ScheduledTrainingServer.__init__() - Code Metrics - Inspection of "Multi-gpu refinement" - zomux/deepy - Measure and Improve Code Quality continuously with Scrutinizer

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by Raphael

created 2016-05-27 07:30 UTC

ScheduledTrainingServer.init() B

↳ Parent: ScheduledTrainingServer

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cc	1
c	1
b	0
f	0
dl	0
loc	36
rs	8.8571

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import print_function
import time
import sys, os
import numpy as np
import logging as loggers
from threading import Lock
logging = loggers.getLogger("ScheduledTrainingServer")
loggers.basicConfig(level=loggers.INFO)

from platoon.channel import Controller
from argparse import ArgumentParser


CONTROLLER_PORT = 5567

class ScheduledTrainingServer(Controller):
    """
    This multi-process controller implements patience-based early-stopping SGD
    """

    def __init__(self, port=CONTROLLER_PORT, start_halving_at=5, end_at=10, step_len=10,
                 valid_freq = 1000,
                 learning_rate = 0.1,
                 easgd_alpha=0.5):
        """
        Initialize the controller.

        Args:
            step_len (int): batches in one training step
            config (dict)
        """

        Controller.__init__(self, port)
        self.epoch_start_halving = start_halving_at
        self.end_at = end_at
        self.step_len = step_len
        self.start_time = None
        self.rand = np.random.RandomState(3)
        self.epoch = 0
        self._current_iter = 0
        self._iters_from_last_valid = 0
        self._evaluating = False
        self._valid_freq = valid_freq
        self._done = False
        self._lr = learning_rate
        self._easgd_alpha = easgd_alpha
        self._training_names = []
        self._evaluation_names = []
        self._best_valid_cost = sys.float_info.max
        self._lock = Lock()

        self.num_train_batches = 0
        self.batch_pool = []
        self._train_costs = []
        self.prepared_worker_pool = set()
        logging.info("multi-gpu server is listening port {}".format(port))

    def prepare_epoch(self):
        """
        Prepare for one epoch.
        Returns:
            bool: False if to stop the training.
        """
        self.epoch += 1
        if self.epoch >= self.epoch_start_halving:
            self._lr *= 0.5
        self._current_iter = 0
        self._iters_from_last_valid = 0
        self._train_costs = []
        self.prepared_worker_pool.clear()
        self.batch_pool = range(self.num_train_batches)
        self.rand.shuffle(self.batch_pool)
        if self.epoch > self.end_at:
            logging.info("Training is done, wait all workers to stop")
            return False
        else:
            logging.info("start epoch {} with lr={}".format(self.epoch, self._lr))
            return True

    def feed_batches(self):
        if not self.batch_pool:
            return None
        else:
            batches = self.batch_pool[:self.step_len]
            self.batch_pool = self.batch_pool[self.step_len:]
            self._current_iter += len(batches)
            self._iters_from_last_valid += len(batches)
            return batches


    def feed_hyperparams(self):
        retval = {
            "epoch": self.epoch,
            "learning_rate": self._lr,
            "easgd_alpha": self._easgd_alpha
        }
        return retval

    def get_monitor_string(self, costs):
        return " ".join(["{}={:.2f}".format(n, c) for (n, c) in costs])


    def handle_control(self, req, worker_id):
        """
        Handles a control_request received from a worker.
        Returns:
            string or dict: response

            'stop' - the worker should quit
            'wait' - wait for 1 second
            'eval' - evaluate on valid and test set to start a new epoch
            'sync_hyperparams' - set learning rate
            'valid' - evaluate on valid and test set, then save the params
            'train' - train next batches
        """
        if self.start_time is None: self.start_time = time.time()
        response = ""

