def custom_feeder(input, task=None):
def write_proc(fin, pipe, task=None):
while True:
data = yield os.read(fin.fileno(), 8 * 1024)
if not data:
break
n = yield pipe.write(data, full=True)
assert n == len(data)
fin.close()
pipe.stdin.close()
def read_proc(pipe, task=None):
# output from sha1sum is small, so read until EOF
data = yield pipe.stdout.read()
pipe.stdout.close()
raise StopIteration(data)
if platform.system() == 'Windows':
# asyncfile.Popen must be used instead of subprocess.Popen
pipe = pycos.asyncfile.Popen([r'\cygwin64\bin\sha1sum.exe'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
else:
pipe = subprocess.Popen(['sha1sum'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
async_pipe = pycos.asyncfile.AsyncPipe(pipe)
reader = pycos.Task(read_proc, async_pipe)
writer = pycos.Task(write_proc, open(input), async_pipe)
stdout = yield reader.finish()
print(' feeder sha1sum: %s' % stdout)
def peer_status(task=None):
client = pycos.Task(rps_client)
rpss = {}
while 1:
status = yield task.receive()
if not isinstance(status, pycos.PeerStatus):
if status == 'quit':
break
pycos.logger.warning('Invalid peer status %s ignored',
type(status))
continue
if status.status == pycos.PeerStatus.Online:
# if peer has rps_log_monitor, run RPS there
def discover_rps(location, task=None):
rps = yield pycos.RPS.locate('rps_log_monitor',
location=location,
timeout=5)
if isinstance(rps, pycos.RPS):
rpss[rps] = rps
server = yield rps(client)
if isinstance(server, pycos.Task):
servers[location] = server
pycos.Task(discover_rps, status.location)
else: # status.status == pycos.PeerStatus.Offline
servers.pop(status.location, None)
for rps in rpss.values():
rps.close()
def create_job(i, task=None):
# create reader and send to rtask so it can send messages to reader
client_reader = pycos.Task(get_output, i)
# schedule rtask on (available) remote server
rtask = yield computation.run(rtask_proc, client_reader, program_path)
if isinstance(rtask, pycos.Task):
print(' job %s processed by %s' % (i, rtask.location))
# sender sends input data to rtask
pycos.Task(send_input, rtask)
# wait for all data to be received
yield client_reader.finish()
print(' job %s done' % i)
else: # failed to schedule
print(' job %s failed: %s' % (i, rtask))
client_reader.terminate()
def client_proc(computation, njobs, task=None):
# schedule computation with the scheduler; scheduler accepts one computation
# at a time, so if scheduler is shared, the computation is queued until it
# is done with already scheduled computations
if (yield computation.schedule()):
raise Exception('Could not schedule computation')
# send 5 requests to remote process (compute_task)
def send_requests(rtask, task=None):
# first send this local task (to whom rtask sends result)
rtask.send(task)
for i in range(5):
# even if recipient doesn't use "yield" (such as executing long-run
# computation, or thread-blocking function such as 'time.sleep' as
# in this case), the message is accepted by another scheduler
# (netpycos.Pycos) at the receiver and put in recipient's message
# queue
rtask.send(random.uniform(10, 20))
# assume delay in input availability
yield task.sleep(random.uniform(2, 5))
# end of input is indicated with None
rtask.send(None)
result = yield task.receive() # get result
print(' %s computed result: %.4f' % (rtask.location, result))
for i in range(njobs):
rtask = yield computation.run(compute_task)
if isinstance(rtask, pycos.Task):
print(' job %d processed by %s' % (i, rtask.location))
pycos.Task(send_requests, rtask)
yield computation.close()
def send_proc(task=None):
# if server is in a remote network, use 'peer' as (optionally enabling
# streaming for efficiency):
# yield pycos.Pycos.instance().peer(pycos.Location('server node/ip', port))
server = yield pycos.Task.locate('chat_server', timeout=5)
if not server:
print('Could not locate server')
raise StopIteration
server.send(('join', task))
client_id = yield task.receive()
# channel is at same location as server task
channel = yield pycos.Channel.locate('chat_channel', server.location)
recv_task = pycos.Task(recv_proc, client_id)
yield channel.subscribe(recv_task)
# since readline is synchronous (blocking) call, use async thread
async_threads = pycos.AsyncThreadPool(1)
if sys.version_info.major > 2:
read_input = input
else:
read_input = raw_input
while True:
try:
line = yield async_threads.async_task(read_input)
line = line.strip()
if line.lower() in ('quit', 'exit'):
break
except:
break
