def benchmark(workers, memory, loopcount, matn):
iterable = [(loopcount, matn) for i in range(workers)]
fexec = FunctionExecutor(runtime_memory=memory)
start_time = time.time()
worker_futures = fexec.map(compute_flops, iterable)
results = fexec.get_result()
end_time = time.time()
worker_stats = [f.stats for f in worker_futures]
total_time = end_time - start_time
print("Total time:", round(total_time, 3))
est_flops = workers * 2 * loopcount * matn**3
print('Estimated GFLOPS:', round(est_flops / 1e9 / total_time, 4))
res = {
'start_time': start_time,
'total_time': total_time,
'est_flops': est_flops,
'worker_stats': worker_stats,
'results': results
}
return res
def read(backend, bucket_name, number, keylist_raw, read_times):
blocksize = 1024 * 1024
def read_object(key_name, storage):
m = hashlib.md5()
bytes_read = 0
print(key_name)
start_time = time.time()
for unused in range(read_times):
fileobj = storage.get_object(bucket_name, key_name, stream=True)
try:
buf = fileobj.read(blocksize)
while len(buf) > 0:
bytes_read += len(buf)
#if bytes_read % (blocksize *10) == 0:
# mb_rate = bytes_read/(time.time()-t1)/1e6
# print('POS:'+str(bytes_read)+' MB Rate: '+ str(mb_rate))
m.update(buf)
buf = fileobj.read(blocksize)
except Exception as e:
print(e)
pass
end_time = time.time()
mb_rate = bytes_read / (end_time - start_time) / 1e6
print('MB Rate: ' + str(mb_rate))
return {
'start_time': start_time,
'end_time': end_time,
'mb_rate': mb_rate,
'bytes_read': bytes_read
}
if number == 0:
keynames = keylist_raw
else:
keynames = [keylist_raw[i % len(keylist_raw)] for i in range(number)]
fexec = FunctionExecutor(backend=backend, runtime_memory=1024)
start_time = time.time()
worker_futures = fexec.map(read_object, keynames)
results = fexec.get_result()
end_time = time.time()
total_time = end_time - start_time
worker_stats = [f.stats for f in worker_futures]
res = {
'start_time': start_time,
'total_time': total_time,
'worker_stats': worker_stats,
'results': results
}
return res
class FuturesList(list):
def _create_executor(self):
if not self.executor:
from lithops import FunctionExecutor
self.executor = FunctionExecutor(config=self.config)
def _extend_futures(self, fs):
for fut in self:
fut._produce_output = False
if not hasattr(self, 'alt_list'):
self.alt_list = []
self.alt_list.extend(self)
self.alt_list.extend(fs)
self.clear()
self.extend(fs)
def map(self, map_function, sync=False, **kwargs):
self._create_executor()
if sync:
self.executor.wait(self)
fs = self.executor.map(map_function, self, **kwargs)
self._extend_futures(fs)
return self
def map_reduce(self, map_function, reduce_function, sync=False, **kwargs):
self._create_executor()
if sync:
self.executor.wait(self)
fs = self.executor.map_reduce(map_function, self, reduce_function,
**kwargs)
self._extend_futures(fs)
return self
def wait(self, **kwargs):
self._create_executor()
fs_tt = self.alt_list if hasattr(self, 'alt_list') else self
return self.executor.wait(fs_tt, **kwargs)
def get_result(self, **kwargs):
self._create_executor()
fs_tt = self.alt_list if hasattr(self, 'alt_list') else self
return self.executor.get_result(fs_tt, **kwargs)
def __reduce__(self):
self.executor = None
return super().__reduce__()
def write(backend, bucket_name, mb_per_file, number, key_prefix):
def write_object(key_name, storage):
bytes_n = mb_per_file * 1024**2
d = RandomDataGenerator(bytes_n)
print(key_name)
start_time = time.time()
storage.put_object(bucket_name, key_name, d)
end_time = time.time()
mb_rate = bytes_n / (end_time - start_time) / 1e6
print('MB Rate: ' + str(mb_rate))
return {
'start_time': start_time,
'end_time': end_time,
'mb_rate': mb_rate
}
# create list of random keys
keynames = [
key_prefix + str(uuid.uuid4().hex.upper()) for unused in range(number)
]
fexec = FunctionExecutor(backend=backend, runtime_memory=1024)
start_time = time.time()
worker_futures = fexec.map(write_object, keynames)
results = fexec.get_result()
end_time = time.time()
worker_stats = [f.stats for f in worker_futures]
total_time = end_time - start_time
res = {
'start_time': start_time,
'total_time': total_time,
'worker_stats': worker_stats,
'bucket_name': bucket_name,
'keynames': keynames,
'results': results
}
return res
class Pool(object):
"""
Class which supports an async version of applying functions to arguments.
