本文整理汇总了Python中multiprocessing.pool.Pool方法的典型用法代码示例。如果您正苦于以下问题：Python pool.Pool方法的具体用法？Python pool.Pool怎么用？Python pool.Pool使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块multiprocessing.pool的用法示例。

在下文中一共展示了pool.Pool方法的27个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: save_tfrecord

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def save_tfrecord(filename, dataset, verbose=False):

observations = len(dataset['length'])

serialized = []

with Pool(processes=4) as pool:

for serialized_string in tqdm(pool.imap(

tfrecord_serializer,

zip(dataset['length'], dataset['source'], dataset['target']),

chunksize=10

), total=observations, disable=not verbose):

serialized.append(serialized_string)

# Save seriealized dataset

writer = tf.python_io.TFRecordWriter(

filename,

options=tf.python_io.TFRecordOptions(

tf.python_io.TFRecordCompressionType.ZLIB

)

)

for serialized_string in tqdm(serialized, disable=not verbose):

writer.write(serialized_string)

writer.close()

开发者ID:distillpub，项目名称:post--memorization-in-rnns，代码行数:26，

示例2: shuffled_analysis

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def shuffled_analysis(iterations: int, meta: pd.DataFrame, counts: pd.DataFrame, interactions: pd.DataFrame,

cluster_interactions: list, base_result: pd.DataFrame, threads: int, separator: str,

suffixes: tuple = ('_1', '_2'), counts_data: str = 'ensembl') -> list:

"""

Shuffles meta and calculates the means for each and saves it in a list.

Runs it in a multiple threads to run it faster

"""

core_logger.info('Running Statistical Analysis')

with Pool(processes=threads) as pool:

statistical_analysis_thread = partial(_statistical_analysis,

base_result,

cluster_interactions,

counts,

interactions,

meta,

separator,

suffixes,

counts_data=counts_data

)

results = pool.map(statistical_analysis_thread, range(iterations))

return results

开发者ID:Teichlab，项目名称:cellphonedb，代码行数:25，

示例3: fill_queue

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def fill_queue(self):

if self.results is None:

self.results = queue.deque(maxlen=self.max_queue)

if self.num_workers > 0:

if self.pool is None:

self.pool = Pool(processes=self.num_workers)

while len(self.results) < self.max_queue:

if self.distinct_levels is not None and self.idx >= self.distinct_levels:

break

elif not self.repeat_levels and self.idx >= len(self.file_data):

break

else:

data = self.get_next_parameters()

if data is None:

break

self.idx += 1

kwargs = {'seed': self._seed.spawn(1)[0]}

if self.num_workers > 0:

result = self.pool.apply_async(_game_from_data, data, kwargs)

else:

result = _game_from_data(*data, **kwargs)

self.results.append((data, result))

开发者ID:PartnershipOnAI，项目名称:safelife，代码行数:25，

示例4: __init__

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def __init__(self, configer=None, num_classes=None, boundary_threshold=0.00088, num_proc=15):

assert configer is not None or num_classes is not None

self.configer = configer

if configer is not None:

self.n_classes = self.configer.get('data', 'num_classes')

else:

self.n_classes = num_classes

self.ignore_index = -1

self.boundary_threshold = boundary_threshold

self.pool = Pool(processes=num_proc)

self.num_proc = num_proc

self._Fpc = 0

self._Fc = 0

self.seg_map_cache = []

self.gt_map_cache = []

开发者ID:openseg-group，项目名称:openseg.pytorch，代码行数:21，

示例5: create_features_from_path

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def create_features_from_path(self, train_path: str, test_path: str) -> Tuple[pd.DataFrame, pd.DataFrame]:

column_pairs = self.get_column_pairs()

col1s = []

col2s = []

latent_vectors = []

gc.collect()

with Pool(4) as p:

for col1, col2, latent_vector in p.map(

partial(self.compute_latent_vectors, train_path=train_path, test_path=test_path), column_pairs):

col1s.append(col1)

col2s.append(col2)

latent_vectors.append(latent_vector.astype(np.float32))

gc.collect()

return self.get_feature(train_path, col1s, col2s, latent_vectors), \

self.get_feature(test_path, col1s, col2s, latent_vectors)

