##// END OF EJS Templates
Reworking parallel examples as notebooks.
Brian E. Granger -
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1 <?xml version="1.0" encoding="utf-8"?>
2 <notebook>
3 <name>helloworld</name>
4 <nbformat>2</nbformat>
5 <worksheets>
6 <worksheet>
7 <cells>
8 <markdowncell>
9 <source># Distributed hello world
10
11 Originally by Ken Kinder (ken at kenkinder dom com)</source>
12 </markdowncell>
13 <codecell>
14 <input>from IPython.parallel import Client</input>
15 <language>python</language>
16 <prompt_number>3</prompt_number>
17 <collapsed>1</collapsed>
18 <outputs />
19 </codecell>
20 <codecell>
21 <input>rc = Client()
22 view = rc.load_balanced_view()</input>
23 <language>python</language>
24 <prompt_number>4</prompt_number>
25 <collapsed>1</collapsed>
26 <outputs />
27 </codecell>
28 <codecell>
29 <input>def sleep_and_echo(t, msg):
30 import time
31 time.sleep(t)
32 return msg</input>
33 <language>python</language>
34 <prompt_number>5</prompt_number>
35 <collapsed>1</collapsed>
36 <outputs />
37 </codecell>
38 <codecell>
39 <input>world = view.apply_async(sleep_and_echo, 3, 'World!')
40 hello = view.apply_async(sleep_and_echo, 2, 'Hello')
41 </input>
42 <language>python</language>
43 <prompt_number>6</prompt_number>
44 <collapsed>1</collapsed>
45 <outputs />
46 </codecell>
47 <codecell>
48 <input>print "Submitted tasks:", hello.msg_ids, world.msg_ids
49 print hello.get(), world.get()</input>
50 <language>python</language>
51 <prompt_number>7</prompt_number>
52 <collapsed>0</collapsed>
53 <outputs>
54 <output>
55 <output_type>stream</output_type>
56 <text>Submitted tasks: [&amp;apos;9e533683-d54e-4588-929e-984dd3eb6dc4&amp;apos;] [&amp;apos;90395f15-723f-44df-a743-a5d88cdeb6a0&amp;apos;]
57 Hello</text>
58 </output>
59 <output>
60 <output_type>stream</output_type>
61 <text>World!</text>
62 </output>
63 </outputs>
64 </codecell>
65 </cells>
66 </worksheet>
67 </worksheets>
68 </notebook>
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1 <?xml version="1.0" encoding="utf-8"?>
2 <notebook>
3 <name>mcpricer</name>
4 <nbformat>2</nbformat>
5 <worksheets>
6 <worksheet>
7 <cells>
8 <markdowncell>
9 <source># Parallel Monto-Carlo options pricing</source>
10 </markdowncell>
11 <markdowncell>
12 <source>## Problem setup</source>
13 </markdowncell>
14 <codecell>
15 <input>import sys
16 import time
17 from IPython.parallel import Client
18 import numpy as np
19 from mcpricer import price_options
20 from matplotlib import pyplot as plt</input>
21 <language>python</language>
22 <prompt_number>27</prompt_number>
23 <collapsed>1</collapsed>
24 <outputs />
25 </codecell>
26 <codecell>
27 <input>cluster_profile = "default"
28 price = 100.0 # Initial price
29 rate = 0.05 # Interest rate
30 days = 260 # Days to expiration
31 paths = 10000 # Number of MC paths
32 n_strikes = 5 # Number of strike values
33 min_strike = 90.0 # Min strike price
34 max_strike = 110.0 # Max strike price
35 n_sigmas = 5 # Number of volatility values
36 min_sigma = 0.1 # Min volatility
37 max_sigma = 0.4 # Max volatility</input>
38 <language>python</language>
39 <prompt_number>28</prompt_number>
40 <collapsed>1</collapsed>
41 <outputs />
42 </codecell>
43 <codecell>
44 <input>strike_vals = np.linspace(min_strike, max_strike, n_strikes)
45 sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)</input>
46 <language>python</language>
