##// END OF EJS Templates
Merge pull request #1548 from minrk/ar_sugar...
Fernando Perez -
r6485:e0b43119 merge
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1 {
2 "metadata": {
3 "name": "Animations_and_Progress"
4 },
5 "nbformat": 3,
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "cell_type": "heading",
11 "level": 1,
12 "source": [
13 "Simple animations, progress bars, and clearing output"
14 ]
15 },
16 {
17 "cell_type": "markdown",
18 "source": [
19 "Sometimes you want to print progress in-place, but don't want",
20 "to keep growing the output area. In terminals, there is the carriage-return",
21 "(`'\\r'`) for overwriting a single line, but the notebook frontend does not support this",
22 "behavior (yet).",
23 "",
24 "What the notebook *does* support is explicit `clear_output`, and you can use this to replace previous",
25 "output (specifying stdout/stderr or the special IPython display outputs)."
26 ]
27 },
28 {
29 "cell_type": "markdown",
30 "source": [
31 "A simple example printing our progress iterating through a list:"
32 ]
33 },
34 {
35 "cell_type": "code",
36 "collapsed": true,
37 "input": [
38 "import sys",
39 "import time"
40 ],
41 "language": "python",
42 "outputs": [],
43 "prompt_number": 16
44 },
45 {
46 "cell_type": "code",
47 "collapsed": false,
48 "input": [
49 "from IPython.core.display import clear_output",
50 "for i in range(10):",
51 " time.sleep(0.25)",
52 " clear_output()",
53 " print i",
54 " sys.stdout.flush()"
55 ],
56 "language": "python",
57 "outputs": [],
58 "prompt_number": 12
59 },
60 {
61 "cell_type": "markdown",
62 "source": [
63 "The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,",
64 "so you can watch as your parallel computation completes.",
65 "",
66 "**This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#tab2)**"
67 ]
68 },
69 {
70 "cell_type": "code",
71 "collapsed": false,
72 "input": [
73 "from IPython import parallel",
74 "rc = parallel.Client()",
75 "view = rc.load_balanced_view()",
76 "",
77 "amr = view.map_async(time.sleep, [0.5]*100)",
78 "",
79 "amr.wait_interactive()"
80 ],
81 "language": "python",
82 "outputs": [],
83 "prompt_number": 13
84 },
85 {
86 "cell_type": "markdown",
87 "source": [
88 "You can also use `clear_output()` to clear figures and plots.",
89 "",
90 "This time, we need to make sure we are using inline pylab (**requires matplotlib**)"
91 ]
92 },
93 {
94 "cell_type": "code",
95 "collapsed": false,
96 "input": [
97 "%pylab inline"
98 ],
99 "language": "python",
100 "outputs": [
101 {
102 "output_type": "stream",
103 "stream": "stdout",
104 "text": [
105 "",
106 "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].",
107 "For more information, type 'help(pylab)'."
108 ]
109 }
110 ],
111 "prompt_number": 17
112 },
113 {
114 "cell_type": "code",
115 "collapsed": false,
116 "input": [
117 "from scipy.special import jn",
118 "x = linspace(0,5)",
119 "f, ax = plt.subplots()",
120 "ax.set_title(\"Bessel functions\")",
121 "",
122 "for n in range(1,10):",
123 " time.sleep(1)",
124 " ax.plot(x, jn(x,n))",
125 " clear_output()",
126 " display(f)",
127 "",
128 "# close the figure at the end, so we don't get a duplicate",
129 "# of the last plot",
130 "plt.close()"
131 ],
132 "language": "python",
133 "outputs": [
134 {
135 "output_type": "display_data",
136 "png": 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7S1h1sIJFcwsY1ZA2nCtLpRR9Wb4As2bNwtKlS4tHnWeNPAsXLix+3rNnT/Ts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\nYII=\n",
137 "text": [
138 "<matplotlib.figure.Figure at 0x1083272d0>"
139 ]
140 }
141 ],
142 "prompt_number": 20
143 },
144 {
145 "cell_type": "heading",
146 "level": 2,
147 "source": [
148 "A Javascript Progress Bar"
149 ]
150 },
151 {
152 "cell_type": "markdown",
153 "source": [
154 "`clear_output()` is still something of a hack, and if you want to do a progress bar in the notebook",
155 "it is better to just use Javascript/HTML if you can.",