开发者ID:flowlight0，项目名称:talkingdata-adtracking-fraud-detection，代码行数:18，

示例6: run

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def run(self):

import gevent

from gevent import monkey

monkey.patch_all()

from gevent import pool

# default 200

# g_pool = pool.Pool(200)

g_pool = pool.Pool(self.coroutine)

tasks = [g_pool.spawn(self.gen_traffic, url) for url in self.url_list]

gevent.joinall(tasks)

traffic_list = []

for i in tasks:

if i.value is not None:

traffic_list.append(i.value)

# save traffic for rescan

Engine.save_traffic(traffic_list, self.id)

开发者ID:lwzSoviet，项目名称:NoXss，代码行数:18，

示例7: verify_async

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def verify_async(case_list,coroutine):

"""

Verify used gevent lib

:param case_list:

:param coroutine:

:return:

"""

from gevent import monkey

monkey.patch_all()

result = []

geventPool = pool.Pool(coroutine)

tasks = [geventPool.spawn(Verify.request_and_verify, case) for case in case_list]

gevent.joinall(tasks)

for i in tasks:

if i.value is not None:

result.append(i.value)

print_info('Total Verify-Case is: %s, %s error happened.' % (len(case_list), Verify.ERROR_COUNT))

return result

开发者ID:lwzSoviet，项目名称:NoXss，代码行数:20，

示例8: deduplicate

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def deduplicate(self, url_list):

print 'Start to deduplicate for all urls.'

filtered_path = self.file + '.filtered'

if os.path.exists(filtered_path):

print '%s has been filtered as %s.' % (self.file, filtered_path)

with open(filtered_path)as f:

filtered = f.read().split('\n')

return filtered

filtered = []

# result = map(filter, url_list)

from multiprocessing import cpu_count

from multiprocessing.pool import Pool

p=Pool(cpu_count())

result=p.map(url_filter,url_list)

for i in result:

if isinstance(i, str):

filtered.append(i)

with open(filtered_path, 'w') as f:

f.write('\n'.join(filtered))

print 'Saved filtered urls to %s.' % filtered_path

return filtered

开发者ID:lwzSoviet，项目名称:NoXss，代码行数:23，

示例9: _schedule_runs_lk

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def _schedule_runs_lk(self, et_pool, job):

""" Schedule runs to execute up to max possible parallelism

suffix '_lk' means caller must already hold lock.

:param et_pool: A multiprocessor pool handle

:type: Pool

:param job: current job

:type: WorkerJob

"""

while (self._has_more_runs_to_schedule(job) and

job.runs_in_flight < job.max_runs_in_flight):

run = job.schedule_next_run()

if run.id is None:

raise ValueError("Unexpected end of runs")

self.etl_helper.etl_step_started(job.msg_dict, run.id, run.step)

log('scheduled: {0}'.format(run.id))

et_pool.apply_async(

run.func,

args=run.args,

callback=self._create_run_complete_callback(job, run.id, run.step),

)

job.runs_in_flight += 1

开发者ID:Yelp，项目名称:mycroft，代码行数:26，

示例10: GenerateMode

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def GenerateMode(corpus, context_token_limit):

for dataset in datasets:

print 'Generating questions for the %s set:' % dataset

urls_filename = '%s/wayback_%s_urls.txt' % (corpus, dataset)

urls = ReadUrls(urls_filename)

p = Pool()

question_context_lists = p.imap_unordered(

GenerateMapper, izip(urls, repeat(corpus), repeat(context_token_limit)))

progress_bar = ProgressBar(len(urls))

for question_context_list in question_context_lists:

if question_context_list:

for question_context in question_context_list:

WriteQuestionContext(question_context, corpus, dataset)

progress_bar.Increment()

开发者ID:deepmind，项目名称:rc-data，代码行数:20，

示例11: fit

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def fit(self, X, y=None):

"""Fit all transformers using X.

Parameters

----------

X : iterable or array-like, depending on transformers

Input data, used to fit transformers.

y : array-like, shape (n_samples, ...), optional

Targets for supervised learning.