47 <prompt_number>29</prompt_number>
48 <collapsed>1</collapsed>
49 <outputs />
50 </codecell>
51 <markdowncell>
52 <source>## Parallel computation across strike prices and volatilities</source>
53 </markdowncell>
54 <markdowncell>
55 <source>The Client is used to setup the calculation and works with all engines.</source>
56 </markdowncell>
57 <codecell>
58 <input>c = Client(profile=cluster_profile)</input>
59 <language>python</language>
60 <prompt_number>30</prompt_number>
61 <collapsed>1</collapsed>
62 <outputs />
63 </codecell>
64 <markdowncell>
65 <source>A LoadBalancedView is an interface to the engines that provides dynamic load
66 balancing at the expense of not knowing which engine will execute the code.</source>
67 </markdowncell>
68 <codecell>
69 <input>view = c.load_balanced_view()</input>
70 <language>python</language>
71 <prompt_number>31</prompt_number>
72 <collapsed>1</collapsed>
73 <outputs />
74 </codecell>
75 <codecell>
76 <input>print "Strike prices: ", strike_vals
77 print "Volatilities: ", sigma_vals</input>
78 <language>python</language>
79 <prompt_number>32</prompt_number>
80 <collapsed>0</collapsed>
81 <outputs>
82 <output>
83 <output_type>stream</output_type>
84 <text>Strike prices: [ 90. 95. 100. 105. 110.]
85 Volatilities: [ 0.1 0.175 0.25 0.325 0.4 ]</text>
86 </output>
87 </outputs>
88 </codecell>
89 <markdowncell>
90 <source>Submit tasks for each (strike, sigma) pair.</source>
91 </markdowncell>
92 <codecell>
93 <input>t1 = time.time()
94 async_results = []
95 for strike in strike_vals:
96 for sigma in sigma_vals:
97 ar = view.apply_async(price_options, price, strike, sigma, rate, days, paths)
98 async_results.append(ar)</input>
99 <language>python</language>
100 <prompt_number>33</prompt_number>
101 <collapsed>1</collapsed>
102 <outputs />
103 </codecell>
104 <codecell>
105 <input>print "Submitted tasks: ", len(async_results)</input>
106 <language>python</language>
107 <prompt_number>34</prompt_number>
108 <collapsed>0</collapsed>
109 <outputs>
110 <output>
111 <output_type>stream</output_type>
112 <text>Submitted tasks: 25</text>
113 </output>
114 </outputs>
115 </codecell>
116 <markdowncell>
117 <source>Block until all tasks are completed.</source>
118 </markdowncell>
119 <codecell>
120 <input>c.wait(async_results)
121 t2 = time.time()
122 t = t2-t1
123
124 print "Parallel calculation completed, time = %s s" % t</input>
125 <language>python</language>
126 <prompt_number>35</prompt_number>
127 <collapsed>0</collapsed>
128 <outputs>
129 <output>
130 <output_type>stream</output_type>
131 <text>Parallel calculation completed, time = 1.8046438694 s</text>
132 </output>
133 </outputs>
134 </codecell>
135 <markdowncell>
136 <source>## Process and visualize results</source>
137 </markdowncell>
138 <markdowncell>
139 <source>Get the results using the `get` method:</source>
140 </markdowncell>
141 <codecell>
142 <input>results = [ar.get() for ar in async_results]
143 </input>
144 <language>python</language>
145 <prompt_number>36</prompt_number>
146 <collapsed>1</collapsed>
147 <outputs />
148 </codecell>
149 <markdowncell>
150 <source>Assemble the result into a structured NumPy array.</source>
151 </markdowncell>
152 <codecell>
153 <input>prices = np.empty(n_strikes*n_sigmas,
154 dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]
155 )
156
157 for i, price in enumerate(results):