"""
_wrap_exception = True
def Process(self, *args, **kwds):
return self._ctx.Process(*args, **kwds)
def __init__(self, processes=None, initializer=None, initargs=(),
maxtasksperchild=None, context=None):
self._ctx = context or get_context()
# self._setup_queues()
self._taskqueue = queue.Queue()
self._cache = {}
self._state = RUN
self._maxtasksperchild = maxtasksperchild
self._initializer = initializer
self._initargs = initargs
if processes is not None and processes < 1:
raise ValueError("Number of processes must be at least 1")
if processes is not None:
if self._initargs:
self._executor = FunctionExecutor(workers=processes, **self._initargs)
else:
self._executor = FunctionExecutor(workers=processes)
self._processes = processes
else:
if self._initargs:
self._executor = FunctionExecutor(**self._initargs)
else:
self._executor = FunctionExecutor()
self._processes = self._executor.invoker.workers
if initializer is not None and not callable(initializer):
raise TypeError('initializer must be a callable')
self._pool = []
def _join_exited_workers(self):
"""
Cleanup after any worker processes which have exited due to reaching
their specified lifetime. Returns True if any workers were cleaned up.
"""
cleaned = False
for i in reversed(range(len(self._pool))):
worker = self._pool[i]
if worker.exitcode is not None:
# worker exited
util.debug('cleaning up worker %d' % i)
worker.join()
cleaned = True
del self._pool[i]
return cleaned
def _repopulate_pool(self):
"""
Bring the number of pool processes up to the specified number,
for use after reaping workers which have exited.
"""
for i in range(self._processes - len(self._pool)):
w = self.Process(target=worker,
args=(self._inqueue, self._outqueue,
self._initializer,
self._initargs, self._maxtasksperchild,
self._wrap_exception)
)
self._pool.append(w)
w.name = w.name.replace('Process', 'PoolWorker')
w.daemon = True
w.start()
util.debug('added worker')
def _maintain_pool(self):
"""
Clean up any exited workers and start replacements for them.
"""
if self._join_exited_workers():
self._repopulate_pool()
def _setup_queues(self):
self._inqueue = self._ctx.SimpleQueue()
self._outqueue = self._ctx.SimpleQueue()
self._quick_put = self._inqueue._writer.send
self._quick_get = self._outqueue._reader.recv
def apply(self, func, args=(), kwds={}):
"""
Equivalent of `func(*args, **kwds)`.
"""
assert self._state == RUN
if kwds and not args:
args = {}
return self.apply_async(func, args, kwds).get()
def map(self, func, iterable, chunksize=None):
"""
Apply `func` to each element in `iterable`, collecting the results
in a list that is returned.
"""
return self._map_async(func, iterable, mapstar, chunksize).get()
def starmap(self, func, iterable, chunksize=None):
"""
Like `map()` method but the elements of the `iterable` are expected to
be iterables as well and will be unpacked as arguments. Hence
`func` and (a, b) becomes func(a, b).
"""
return self._map_async(func, iterable, starmapstar, chunksize).get()
def starmap_async(self, func, iterable, chunksize=None, callback=None,
error_callback=None):
"""
Asynchronous version of `starmap()` method.
"""
return self._map_async(func, iterable, starmapstar, chunksize,
callback, error_callback)
def _guarded_task_generation(self, result_job, func, iterable):
"""
Provides a generator of tasks for imap and imap_unordered with
appropriate handling for iterables which throw exceptions during
iteration.
"""
try:
i = -1
for i, x in enumerate(iterable):
yield (result_job, i, func, (x,), {})
except Exception as e:
yield (result_job, i + 1, _helper_reraises_exception, (e,), {})
def imap(self, func, iterable, chunksize=1):
"""
Equivalent of `map()` -- can be MUCH slower than `Pool.map()`.