# send message to channel
channel.send((line, client_id))
server.send(('quit', client_id))
yield channel.unsubscribe(recv_task)
def client_proc(computation, task=None):
# schedule computation with the scheduler; if remote scheduler is not
# automatically discovered (e.g., if it is on remote network, or UDP is
# lossy), use 'peer' method to discover, e.g., with
# yield pycos.Pycos.instance().peer(pycos.Location('hostname_or_ip_of_scheduler', 9706))
if (yield computation.schedule()):
raise Exception('Could not schedule computation')
# remote tasks send results to this process
def reply_proc(task=None):
task.set_daemon()
while 1:
msg = yield task.recv()
print(' Received reply for %s from %s: %s' %
(msg[0], msg[1], msg[2]))
reply_task = pycos.Task(reply_proc)
i = 0
while True:
n = yield task.receive()
if n is None:
break
i += 1
rtask = yield computation.run(compute, i, n, reply_task)
if isinstance(rtask, pycos.Task):
print(' Task %s created for %s at %s' % (i, n, rtask.location))
# wait for all jobs to be done and close computation
yield computation.close()
def client_proc(computation, njobs, task=None):
# schedule computation with the scheduler; scheduler accepts one computation
# at a time, so if scheduler is shared, the computation is queued until it
# is done with already scheduled computations
if (yield computation.schedule()):
raise Exception('Could not schedule computation')
# create a separate task to receive results, so they can be processed
# as soon as received
def recv_results(task=None):
for i in range(njobs):
msg = yield task.receive()
print(' result for job %d: %s' % (i, msg))
# remote tasks send replies as messages to this task
results_task = pycos.Task(recv_results)
# run njobs; each job will be executed by one dispycos server
for i in range(njobs):
cobj = C(i)
cobj.n = random.uniform(5, 10)
# as noted in 'dispycos_client2.py', 'run' method is used to run jobs
# sequentially; use 'run_async' to run multiple jobs on one server
# concurrently
print(' request %d: %s' % (i, cobj.n))
rtask = yield computation.run(compute, cobj, results_task)
if not isinstance(rtask, pycos.Task):
print('failed to create rtask %s: %s' % (i, rtask))
# wait for all results and close computation
yield computation.close()
def server_task(task=None):
task.set_daemon()
task.register('server_task')
while True:
msg = yield task.receive()
# create task to process message
pycos.Task(process, msg)
def client_proc(task=None):
# create channel
channel = pycos.Channel('sum_prod')
# create tasks to compute sum and product of numbers sent
sum_task = pycos.Task(seqsum)
prod_task = pycos.Task(seqprod)
# subscribe tasks to channel so they receive messages
yield channel.subscribe(sum_task)
yield channel.subscribe(prod_task)
# send 4 numbers to channel
for _ in range(4):
r = random.uniform(0.5, 3)
channel.send(r)
print('sent %f' % r)
# send None to indicate end of data
channel.send(None)
yield channel.unsubscribe(sum_task)
yield channel.unsubscribe(prod_task)
def status_proc(task=None):
task.set_daemon()
i = 0
while 1:
msg = yield task.receive()
if not isinstance(msg, DispycosStatus):
continue
if msg.status == Scheduler.ServerDiscovered:
pycos.Task(server_available, msg.info, data_files[i])
i += 1
def server(host, port, task=None):
task.set_daemon()
sock = pycos.AsyncSocket(socket.socket(socket.AF_INET, socket.SOCK_STREAM))
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.bind((host, port))
sock.listen(5000)
while True:
conn, addr = yield sock.accept()
pycos.Task(process, conn)
def hcwst(host,
port,
repeater_ws,
proxy_host,
proxy_port,
proxy_username,
proxy_password,
ssl_verify,
task=None):
task.set_daemon()
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
#sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
sock = pycos.AsyncSocket(sock)
sock.bind((host, int(port)))
sock.listen(1)
print('Tunnel listening at %s' % str(sock.getsockname()))
if ssl_verify:
ws = websocket.WebSocket()
else:
ws = websocket.WebSocket(sslopt={"cert_reqs": ssl.CERT_NONE})
if not proxy_host:
ws.connect(repeater_ws,
subprotocols=["binary"],
sockopt=(socket.IPPROTO_TCP, socket.TCP_NODELAY))
else:
ws.connect(repeater_ws,
http_proxy_host=proxy_host,
http_proxy_port=proxy_port,
http_proxy_auth=proxy_auth,
subprotocols=["binary"],
sockopt=(socket.IPPROTO_TCP, socket.TCP_NODELAY))
print('Tunnel connected to %s' % repeater_ws)
conn, _ = yield sock.accept()
pycos.Task(client_send, conn, ws)
pycos.Task(ws_send, conn, ws)
def server(host, port, task=None):
