.info(str(tempres))
logger.info("Retrieved %i/%i results" % (int(retrieved), self.number_of_tasks))
if len(return_particles) != len(self.particles):
logger.warn("Some particles failed and are not included in the output")
# The results queue should be empty at this point
assert self.results.empty() is True
# Should be good to join on the tasks now that the queue is empty
logger.info("Joining the task queue")
self.tasks.join()
self.particles = return_particles
finally:
# Join all processes
logger.info("Joining the processes")
for w in self.procs + [self.data_controller_process]:
# Wait 20 seconds
w.join(20.)
if w.is_alive():
# Process is hanging, kill it.
logger.info("Terminating %s forcefully. This should have exited itself." % w.name)
w.terminate()
def setup_run(self, **kwargs):
logger.setLevel(logging.PROGRESS)
self.redis_url = None
self.redis_log_channel = None
self.redis_results_channel = None
if "redis" in kwargs.get("output_formats", []):
from paegan.logger.redis_handler import RedisHandler
self.redis_url = kwargs.get("redis_url")
self.redis_log_channel = kwargs.get("redis_log_channel")
self.redis_results_channel = kwargs.get("redis_results_channel")
rhandler = RedisHandler(self.redis_log_channel, self.redis_url)
rhandler.setLevel(logging.PROGRESS)
logger.addHandler(rhandler)
# Relax.
time.sleep(0.5)
# Add ModelController description to logfile
logger.info(unicode(self))
# Add the model descriptions to logfile
for m in self._models:
logger.info(unicode(m))
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
self.times = range(0, (self._step*self._nstep)+1, self._step)
# Calculate a datetime object for each model timestep
# This method is duplicated in CachingDataController and CachingForcer
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
self.modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(self.times, start=self.start)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(self.geometry, MultiPolygon):
point_locations = [Location4D(latitude=loc.y, longitude=loc.x, depth=self._depth, time=self.start) for loc in AsaTransport.fill_polygon_with_points(goal=self._npart, polygon=self.geometry)]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in xrange(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
if kwargs.get("manager", True):
# Get the number of cores (may take some tuning) and create that
# many workers then pass particles into the queue for the workers
self.mgr = multiprocessing.Manager()
# This tracks if the system is 'alive'. Most looping whiles will check this
# and break out if it is False. This is True until something goes very wrong.
self.active = self.mgr.Value('bool', True)
# Each particle is a task, plus the CachingDataController
self.number_of_tasks = self.get_number_of_tasks()
# Either spin up the number of cores, or the number of tasks
self.nproc = min(multiprocessing.cpu_count() - 1, self.number_of_tasks)
# Number of tasks that we need to run. This is decremented everytime something exits.
self.n_run = self.mgr.Value('int', self.number_of_tasks)
# The lock that controls access to the 'n_run' variable
self.nproc_lock = self.mgr.Lock()
# Create the task queue for all of the particles and the CachingDataController
self.tasks = multiprocessing.JoinableQueue(self.number_of_tasks)
# Create the result queue for all of the particles and the CachingDataController
self.results = self.mgr.Queue(self.number_of_tasks)
logger.progress((3, "Initializing and caching hydro model's grid"))
try:
ds = CommonDataset.open(self.hydrodataset)
except Exception:
logger.exception("Failed to access dataset %s" % self.hydrodataset)
raise BaseDataControllerError("Inaccessible Dataset: %s" % self.hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
self.common_variables = self.get_common_variables_from_dataset(ds)
self.timevar = None
try:
assert self.common_variables.get("u") in ds._current_variables
assert self.common_variables.get("v") in ds._current_variables
assert self.common_variables.get("x") in ds._current_variables
assert self.common_variables.get("y") in ds._current_variables
self.timevar = ds.gettimevar(self.common_variables.get("u"))
except AssertionError:
logger.exception("Could not locate variables needed to run model: %s" % unicode(self.common_variables))
raise BaseDataControllerError("A required data variable was not found in %s" % self.hydrodataset)
model_start = self.timevar.get_dates()[0]
model_end = self.timevar.get_dates()[-1]
try:
assert self.start > model_start
assert self.start < model_end
except AssertionError:
raise BaseDataControllerError("Start time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[0], model_start, model_end))
try:
assert self.datetimes[-1] > model_start
assert self.datetimes[-1] < model_end
except AssertionError:
raise BaseDataControllerError("End time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[-1], model_start, model_end))
ds.closenc()
def listen_for_results(self):
try:
# Get results back from queue, test for failed particles
return_particles = []
retrieved = 0.