158 prices[i] = tuple(price)
159
160 prices.shape = (n_strikes, n_sigmas)
161 strike_mesh, sigma_mesh = np.meshgrid(strike_vals, sigma_vals)</input>
162 <language>python</language>
163 <prompt_number>37</prompt_number>
164 <collapsed>1</collapsed>
165 <outputs />
166 </codecell>
167 <markdowncell>
168 <source>Plot the value of the European call in (volatility, strike) space.</source>
169 </markdowncell>
170 <codecell>
171 <input>plt.contourf(sigma_mesh, strike_mesh, prices['ecall'])
172 plt.axis('tight')
173 plt.colorbar()
174 plt.title('European Call')
175 plt.xlabel("Volatility")
176 plt.ylabel("Strike Price")</input>
177 <language>python</language>
178 <prompt_number>38</prompt_number>
179 <collapsed>0</collapsed>
180 <outputs>
181 <output>
182 <output_type>pyout</output_type>
183 <text>&amp;lt;matplotlib.text.Text at 0x3f7ed90&amp;gt;</text>
184 <prompt_number>38</prompt_number>
185 </output>
186 <output>
187 <output_type>display_data</output_type>
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608 </png>
609 </output>
610 </outputs>
611 </codecell>
612 <markdowncell>
613 <source>Plot the value of the Asian call in (volatility, strike) space.</source>
614 </markdowncell>
615 <codecell>
616 <input>plt.contourf(sigma_mesh, strike_mesh, prices['acall'])
617 plt.axis('tight')
618 plt.colorbar()
619 plt.title("Asian Call")
620 plt.xlabel("Volatility")
621 plt.ylabel("Strike Price")</input>
622 <language>python</language>
623 <prompt_number>39</prompt_number>
624 <collapsed>0</collapsed>
625 <outputs>
626 <output>
627 <output_type>pyout</output_type>
628 <text>&amp;lt;matplotlib.text.Text at 0x7fda543977d0&amp;gt;</text>
629 <prompt_number>39</prompt_number>
630 </output>
631 <output>
632 <output_type>display_data</output_type>
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1084 </png>
1085 </output>
1086 </outputs>
1087 </codecell>
1088 <markdowncell>
1089 <source>Plot the value of the European put in (volatility, strike) space.</source>
1090 </markdowncell>
1091 <codecell>
1092 <input>plt.contourf(sigma_mesh, strike_mesh, prices['eput'])
1093 plt.axis('tight')
1094 plt.colorbar()
1095 plt.title("European Put")
1096 plt.xlabel("Volatility")
1097 plt.ylabel("Strike Price")</input>
1098 <language>python</language>
1099 <prompt_number>40</prompt_number>
1100 <collapsed>0</collapsed>
1101 <outputs>
1102 <output>
1103 <output_type>pyout</output_type>
1104 <text>&amp;lt;matplotlib.text.Text at 0x7fda5453d350&amp;gt;</text>
1105 <prompt_number>40</prompt_number>
1106 </output>
1107 <output>
1108 <output_type>display_data</output_type>
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1582 </png>
1583 </output>
1584 </outputs>
1585 </codecell>
1586 <markdowncell>
1587 <source>Plot the value of the Asian put in (volatility, strike) space.</source>
1588 </markdowncell>
1589 <codecell>
1590 <input>plt.contourf(sigma_mesh, strike_mesh, prices['aput'])
1591 plt.axis('tight')
1592 plt.colorbar()
1593 plt.title("Asian Put")
1594 plt.xlabel("Volatility")
1595 plt.ylabel("Strike Price")
1596 </input>
1597 <language>python</language>
1598 <prompt_number>25</prompt_number>
1599 <collapsed>0</collapsed>
1600 <outputs>
1601 <output>
1602 <output_type>pyout</output_type>
1603 <text>&amp;lt;matplotlib.text.Text at 0x3bd67d0&amp;gt;</text>
1604 <prompt_number>25</prompt_number>
1605 </output>
1606 <output>