        if req == 'next':
            if self.num_train_batches == 0:
                response = "get_num_batches"
            elif self._done:
                response = "stop"
                self.worker_is_done(worker_id)
            elif self._evaluating:
                response = 'wait'
            elif not self.batch_pool:
                # End of one iter
                if self._train_costs:
                    with self._lock:
                        sys.stdout.write("\r")
                        sys.stdout.flush()
                        mean_costs = []
                        for i in range(len(self._training_names)):
                            mean_costs.append(np.mean([c[i] for c in self._train_costs]))
                        logging.info("train   (epoch={:2d}) {}".format(
                            self.epoch,
                            self.get_monitor_string(zip(self._training_names, mean_costs)))
                        )
                response = {'eval': None, 'best_valid_cost': self._best_valid_cost}
                self._evaluating = True
            else:
                # Continue training
                if worker_id not in self.prepared_worker_pool:
                    response = {"sync_hyperparams": self.feed_hyperparams()}
                    self.prepared_worker_pool.add(worker_id)
                elif self._iters_from_last_valid >= self._valid_freq:
                    response = {'valid': None, 'best_valid_cost': self._best_valid_cost}
                    self._iters_from_last_valid = 0
                else:
                    response = {"train": self.feed_batches()}
        elif 'eval_done' in req:
            with self._lock:
                self._evaluating = False
                sys.stdout.write("\r")
                sys.stdout.flush()
                if 'test_costs' in req:
                    logging.info("test    (epoch={:2d}) {}".format(
                        self.epoch,
                        self.get_monitor_string(req['test_costs']))
                    )
                if 'valid_costs' in req:
                    valid_J = req['valid_costs'][0][1]
                    if valid_J < self._best_valid_cost:
                        self._best_valid_cost = valid_J
                        star_str = "*"
                    else:
                        star_str = ""
                    logging.info("valid   (epoch={:2d}) {} {}".format(
                        self.epoch,
                        self.get_monitor_string(req['valid_costs']),
                        star_str))
                continue_training = self.prepare_epoch()
                if not continue_training:
                    self._done = True
                    logging.info("training time {:.4f}s".format(time.time() - self.start_time))
                    response = "stop"
        elif 'valid_done' in req:
            with self._lock:
                sys.stdout.write("\r")
                sys.stdout.flush()
                if 'valid_costs' in req:
                    valid_J = req['valid_costs'][0][1]
                    if valid_J < self._best_valid_cost:
                        self._best_valid_cost = valid_J
                        star_str = "*"
                    else:
                        star_str = ""
                    logging.info("valid   ( dryrun ) {} {}".format(
                        self.get_monitor_string(req['valid_costs']),
                        star_str
                    ))
        elif 'train_done' in req:
            costs = req['costs']
            self._train_costs.append(costs)
            sys.stdout.write("\x1b[2K\r> %d%% | J=%.2f" % (self._current_iter * 100 / self.num_train_batches,
                                                           costs[0]))
            sys.stdout.flush()
        elif 'get_num_batches_done' in req:
            self.num_train_batches = req['get_num_batches_done']
        elif 'get_easgd_alpha' in req:
            response = self._easgd_alpha
        elif 'sync_hyperparams' in req:
            response = {"sync_hyperparams": self.feed_hyperparams()}
        elif 'set_names' in req:
            self._training_names = req['training_names']
            self._evaluation_names = req['evaluation_names']

        return response

if __name__ == '__main__':
    ap = ArgumentParser()
    ap.add_argument("--port", type=int, default=5567)
    ap.add_argument("--learning_rate", type=float, default=0.01)
    ap.add_argument("--start_halving_at", type=int, default=5)
    ap.add_argument("--end_at", type=int, default=10)
    ap.add_argument("--step_len", type=int, default=10)
    ap.add_argument("--valid_freq", type=int, default=1500)
    ap.add_argument("--easgd_alpha", type=float, default=0.5)
    args = ap.parse_args()

    server = ScheduledTrainingServer(
        port=args.port, learning_rate=args.learning_rate,
        start_halving_at=args.start_halving_at,
        end_at=args.end_at,
        step_len=args.step_len,
        valid_freq=args.valid_freq,
        easgd_alpha=args.easgd_alpha)
    server.serve()