self.error_code = 0
logger.info("Waiting for %i particle results" % len(self.particles))
logger.progress((5, "Running model"))
while retrieved < self.number_of_tasks:
try:
# Returns a tuple of code, result
code, tempres = self.results.get(timeout=240)
except Queue.Empty:
new_procs = []
old_procs = []
for p in self.procs:
if not p.is_alive() and p.exitcode != 0:
# Do what the Consumer would do if something finished.
# Add something to results queue
self.results.put((-3, "Zombie"))
# Decrement nproc (Consumer exits when this is 0)
with self.nproc_lock:
self.n_run.value = self.n_run.value - 1
# Remove task from queue (so they can be joined later on)
self.tasks.task_done()
# Start a new Consumer. It will exit if there are no tasks available.
np = Consumer(self.tasks, self.results, self.n_run, self.nproc_lock, self.active, None, name=p.name)
new_procs.append(np)
old_procs.append(p)
for p in old_procs:
try:
self.procs.remove(p)
except ValueError:
logger.warn("Did not find %s in the list of processes. Continuing on." % p.name)
for p in new_procs:
self.procs.append(p)
logger.warn("Started a new consumer (%s) to replace a zombie consumer" % p.name)
p.start()
else:
# We got one.
retrieved += 1
if code is None:
logger.warn("Got an unrecognized response from a task.")
elif code == -1:
logger.warn("Particle %s has FAILED!!" % tempres.uid)
elif code == -3:
self.error_code = code
logger.info("A zombie process was caught and task was removed from queue")
elif isinstance(tempres, Particle):
logger.info("Particle %d finished" % tempres.uid)
return_particles.append(tempres)
# We mulitply by 95 here to save 5% for the exporting
logger.progress((round((retrieved / self.number_of_tasks) * 90., 1), "Particle %d finished" % tempres.uid))
else:
logger.info("Got a strange result on results queue: %s" % str(tempres))
logger.info("Retrieved %i/%i results" % (int(retrieved), self.number_of_tasks))
if len(return_particles) != len(self.particles):
logger.warn("Some particles failed and are not included in the output")
# The results queue should be empty at this point
assert self.results.empty() is True
# Should be good to join on the tasks now that the queue is empty
logger.info("Joining the task queue")
self.tasks.join()
self.particles = return_particles
finally:
# Join all processes
logger.info("Joining the processes")
for w in self.procs:
# Wait 20 seconds
w.join(20.)
if w.is_alive():
# Process is hanging, kill it.
logger.info("Terminating %s forcefully. This should have exited itself." % w.name)
w.terminate()
def setup_run(self, hydrodataset, **kwargs):
self.hydrodataset = hydrodataset
logger.setLevel(logging.PROGRESS)
