def trainerLearnScoreParallel(lrLearner, svmLearner, knnLearner):
manager = Manager()
lrScore, svmScore, knnScore = manager.Value('d', 0.0), manager.Value('d', 0.0), manager.Value('d', 0.0)
temp = manager.Namespace()
temp.learner = lrLearner
lrLearner = temp
temp = manager.Namespace()
temp.learner = svmLearner
svmLearner = temp
temp = manager.Namespace()
temp.learner = knnLearner
knnLearner = temp
lrP = Process(target=trainerLearnScore, args=(lrLearner, 'LogReg', finalFeatures, finalAnswers, testFeatures, testAnswers, lrScore))
svmP = Process(target=trainerLearnScore, args=(svmLearner, 'SVM', finalFeatures, finalAnswers, testFeatures, testAnswers, svmScore))
knnP = Process(target=trainerLearnScore, args=(knnLearner, 'kNN', finalFeatures, finalAnswers, testFeatures, testAnswers, knnScore))
lrP.start()
svmP.start()
knnP.start()
lrP.join()
svmP.join()
knnP.join()
lrLearner = lrLearner.learner
svmLearner = svmLearner.learner
knnLearner = knnLearner.learner
return (lrLearner, svmLearner, knnLearner, lrScore, svmScore, knnScore)
def make_context():
manager = Manager()
context = {
'status': manager.Namespace(),
'configLock': manager.Lock(),
'streams': manager.Namespace(),
'filecopy': manager.Namespace(),
'logs': manager.Queue()
}
return context
def _main(*, input_files, clean, preprocess_args=None, plot_args, output_path):
if clean:
shutil.rmtree(output_path, ignore_errors=True)
ensure_directory(output_path)
dfs = [pd.read_parquet(f.format(**os.environ)) for f in input_files]
df = pd.concat(dfs)
if preprocess_args:
df = preprocess(df, **preprocess_args)
print(df.head())
for col in df.columns:
if df[col].nunique() < 20:
values = ', '.join(map(str, df[col].unique()))
print(f'{col} has the values: {values}')
plot_args = list(expand(df, plot_args))
plot_args = [{**pa, 'output_path': output_path} for pa in plot_args]
print(f"Make {len(plot_args)} plots.")
mgr = Manager()
ns = mgr.Namespace()
ns.df = df
pool = Pool(20)
data_args = list(zip(plot_args, [ns] * len(plot_args)))
pool.map(_plot, data_args)
def test_call_aws_cli_called(mock_aws):
"""
Test that the aws cli is called since the object storage class is standard
"""
scripts.aws_replicate.logger = MagicMock()
subprocess.Popen = MagicMock()
utils.get_aws_bucket_name = MagicMock()
mock_aws.return_value = "tcga-open"
scripts.aws_replicate.bucket_exists = MagicMock()
scripts.aws_replicate.bucket_exists.return_value = True
scripts.aws_replicate.object_exists = MagicMock()
scripts.aws_replicate.object_exists.return_value = True
scripts.aws_replicate.get_object_storage_class = MagicMock()
boto3.session.Session = MagicMock()
scripts.aws_replicate.get_object_storage_class.return_value = "STANDARD"
manager = Manager()
manager_ns = manager.Namespace()
manager_ns.total_processed_files = 0
manager_ns.total_copied_data = 0
lock = manager.Lock()
job_info = scripts.aws_replicate.JobInfo({},
gen_mock_manifest_data()[0], 1, 1,
"", {}, {}, manager_ns, "bucket")
scripts.aws_replicate.exec_aws_copy(lock, False, job_info)
assert subprocess.Popen.call_count == 1
def multi_run(producer,consumer,pool_cnt=20,producer_cnt=10,consumer_cnt=10):
global token
global last_down_w
global finished
manager = Manager()
q1 = manager.Queue(10000)
ws = [i for i in range(1, int(no_of_images)+1, 100)]
for i in ws: q1.put(i)
last_down_w = ws[-1]
q = manager.Queue(10000)
p = Pool(pool_cnt)
nm = manager.Namespace()
nm.running = True
for i in xrange(producer_cnt):