"""
if self._state != RUN:
raise ValueError("Pool not running")
if chunksize == 1:
result = IMapIterator(self._cache)
self._taskqueue.put(
(
self._guarded_task_generation(result._job, func, iterable),
result._set_length
))
return result
else:
assert chunksize > 1
task_batches = Pool._get_tasks(func, iterable, chunksize)
result = IMapIterator(self._cache)
self._taskqueue.put(
(
self._guarded_task_generation(result._job,
mapstar,
task_batches),
result._set_length
))
return (item for chunk in result for item in chunk)
def imap_unordered(self, func, iterable, chunksize=1):
"""
Like `imap()` method but ordering of results is arbitrary.
"""
if self._state != RUN:
raise ValueError("Pool not running")
if chunksize == 1:
result = IMapUnorderedIterator(self._cache)
self._taskqueue.put(
(
self._guarded_task_generation(result._job, func, iterable),
result._set_length
))
return result
else:
assert chunksize > 1
task_batches = Pool._get_tasks(func, iterable, chunksize)
result = IMapUnorderedIterator(self._cache)
self._taskqueue.put(
(
self._guarded_task_generation(result._job,
mapstar,
task_batches),
result._set_length
))
return (item for chunk in result for item in chunk)
def apply_async(self, func, args=(), kwds={}, callback=None, error_callback=None):
"""
Asynchronous version of `apply()` method.
"""
if self._state != RUN:
raise ValueError("Pool not running")
futures = self._executor.call_async(func, data=args)
result = ApplyResult(self._executor, [futures], callback, error_callback)
return result
def map_async(self, func, iterable, chunksize=None, callback=None,
error_callback=None):
"""
Asynchronous version of `map()` method.
"""
return self._map_async(func, iterable, mapstar, chunksize, callback,
error_callback)
def _map_async(self, func, iterable, mapper, chunksize=None, callback=None,
error_callback=None):
"""
Helper function to implement map, starmap and their async counterparts.
"""
if self._state != RUN:
raise ValueError("Pool not running")
if not hasattr(iterable, '__len__'):
iterable = list(iterable)
futures = self._executor.map(func, iterable)
result = MapResult(self._executor, futures, callback, error_callback)
return result
@staticmethod
def _handle_workers(pool):
thread = threading.current_thread()
# Keep maintaining workers until the cache gets drained, unless the pool
# is terminated.
while thread._state == RUN or (pool._cache and thread._state != TERMINATE):
pool._maintain_pool()
time.sleep(0.1)
# send sentinel to stop workers
pool._taskqueue.put(None)
util.debug('worker handler exiting')
@staticmethod
def _handle_tasks(taskqueue, put, outqueue, pool, cache):
thread = threading.current_thread()
for taskseq, set_length in iter(taskqueue.get, None):
task = None
try:
# iterating taskseq cannot fail
for task in taskseq:
if thread._state:
util.debug('task handler found thread._state != RUN')
break
try:
put(task)
except Exception as e:
job, idx = task[:2]
try:
cache[job]._set(idx, (False, e))
except KeyError:
pass
else:
if set_length:
util.debug('doing set_length()')
idx = task[1] if task else -1
set_length(idx + 1)
continue
break
finally:
task = taskseq = job = None
else:
util.debug('task handler got sentinel')
try:
# tell result handler to finish when cache is empty
util.debug('task handler sending sentinel to result handler')
outqueue.put(None)
# tell workers there is no more work
util.debug('task handler sending sentinel to workers')
for p in pool:
put(None)
except OSError:
util.debug('task handler got OSError when sending sentinels')
util.debug('task handler exiting')
@staticmethod
def _handle_results(outqueue, get, cache):
thread = threading.current_thread()
while 1:
try:
task = get()
except (OSError, EOFError):
util.debug('result handler got EOFError/OSError -- exiting')
return
if thread._state:
assert thread._state == TERMINATE
util.debug('result handler found thread._state=TERMINATE')
break
if task is None:
util.debug('result handler got sentinel')
break
job, i, obj = task
try:
cache[job]._set(i, obj)
except KeyError:
pass
task = job = obj = None
while cache and thread._state != TERMINATE:
try:
task = get()
except (OSError, EOFError):
util.debug('result handler got EOFError/OSError -- exiting')
return
if task is None:
util.debug('result handler ignoring extra sentinel')
continue
job, i, obj = task
try:
cache[job]._set(i, obj)
except KeyError:
pass
task = job = obj = None
if hasattr(outqueue, '_reader'):
util.debug('ensuring that outqueue is not full')