1607 <output_type>display_data</output_type>
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2046 </png>
2047 </output>
2048 </outputs>
2049 </codecell>
2050 </cells>
2051 </worksheet>
2052 </worksheets>
2053 </notebook>
@@ -0,0 +1,173 b''
1 # <nbformat>2</nbformat>
2
3 # <markdowncell>
4
5 # # Parallel Monto-Carlo options pricing
6
7 # <markdowncell>
8
9 # ## Problem setup
10
11 # <codecell>
12
13 import sys
14 import time
15 from IPython.parallel import Client
16 import numpy as np
17 from mcpricer import price_options
18 from matplotlib import pyplot as plt
19
20 # <codecell>
21
22 cluster_profile = "default"
23 price = 100.0 # Initial price
24 rate = 0.05 # Interest rate
25 days = 260 # Days to expiration
26 paths = 10000 # Number of MC paths
27 n_strikes = 5 # Number of strike values
28 min_strike = 90.0 # Min strike price
29 max_strike = 110.0 # Max strike price
30 n_sigmas = 5 # Number of volatility values
31 min_sigma = 0.1 # Min volatility
32 max_sigma = 0.4 # Max volatility
33
34 # <codecell>
35
36 strike_vals = np.linspace(min_strike, max_strike, n_strikes)
37 sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)
38
39 # <markdowncell>
40
41 # ## Parallel computation across strike prices and volatilities
42
43 # <markdowncell>
44
45 # The Client is used to setup the calculation and works with all engines.
46
47 # <codecell>
48
49 c = Client(profile=cluster_profile)
50
51 # <markdowncell>
52
53 # A LoadBalancedView is an interface to the engines that provides dynamic load
54 # balancing at the expense of not knowing which engine will execute the code.
55
56 # <codecell>
57
58 view = c.load_balanced_view()
59
60 # <codecell>
61
62 print "Strike prices: ", strike_vals
63 print "Volatilities: ", sigma_vals
64
65 # <markdowncell>
66
67 # Submit tasks for each (strike, sigma) pair.
68
69 # <codecell>
70
71 t1 = time.time()
72 async_results = []
73 for strike in strike_vals:
74 for sigma in sigma_vals:
75 ar = view.apply_async(price_options, price, strike, sigma, rate, days, paths)
76 async_results.append(ar)
77
78 # <codecell>
79
80 print "Submitted tasks: ", len(async_results)
81
82 # <markdowncell>
83
84 # Block until all tasks are completed.
85
86 # <codecell>
87
88 c.wait(async_results)
89 t2 = time.time()
90 t = t2-t1
91
92 print "Parallel calculation completed, time = %s s" % t
93
94 # <markdowncell>
95
96 # ## Process and visualize results
97
98 # <markdowncell>
99
100 # Get the results using the `get` method:
101
102 # <codecell>
103
104 results = [ar.get() for ar in async_results]
105
106 # <markdowncell>
107
108 # Assemble the result into a structured NumPy array.
109
110 # <codecell>
111
112 prices = np.empty(n_strikes*n_sigmas,
113 dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]
114 )
115
116 for i, price in enumerate(results):
117 prices[i] = tuple(price)
118
119 prices.shape = (n_strikes, n_sigmas)
120 strike_mesh, sigma_mesh = np.meshgrid(strike_vals, sigma_vals)
121
122 # <markdowncell>
123
124 # Plot the value of the European call in (volatility, strike) space.
125
126 # <codecell>
127
128 plt.contourf(sigma_mesh, strike_mesh, prices['ecall'])
129 plt.axis('tight')
130 plt.colorbar()
131 plt.title('European Call')
132 plt.xlabel("Volatility")
133 plt.ylabel("Strike Price")
134
135 # <markdowncell>
136
137 # Plot the value of the Asian call in (volatility, strike) space.