pw = p.apply_async(producer,args=(q1,q,nm,))
print 'new producer'
sys.stdout.flush()
time.sleep(0.1)
for i in xrange(consumer_cnt):
p.apply_async(consumer,args=(q1,q,nm,))
print 'new consumer'
sys.stdout.flush()
p.close()
p.join()
def _random(self, random_changes=None, random_upgrades=None, tries=100):
manager = Manager()
glob = manager.Namespace()
# start = time.time()
glob.best_grid = self.copy()
glob.best_score = glob.best_grid.score()
lock = Lock()
def f(i, glob, lock):
start_grid = self.copy()
start_grid._random_upgrade(random_upgrades)
# start_grid._random_change(random_changes)
# start_grid.brute_upgrade()
score = start_grid.score()
with lock:
if score > glob.best_score:
print(i, score)
glob.best_grid = start_grid.copy()
glob.best_score = score
return
ps = [Process(target=f, args=(i, glob, lock)) for i in range(tries)]
[p.start() for p in ps]
[p.join() for p in ps]
# try:
# except KeyboardInterrupt:
# pass
self = glob.best_grid
# print("%d : %d : %.2fs" % (i, score, (time.time()-start)))
return self
def __init__(self) -> None:
self.Xd_shared = Array('d',[0,0,0])
self.dXd_shared = Array('d',[0,0,0])
# self.dXd_shared = [0,0,0]
self.force = Value('d',0.0)
mgr = Manager()
self.namespace = mgr.Namespace()
self.namespace.my_list = []
def scrape_recommendations(client_id, client_secret):
manager = Manager()
namespace = manager.Namespace()
namespace.credentials_manager = SpotifyClientCredentials(client_id=client_id, client_secret=client_secret)
sp = spotipy.Spotify(client_credentials_manager=namespace.credentials_manager)
genres = sp.recommendation_genre_seeds()["genres"]
worker = partial(scraping_worker, namespace)
with Pool(processes=len(genres)) as pool:
pool.map(worker, genres)
def __init__(self):
process_manager = Manager()
self._shared_data = process_manager.Namespace()
self._shared_data.alive = True
self._queue = Queue()
self._worker = Process(target=input_worker,
args=(self._shared_data, self._queue))
self._worker.start()
def main():
processes = []
manager = Manager()
shared_namespace = manager.Namespace()
shared_namespace.hashes = []
for i in range(NB_POOLS):
proc = Process(target=process_job, args=(i, shared_namespace))
proc.start()
processes.append(proc)
for proc in processes:
proc.join()
def run_downloader(db_connection, logger):
manager = Manager()
shared_state = manager.Namespace()
shared_state.default_date = get_default_date()
shared_state.past_date = None
job_serializer = Serializer(db_connection, job)
# TODO: Check if today's job is already running.
# If so, just go with missed jobs.
logger.info('Starting the main workflow')
p = start_main_job(shared_state)
if environ.get('JUST_MAIN', False):
p.join()
return
end_time = datetime.now()\
.replace(hour=23, minute=30, second=0, microsecond=0)
while True:
sleep(5)
p.join(timeout=0)
if not p.is_alive():
job_id = shared_state.job_id
completed = shared_state.completed
if job_id is not None and not completed:
job_serializer.put(job_id, {
'status': JobStatus.FAILED,
})
if p.exitcode != 0:
logger.error('Job exited unexpectedly',
f'Exit code: {p.exitcode}\nJob id: {job_id}')
if datetime.now() >= end_time:
break
p = start_past_job(shared_state)
if p is None:
break
elif datetime.now() >= end_time:
job_id = shared_state.job_id
completed = shared_state.completed
if job_id is not None and not completed:
# Time to end.
p.terminate()
job_serializer.put(job_id, {
'status': JobStatus.FAILED,
})
logger.info('All jobs finished.')