# If we don't make room available in outqueue then
# attempts to add the sentinel (None) to outqueue may
# block. There is guaranteed to be no more than 2 sentinels.
try:
for i in range(10):
if not outqueue._reader.poll():
break
get()
except (OSError, EOFError):
pass
util.debug('result handler exiting: len(cache)=%s, thread._state=%s',
len(cache), thread._state)
@staticmethod
def _get_tasks(func, it, size):
it = iter(it)
while 1:
x = tuple(itertools.islice(it, size))
if not x:
return
yield (func, x)
def __reduce__(self):
raise NotImplementedError(
'pool objects cannot be passed between processes or pickled'
)
def close(self):
util.debug('closing pool')
if self._state == RUN:
self._state = CLOSE
# self._worker_handler._state = CLOSE
def terminate(self):
util.debug('terminating pool')
self._state = TERMINATE
# self._worker_handler._state = TERMINATE
# self._terminate()
self._executor.clean()
def join(self):
util.debug('joining pool')
assert self._state in (CLOSE, TERMINATE)
# self._worker_handler.join()
# self._task_handler.join()
# self._result_handler.join()
# for p in self._pool:
# p.join()
@staticmethod
def _help_stuff_finish(inqueue, task_handler, size):
# task_handler may be blocked trying to put items on inqueue
util.debug('removing tasks from inqueue until task handler finished')
while task_handler.is_alive() and inqueue._reader.poll():
inqueue._reader.recv()
time.sleep(0)
@classmethod
def _terminate_pool(cls, taskqueue, inqueue, outqueue, pool,
worker_handler, task_handler, result_handler, cache):
# this is guaranteed to only be called once
util.debug('finalizing pool')
worker_handler._state = TERMINATE
task_handler._state = TERMINATE
util.debug('helping task handler/workers to finish')
cls._help_stuff_finish(inqueue, task_handler, len(pool))
assert result_handler.is_alive() or len(cache) == 0
result_handler._state = TERMINATE
outqueue.put(None) # sentinel
# We must wait for the worker handler to exit before terminating
# workers because we don't want workers to be restarted behind our back.
util.debug('joining worker handler')
if threading.current_thread() is not worker_handler:
worker_handler.join()
# Terminate workers which haven't already finished.
if pool and hasattr(pool[0], 'terminate'):
util.debug('terminating workers')
for p in pool:
if p.exitcode is None:
p.terminate()
util.debug('joining task handler')
if threading.current_thread() is not task_handler:
task_handler.join()
util.debug('joining result handler')
if threading.current_thread() is not result_handler:
result_handler.join()
if pool and hasattr(pool[0], 'terminate'):
util.debug('joining pool workers')
for p in pool:
if p.is_alive():
# worker has not yet exited
util.debug('cleaning up worker %d' % p.pid)
p.join()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.terminate()
class Pool(object):
"""
Class which supports an async version of applying functions to arguments.
"""
_wrap_exception = True
Process = CloudProcess
def __init__(self,
processes=None,
initializer=None,
initargs=None,
maxtasksperchild=None,
context=None):
if initargs is None:
initargs = ()
self._taskqueue = queue.Queue()
self._cache = {}
self._state = RUN
self._maxtasksperchild = maxtasksperchild
self._initializer = initializer
self._initargs = initargs
self._remote_logger = None
if processes is not None and processes < 1:
raise ValueError("Number of processes must be at least 1")
lithops_conf = mp_config.get_parameter(mp_config.LITHOPS_CONFIG)
if processes is not None:
self._processes = processes
self._executor = FunctionExecutor(workers=processes,
**lithops_conf)
else:
self._executor = FunctionExecutor(**lithops_conf)
self._processes = self._executor.invoker.workers
if initializer is not None and not callable(initializer):
raise TypeError('initializer must be a callable')
def apply(self, func, args=(), kwds={}):
"""
Equivalent of `func(*args, **kwds)`.