1			#!/usr/bin/env python
2			# -- coding: utf-8 --
3
4			from __future__ import print_function
5			import time
6			import sys, os
7			import numpy as np
8			import logging as loggers
9			from threading import Lock
10			logging = loggers.getLogger("ScheduledTrainingServer")
11			loggers.basicConfig(level=loggers.INFO)
12
13			from platoon.channel import Controller
14			from argparse import ArgumentParser
15
16
17			CONTROLLER_PORT = 5567
18
19			class ScheduledTrainingServer(Controller):
20			"""
21			This multi-process controller implements patience-based early-stopping SGD
22			"""
23
24			def __init__(self, port=CONTROLLER_PORT, start_halving_at=5, end_at=10, step_len=10,
25			valid_freq = 1000,
26			learning_rate = 0.1,
27			easgd_alpha=0.5):
28			"""
29			Initialize the controller.
30
31			Args:
32			step_len (int): batches in one training step
33			config (dict)
34			"""
35
36			Controller.__init__(self, port)
37			self.epoch_start_halving = start_halving_at
38			self.end_at = end_at
39			self.step_len = step_len
40			self.start_time = None
41			self.rand = np.random.RandomState(3)
42			self.epoch = 0
43			self._current_iter = 0
44			self._iters_from_last_valid = 0
45			self._evaluating = False
46			self._valid_freq = valid_freq
47			self._done = False
48			self._lr = learning_rate
49			self._easgd_alpha = easgd_alpha
50			self._training_names = []
51			self._evaluation_names = []
52			self._best_valid_cost = sys.float_info.max
53			self._lock = Lock()
54
55			self.num_train_batches = 0
56			self.batch_pool = []
57			self._train_costs = []
58			self.prepared_worker_pool = set()
59			logging.info("multi-gpu server is listening port {}".format(port))
60
61			def prepare_epoch(self):
62			"""
63			Prepare for one epoch.
64			Returns:
65			bool: False if to stop the training.
66			"""
67			self.epoch += 1
68			if self.epoch >= self.epoch_start_halving:
69			self._lr *= 0.5
70			self._current_iter = 0
71			self._iters_from_last_valid = 0
72			self._train_costs = []
73			self.prepared_worker_pool.clear()
74			self.batch_pool = range(self.num_train_batches)
75			self.rand.shuffle(self.batch_pool)
76			if self.epoch > self.end_at:
77			logging.info("Training is done, wait all workers to stop")
78			return False
79			else:
80			logging.info("start epoch {} with lr={}".format(self.epoch, self._lr))
81			return True
82
83			def feed_batches(self):
84			if not self.batch_pool:
85			return None
86			else:
87			batches = self.batch_pool[:self.step_len]
88			self.batch_pool = self.batch_pool[self.step_len:]
89			self._current_iter += len(batches)
90			self._iters_from_last_valid += len(batches)
91			return batches
92
93
94			def feed_hyperparams(self):
95			retval = {
96			"epoch": self.epoch,
97			"learning_rate": self._lr,
98			"easgd_alpha": self._easgd_alpha
99			}
100			return retval
101
102			def get_monitor_string(self, costs):
103			return " ".join(["{}={:.2f}".format(n, c) for (n, c) in costs])
104
105
106			def handle_control(self, req, worker_id):
107			"""
108			Handles a control_request received from a worker.
109			Returns:
110			string or dict: response
111
112			'stop' - the worker should quit
113			'wait' - wait for 1 second
114			'eval' - evaluate on valid and test set to start a new epoch
115			'sync_hyperparams' - set learning rate
116			'valid' - evaluate on valid and test set, then save the params
117			'train' - train next batches
118			"""
119			if self.start_time is None: self.start_time = time.time()
120			response = ""
121
122			if req == 'next':
123			if self.num_train_batches == 0:
124			response = "get_num_batches"
125			elif self._done:
126			response = "stop"
127			self.worker_is_done(worker_id)
128			elif self._evaluating:
129			response = 'wait'
130			elif not self.batch_pool:
131			# End of one iter