156 "",
157 "Here is a simple progress bar using HTML/Javascript:"
158 ]
159 },
160 {
161 "cell_type": "code",
162 "collapsed": false,
163 "input": [
164 "import uuid",
165 "from IPython.core.display import HTML, Javascript, display",
166 "",
167 "divid = str(uuid.uuid4())",
168 "",
169 "pb = HTML(",
170 "\"\"\"",
171 "<div style=\"border: 1px solid black; width:500px\">",
172 " <div id=\"%s\" style=\"background-color:blue; width:0%%\">&nbsp;</div>",
173 "</div> ",
174 "\"\"\" % divid)",
175 "display(pb)",
176 "for i in range(1,101):",
177 " time.sleep(0.1)",
178 " ",
179 " display(Javascript(\"$('div#%s').width('%i%%')\" % (divid, i)))"
180 ],
181 "language": "python",
182 "outputs": [],
183 "prompt_number": 15
184 },
185 {
186 "cell_type": "markdown",
187 "source": [
188 "The above simply makes a div that is a box, and a blue div inside it with a unique ID ",
189 "(so that the javascript won't collide with other similar progress bars on the same page). ",
190 "",
191 "Then, at every progress point, we run a simple jQuery call to resize the blue box to",
192 "the appropriate fraction of the width of its containing box, and voil\u00e0 a nice",
193 "HTML/Javascript progress bar!"
194 ]
195 },
196 {
197 "cell_type": "heading",
198 "level": 2,
199 "source": [
200 "ProgressBar class"
201 ]
202 },
203 {
204 "cell_type": "markdown",
205 "source": [
206 "And finally, here is a progress bar *class* extracted from [PyMC](http://code.google.com/p/pymc/), which will work in regular Python as well as in the IPython Notebook"
207 ]
208 },
209 {
210 "cell_type": "code",
211 "collapsed": true,
212 "input": [
213 "import sys, time",
214 "try:",
215 " from IPython.core.display import clear_output",
216 " have_ipython = True",
217 "except ImportError:",
218 " have_ipython = False",
219 "",
220 "class ProgressBar:",
221 " def __init__(self, iterations):",
222 " self.iterations = iterations",
223 " self.prog_bar = '[]'",
224 " self.fill_char = '*'",
225 " self.width = 40",
226 " self.__update_amount(0)",
227 " if have_ipython:",
228 " self.animate = self.animate_ipython",
229 " else:",
230 " self.animate = self.animate_noipython",
231 "",
232 " def animate_ipython(self, iter):",
233 " clear_output()",
234 " print '\\r', self,",
235 " sys.stdout.flush()",
236 " self.update_iteration(iter + 1)",
237 "",
238 " def update_iteration(self, elapsed_iter):",
239 " self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)",
240 " self.prog_bar += ' %d of %s complete' % (elapsed_iter, self.iterations)",
241 "",
242 " def __update_amount(self, new_amount):",
243 " percent_done = int(round((new_amount / 100.0) * 100.0))",
244 " all_full = self.width - 2",
245 " num_hashes = int(round((percent_done / 100.0) * all_full))",
246 " self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'",
247 " pct_place = (len(self.prog_bar) / 2) - len(str(percent_done))",
248 " pct_string = '%d%%' % percent_done",
249 " self.prog_bar = self.prog_bar[0:pct_place] + \\",
250 " (pct_string + self.prog_bar[pct_place + len(pct_string):])",
251 "",
252 " def __str__(self):",
253 " return str(self.prog_bar)"
254 ],
255 "language": "python",
256 "outputs": [],
257 "prompt_number": 10
258 },
259 {
260 "cell_type": "code",
261 "collapsed": false,
262 "input": [
263 "p = ProgressBar(1000)",
264 "for i in range(1001):",
265 " p.animate(i)"
266 ],
267 "language": "python",
268 "outputs": [],
269 "prompt_number": 11
270 }
271 ]
272 }
273 ]
274 } No newline at end of file
@@ -0,0 +1,116 b''
1 .. _parallel_asyncresult:
2
3 ======================
4 The AsyncResult object
5 ======================
6
7 In non-blocking mode, :meth:`apply` submits the command to be executed and
8 then returns a :class:`~.AsyncResult` object immediately. The
9 AsyncResult object gives you a way of getting a result at a later
10 time through its :meth:`get` method, but it also collects metadata
11 on execution.