# Relax.
time.sleep(0.5)
# Add ModelController description to logfile
logger.info(str(self))
# Add the model descriptions to logfile
for m in self._models:
logger.info(str(m))
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
self.times = list(range(0, (self._step * self._nstep) + 1, self._step))
# Calculate a datetime object for each model timestep
# This method is duplicated in CachingDataController and CachingForcer
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
self.modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(
self.times, start=self.start)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(
self.geometry, MultiPolygon):
point_locations = [
Location4D(latitude=loc.y,
longitude=loc.x,
depth=self._depth,
time=self.start)
for loc in AsaTransport.fill_polygon_with_points(
goal=self._npart, polygon=self.geometry)
]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in range(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
logger.progress((3, "Initializing and caching hydro model's grid %s" %
self.hydrodataset))
try:
ds = CommonDataset.open(self.hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
self.common_variables = self.get_common_variables_from_dataset(ds)
except Exception:
logger.exception("Failed to access dataset %s" % self.hydrodataset)
raise BaseDataControllerError("Inaccessible Dataset: %s" %
self.hydrodataset)
self.timevar = None
try:
assert self.common_variables.get("u") in ds._current_variables
assert self.common_variables.get("v") in ds._current_variables
assert self.common_variables.get("x") in ds._current_variables
assert self.common_variables.get("y") in ds._current_variables
self.timevar = ds.gettimevar(self.common_variables.get("u"))
model_start = self.timevar.get_dates()[0]
model_end = self.timevar.get_dates()[-1]
except AssertionError:
logger.exception(
"Could not locate variables needed to run model: %s" %
str(self.common_variables))
raise BaseDataControllerError(
"A required data variable was not found in %s" %
self.hydrodataset)
finally:
ds.closenc()
try:
assert self.start > model_start
assert self.start < model_end
except AssertionError:
raise BaseDataControllerError(
"Start time for model (%s) is not available in source dataset (%s/%s)"
% (self.datetimes[0], model_start, model_end))
try:
assert self.datetimes[-1] > model_start
assert self.datetimes[-1] < model_end
except AssertionError:
raise BaseDataControllerError(
"End time for model (%s) is not available in source dataset (%s/%s)"
% (self.datetimes[-1], model_start, model_end))
def setup_run(self, hydrodataset, **kwargs):
self.hydrodataset = hydrodataset
logger.setLevel(logging.PROGRESS)
# Relax.
time.sleep(0.5)
# Add ModelController description to logfile
logger.info(str(self))
# Add the model descriptions to logfile
for m in self._models:
logger.info(str(m))
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
self.times = list(range(0, (self._step*self._nstep)+1, self._step))
# Calculate a datetime object for each model timestep
# This method is duplicated in CachingDataController and CachingForcer
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
self.modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(self.times, start=self.start)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(self.geometry, MultiPolygon):
point_locations = [Location4D(latitude=loc.y, longitude=loc.x, depth=self._depth, time=self.start) for loc in AsaTransport.fill_polygon_with_points(goal=self._npart, polygon=self.geometry)]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in range(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
logger.progress((3, "Initializing and caching hydro model's grid %s" % self.hydrodataset))
try:
ds = CommonDataset.open(self.hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
self.common_variables = self.get_common_variables_from_dataset(ds)
except Exception:
logger.exception("Failed to access dataset %s" % self.hydrodataset)
raise BaseDataControllerError("Inaccessible Dataset: %s" % self.hydrodataset)
self.timevar = None
try:
assert self.common_variables.get("u") in ds._current_variables
assert self.common_variables.get("v") in ds._current_variables
assert self.common_variables.get("x") in ds._current_variables
assert self.common_variables.get("y") in ds._current_variables
self.timevar = ds.gettimevar(self.common_variables.get("u"))
model_start = self.timevar.get_dates()[0]
model_end = self.timevar.get_dates()[-1]
except AssertionError:
logger.exception("Could not locate variables needed to run model: %s" % str(self.common_variables))
raise BaseDataControllerError("A required data variable was not found in %s" % self.hydrodataset)
finally:
ds.closenc()
try:
assert self.start > model_start
assert self.start < model_end
except AssertionError:
raise BaseDataControllerError("Start time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[0], model_start, model_end))
try:
assert self.datetimes[-1] > model_start
assert self.datetimes[-1] < model_end
except AssertionError:
raise BaseDataControllerError("End time for model (%s) is not available in source dataset (%s/%s)" % (self.datetimes[-1], model_start, model_end))
def run(self, hydrodataset, **kwargs):
# Add ModelController description to logfile
logger.info(self)
# Add the model descriptions to logfile
for m in self._models:
logger.info(m)
# Calculate the model timesteps
# We need times = len(self._nstep) + 1 since data is stored one timestep
# after a particle is forced with the final timestep's data.
times = range(0,(self._step*self._nstep)+1,self._step)
# Calculate a datetime object for each model timestep
# This method is duplicated in DataController and ForceParticle
# using the 'times' variables above. Will be useful in those other
# locations for particles released at different times
# i.e. released over a few days
modelTimestep, self.datetimes = AsaTransport.get_time_objects_from_model_timesteps(times, start=self.start)
time_chunk = self._time_chunk
horiz_chunk = self._horiz_chunk
low_memory = kwargs.get("low_memory", False)