def trainOnSet(hyperParams, examples, theta, histGrad, cores):
try: fixWords = hyperParams['fixEmb']
except: fixWords = False
try: fixWeights = hyperParams['fixW']
except: fixWeights = False
# print 'fixEmb:',fixWords, ',fixW:', fixWeights
adagrad = hyperParams['ada']
mgr = Manager()
ns= mgr.Namespace()
ns.lamb = hyperParams['lambda']
batchsize = hyperParams['bSize']
random.shuffle(examples) # randomly split the data into parts of batchsize
avErrors = []
for batch in xrange((len(examples)+batchsize-1)//batchsize):
ns.theta = theta
minibatch = examples[batch*batchsize:(batch+1)*batchsize]
s = (len(minibatch)+cores-1)//cores
trainPs = []
q = Queue()
if cores<2:
trainBatch(ns, minibatch,q, fixWords,fixWeights) #don't start a subprocess
trainPs.append('') # But do put a placeholder in the queue
else:
for j in xrange(cores):
p = Process(name='minibatch'+str(batch)+'-'+str(j), target=trainBatch, args=(ns, minibatch[j*s:(j+1)*s],q,fixWords,fixWeights))
trainPs.append(p)
p.start()
errors = []
theta.regularize(hyperParams['alpha']/len(examples), hyperParams['lambda'])
for j in xrange(len(trainPs)):
(grad, error) = q.get()
if grad is None: continue
theta.add2Theta(grad,hyperParams['alpha'],histGrad)
errors.append(error)
# make sure all worker processes have finished and are killed
if cores>1:
for p in trainPs: p.join()
try: avError = sum(errors)/len(errors)
except:
avError = 0
print 'batch size zero!'
if batch % 25 == 0:
print '\t\tBatch', batch, ', average error:',avError , ', theta norm:', theta.norm()
avErrors.append(avError)
return sum(avErrors)/len(avErrors)
def __init__(self):
self.i3 = i3ipc.Connection()
self._actived = False
self._win_rect = (0, 0, 0, 0)
process_manager = Manager()
self._shared_data = process_manager.Namespace()
self._shared_data.alive = True
self._shared_data.actived = False
self._lock = Lock()
self._worker = Process(target=info_worker,
args=(self._lock, self._shared_data))
self._worker.start()
class ParallelContext:
"""Holds the objects needed to coordinate parallelism."""
def __init__(self,
spawner: GeneSpawner,
evaluator: Evaluator,
n_proc: Optional[int] = None):
self.manager = Manager()
self.ns = self.manager.Namespace()
self.ns.spawner = spawner
self.ns.evaluator = evaluator
if n_proc is None:
self.pool = Pool()
else:
self.pool = Pool(n_proc)
def main():
final_result = []
args = cmd_args_parser()
case_id = int(args.case_num[3:])
prefix = args.case_num[:3]
case_type_filter = None
if hasattr(args, "case_type_filter"):
case_type_filter = args.case_type_filter
lock = Lock()
jobs = []
mgr = Manager()
ns = mgr.Namespace()
ns.df = final_result
start = case_id - args.batch
end = case_id + args.batch
total_num = end - start + 1
if total_num > 20:
batch_result = get_batch_pair(total_num, start, end)
for i in range(len(batch_result)):
p = multiprocessing.Process(target=query_website,
args=(
ns,
batch_result[i],
prefix,
case_type_filter,
lock,
args.verbose,
))
jobs.append(p)
p.start()
for job in jobs:
job.join()
final_result = ns.df
else:
for i in range(start, end):
result = get_result(i, prefix, case_type_filter, args.verbose)
if bool(result):
final_result.append(result)
json_type = json.dumps(final_result, indent=4)
now = datetime.datetime.now()
with open("data-%s.yml" % now.strftime("%Y-%m-%d"), "w") as outfile:
yaml.dump(yaml.load(json_type), outfile, allow_unicode=True)
print yaml.dump(yaml.load(json_type), allow_unicode=True, width=256)