Returns

-------

self : FeatureUnion

This estimator

"""

self.transformer_list = list(self.transformer_list)

self._validate_transformers()

with Pool(self.n_jobs) as pool:

transformers = pool.starmap(_fit_one_transformer,

((trans, X[trans.steps[0][1].columns], y) for _, trans, _ in self._iter()))

self._update_transformer_list(transformers)

return self

开发者ID:pjankiewicz，项目名称:mercari-solution，代码行数:25，

示例12: transform

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def transform(self, X):

"""Transform X separately by each transformer, concatenate results.

Parameters

----------

X : iterable or array-like, depending on transformers

Input data to be transformed.

Returns

-------

X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)

hstack of results of transformers. sum_n_components is the

sum of n_components (output dimension) over transformers.

"""

with Pool(self.n_jobs) as pool:

Xs = pool.starmap(_transform_one, ((trans, weight, X[trans.steps[0][1].columns])

for name, trans, weight in self._iter()))

if not Xs:

# All transformers are None

return np.zeros((X.shape[0], 0))

if any(sparse.issparse(f) for f in Xs):

Xs = sparse.hstack(Xs).tocsr()

else:

Xs = np.hstack(Xs)

return Xs

开发者ID:pjankiewicz，项目名称:mercari-solution，代码行数:27，

示例13: calculate_cell_score_selim

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def calculate_cell_score_selim(y_true, y_pred, num_threads=32, ids=None):

yps = []

for m in range(len(y_true)):

yps.append((y_true[m].copy(), y_pred[m].copy()))

pool = Pool(num_threads)

results = pool.map(calculate_jaccard, yps)

if ids:

import pandas as pd

s_iou = np.argsort(results)

d = []

for i in range(len(s_iou)):

id = ids[s_iou[i]]

res = results[s_iou[i]]

d.append([id, res])

pd.DataFrame(d, columns=["ID", "METRIC_SCORE"]).to_csv("gt_vs_oof.csv", index=False)

return np.array(results).mean()

开发者ID:selimsef，项目名称:dsb2018_topcoders，代码行数:19，

示例14: process_specification_directory

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def process_specification_directory(glob_pattern, outfile_name, namespace, write_baseclass=True,):

with open(os.path.join(options.out_path, outfile_name), 'w+') as out_file:

paths = [p for p in glob.glob(os.path.join(options.spec_path, glob_pattern))]

classes = list()

func = functools.partial(process_file, namespace)

with Pool() as pool:

classes.extend(pool.map(func, paths))

print("Formatting...")

formatted_code = FormatCode("\n".join(sorted(classes)))[0]

if write_baseclass:

header = BASE_CLASS

else:

header = "from zenpy.lib.api_objects import BaseObject\nimport dateutil.parser"

out_file.write("\n\n\n".join((header, formatted_code)))

开发者ID:facetoe，项目名称:zenpy，代码行数:18，

示例15: main

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def main():

# Parameters

process_num = 24

image_size = (512, 512)

url = 'http://v18.proteinatlas.org/images/'

csv_path = "data/HPAv18RBGY_wodpl.csv"

save_dir = "data/raw/external"

os.makedirs(save_dir, exist_ok=True)

print('Parent process %s.' % os.getpid())

img_list = list(pd.read_csv(csv_path)['Id'])

img_splits = np.array_split(img_list, process_num)

assert sum([len(v) for v in img_splits]) == len(img_list)

p = Pool(process_num)

for i, split in enumerate(img_splits):

p.apply_async(

download, args=(str(i), list(split), url, save_dir, image_size)

)

print('Waiting for all subprocesses done...')

p.close()

p.join()

print('All subprocesses done.')

开发者ID:pudae，项目名称:kaggle-hpa，代码行数:25，

示例16: main

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def main():

print('\nWelcome here to get pictures from www.toutiao.com!')

keyword = input('Please input your search keywords > ')

count = None

while count == None:

number_str = input(

'Please input count of picture collection that you want(Divisible by 20 ) > ')

try:

count = int(number_str)

except ValueError:

print('Please input a valid number!')

if count > 0:

print('Getting %s pictures...' % keyword)

page_num = count // 20 + (0 if count % 20 == 0 else 1)

offset_list = [x * 20 for x in range(0, page_num)]

pool = Pool()

partial_getter = partial(get_images_of, keyword=keyword)

pool.map(partial_getter, offset_list)

pool.close()

pool.join()

else:

print('Get Cancel!')