12
13
14 Beyond multiprocessing's AsyncResult
15 ====================================
16
17 .. Note::
18
19 The :class:`~.AsyncResult` object provides a superset of the interface in
20 :py:class:`multiprocessing.pool.AsyncResult`. See the
21 `official Python documentation <http://docs.python.org/library/multiprocessing#multiprocessing.pool.AsyncResult>`_
22 for more on the basics of this interface.
23
24 Our AsyncResult objects add a number of convenient features for working with
25 parallel results, beyond what is provided by the original AsyncResult.
26
27
28 get_dict
29 --------
30
31 First, is :meth:`.AsyncResult.get_dict`, which pulls results as a dictionary
32 keyed by engine_id, rather than a flat list. This is useful for quickly
33 coordinating or distributing information about all of the engines.
34
35 As an example, here is a quick call that gives every engine a dict showing
36 the PID of every other engine:
37
38 .. sourcecode:: ipython
39
40 In [10]: ar = rc[:].apply_async(os.getpid)
41 In [11]: pids = ar.get_dict()
42 In [12]: rc[:]['pid_map'] = pids
43
44 This trick is particularly useful when setting up inter-engine communication,
45 as in IPython's :file:`examples/parallel/interengine` examples.
46
47
48 Metadata
49 ========
50
51 IPython.parallel tracks some metadata about the tasks, which is stored
52 in the :attr:`.Client.metadata` dict. The AsyncResult object gives you an
53 interface for this information as well, including timestamps stdout/err,
54 and engine IDs.
55
56
57 Timing
58 ------
59
60 IPython tracks various timestamps as :py:class:`.datetime` objects,
61 and the AsyncResult object has a few properties that turn these into useful
62 times (in seconds as floats).
63
64 For use while the tasks are still pending:
65
66 * :attr:`ar.elapsed` is just the elapsed seconds since submission, for use
67 before the AsyncResult is complete.
68 * :attr:`ar.progress` is the number of tasks that have completed. Fractional progress
69 would be::
70
71 1.0 * ar.progress / len(ar)
72
73 * :meth:`AsyncResult.wait_interactive` will wait for the result to finish, but
74 print out status updates on progress and elapsed time while it waits.
75
76 For use after the tasks are done:
77
78 * :attr:`ar.serial_time` is the sum of the computation time of all of the tasks
79 done in parallel.
80 * :attr:`ar.wall_time` is the time between the first task submitted and last result
81 received. This is the actual cost of computation, including IPython overhead.
82
83
84 .. note::
85
86 wall_time is only precise if the Client is waiting for results when
87 the task finished, because the `received` timestamp is made when the result is
88 unpacked by the Client, triggered by the :meth:`~Client.spin` call. If you
89 are doing work in the Client, and not waiting/spinning, then `received` might
90 be artificially high.
91
92 An often interesting metric is the time it actually cost to do the work in parallel
93 relative to the serial computation, and this can be given simply with
94
95 .. sourcecode:: python
96
97 speedup = ar.serial_time / ar.wall_time
98
99
100 Map results are iterable!
101 =========================
102
103 When an AsyncResult object has multiple results (e.g. the :class:`~AsyncMapResult`
104 object), you can actually iterate through them, and act on the results as they arrive:
105
106 .. literalinclude:: ../../examples/parallel/itermapresult.py
107 :language: python
108 :lines: 20-66
109
110 .. seealso::
111
112 When AsyncResult or the AsyncMapResult don't provide what you need (for instance,
113 handling individual results as they arrive, but with metadata), you can always
114 just split the original result's ``msg_ids`` attribute, and handle them as you like.