def findTrainerErrorParallel(lrLearner, svmLearner, knnLearner, finalFeatures, finalAnswers, testFeatures, testAnswers):
manager = Manager()
lrTrainingError, lrTestingError, lrIndices = manager.list(), manager.list(), manager.list()
svmTrainingError, svmTestingError, svmIndices = manager.list(), manager.list(), manager.list()
knnTrainingError, knnTestingError, knnIndices = manager.list(), manager.list(), manager.list()
temp = manager.Namespace()
temp.learner = lrLearner
lrLearner = temp
temp = manager.Namespace()
temp.learner = svmLearner
svmLearner = temp
temp = manager.Namespace()
temp.learner = knnLearner
knnLearner = temp
lrP = Process(target=findTrainerError, args=(lrLearner, 'LogReg', finalFeatures, finalAnswers, testFeatures, testAnswers, lrTrainingError, lrTestingError, lrIndices))
svmP = Process(target=findTrainerError, args=(svmLearner, 'SVM', finalFeatures, finalAnswers, testFeatures, testAnswers, svmTrainingError, svmTestingError, svmIndices))
knnP = Process(target=findTrainerError, args=(knnLearner, 'kNN', finalFeatures, finalAnswers, testFeatures, testAnswers, knnTrainingError, knnTestingError, knnIndices))
lrP.start()
svmP.start()
knnP.start()
lrP.join()
svmP.join()
knnP.join()
lrLearner = lrLearner.learner
svmLearner = svmLearner.learner
knnLearner = knnLearner.learner
lrTrainingError, lrTestingError, lrIndices = np.asarray(lrTrainingError), np.asarray(lrTestingError), np.asarray(lrIndices)
svmTrainingError, svmTestingError, svmIndices = np.asarray(svmTrainingError), np.asarray(svmTestingError), np.asarray(svmIndices)
knnTrainingError, knnTestingError, knnIndices = np.asarray(knnTrainingError), np.asarray(knnTestingError), np.asarray(knnIndices)
return (lrLearner, svmLearner, knnLearner, lrTrainingError, lrTestingError, lrIndices, svmTrainingError, svmTestingError, svmIndices, knnTrainingError, knnTestingError, knnIndices)
def main():
final_result = []
reminder_result = []
args = cmdArgumentParser()
case_numberic = int(args.case_num[3:])
prefix = args.case_num[:3]
lock = Lock()
jobs = []
mgr = Manager()
ns = mgr.Namespace()
ns.df = final_result
start = case_numberic - args.batch
end = case_numberic + args.batch
total_num = end - start + 1
rmnder = total_num % CPU_CORES
if total_num > 20:
batch_result = get_batch_pair(total_num, start, end)
for i in range(len(batch_result)):
p = multiprocessing.Process(target=query_website,
args=(
ns,
batch_result[i],
prefix,
lock,
args.verbose,
))
jobs.append(p)
p.start()
for job in jobs:
job.join()
final_result = ns.df
# for i in range(end - rmnder + 1,end):
# reminder_result.append(get_result(i,prefix))
else:
for i in range(start, end):
final_result.append(get_result(i, prefix, args.verbose))
json_type = json.dumps(final_result, indent=4)
now = datetime.datetime.now()
with open('data-%s.yml' % now.strftime("%Y-%m-%d"), 'w') as outfile:
yaml.dump(yaml.load(json_type), outfile, allow_unicode=True)
print yaml.dump(yaml.load(json_type), allow_unicode=True)
def main():
"""Gets input hash and salt from command line,
spawns worker threads, initializes queue,
calls worker function"""
# Check user input
if len(sys.argv) != 2:
sys.exit("Usage: python crack.py hash")
# Extract hash and salt from user input
user_hash = sys.argv[1]
salt = user_hash[:2]
# Input lists
inputs = [HASH_INPUTS_1, HASH_INPUTS_2, HASH_INPUTS_3, HASH_INPUTS_4]
# Manager to create shared Namespace
mgr = Manager()
namespace = mgr.Namespace()