task.set_daemon()
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
# setup socket for asynchronous I/O with pycos
sock = pycos.AsyncSocket(sock)
sock.bind((host, port))
sock.listen(128)
while True:
conn, addr = yield sock.accept()
# create a task to process connection
pycos.Task(process, conn)
def client_proc(computation, program_path, n, task=None):
# schedule computation with the scheduler; scheduler accepts one computation
# at a time, so if scheduler is shared, the computation is queued until it
# is done with already scheduled computations
if (yield computation.schedule()):
raise Exception('Could not schedule computation')
# send 10 random numbers to remote process (rtask_proc)
def send_input(rtask, task=None):
for i in range(10):
# encode strings so works with both Python 2.7 and 3
rtask.send(('%.2f' % random.uniform(0, 5)).encode())
# assume delay in input availability
yield task.sleep(random.uniform(0, 2))
# end of input is indicated with None
rtask.send(None)
# read output (messages sent by 'reader_proc' on remote process)
def get_output(i, task=None):
while True:
line = yield task.receive()
if not line: # end of output
break
print(' job %s output: %s' % (i, line.strip().decode()))
def create_job(i, task=None):
# create reader and send to rtask so it can send messages to reader
client_reader = pycos.Task(get_output, i)
# schedule rtask on (available) remote server
rtask = yield computation.run(rtask_proc, client_reader, program_path)
if isinstance(rtask, pycos.Task):
print(' job %s processed by %s' % (i, rtask.location))
# sender sends input data to rtask
pycos.Task(send_input, rtask)
# wait for all data to be received
yield client_reader.finish()
print(' job %s done' % i)
else: # failed to schedule
print(' job %s failed: %s' % (i, rtask))
client_reader.terminate()
# create n jobs (that run concurrently)
job_tasks = []
for i in range(1, n + 1):
job_tasks.append(pycos.Task(create_job, i))
# wait for jobs to finish
for job_task in job_tasks:
yield job_task.finish()
yield computation.close()
def client_proc(computation, task=None):
# schedule computation with the scheduler; scheduler accepts one computation
# at a time, so if scheduler is shared, the computation is queued until it
# is done with already scheduled computations
if (yield computation.schedule()):
raise Exception('Could not schedule computation')
# in dispycos_client6.py, data is sent to each remote task; here, data
# is broadcast over channel and remote tasks subscribe to it
data_channel = pycos.Channel('data_channel')
# not necessary to register channel in this case, as it is sent to remote
# tasks; if they were to 'locate' it, it should be registered
# data_channel.register()
trend_task = pycos.Task(trend_proc)
rtask_avg = yield computation.run(rtask_avg_proc, data_channel, 0.4,
trend_task, 10)
assert isinstance(rtask_avg, pycos.Task)
rtask_save = yield computation.run(rtask_save_proc, data_channel)
assert isinstance(rtask_save, pycos.Task)
# make sure both remote tasks have subscribed to channel ('deliver'
# should return 2 if they both are)
assert (yield data_channel.deliver('start', n=2)) == 2
# if data is sent frequently (say, many times a second), enable
# streaming data to remote peer; this is more efficient as
# connections are kept open (so the cost of opening and closing
# connections is avoided), but keeping too many connections open
# consumes system resources
yield pycos.Pycos.instance().peer(rtask_avg.location, stream_send=True)
yield pycos.Pycos.instance().peer(rtask_save.location, stream_send=True)
# send 1000 items of random data to remote tasks
for i in range(1000):
n = random.uniform(-1, 1)
item = (i, n)
# data can be sent to remote tasks either with 'send' or
# 'deliver'; 'send' is more efficient but no guarantee data
# has been sent successfully whereas 'deliver' indicates
# errors right away
data_channel.send(item)
yield task.sleep(0.02)
item = (i, None)
data_channel.send(item)
yield computation.close()
data_channel.close()
def rtask_proc(client, program, task=None):
import sys
import os
import subprocess
import pycos.asyncfile
if program.endswith('.py'):
# Computation dependencies are saved in parent directory
program = [sys.executable, os.path.join('..', program)]
# start program as a subprocess (to read from and write to pipe)
if os.name == 'nt': # create pipe with asyncfile under Windows
pipe = pycos.asyncfile.Popen(program,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
else:
pipe = subprocess.Popen(program,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
# convert to asynchronous pipe; see 'pipe_csum.py' and 'pipe_grep.py' for
# chaining pipes
pipe = pycos.asyncfile.AsyncPipe(pipe)
# reader reads (output) from pipe and sends to client as messages
def reader_proc(task=None):
while True:
line = yield pipe.readline()
if not line:
break
# send output to client
client.send(line)
pipe.stdout.close()
if os.name == 'nt':
pipe.close()
client.send(None)
reader = pycos.Task(reader_proc)
# writer gets messages from client and writes them (input) to pipe
while True:
data = yield task.receive()
if not data:
break
# write data as lines to program
yield pipe.write(data + '\n'.encode(), full=True)
pipe.stdin.close()
# wait for all data to be read (subprocess to end)
yield reader.finish()
raise StopIteration(pipe.poll())
def server_proc(host, port, task=None):
task.set_daemon()
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
# convert sock to asynchronous
sock = pycos.AsyncSocket(sock)
sock.bind((host, port))
sock.listen(128)
print('server at %s' % str(sock.getsockname()))
try:
while True:
conn, addr = yield sock.accept()
pycos.Task(client_conn_proc, conn)
except:
msg_bcast_task.terminate()
def run_jobs_proc(computation, data_files, task=None):
# schedule computation with the scheduler; scheduler accepts one computation
# at a time, so if scheduler is shared, the computation is queued until it
# is done with already scheduled computations
if (yield computation.schedule()):
raise Exception('Could not schedule computation')
for i in range(len(data_files)):
data_file = data_files[i]
# create remote task
rtask = yield computation.run(rtask_proc)
if isinstance(rtask, pycos.Task):
# create local task to send input file and data to rtask
pycos.Task(client_proc, i, data_file, rtask)
else:
print(' job %s failed: %s' % (i, rtask))
yield computation.close()
def receiver_proc2(task=None):
# if server is in remote network, add it explicitly
# scheduler = pycos.Pycos.instance()
# yield scheduler.peer(pycos.Location('remote.ip', tcp_port))
rchannel = yield pycos.Channel.locate('2clients', timeout=5)
if not rchannel:
print('Could not locate server!')
raise StopIteration
# this task subscribes to the channel to get messages to server channel
print('server is at %s' % rchannel.location)
if (yield rchannel.subscribe(task)) != 0:
raise Exception('subscription failed')
sender = pycos.Task(sender_proc, rchannel)
while True:
msg = yield task.receive()
print('Received "%s" from %s at %s' %
(msg['msg'], msg['sender'].name, msg['sender'].location))
if msg['msg'] is None and msg['sender'] == sender:
break
yield rchannel.unsubscribe(task)
def rti_test(task=None):
# if server is on remote network, automatic discovery won't work,
# so add it explicitly
# yield scheduler.peer(pycos.Location('192.168.21.5', 9705))
# get reference to RTI at server
rti1 = yield pycos.RTI.locate('rti_1')
print('RTI is at %s' % rti1.location)
# 5 (remote) tasks are created with rti1
n = 5
# set monitor (monitor_proc task) for tasks created for this RTI
yield rti1.monitor(pycos.Task(monitor_proc, n))
for i in range(n):
rtask = yield rti1('test%s' % i, b=i)
pycos.logger.debug('RTI %s created' % rtask)
# If necessary, each rtask can also be set (different) 'monitor'
rtask.send('msg:%s' % i)
yield task.sleep(random.uniform(0, 1))
def chat(host, port, task=None):
task.set_daemon()
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock = pycos.AsyncSocket(sock)
sock.bind((host, port))
sock.listen(128)
print('server at %s' % str(sock.getsockname()))
clients = set()
try:
while True:
conn, addr = yield sock.accept()
clients.add(conn)
pycos.Task(client_send, clients, conn)
except:
for client in clients:
client.close()
raise
def client_proc(computation, task=None):
# schedule computation with the scheduler; scheduler accepts one computation
# at a time, so if scheduler is shared, the computation is queued until it
# is done with already scheduled computations
if (yield computation.schedule()):
raise Exception('Could not schedule computation')
trend_task = pycos.Task(trend_proc)
# run average and save tasks at two different servers
rtask_avg = yield computation.run(rtask_avg_proc, 0.4, trend_task, 10)
assert isinstance(rtask_avg, pycos.Task)
rtask_save = yield computation.run(rtask_save_proc)
assert isinstance(rtask_save, pycos.Task)