# Should we remove the cache file at the end of the run?
remove_cache = kwargs.get("remove_cache", True)
self.bathy_path = kwargs.get("bathy", None)
self.cache_path = kwargs.get("cache", None)
if self.cache_path is None:
# Generate temp filename for dataset cache
default_cache_dir = os.path.join(os.path.dirname(__file__), "_cache")
temp_name = AsaRandom.filename(prefix=str(datetime.now().microsecond), suffix=".nc")
self.cache_path = os.path.join(default_cache_dir, temp_name)
logger.progress((1, "Setting up particle start locations"))
point_locations = []
if isinstance(self.geometry, Point):
point_locations = [self.reference_location] * self._npart
elif isinstance(self.geometry, Polygon) or isinstance(self.geometry, MultiPolygon):
point_locations = [Location4D(latitude=loc.y, longitude=loc.x, depth=self._depth, time=self.start) for loc in AsaTransport.fill_polygon_with_points(goal=self._npart, polygon=self.geometry)]
# Initialize the particles
logger.progress((2, "Initializing particles"))
for x in xrange(0, self._npart):
p = LarvaParticle(id=x)
p.location = point_locations[x]
# We don't need to fill the location gaps here for environment variables
# because the first data collected actually relates to this original
# position.
# We do need to fill in fields such as settled, halted, etc.
p.fill_status_gap()
# Set the inital note
p.note = p.outputstring()
p.notes.append(p.note)
self.particles.append(p)
# This is where it makes sense to implement the multiprocessing
# looping for particles and models. Can handle each particle in
# parallel probably.
#
# Get the number of cores (may take some tuning) and create that
# many workers then pass particles into the queue for the workers
mgr = multiprocessing.Manager()
nproc = multiprocessing.cpu_count() - 1
if nproc <= 0:
raise ValueError("Model does not run using less than two CPU cores")
# Each particle is a task, plus the DataController
number_of_tasks = len(self.particles) + 1
# We need a process for each particle and one for the data controller
nproc = min(number_of_tasks, nproc)
# When a particle requests data
data_request_lock = mgr.Lock()
# PID of process with lock
has_data_request_lock = mgr.Value('int',-1)
nproc_lock = mgr.Lock()
# Create the task queue for all of the particles and the DataController
tasks = multiprocessing.JoinableQueue(number_of_tasks)
# Create the result queue for all of the particles and the DataController
results = mgr.Queue(number_of_tasks)
# Create the shared state objects
get_data = mgr.Value('bool', True)
# Number of tasks
n_run = mgr.Value('int', number_of_tasks)
updating = mgr.Value('bool', False)
# When something is reading from cache file
read_lock = mgr.Lock()
# list of PIDs that are reading
has_read_lock = mgr.list()
read_count = mgr.Value('int', 0)
# When something is writing to the cache file
write_lock = mgr.Lock()
# PID of process with lock
has_write_lock = mgr.Value('int',-1)
point_get = mgr.Value('list', [0, 0, 0])
active = mgr.Value('bool', True)
logger.progress((3, "Initializing and caching hydro model's grid"))
try:
ds = CommonDataset.open(hydrodataset)
# Query the dataset for common variable names
# and the time variable.
logger.debug("Retrieving variable information from dataset")
common_variables = self.get_common_variables_from_dataset(ds)
logger.debug("Pickling time variable to disk for particles")
timevar = ds.gettimevar(common_variables.get("u"))
f, timevar_pickle_path = tempfile.mkstemp()
os.close(f)
f = open(timevar_pickle_path, "wb")
pickle.dump(timevar, f)
f.close()
ds.closenc()
except:
logger.warn("Failed to access remote dataset %s" % hydrodataset)
raise DataControllerError("Inaccessible DAP endpoint: %s" % hydrodataset)
# Add data controller to the queue first so that it
# can get the initial data and is not blocked
logger.debug('Starting DataController')
logger.progress((4, "Starting processes"))
data_controller = parallel.DataController(hydrodataset, common_variables, n_run, get_data, write_lock, has_write_lock, read_lock, read_count,
time_chunk, horiz_chunk, times,
self.start, point_get, self.reference_location,
low_memory=low_memory,
cache=self.cache_path)
tasks.put(data_controller)
# Create DataController worker
data_controller_process = parallel.Consumer(tasks, results, n_run, nproc_lock, active, get_data, name="DataController")
data_controller_process.start()
logger.debug('Adding %i particles as tasks' % len(self.particles))
for part in self.particles:
forcing = parallel.ForceParticle(part,
hydrodataset,
common_variables,
timevar_pickle_path,
times,
self.start,
self._models,
self.reference_location.point,
self._use_bathymetry,
self._use_shoreline,
self._use_seasurface,
get_data,
n_run,
read_lock,
has_read_lock,
read_count,
point_get,
data_request_lock,
has_data_request_lock,
reverse_distance=self.reverse_distance,
bathy=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
cache=self.cache_path,
time_method=self.time_method)
tasks.put(forcing)