# Found to catch found event on threads
namespace.found = False
# The function to crack the DES hashed password.
# It is the callback function to main's calls to set_tuple_permutations
# via pool.apply_async. When the pool of child processes return results,
# the main process will process them via the callback function DES_crack.
# Spawn pool of child processes calling the worker
# function: worker_crack in parallel and
# applying async, with callback function: print_n_terminate
# to the result returned from the workers.
start_time = time.time()
pool = Pool(NUM_PHYS_CORES)
for aninput in inputs:
pool.apply_async(worker_crack,
args=(aninput, user_hash, salt, namespace),
callback=print_n_terminate)
pool.close()
pool.join()
duration = time.time() - start_time
print(f"Cracking password took {duration:.2f} seconds")
# Success
print('success - exiting')
sys.exit(0)
def main():
# Create a multiprocessing manager to use as the token store
global tokens, refresh_lock
manager = Manager()
tokens = manager.Namespace()
refresh_lock = manager.Lock()
# Authenticate in master process
oauth2, tokens.access, tokens.refresh = authenticate(CooperativelyManagedOAuth2)
# Create 2 worker processes and wait on them to finish
workers = []
for _ in range(2):
worker_process = Process(target=worker)
worker_process.start()
workers.append(worker_process)
for worker_process in workers:
worker_process.join()
def iter_latest_asynchonously(gen_func,
timeout=None,
empty_value=None,
use_forkserver=False,
uninitialized_wait=None):
"""
Given a generator function, make an iterator that pulls the latest value yielded when running it asynchronously.
If a value has never been set, or timeout is exceeded, yield empty_value instead.
:param gen_func: A generator function (a function returning a generator);
:return:
"""
if use_forkserver:
from multiprocessing import set_start_method # Only Python 3.X
set_start_method(
'forkserver'
) # On macos this is necessary to start camera in separate thread
m = Manager()
namespace = m.Namespace()
lock = Lock()
with lock:
namespace.time_and_data = (-float('inf'), Uninitialized)
p = Process(target=_async_value_setter, args=(gen_func, namespace, lock))
p.start()
while True:
with lock:
lasttime, item = namespace.time_and_data
if item is PoisonPill: # The generator has terminated
break
elif item is Uninitialized:
if uninitialized_wait is not None:
time.sleep(uninitialized_wait)
continue
else:
yield empty_value
elif timeout is not None and (time.time(
) - lasttime) > timeout: # Nothing written or nothing recent enough
yield empty_value
else:
yield item
def start(self):
p = current_process()
print('parent id: {}'.format(id(p)))
manager = Manager()
ns = manager.Namespace()
ns.a = 1
lst_proxy = manager.list()
lst_proxy.append(1)
dct_proxy = manager.dict()
dct_proxy['b'] = 2
print(ns.a)
print(lst_proxy)
print(dct_proxy)
p = Process(target=self.modify, args=(ns, lst_proxy, dct_proxy))
p.start()
p.join()
print(ns.a)
print(lst_proxy)
print(dct_proxy)
def __init__(self):
process_manager = Manager()
self._shared_data = process_manager.Namespace()
self._shared_data.alive = True
self._shared_data.DEFAULT_MOUSE = [
uinput.BTN_LEFT,
uinput.BTN_RIGHT,
uinput.BTN_MIDDLE,
uinput.REL_X,
uinput.REL_Y,
# TODO detect full screen size
uinput.ABS_X + (0, 1920, 0, 0),
uinput.ABS_Y + (0, 1080, 0, 0),
]
self._queue = Queue()
self._worker = Process(target=input_worker,
args=(self._shared_data, self._queue))
self._worker.start()
def main():