# if data is sent frequently (say, many times a second), enable streaming
# data to remote peer; this is more efficient as connections are kept open
# (so the cost of opening and closing connections is avoided), but keeping
# too many connections open consumes system resources
yield pycos.Pycos.instance().peer(rtask_avg.location, stream_send=True)
yield pycos.Pycos.instance().peer(rtask_save.location, stream_send=True)
# send 1000 items of random data to remote tasks
for i in range(1000):
n = random.uniform(-1, 1)
item = (i, n)
# data can be sent to remote tasks either with 'send' or 'deliver';
# 'send' is more efficient but no guarantee data has been sent
# successfully whereas 'deliver' indicates errors right away;
# alternately, messages can be sent with a channel, which is more
# convenient if there are multiple (unknown) recipients
rtask_avg.send(item)
rtask_save.send(item)
yield task.sleep(0.01)
item = (i, None)
rtask_avg.send(item)
rtask_save.send(item)
yield computation.close()
if __name__ == '__main__':
# pycos.logger.setLevel(pycos.logger.DEBUG)
# optional arg 1 is host IP address and arg 2 is port to use
host, port = '', 3456
if len(sys.argv) > 1:
host = sys.argv[1]
if len(sys.argv) > 2:
port = int(sys.argv[2])
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect((host, port))
sock = pycos.AsyncSocket(sock)
# same connection is used to receive messages in one task and to send
# messages in another task
pycos.Task(client_recv, sock)
sender = pycos.Task(client_send, sock)
if sys.version_info.major > 2:
read_input = input
else:
read_input = raw_input
while True:
try:
line = read_input().strip()
if line.lower() in ('quit', 'exit'):
break
if not line:
continue
except:
break
recvd += 1
# server accepts connections and creates tasks to deal with them
def server(host, port, task=None):
task.set_daemon()
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
# setup socket for asynchronous I/O with pycos
sock = pycos.AsyncSocket(sock)
sock.bind((host, port))
sock.listen(128)
while True:
conn, addr = yield sock.accept()
# create a task to process connection
pycos.Task(process, conn)
# pycos.logger.setLevel(pycos.Logger.DEBUG)
recvd = 0
pycos.Task(server, '', 8010)
if sys.version_info.major > 2:
read_input = input
else:
read_input = raw_input
while True:
cmd = read_input('Enter "quit" or "exit" to terminate: ').strip().lower()
if cmd == 'exit' or cmd == 'quit':
break
print('Received %d messages' % recvd)
rtask = yield computation.run(rtask_proc)
if isinstance(rtask, pycos.Task):
# create local task to send input file and data to rtask
pycos.Task(client_proc, i, data_file, rtask)
else:
print(' job %s failed: %s' % (i, rtask))
yield computation.close()
if __name__ == '__main__':
import random, os, sys, glob
# pycos.logger.setLevel(pycos.Logger.DEBUG)
if os.path.dirname(sys.argv[0]):
os.chdir(os.path.dirname(sys.argv[0]))
data_files = glob.glob('dispycos_client*.py')
if not data_files:
raise Exception('No data files to process')
if len(sys.argv) > 1:
data_files = data_files[:int(sys.argv[1])]
# if scheduler is not already running (on a node as a program), start it
# (private scheduler):
Scheduler()
# unlike in earlier examples, rtask_proc is not sent with computation (as it
# is not included in 'components'; instead, it is sent each time a job is
# submitted, which is a bit inefficient
computation = Computation([C])
pycos.Task(run_jobs_proc, computation, data_files)
# computations, use 'run_async' to run more than one computation at a
# server at the same time.
rtask = yield computation.run(compute, random.uniform(5, 10))
if isinstance(rtask, pycos.Task):
print(' job %s processed by %s' % (i, rtask.location))
else:
print('rtask %s failed: %s' % (i, rtask))
# wait for all jobs to be done and close computation
yield computation.close()
if __name__ == '__main__':
import random, sys, pycos.dispycos
# pycos.logger.setLevel(pycos.Logger.DEBUG)
# if scheduler is not already running (on a node as a program), start
# private scheduler:
Scheduler()
# send 'compute' generator function; use MinPulseInterval so node status
# updates are sent more frequently (instead of default 2*MinPulseInterval)
computation = Computation([compute], pulse_interval=pycos.dispycos.MinPulseInterval)
# to illustrate relaying of status messages to multiple tasks, httpd is
# also used in this example; this sets computation's status_task to httpd's status_task
httpd = pycos.httpd.HTTPServer(computation)
# run 10 (or given number of) jobs
pycos.Task(client_proc, computation, 10 if len(sys.argv) < 2 else int(sys.argv[1])).value()