# Create workers for the particles.
procs = [ parallel.Consumer(tasks, results, n_run, nproc_lock, active, get_data, name="ForceParticle-%d"%i)
for i in xrange(nproc - 1) ]
for w in procs:
w.start()
logger.debug('Started %s' % w.name)
# Get results back from queue, test for failed particles
return_particles = []
retrieved = 0.
error_code = 0
logger.info("Waiting for %i particle results" % len(self.particles))
logger.progress((5, "Running model"))
while retrieved < number_of_tasks:
try:
# Returns a tuple of code, result
code, tempres = results.get(timeout=240)
except Queue.Empty:
# Poll the active processes to make sure they are all alive and then continue with loop
if not data_controller_process.is_alive() and data_controller_process.exitcode != 0:
# Data controller is zombied, kill off other processes.
get_data.value == False
results.put((-2, "DataController"))
new_procs = []
old_procs = []
for p in procs:
if not p.is_alive() and p.exitcode != 0:
# Do what the Consumer would do if something finished.
# Add something to results queue
results.put((-3, "ZombieParticle"))
# Decrement nproc (DataController exits when this is 0)
with nproc_lock:
n_run.value = n_run.value - 1
# Remove task from queue (so they can be joined later on)
tasks.task_done()
# Start a new Consumer. It will exit if there are no tasks available.
np = parallel.Consumer(tasks, results, n_run, nproc_lock, active, get_data, name=p.name)
new_procs.append(np)
old_procs.append(p)
# Release any locks the PID had
if p.pid in has_read_lock:
with read_lock:
read_count.value -= 1
has_read_lock.remove(p.pid)
if has_data_request_lock.value == p.pid:
has_data_request_lock.value = -1
try:
data_request_lock.release()
except:
pass
if has_write_lock.value == p.pid:
has_write_lock.value = -1
try:
write_lock.release()
except:
pass
for p in old_procs:
try:
procs.remove(p)
except ValueError:
logger.warn("Did not find %s in the list of processes. Continuing on." % p.name)
for p in new_procs:
procs.append(p)
logger.warn("Started a new consumer (%s) to replace a zombie consumer" % p.name)
p.start()
else:
# We got one.
retrieved += 1
if code == None:
logger.warn("Got an unrecognized response from a task.")
elif code == -1:
logger.warn("Particle %s has FAILED!!" % tempres.uid)
elif code == -2:
error_code = code
logger.warn("DataController has FAILED!! Removing cache file so the particles fail.")
try:
os.remove(self.cache_path)
except OSError:
logger.debug("Could not remove cache file, it probably never existed")
pass
elif code == -3:
error_code = code
logger.info("A zombie process was caught and task was removed from queue")
elif isinstance(tempres, Particle):
logger.info("Particle %d finished" % tempres.uid)
return_particles.append(tempres)
# We mulitply by 95 here to save 5% for the exporting
logger.progress((round((retrieved / number_of_tasks) * 90.,1), "Particle %d finished" % tempres.uid))
elif tempres == "DataController":
logger.info("DataController finished")
logger.progress((round((retrieved / number_of_tasks) * 90.,1), "DataController finished"))
else:
logger.info("Got a strange result on results queue")
logger.info(str(tempres))
logger.info("Retrieved %i/%i results" % (int(retrieved),number_of_tasks))
if len(return_particles) != len(self.particles):
logger.warn("Some particles failed and are not included in the output")
# The results queue should be empty at this point
assert results.empty() is True
# Should be good to join on the tasks now that the queue is empty
logger.info("Joining the task queue")
tasks.join()
# Join all processes
logger.info("Joining the processes")
for w in procs + [data_controller_process]:
# Wait 10 seconds
w.join(10.)