开发者ID:Litreily，项目名称:capturer，代码行数:25，

示例17: main

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def main():

parser = argparse.ArgumentParser(

description="Downloads wheels from AppVeyor")

parser.add_argument("--buildid", "-id", type=int,

default=None, help='AppVeyor build id')

parser.add_argument("--pool", "-p", type=int, default=6,

help="Multiprocess pool size")

parser.add_argument("--dir", "-d", type=check_dir, default=getcwd(),

help='Directory to download the files into.')

options = parser.parse_args()

download_urls = []

for job in get_jobs(options.buildid):

download_urls += get_artifacts(job)

# Download them in parallel

pool = Pool(options.pool)

for download_url in download_urls:

pool.apply_async(download_file, args=(download_url, options.dir))

pool.close()

pool.join()

print("Done")

开发者ID:piqueserver，项目名称:piqueserver，代码行数:23，

示例18: init_pool

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def init_pool(self, worker_count):

return Pool(worker_count)

开发者ID:JohnStarich，项目名称:python-pool-performance，代码行数:4，

示例19: cloud_tpu

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def cloud_tpu(vm_name, tpu_name, delete_on_done=False, skip_confirmation=False):

"""Gets or creates a VM and TPU instance, and forwards ports.

Args:

vm_name: str, name of VM.

tpu_name: str, name of TPU instance.

delete_on_done: bool, whether to delete the instances when done.

skip_confirmation: bool, whether to skip launch confirmations.

Yields:

master: str, grpc master pointing to the TPU instance.

"""

state = CloudState()

# Read state from previous processes and possibly cleanup

state.cleanup(current_vm_name=vm_name, current_tpu_name=tpu_name,

skip_confirmation=skip_confirmation)

done_str = "" if delete_on_done else "NOT "

print("Will %sdelete VM and TPU instance on done." % done_str)

if not skip_confirmation:

assert confirm()

_, tpu_ip = create_vm_tpu_pair(vm_name, tpu_name,

skip_confirmation=skip_confirmation)

with tpu_tunnel(vm_name, tpu_ip) as (local_ports, tunnel_pid):

master = "grpc://localhost:%d" % local_ports["tpu"]

state.add_current(tunnel_pid, vm_name, tpu_name, delete_on_done)

yield master

if delete_on_done:

pool = mp.Pool(2)

vm_res = pool.apply_async(delete_vm, (vm_name,))

tpu_res = pool.apply_async(delete_tpu, (tpu_name,))

vm_res.get()

tpu_res.get()

# Cleanup state from this process

state.delete_current()

开发者ID:akzaidi，项目名称:fine-lm，代码行数:41，

示例20: Pool

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def Pool(processes=None, initializer=None, initargs=(), maxtasksperchild=None):

'''

Returns a process pool object

'''

from multiprocessing.pool import Pool

return Pool(processes, initializer, initargs, maxtasksperchild)

开发者ID:war-and-code，项目名称:jawfish，代码行数:8，

示例21: parallel_variability_analysis

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def parallel_variability_analysis(tmodel, kind='reactions', proc_num = BEST_THREAD_RATIO):

"""

WIP.

:param tmodel:

:param kind:

:param proc_num:

:return:

"""

raise(NotImplementedError)

objective = tmodel.objective

if kind == Reaction or kind.lower() in ['reaction','reactions']:

these_vars = tmodel.reactions

else:

these_vars = tmodel.get_variables_of_type(kind)

func = partial(_variability_analysis_element, tmodel)

pool = Pool(processes=proc_num)

async_result = pool.map_async(func, these_vars)

pool.close()

pool.join()

# aggregated_result = pd.DataFrame(async_result.get(),

# columns = ['minimize','maximize'])

tmodel.objective = objective

return async_result

开发者ID:EPFL-LCSB，项目名称:pytfa，代码行数:33，

示例22: __init__

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def __init__(self, processes: int = None):

super().__init__()

self.pool = Pool(processes)