"""Gets input hash and salt from command line,
spawns worker processes, initializes Sync_Manager,
calls worker function"""
# Check user input
if len(sys.argv) != 2:
sys.exit("Usage: python crack.py hash")
# Extract hash and salt from user input
user_hash = sys.argv[1]
salt = user_hash[:2]
# Input lists
inputs = [HASH_INPUTS_1, HASH_INPUTS_2, HASH_INPUTS_3, HASH_INPUTS_4]
# Manager to create shared Namespace
mgr = Manager()
namespace = mgr.Namespace()
# Found to catch found event on threads
namespace.found = False
# Spawn processes calling the worker
start_time = time.time()
processes = []
for i in range(len(inputs)):
process = Process(target=worker_crack_password,
args=(inputs[i], user_hash, salt, namespace),
daemon=True)
processes.append(process)
process.start()
for process in processes:
process.join()
duration = time.time() - start_time
print(
f"Cracking password took {duration:.2f} seconds using mp.Pool.apply_async()"
)
# Success
sys.exit(0)
def run(thread_num, global_config, job_name, manifest_file, bucket=None):
"""
start threads and log after they finish
"""
tasks, _ = prepare_data(manifest_file, global_config)
manager = Manager()
manager_ns = manager.Namespace()
manager_ns.total_processed_files = 0
jobInfos = []
for task in tasks:
job = JobInfo(global_config, task, len(tasks), job_name, {},
manager_ns, bucket)
jobInfos.append(job)
# Make the Pool of workers
pool = Pool(thread_num)
results = []
if job_name == "copying":
results = pool.map(exec_google_copy, jobInfos)
elif job_name == "indexing":
results = pool.map(check_and_index_the_data, jobInfos)
# close the pool and wait for the work to finish
pool.close()
pool.join()
filename = global_config.get("log_file", "{}_log.json".format(job_name))
timestr = time.strftime("%Y%m%d-%H%M%S")
filename = timestr + "_" + filename
if job_name == "copying":
results = [{"data": results}]
json_log = {}
for result in results:
json_log.update(result)
class ParallelMgr():
def __init__(self, **kwargs):
num_pes = kwargs.get('num_pes', 1)
# TODO: compare num_pes to multiprocessing.cpu_count()
# manager for manager-to-worker communication & shared namespace
self.mpMgr = Manager()
self.namespace = self.mpMgr.Namespace()
# need a copy of num_pes
self.namespace.num_pes = num_pes
# manager for manager-to-worker communication
self.namespace.masterList = [self.mpMgr.list() for i in range(num_pes)]
# self.masterList = [ self.commMgr.list() for i in range(self.num_pes) ]
# separate lists for direct PE-to-PE communication
self.namespace.msgLists = [self.mpMgr.list() for i in range(num_pes)]
# self.msgLists = [ self.commMgr.list() for i in range(self.num_pes) ]
print('ParallelMgr init')
sys.stdout.flush()
def runWorkers(self, workerModule):
# self.peList = [ workerModule(i,self.namespace) for i in range(self.namespace.num_pes) ]
self.peList = []
for i in range(self.namespace.num_pes):
commMgr = CommMgr(tid=i, namespace=self.namespace)
pid = workerModule(i, commMgr)
self.peList.append(pid)
for pe in self.peList:
print('starting worker ' + str(pe))
pe.start()
# TODO: check for errors
def finalize(self):
print('waiting for workers to finish')
for pe in self.peList:
print('worker ' + str(pe) + ' is_alive=' + str(pe.is_alive()))
for pe in self.peList:
pe.join()
class Test(object):
def __init__(self):
self.manager = Manager()
self.namespace = self.manager.Namespace()
self.process_list = []
self.namespace.d=self.manager.dict()
def print_me(self, ns, i):
print("\nBefore:{0}, {1}, {2} ".format(current_process().name, i, ns.d))