if w.is_alive():
# Process is hanging, kill it.
logger.info("Terminating %s forcefully. This should have exited itself." % w.name)
w.terminate()
logger.info('Workers complete')
self.particles = return_particles
# Remove Manager so it shuts down
del mgr
# Remove pickled timevar
os.remove(timevar_pickle_path)
# Remove the cache file
if remove_cache is True:
try:
os.remove(self.cache_path)
except OSError:
logger.debug("Could not remove cache file, it probably never existed")
logger.progress((96, "Exporting results"))
if len(self.particles) > 0:
# If output_formats and path specified,
# output particle run data to disk when completed
if "output_formats" in kwargs:
# Make sure output_path is also included
if kwargs.get("output_path", None) != None:
formats = kwargs.get("output_formats")
output_path = kwargs.get("output_path")
if isinstance(formats, list):
for format in formats:
logger.info("Exporting to: %s" % format)
try:
self.export(output_path, format=format)
except:
logger.error("Failed to export to: %s" % format)
else:
logger.warn('The output_formats parameter should be a list, not saving any output!')
else:
logger.warn('No output path defined, not saving any output!')
else:
logger.warn('No output format defined, not saving any output!')
else:
logger.warn("Model didn't actually do anything, check the log.")
if error_code == -2:
raise DataControllerError("Error in the DataController")
else:
raise ModelError("Error in the model")
logger.progress((99, "Model Run Complete"))
return
def start_tasks(self, **kwargs):
try:
logger.info('Starting CachingDataController')
# Add data controller to the queue first so that it
# can get the initial data and is not blocked
data_controller = CachingDataController(self.hydrodataset,
self.common_variables,
self.n_run,
self.get_data,
self.write_lock,
self.has_write_lock,
self.read_lock,
self.read_count,
self.time_chunk,
self.horiz_chunk,
self.times,
self.start,
self.point_get,
self.reference_location,
cache_path=self.cache_path)
self.tasks.put(data_controller)
# Create CachingDataController worker
self.data_controller_process = Consumer(
self.tasks,
self.results,
self.n_run,
self.nproc_lock,
self.active,
self.get_data,
name="CachingDataController")
self.data_controller_process.start()
logger.info('Adding %i particles as tasks' %
self.total_particle_count())
for part in self.particles:
forcer = CachingForcer(
self.cache_path,
particle=part,
common_variables=self.common_variables,
timevar=self.timevar,
times=self.times,
start_time=self.start,
models=self._models,
release_location_centroid=self.reference_location.point,
usebathy=self._use_bathymetry,
useshore=self._use_shoreline,
usesurface=self._use_seasurface,
reverse_distance=self.reverse_distance,
bathy_path=self.bathy_path,
shoreline_path=self.shoreline_path,
shoreline_feature=self.shoreline_feature,
time_method=self.time_method,
shoreline_index_buffer=self.shoreline_index_buffer,
get_data=self.get_data,
read_lock=self.read_lock,
has_read_lock=self.has_read_lock,
read_count=self.read_count,
point_get=self.point_get,
data_request_lock=self.data_request_lock,
has_data_request_lock=self.has_data_request_lock)
self.tasks.put(forcer)
# Create workers for the particles.
self.procs = [
Consumer(self.tasks,
self.results,
self.n_run,
self.nproc_lock,
self.active,
self.get_data,
name="CachingForcer-%d" % i)
for i in range(self.nproc - 1)
]
logger.progress((5, 'Running model'))
for w in self.procs:
w.start()
logger.info('Started %s' % w.name)
return True
except Exception:
logger.exception("Something didn't start correctly!")
return False
def listen_for_results(self, output_h5_file, total_particles):
try:
# Get results back from queue, test for failed particles
return_particles = []
retrieved = 0.