115
116 For an example of this, see :file:`docs/examples/parallel/customresult.py`
@@ -15,13 +15,17 b' Authors:'
15 15 # Imports
16 16 #-----------------------------------------------------------------------------
17 17
18 import sys
18 19 import time
20 from datetime import datetime
19 21
20 22 from zmq import MessageTracker
21 23
24 from IPython.core.display import clear_output
22 25 from IPython.external.decorator import decorator
23 26 from IPython.parallel import error
24 27
28
25 29 #-----------------------------------------------------------------------------
26 30 # Classes
27 31 #-----------------------------------------------------------------------------
@@ -255,7 +259,112 b' class AsyncResult(object):'
255 259 # already done
256 260 for r in rlist:
257 261 yield r
258
262
263 def __len__(self):
264 return len(self.msg_ids)
265
266 #-------------------------------------
267 # Sugar methods and attributes
268 #-------------------------------------
269
270 def timedelta(self, start, end, start_key=min, end_key=max):
271 """compute the difference between two sets of timestamps
272
273 The default behavior is to use the earliest of the first
274 and the latest of the second list, but this can be changed
275 by passing a different
276
277 Parameters
278 ----------
279
280 start : one or more datetime objects (e.g. ar.submitted)
281 end : one or more datetime objects (e.g. ar.received)
282 start_key : callable
283 Function to call on `start` to extract the relevant
284 entry [defalt: min]
285 end_key : callable
286 Function to call on `end` to extract the relevant
287 entry [default: max]
288
289 Returns
290 -------
291
292 dt : float
293 The time elapsed (in seconds) between the two selected timestamps.
294 """
295 if not isinstance(start, datetime):
296 # handle single_result AsyncResults, where ar.stamp is single object,
297 # not a list
298 start = start_key(start)
299 if not isinstance(end, datetime):
300 # handle single_result AsyncResults, where ar.stamp is single object,
301 # not a list
302 end = end_key(end)
303 return (end - start).total_seconds()
304
305 @property
306 def progress(self):
307 """the number of tasks which have been completed at this point.
308
309 Fractional progress would be given by 1.0 * ar.progress / len(ar)
310 """
311 self.wait(0)
312 return len(self) - len(set(self.msg_ids).intersection(self._client.outstanding))
313
314 @property
315 def elapsed(self):
316 """elapsed time since initial submission"""
317 if self.ready():
318 return self.wall_time
319
320 now = submitted = datetime.now()
321 for msg_id in self.msg_ids:
322 if msg_id in self._client.metadata:
323 stamp = self._client.metadata[msg_id]['submitted']
324 if stamp and stamp < submitted:
325 submitted = stamp
326 return (now-submitted).total_seconds()
327
328 @property
329 @check_ready
330 def serial_time(self):
331 """serial computation time of a parallel calculation
332
333 Computed as the sum of (completed-started) of each task
334 """
335 t = 0
336 for md in self._metadata:
337 t += (md['completed'] - md['started']).total_seconds()
338 return t
339
340 @property
341 @check_ready
342 def wall_time(self):
343 """actual computation time of a parallel calculation
344
345 Computed as the time between the latest `received` stamp
346 and the earliest `submitted`.
347
348 Only reliable if Client was spinning/waiting when the task finished, because
349 the `received` timestamp is created when a result is pulled off of the zmq queue,
350 which happens as a result of `client.spin()`.
351
352 For similar comparison of other timestamp pairs, check out AsyncResult.timedelta.
353
354 """
355 return self.timedelta(self.submitted, self.received)
356
357 def wait_interactive(self, interval=1., timeout=None):
358 """interactive wait, printing progress at regular intervals"""
359 N = len(self)
360 tic = time.time()
361 while not self.ready() and (timeout is None or time.time() - tic <= timeout):
362 self.wait(interval)
363 clear_output()