132			if self._train_costs:
133			with self._lock:
134			sys.stdout.write("\r")
135			sys.stdout.flush()
136			mean_costs = []
137			for i in range(len(self._training_names)):
138			mean_costs.append(np.mean([c[i] for c in self._train_costs]))
139			logging.info("train (epoch={:2d}) {}".format(
140			self.epoch,
141			self.get_monitor_string(zip(self._training_names, mean_costs)))
142			)
143			response = {'eval': None, 'best_valid_cost': self._best_valid_cost}
144			self._evaluating = True
145			else:
146			# Continue training
147			if worker_id not in self.prepared_worker_pool:
148			response = {"sync_hyperparams": self.feed_hyperparams()}
149			self.prepared_worker_pool.add(worker_id)
150			elif self._iters_from_last_valid >= self._valid_freq:
151			response = {'valid': None, 'best_valid_cost': self._best_valid_cost}
152			self._iters_from_last_valid = 0
153			else:
154			response = {"train": self.feed_batches()}
155			elif 'eval_done' in req:
156			with self._lock:
157			self._evaluating = False
158			sys.stdout.write("\r")
159			sys.stdout.flush()
160			if 'test_costs' in req:
161			logging.info("test (epoch={:2d}) {}".format(
162			self.epoch,
163			self.get_monitor_string(req['test_costs']))
164			)
165			if 'valid_costs' in req:
166			valid_J = req['valid_costs'][0][1]
167			if valid_J < self._best_valid_cost:
168			self._best_valid_cost = valid_J
169			star_str = "*"
170			else:
171			star_str = ""
172			logging.info("valid (epoch={:2d}) {} {}".format(
173			self.epoch,
174			self.get_monitor_string(req['valid_costs']),
175			star_str))
176			continue_training = self.prepare_epoch()
177			if not continue_training:
178			self._done = True
179			logging.info("training time {:.4f}s".format(time.time() - self.start_time))
180			response = "stop"
181			elif 'valid_done' in req:
182			with self._lock:
183			sys.stdout.write("\r")
184			sys.stdout.flush()
185			if 'valid_costs' in req:
186			valid_J = req['valid_costs'][0][1]
187			if valid_J < self._best_valid_cost:
188			self._best_valid_cost = valid_J
189			star_str = "*"
190			else:
191			star_str = ""
192			logging.info("valid ( dryrun ) {} {}".format(
193			self.get_monitor_string(req['valid_costs']),
194			star_str
195			))
196			elif 'train_done' in req:
197			costs = req['costs']
198			self._train_costs.append(costs)
199			sys.stdout.write("\x1b[2K\r> %d%% \| J=%.2f" % (self._current_iter * 100 / self.num_train_batches,
200			costs[0]))
201			sys.stdout.flush()
202			elif 'get_num_batches_done' in req:
203			self.num_train_batches = req['get_num_batches_done']
204			elif 'get_easgd_alpha' in req:
205			response = self._easgd_alpha
206			elif 'sync_hyperparams' in req:
207			response = {"sync_hyperparams": self.feed_hyperparams()}
208			elif 'set_names' in req:
209			self._training_names = req['training_names']
210			self._evaluation_names = req['evaluation_names']
211
212			return response
213
214			if __name__ == '__main__':
215			ap = ArgumentParser()
216			ap.add_argument("--port", type=int, default=5567)
217			ap.add_argument("--learning_rate", type=float, default=0.01)
218			ap.add_argument("--start_halving_at", type=int, default=5)
219			ap.add_argument("--end_at", type=int, default=10)
220			ap.add_argument("--step_len", type=int, default=10)
221			ap.add_argument("--valid_freq", type=int, default=1500)
222			ap.add_argument("--easgd_alpha", type=float, default=0.5)
223			args = ap.parse_args()
224
225			server = ScheduledTrainingServer(
226			port=args.port, learning_rate=args.learning_rate,
227			start_halving_at=args.start_halving_at,
228			end_at=args.end_at,
229			step_len=args.step_len,
230			valid_freq=args.valid_freq,
231			easgd_alpha=args.easgd_alpha)
232			server.serve()
233

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ScheduledTrainingServer.init() B