# shutdown httpd only after computation is closed; alternately, close it in
# 'client_proc' after the computation is closed.
httpd.shutdown()
conn, addr = yield sock.accept()
pycos.Task(client_conn_proc, conn)
except:
msg_bcast_task.terminate()
if __name__ == '__main__':
# optional arg 1 is host IP address and arg 2 is port to use
host, port = '', 3456
if len(sys.argv) > 1:
host = sys.argv[1]
if len(sys.argv) > 2:
port = int(sys.argv[2])
if sys.version_info.major > 2:
read_input = input
else:
read_input = raw_input
msg_bcast_task = pycos.Task(msg_bcast_proc)
pycos.Task(server_proc, host, port)
while True:
try:
cmd = read_input(
'Enter "quit" or "exit" to terminate: ').strip().lower()
if cmd in ('quit', 'exit'):
break
except:
break
msg_bcast_task.terminate()
for i in range(1000):
n = random.uniform(-1, 1)
item = (i, n)
# data can be sent to remote tasks either with 'send' or
# 'deliver'; 'send' is more efficient but no guarantee data
# has been sent successfully whereas 'deliver' indicates
# errors right away
data_channel.send(item)
yield task.sleep(0.02)
item = (i, None)
data_channel.send(item)
yield computation.close()
data_channel.close()
if __name__ == '__main__':
import sys, random
# pycos.logger.setLevel(pycos.Logger.DEBUG)
# PyPI / pip packaging adjusts assertion below for Python 3.7+
if sys.version_info.major == 3:
assert sys.version_info.minor < 7, \
('"%s" is not suitable for Python version %s.%s; use file installed by pip instead' %
(__file__, sys.version_info.major, sys.version_info.minor))
# if scheduler is shared (i.e., running as program), nothing needs
# to be done (its location can optionally be given to 'schedule');
# othrwise, start private scheduler:
Scheduler()
computation = Computation([])
pycos.Task(client_proc, computation)
import sys, socket
import pycos
def server_proc(n, sock, task=None):
for i in range(n):
msg, addr = yield sock.recvfrom(1024)
print('Received "%s" from %s:%s' % (msg, addr[0], addr[1]))
sock.close()
def client_proc(host, port, task=None):
sock = pycos.AsyncSocket(socket.socket(socket.AF_INET, socket.SOCK_DGRAM))
msg = 'client socket: %s' % (sock.fileno())
if sys.version_info.major >= 3:
msg = bytes(msg, 'ascii')
yield sock.sendto(msg, (host, port))
sock.close()
if __name__ == '__main__':
sock = pycos.AsyncSocket(socket.socket(socket.AF_INET, socket.SOCK_DGRAM))
sock.bind(('127.0.0.1', 0))
host, port = sock.getsockname()
n = 50
server_task = pycos.Task(server_proc, n, sock)
for i in range(n):
pycos.Task(client_proc, host, port)
server_task.value()
# output from sha1sum is small, so read until EOF
data = yield pipe.stdout.read()
pipe.stdout.close()
raise StopIteration(data)
if platform.system() == 'Windows':
# asyncfile.Popen must be used instead of subprocess.Popen
pipe = pycos.asyncfile.Popen([r'\cygwin64\bin\sha1sum.exe'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
else:
pipe = subprocess.Popen(['sha1sum'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
async_pipe = pycos.asyncfile.AsyncPipe(pipe)
reader = pycos.Task(read_proc, async_pipe)
writer = pycos.Task(write_proc, open(input), async_pipe)
stdout = yield reader.finish()
print(' feeder sha1sum: %s' % stdout)
# pycos.logger.setLevel(pycos.Logger.DEBUG)
# simpler version using 'communicate'
task = pycos.Task(communicate,
sys.argv[1] if len(sys.argv) > 1 else sys.argv[0])
task.value() # wait for it to finish
# alternate version with custom read and write processes
pycos.Task(custom_feeder, sys.argv[1] if len(sys.argv) > 1 else sys.argv[0])