364 print "%4i/%i tasks finished after %4i s" % (self.progress, N, self.elapsed),
365 sys.stdout.flush()
366 print
367 print "done"
259 368
260 369
261 370 class AsyncMapResult(AsyncResult):
@@ -18,6 +18,7 b' Authors:'
18 18 import os
19 19 import json
20 20 import sys
21 from threading import Thread, Event
21 22 import time
22 23 import warnings
23 24 from datetime import datetime
@@ -36,7 +37,7 b' from IPython.utils.jsonutil import rekey'
36 37 from IPython.utils.localinterfaces import LOCAL_IPS
37 38 from IPython.utils.path import get_ipython_dir
38 39 from IPython.utils.traitlets import (HasTraits, Integer, Instance, Unicode,
39 Dict, List, Bool, Set)
40 Dict, List, Bool, Set, Any)
40 41 from IPython.external.decorator import decorator
41 42 from IPython.external.ssh import tunnel
42 43
@@ -249,6 +250,8 b' class Client(HasTraits):'
249 250 metadata = Instance('collections.defaultdict', (Metadata,))
250 251 history = List()
251 252 debug = Bool(False)
253 _spin_thread = Any()
254 _stop_spinning = Any()
252 255
253 256 profile=Unicode()
254 257 def _profile_default(self):
@@ -299,6 +302,7 b' class Client(HasTraits):'
299 302 if context is None:
300 303 context = zmq.Context.instance()
301 304 self._context = context
305 self._stop_spinning = Event()
302 306
303 307 self._setup_profile_dir(self.profile, profile_dir, ipython_dir)
304 308 if self._cd is not None:
@@ -785,12 +789,59 b' class Client(HasTraits):'
785 789 def close(self):
786 790 if self._closed:
787 791 return
792 self.stop_spin_thread()
788 793 snames = filter(lambda n: n.endswith('socket'), dir(self))
789 794 for socket in map(lambda name: getattr(self, name), snames):
790 795 if isinstance(socket, zmq.Socket) and not socket.closed:
791 796 socket.close()
792 797 self._closed = True
793 798
799 def _spin_every(self, interval=1):
800 """target func for use in spin_thread"""
801 while True:
802 if self._stop_spinning.is_set():
803 return
804 time.sleep(interval)
805 self.spin()
806
807 def spin_thread(self, interval=1):
808 """call Client.spin() in a background thread on some regular interval
809
810 This helps ensure that messages don't pile up too much in the zmq queue
811 while you are working on other things, or just leaving an idle terminal.
812
813 It also helps limit potential padding of the `received` timestamp
814 on AsyncResult objects, used for timings.
815
816 Parameters
817 ----------
818
819 interval : float, optional
820 The interval on which to spin the client in the background thread
821 (simply passed to time.sleep).
822
823 Notes
824 -----
825
826 For precision timing, you may want to use this method to put a bound
827 on the jitter (in seconds) in `received` timestamps used
828 in AsyncResult.wall_time.
829
830 """
831 if self._spin_thread is not None:
832 self.stop_spin_thread()
833 self._stop_spinning.clear()
834 self._spin_thread = Thread(target=self._spin_every, args=(interval,))
835 self._spin_thread.daemon = True
836 self._spin_thread.start()
837
838 def stop_spin_thread(self):
839 """stop background spin_thread, if any"""
840 if self._spin_thread is not None:
841 self._stop_spinning.set()
842 self._spin_thread.join()
843 self._spin_thread = None
844
794 845 def spin(self):
795 846 """Flush any registration notifications and execution results
796 847 waiting in the ZMQ queue.
@@ -1277,6 +1328,8 b' class Client(HasTraits):'
1277 1328
1278 1329 md = self.metadata[msg_id]
1279 1330 md.update(self._extract_metadata(header, parent, rcontent))
1331 if rec.get('received'):
1332 md['received'] = rec['received']
1280 1333 md.update(iodict)
1281 1334
1282 1335 if rcontent['status'] == 'ok':
@@ -63,6 +63,7 b' def empty_record():'
63 63 'started': None,
64 64 'completed': None,
65 65 'resubmitted': None,
66 'received': None,
66 67 'result_header' : None,
67 68 'result_content' : None,
68 69 'result_buffers' : None,
@@ -88,6 +89,7 b' def init_record(msg):'
88 89 'started': None,
89 90 'completed': None,
90 91 'resubmitted': None,
92 'received': None,
91 93 'result_header' : None,