开发者ID:jMetal，项目名称:jMetalPy，代码行数:5，

示例23: __init__

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def __init__(self, args):

super().__init__()

self.logger = set_logger(colored('VENTILATOR', 'magenta'), args.verbose)

self.model_dir = args.model_dir

self.max_seq_len = args.max_seq_len

self.num_worker = args.num_worker

self.max_batch_size = args.max_batch_size

self.num_concurrent_socket = max(8, args.num_worker * 2) # optimize concurrency for multi-clients

self.port = args.port

self.args = args

self.status_args = {k: (v if k != 'pooling_strategy' else v.value) for k, v in sorted(vars(args).items())}

self.status_static = {

'tensorflow_version': _tf_ver_,

'python_version': sys.version,

'server_version': __version__,

'pyzmq_version': zmq.pyzmq_version(),

'zmq_version': zmq.zmq_version(),

'server_start_time': str(datetime.now()),

}

self.processes = []

self.logger.info('freeze, optimize and export graph, could take a while...')

with Pool(processes=1) as pool:

# optimize the graph, must be done in another process

from .graph import optimize_graph

self.graph_path, self.bert_config = pool.apply(optimize_graph, (self.args,))

# from .graph import optimize_graph

# self.graph_path = optimize_graph(self.args, self.logger)

if self.graph_path:

self.logger.info('optimized graph is stored at: %s' % self.graph_path)

else:

raise FileNotFoundError('graph optimization fails and returns empty result')

self.is_ready = threading.Event()

开发者ID:hanxiao，项目名称:bert-as-service，代码行数:35，

示例24: match_plans

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def match_plans(self):

pool = Pool(multiprocessing.cpu_count() - 1)

plans = list(reversed(list(tqdm(pool.imap(match_plan, list(reversed(self.data))), total=len(self.data)))))

self.data = [d.set_plan(p) for d, plans in zip(self.data, plans) for p in plans]

return self

开发者ID:AmitMY，项目名称:chimera，代码行数:8，

示例25: exhaustive_plan

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def exhaustive_plan(self, planner):

unique = {d.graph.unique_key(): d.graph for d in self.data}

unique_graphs = list(reversed(list(unique.values())))

plan_iter = ((g, planner) for g in unique_graphs)

# pool = Pool(multiprocessing.cpu_count() - 1)

# plans = list(tqdm(pool.imap(exhaustive_plan_compress, plan_iter),

# total=len(unique_graphs)))

plans = [exhaustive_plan_compress(g_p) for g_p in tqdm(list(plan_iter))]

graph_plans = {g.unique_key(): p for g, p in zip(unique_graphs, plans)}

self.data = [d.set_plans(graph_plans[d.graph.unique_key()]) for d in self.data]

return self

开发者ID:AmitMY，项目名称:chimera，代码行数:16，

示例26: create_plans

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​

# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def create_plans(self, planner):

assert planner is not None

unique = {d.graph.unique_key(): d.graph for d in self.data}

unique_graphs = list(reversed(list(unique.values())))

# if planner.is_parallel:

# pool = Pool(multiprocessing.cpu_count() - 1)

# plans = list(tqdm(pool.imap(planner.plan_best, unique_graphs), total=len(unique_graphs)))

# else:

plans = []

for g in tqdm(unique_graphs):

start = time.time()

plans.append(planner.plan_best(g))

g_size = len(g.edges)

if g_size not in self.timing:

self.timing[g_size] = []

self.timing[g_size].append(time.time() - start)

graph_plan = {g.unique_key(): p for g, p in zip(unique_graphs, plans)}

for d in self.data:

plans = graph_plan[d.graph.unique_key()]

if isinstance(plans, list):

d.set_plan(plans[0])

d.set_plans(plans[1:])

else:

d.set_plan(plans)

return self

开发者ID:AmitMY，项目名称:chimera，代码行数:30，

示例27: process_all

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# 需要导入模块: from multiprocessing import pool [as 别名]

# 或者: from multiprocessing.pool import Pool [as 别名]

def process_all(self, pool: Pool):

"""Process all masks."""

images = [

filename

for filename in self.in_dir.iterdir()

if has_image_extension(str(filename))

]

tqdm_parallel_imap(self.preprocess, images, pool)

开发者ID:catalyst-team，项目名称:segmentation，代码行数:10，

注：本文中的multiprocessing.pool.Pool方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。