self.error_code = 0
logger.info("Waiting for %i particle results" % total_particles)
while retrieved < self.total_task_count(
): # One for the CachingDataController
logger.info("looping in listen_for_results")
try:
# Returns a tuple of code, result
code, tempres = self.results.get(timeout=240)
except queue.Empty:
# Poll the active processes to make sure they are all alive and then continue with loop
if not self.data_controller_process.is_alive(
) and self.data_controller_process.exitcode != 0:
# Data controller is zombied, kill off other processes.
self.get_data.value is False
self.results.put((-2, "CachingDataController"))
new_procs = []
old_procs = []
for p in self.procs:
if not p.is_alive() and p.exitcode != 0:
# Do what the Consumer would do if something finished.
# Add something to results queue
self.results.put((-3, "ZombieParticle"))
# Decrement nproc (CachingDataController exits when this is 0)
with self.nproc_lock:
self.n_run.value = self.n_run.value - 1
# Remove task from queue (so they can be joined later on)
self.tasks.task_done()
# Start a new Consumer. It will exit if there are no tasks available.
np = Consumer(self.tasks,
self.results,
self.n_run,
self.nproc_lock,
self.active,
self.get_data,
name=p.name)
new_procs.append(np)
old_procs.append(p)
# Release any locks the PID had
if p.pid in self.has_read_lock:
with self.read_lock:
self.read_count.value -= 1
self.has_read_lock.remove(p.pid)
if self.has_data_request_lock.value == p.pid:
self.has_data_request_lock.value = -1
try:
self.data_request_lock.release()
except:
pass
if self.has_write_lock.value == p.pid:
self.has_write_lock.value = -1
try:
self.write_lock.release()
except:
pass
for p in old_procs:
try:
self.procs.remove(p)
except ValueError:
logger.warn(
"Did not find %s in the list of processes. Continuing on."
% p.name)
for p in new_procs:
self.procs.append(p)
logger.warn(
"Started a new consumer (%s) to replace a zombie consumer"
% p.name)
p.start()
else:
# We got one.
retrieved += 1
if code is None:
logger.warn(
"Got an unrecognized response from a task.")
elif code == -1:
logger.warn("Particle %s has FAILED!!" % tempres.uid)
elif code == -2:
self.error_code = code
logger.warn(
"CachingDataController has FAILED!! Removing cache file so the particles fail."
)
try:
os.remove(self.cache_path)
except OSError:
logger.debug(
"Could not remove cache file, it probably never existed"
)
pass
elif code == -3:
self.error_code = code
logger.info(
"A zombie process was caught and task was removed from queue"
)
elif isinstance(tempres, Particle):
logger.info("Particle %d finished" % tempres.uid)
return_particles.append(tempres)
# We mulitply by 95 here to save 5% for the exporting
logger.progress(
(round((retrieved / self.total_task_count()) * 90.,
1), "Particle %d finished" % tempres.uid))
elif tempres == "CachingDataController":
logger.info("CachingDataController finished")
logger.progress(
(round((retrieved / self.total_task_count()) * 90.,
1), "CachingDataController finished"))
else:
logger.info("Got a strange result on results queue")
logger.info(str(tempres))
logger.info("Retrieved %i/%i results" %
(int(retrieved), self.total_task_count()))
# Relax
time.sleep(1)
if len(return_particles) != total_particles:
logger.warn(
"Some particles failed and are not included in the output")
# The results queue should be empty at this point
assert self.results.empty() is True
# Should be good to join on the tasks now that the queue is empty
logger.info("Joining the task queue")
self.tasks.join()
self.tasks.close()
self.tasks.join_thread()
finally:
# Join all processes
logger.info("Joining the processes")
for w in self.procs + [self.data_controller_process]:
# Wait 20 seconds
w.join(20.)
if w.is_alive():
# Process is hanging, kill it.
logger.info(
"Terminating %s forcefully. This should have exited itself."
% w.name)
w.terminate()
if self.error_code == -2:
raise ValueError(
"Error in the BaseDataController (error_code was -2)")
results = ex.ResultsPyTable(output_h5_file)
for p in return_particles:
for x in range(len(p.locations)):
results.write(p.timestep_index_dump(x))
results.compute()
results.close()
return