92 94 'result_content' : None,
93 95 'result_buffers' : None,
@@ -632,6 +634,7 b' class Hub(SessionFactory):'
632 634 result = {
633 635 'result_header' : rheader,
634 636 'result_content': msg['content'],
637 'received': datetime.now(),
635 638 'started' : started,
636 639 'completed' : completed
637 640 }
@@ -734,7 +737,8 b' class Hub(SessionFactory):'
734 737 'result_content': msg['content'],
735 738 'started' : started,
736 739 'completed' : completed,
737 'engine_uuid': engine_uuid
740 'received' : datetime.now(),
741 'engine_uuid': engine_uuid,
738 742 }
739 743
740 744 result['result_buffers'] = msg['buffers']
@@ -1178,11 +1182,12 b' class Hub(SessionFactory):'
1178 1182 def _extract_record(self, rec):
1179 1183 """decompose a TaskRecord dict into subsection of reply for get_result"""
1180 1184 io_dict = {}
1181 for key in 'pyin pyout pyerr stdout stderr'.split():
1185 for key in ('pyin', 'pyout', 'pyerr', 'stdout', 'stderr'):
1182 1186 io_dict[key] = rec[key]
1183 1187 content = { 'result_content': rec['result_content'],
1184 1188 'header': rec['header'],
1185 1189 'result_header' : rec['result_header'],
1190 'received' : rec['received'],
1186 1191 'io' : io_dict,
1187 1192 }
1188 1193 if rec['result_buffers']:
@@ -117,6 +117,7 b' class SQLiteDB(BaseDB):'
117 117 'started',
118 118 'completed',
119 119 'resubmitted',
120 'received',
120 121 'result_header' ,
121 122 'result_content' ,
122 123 'result_buffers' ,
@@ -138,6 +139,7 b' class SQLiteDB(BaseDB):'
138 139 'started' : 'timestamp',
139 140 'completed' : 'timestamp',
140 141 'resubmitted' : 'timestamp',
142 'received' : 'timestamp',
141 143 'result_header' : 'dict text',
142 144 'result_content' : 'dict text',
143 145 'result_buffers' : 'bufs blob',
@@ -245,6 +247,7 b' class SQLiteDB(BaseDB):'
245 247 started timestamp,
246 248 completed timestamp,
247 249 resubmitted timestamp,
250 received timestamp,
248 251 result_header dict text,
249 252 result_content dict text,
250 253 result_buffers bufs blob,
@@ -16,10 +16,11 b' Authors:'
16 16 # Imports
17 17 #-------------------------------------------------------------------------------
18 18
19 import time
19 20
20 21 from IPython.parallel.error import TimeoutError
21 22
22 from IPython.parallel import error
23 from IPython.parallel import error, Client
23 24 from IPython.parallel.tests import add_engines
24 25 from .clienttest import ClusterTestCase
25 26
@@ -121,5 +122,84 b' class AsyncResultTest(ClusterTestCase):'
121 122 ar2.abort()
122 123 self.assertRaises(error.TaskAborted, ar2.get)
123 124 ar.get()
125
126 def test_len(self):
127 v = self.client.load_balanced_view()
128 ar = v.map_async(lambda x: x, range(10))
129 self.assertEquals(len(ar), 10)
130 ar = v.apply_async(lambda x: x, range(10))
131 self.assertEquals(len(ar), 1)
132 ar = self.client[:].apply_async(lambda x: x, range(10))
133 self.assertEquals(len(ar), len(self.client.ids))
134
135 def test_wall_time_single(self):
136 v = self.client.load_balanced_view()
137 ar = v.apply_async(time.sleep, 0.25)
138 self.assertRaises(TimeoutError, getattr, ar, 'wall_time')
139 ar.get(2)
140 self.assertTrue(ar.wall_time < 1.)
141 self.assertTrue(ar.wall_time > 0.2)
142
143 def test_wall_time_multi(self):
144 self.minimum_engines(4)
145 v = self.client[:]
146 ar = v.apply_async(time.sleep, 0.25)
147 self.assertRaises(TimeoutError, getattr, ar, 'wall_time')
148 ar.get(2)
149 self.assertTrue(ar.wall_time < 1.)
150 self.assertTrue(ar.wall_time > 0.2)
151
152 def test_serial_time_single(self):
153 v = self.client.load_balanced_view()
154 ar = v.apply_async(time.sleep, 0.25)
155 self.assertRaises(TimeoutError, getattr, ar, 'serial_time')
156 ar.get(2)
157 self.assertTrue(ar.serial_time < 0.5)
158 self.assertTrue(ar.serial_time > 0.2)
159
160 def test_serial_time_multi(self):
161 self.minimum_engines(4)
162 v = self.client[:]
163 ar = v.apply_async(time.sleep, 0.25)
164 self.assertRaises(TimeoutError, getattr, ar, 'serial_time')
165 ar.get(2)
166 self.assertTrue(ar.serial_time < 2.)