"""
assert self._state == RUN
if kwds and not args:
args = {}
return self.apply_async(func, args, kwds).get()
def map(self, func, iterable, chunksize=None):
"""
Apply `func` to each element in `iterable`, collecting the results
in a list that is returned.
"""
return self._map_async(func, iterable, chunksize).get()
def starmap(self, func, iterable, chunksize=None):
"""
Like `map()` method but the elements of the `iterable` are expected to
be iterables as well and will be unpacked as arguments. Hence
`func` and (a, b) becomes func(a, b).
"""
return self._map_async(func, iterable, chunksize=chunksize).get()
def starmap_async(self,
func,
iterable,
chunksize=None,
callback=None,
error_callback=None):
"""
Asynchronous version of `starmap()` method.
"""
return self._map_async(func,
iterable,
chunksize=chunksize,
callback=callback,
error_callback=error_callback)
def imap(self, func, iterable, chunksize=1):
"""
Equivalent of `map()` -- can be MUCH slower than `Pool.map()`.
"""
res = self.map(func, iterable, chunksize=chunksize)
return IMapIterator(res)
def imap_unordered(self, func, iterable, chunksize=1):
"""
Like `imap()` method but ordering of results is arbitrary.
"""
res = self.map(func, iterable, chunksize=chunksize)
return IMapIterator(res)
def apply_async(self,
func,
args=(),
kwds={},
callback=None,
error_callback=None):
"""
Asynchronous version of `apply()` method.
"""
if self._state != RUN:
raise ValueError("Pool not running")
cloud_worker = CloudWorker(func=func,
initializer=self._initializer,
initargs=self._initargs)
if mp_config.get_parameter(mp_config.STREAM_STDOUT):
stream = self._executor.executor_id
util.debug('Log streaming enabled, stream name: {}'.format(stream))
self._remote_logger = util.RemoteLoggingFeed(stream)
self._remote_logger.start()
cloud_worker.log_stream = stream
extra_env = mp_config.get_parameter(mp_config.ENV_VARS)
futures = self._executor.call_async(cloud_worker,
data={
'args': args,
'kwargs': kwds
},
extra_env=extra_env)
result = ApplyResult(self._executor, [futures], callback,
error_callback)
return result
def map_async(self,
func,
iterable,
chunksize=None,
callback=None,
error_callback=None):
"""
Asynchronous version of `map()` method.
"""
return self._map_async(func, iterable, chunksize, callback,
error_callback)
def _map_async(self,
func,
iterable,
chunksize=None,
callback=None,
error_callback=None):
"""
Helper function to implement map, starmap and their async counterparts.
"""
if self._state != RUN:
raise ValueError("Pool not running")
if not hasattr(iterable, '__len__'):
iterable = list(iterable)
cloud_worker = CloudWorker(func=func,
initializer=self._initializer,
initargs=self._initargs)
if isinstance(iterable[0], dict):
fmt_args = [{'args': (), 'kwargs': kwargs} for kwargs in iterable]
elif isinstance(iterable[0], tuple) or isinstance(iterable[0], list):
fmt_args = [{'args': args, 'kwargs': {}} for args in iterable]
else:
fmt_args = [{'args': (args, ), 'kwargs': {}} for args in iterable]
if mp_config.get_parameter(mp_config.STREAM_STDOUT):
stream = self._executor.executor_id
util.debug('Log streaming enabled, stream name: {}'.format(stream))
self._remote_logger = util.RemoteLoggingFeed(stream)
self._remote_logger.start()
cloud_worker.log_stream = stream
extra_env = mp_config.get_parameter(mp_config.ENV_VARS)
futures = self._executor.map(cloud_worker,
fmt_args,
extra_env=extra_env)
result = MapResult(self._executor, futures, callback, error_callback)
return result
def __reduce__(self):
raise NotImplementedError(
'pool objects cannot be passed between processes or pickled')
def close(self):
util.debug('closing pool')
if self._state == RUN:
self._state = CLOSE
def terminate(self):
util.debug('terminating pool')
self._state = TERMINATE
if self._remote_logger:
self._remote_logger.stop()
self._remote_logger = None
def join(self):
util.debug('joining pool')
assert self._state in (CLOSE, TERMINATE)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.terminate()