temp = i * i
ns.d[i] = temp
print("\nAfter:{0}, {1}, {2} ".format(current_process().name, i, ns.d))
time.sleep(5)
return
def spawn_processes(self):
for i in range(5):
p = Process(target=self.print_me, args=(self.namespace, i,))
self.process_list.append(p)
p.start()
for i in self.process_list:
i.join()
print(self.namespace.d)
def spawn_processes22(self):
for i in range(5):
p = Process(target=self.print_me, args=(self.namespace, i,))
self.process_list.append(p)
p.start()
print("exitcode is:{0}".format(p.exitcode))
while len(self.process_list) is not 0:
for i in self.process_list:
if not i.is_alive():
print("exitcode is:{0}".format(i.exitcode))
print("process:{0} is done, exit code:{1}".format(i.name, i.exitcode));
self.process_list.remove(i)
i.terminate()
print(self.namespace.d)
def get_prices(self):
mgr = Manager()
ns = mgr.Namespace()
ns.prices = pd.DataFrame()
d = mgr.dict()
filenames = os.listdir(self.prices_dir)
count = len(filenames)
jobs = []
for filename in tqdm(filenames):
job = Process(target=self.worker, args=(ns, filename))
job.start()
jobs.append(job)
for job in jobs:
job.join()
prices = ns.prices
#print(prices.head())
self.prices = prices
def upload_parts(self):
args_list = []
if OS_WINDOWS:
self.ns = FakeNamespace()
else:
manager = Manager()
self.ns = manager.Namespace()
self.ns.completed = 0
part_amount = int(math.ceil(self.file_size / float(self.part_size)))
self.total_parts = part_amount
self.pbar = ProgressBar(widgets=[Percentage(), Bar()],
maxval=self.total_parts).start()
try:
for i in xrange(part_amount):
offset = i * self.part_size
remaining_bytes = self.file_size - offset
bytes = min(remaining_bytes, self.part_size)
if not self.multiparts.uploaded(i + 1):
args_list.append([
self.file_path, offset, bytes, self.url,
self.upload_id, i + 1, self.headers, self.verify,
self.pbar, self.ns
])
else:
self.total_parts -= 1
if self.total_parts == 0:
return
self.pbar.maxval = self.total_parts
pool = Pool(processes=self.processes)
pool.map_async(upload_multipart_wrapper, args_list).get(9999999)
pool.close()
pool.join()
except KeyboardInterrupt:
log.error("Caught KeyboardInterrupt, terminating workers")
pool.terminate()
pool.join()
raise Exception("Process canceled by user")
def test_call_streamming_method_called(mock_aws):
"""
Test that the streamming method is called since the object is Glacier
"""
scripts.aws_replicate.logger = MagicMock()
subprocess.Popen = MagicMock()
scripts.aws_replicate.stream_object_from_gdc_api = MagicMock()
mock_aws.return_value = "tcga-open"
scripts.aws_replicate.bucket_exists = MagicMock()
scripts.aws_replicate.bucket_exists.return_value = True
scripts.aws_replicate.object_exists = MagicMock()
scripts.aws_replicate.object_exists.return_value = False
source_objects = {"11111111111111111/abc.bam": {"StorageClass": "GLACIER"}}
copied_objects = {}
manager = Manager()
manager_ns = manager.Namespace()
manager_ns.total_processed_files = 0
manager_ns.total_copied_data = 0
lock = manager.Lock()
job_info = scripts.aws_replicate.JobInfo(
{},
gen_mock_manifest_data()[0],
1,
1,
"",
copied_objects,
source_objects,
manager_ns,
"bucket",
)
scripts.aws_replicate.exec_aws_copy(lock, False, job_info)
assert subprocess.Popen.call_count == 0
assert scripts.aws_replicate.stream_object_from_gdc_api.call_count == 1
class Pipeline:
def __init__(self,
granule_loader: GranuleLoader,
slicer: TileSlicer,
data_store_factory,
metadata_store_factory,
tile_processors: List[TileProcessor],
max_concurrency: int):