167 self.assertTrue(ar.serial_time > 0.8)
168
169 def test_elapsed_single(self):
170 v = self.client.load_balanced_view()
171 ar = v.apply_async(time.sleep, 0.25)
172 while not ar.ready():
173 time.sleep(0.01)
174 self.assertTrue(ar.elapsed < 0.3)
175 self.assertTrue(ar.elapsed < 0.3)
176 ar.get(2)
177
178 def test_elapsed_multi(self):
179 v = self.client[:]
180 ar = v.apply_async(time.sleep, 0.25)
181 while not ar.ready():
182 time.sleep(0.01)
183 self.assertTrue(ar.elapsed < 0.3)
184 self.assertTrue(ar.elapsed < 0.3)
185 ar.get(2)
186
187 def test_hubresult_timestamps(self):
188 self.minimum_engines(4)
189 v = self.client[:]
190 ar = v.apply_async(time.sleep, 0.25)
191 ar.get(2)
192 rc2 = Client(profile='iptest')
193 # must have try/finally to close second Client, otherwise
194 # will have dangling sockets causing problems
195 try:
196 time.sleep(0.25)
197 hr = rc2.get_result(ar.msg_ids)
198 self.assertTrue(hr.elapsed > 0., "got bad elapsed: %s" % hr.elapsed)
199 hr.get(1)
200 self.assertTrue(hr.wall_time < ar.wall_time + 0.2, "got bad wall_time: %s > %s" % (hr.wall_time, ar.wall_time))
201 self.assertEquals(hr.serial_time, ar.serial_time)
202 finally:
203 rc2.close()
124 204
125 205
@@ -328,4 +328,22 b' class TestClient(ClusterTestCase):'
328 328 self.client.purge_results('all')
329 329 hist = self.client.hub_history()
330 330 self.assertEquals(len(hist), 0)
331
332 def test_spin_thread(self):
333 self.client.spin_thread(0.01)
334 ar = self.client[-1].apply_async(lambda : 1)
335 time.sleep(0.1)
336 self.assertTrue(ar.wall_time < 0.1,
337 "spin should have kept wall_time < 0.1, but got %f" % ar.wall_time
338 )
339
340 def test_stop_spin_thread(self):
341 self.client.spin_thread(0.01)
342 self.client.stop_spin_thread()
343 ar = self.client[-1].apply_async(lambda : 1)
344 time.sleep(0.15)
345 self.assertTrue(ar.wall_time > 0.1,
346 "Shouldn't be spinning, but got wall_time=%f" % ar.wall_time
347 )
348
331 349
@@ -11,6 +11,7 b' Using IPython for parallel computing'
11 11 parallel_process.txt
12 12 parallel_multiengine.txt
13 13 parallel_task.txt
14 asyncresult.txt
14 15 parallel_mpi.txt
15 16 parallel_db.txt
16 17 parallel_security.txt
@@ -275,13 +275,9 b' then returns a :class:`AsyncResult` object immediately. The'
275 275 :class:`AsyncResult` object gives you a way of getting a result at a later
276 276 time through its :meth:`get` method.
277 277
278 .. Note::
279
280 The :class:`AsyncResult` object provides a superset of the interface in
281 :py:class:`multiprocessing.pool.AsyncResult`. See the
282 `official Python documentation <http://docs.python.org/library/multiprocessing#multiprocessing.pool.AsyncResult>`_
283 for more.
278 .. seealso::
284 279
280 Docs on the :ref:`AsyncResult <parallel_asyncresult>` object.
285 281
286 282 This allows you to quickly submit long running commands without blocking your
287 283 local Python/IPython session:
@@ -111,27 +111,6 b' that turns any Python function into a parallel function:'
111 111 In [11]: f.map(range(32)) # this is done in parallel
112 112 Out[11]: [0.0,10.0,160.0,...]
113 113
114 .. _parallel_taskmap:
115
116 Map results are iterable!
117 -------------------------
118
119 When an AsyncResult object actually maps multiple results (e.g. the :class:`~AsyncMapResult`
120 object), you can actually iterate through them, and act on the results as they arrive:
121
122 .. literalinclude:: ../../examples/parallel/itermapresult.py
123 :language: python
124 :lines: 9-34
125
126 .. seealso::
127
128 When AsyncResult or the AsyncMapResult don't provide what you need (for instance,
129 handling individual results as they arrive, but with metadata), you can always
130 just split the original result's ``msg_ids`` attribute, and handle them as you like.
131
132 For an example of this, see :file:`docs/examples/parallel/customresult.py`
133
134
135 114 .. _parallel_dependencies:
136 115
137 116 Dependencies
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