self._granule_loader = granule_loader
self._tile_processors = tile_processors
self._slicer = slicer
self._data_store_factory = data_store_factory
self._metadata_store_factory = metadata_store_factory
self._max_concurrency = max_concurrency
# Create a SyncManager so that we can to communicate exceptions from the
# worker processes back to the main process.
self._manager = Manager()
def __del__(self):
self._manager.shutdown()
@classmethod
def from_string(cls, config_str: str, data_store_factory, metadata_store_factory, max_concurrency: int = 16):
try:
config = yaml.load(config_str, yaml.FullLoader)
cls._validate_config(config)
return cls._build_pipeline(config,
data_store_factory,
metadata_store_factory,
processor_module_mappings,
max_concurrency)
except yaml.scanner.ScannerError:
raise PipelineBuildingError("Cannot build pipeline because of a syntax error in the YAML.")
# TODO: this method should validate the config against an actual schema definition
@staticmethod
def _validate_config(config: dict):
if type(config) is not dict:
raise PipelineBuildingError("Cannot build pipeline; the pipeline configuration that " +
"was received is not valid YAML.")
@classmethod
def _build_pipeline(cls,
config: dict,
data_store_factory,
metadata_store_factory,
module_mappings: dict,
max_concurrency: int):
try:
granule_loader = GranuleLoader(**config['granule'])
slicer_config = config['slicer']
slicer = cls._parse_module(slicer_config, module_mappings)
tile_processors = []
for processor_config in config['processors']:
module = cls._parse_module(processor_config, module_mappings)
tile_processors.append(module)
return cls(granule_loader,
slicer,
data_store_factory,
metadata_store_factory,
tile_processors,
max_concurrency)
except PipelineBuildingError:
raise
except KeyError as e:
raise PipelineBuildingError(f"Cannot build pipeline because {e} is missing from the YAML.")
except Exception as e:
logger.exception(e)
raise PipelineBuildingError(f"Cannot build pipeline because of the following error: {e}")
@classmethod
def _parse_module(cls, module_config: dict, module_mappings: dict):
module_name = module_config.pop('name')
try:
module_class = module_mappings[module_name]
logger.debug("Loaded processor {}.".format(module_class))
processor_module = module_class(**module_config)
except KeyError:
raise PipelineBuildingError(f"'{module_name}' is not a valid processor.")
except Exception as e:
raise PipelineBuildingError(f"Parsing module '{module_name}' failed because of the following error: {e}")
return processor_module
async def run(self):
async with self._granule_loader as (dataset, granule_name):
start = time.perf_counter()
shared_memory = self._manager.Namespace()
async with Pool(initializer=_init_worker,
initargs=(self._tile_processors,
dataset,
self._data_store_factory,
self._metadata_store_factory,
shared_memory),
maxtasksperchild=self._max_concurrency,
childconcurrency=self._max_concurrency) as pool:
serialized_tiles = [nexusproto.NexusTile.SerializeToString(tile) for tile in
self._slicer.generate_tiles(dataset, granule_name)]
# aiomultiprocess is built on top of the stdlib multiprocessing library, which has the limitation that
# a queue can't have more than 2**15-1 tasks. So, we have to batch it.
for chunk in self._chunk_list(serialized_tiles, MAX_CHUNK_SIZE):
try:
await pool.map(_process_tile_in_worker, chunk)
except ProxyException:
pool.terminate()
# Give the shared memory manager some time to write the exception
# await asyncio.sleep(1)
raise pickle.loads(shared_memory.error)
end = time.perf_counter()
logger.info("Pipeline finished in {} seconds".format(end - start))
@staticmethod
def _chunk_list(items, chunk_size: int):
return [items[i:i + chunk_size] for i in range(0, len(items), chunk_size)]