138
139 # <codecell>
140
141 plt.contourf(sigma_mesh, strike_mesh, prices['acall'])
142 plt.axis('tight')
143 plt.colorbar()
144 plt.title("Asian Call")
145 plt.xlabel("Volatility")
146 plt.ylabel("Strike Price")
147
148 # <markdowncell>
149
150 # Plot the value of the European put in (volatility, strike) space.
151
152 # <codecell>
153
154 plt.contourf(sigma_mesh, strike_mesh, prices['eput'])
155 plt.axis('tight')
156 plt.colorbar()
157 plt.title("European Put")
158 plt.xlabel("Volatility")
159 plt.ylabel("Strike Price")
160
161 # <markdowncell>
162
163 # Plot the value of the Asian put in (volatility, strike) space.
164
165 # <codecell>
166
167 plt.contourf(sigma_mesh, strike_mesh, prices['aput'])
168 plt.axis('tight')
169 plt.colorbar()
170 plt.title("Asian Put")
171 plt.xlabel("Volatility")
172 plt.ylabel("Strike Price")
173
@@ -0,0 +1,78 b''
1 <?xml version="1.0" encoding="utf-8"?>
2 <notebook>
3 <name>task_1</name>
4 <nbformat>2</nbformat>
5 <worksheets>
6 <worksheet>
7 <cells>
8 <markdowncell>
9 <source># Simple task farming example</source>
10 </markdowncell>
11 <codecell>
12 <input>from IPython.parallel import Client</input>
13 <language>python</language>
14 <prompt_number>3</prompt_number>
15 <collapsed>1</collapsed>
16 <outputs />
17 </codecell>
18 <markdowncell>
19 <source>A `Client.load_balanced_view` is used to get the object used for working with load balanced tasks.</source>
20 </markdowncell>
21 <codecell>
22 <input>rc = Client()
23 v = rc.load_balanced_view()</input>
24 <language>python</language>
25 <prompt_number>4</prompt_number>
26 <collapsed>1</collapsed>
27 <outputs />
28 </codecell>
29 <markdowncell>
30 <source>Set the variable `d` on all engines:</source>
31 </markdowncell>
32 <codecell>
33 <input>rc[:]['d'] = 30</input>
34 <language>python</language>
35 <prompt_number>5</prompt_number>
36 <collapsed>1</collapsed>
37 <outputs />
38 </codecell>
39 <markdowncell>
40 <source>Define a function that will be our task:</source>
41 </markdowncell>
42 <codecell>
43 <input>def task(a):
44 return a, 10*d, a*10*d</input>
45 <language>python</language>
46 <prompt_number>6</prompt_number>
47 <collapsed>1</collapsed>
48 <outputs />
49 </codecell>
50 <markdowncell>
51 <source>Run the task once:</source>
52 </markdowncell>
53 <codecell>
54 <input>ar = v.apply(task, 5)</input>
55 <language>python</language>
56 <prompt_number>7</prompt_number>
57 <collapsed>1</collapsed>
58 <outputs />
59 </codecell>
60 <markdowncell>
61 <source>Print the results:</source>
62 </markdowncell>
63 <codecell>
64 <input>print "a, b, c: ", ar.get()</input>
65 <language>python</language>
66 <prompt_number>8</prompt_number>
67 <collapsed>0</collapsed>
68 <outputs>
69 <output>
70 <output_type>stream</output_type>
71 <text>a, b, c: [5, 300, 1500]</text>
72 </output>
73 </outputs>
74 </codecell>
75 </cells>
76 </worksheet>
77 </worksheets>
78 </notebook>
@@ -0,0 +1,74 b''
1 <?xml version="1.0" encoding="utf-8"?>
2 <notebook>
3 <name>taskmap</name>
4 <nbformat>2</nbformat>
5 <worksheets>
6 <worksheet>
7 <cells>
8 <markdowncell>
9 <source># Load balanced map and parallel function decorator</source>
10 </markdowncell>
11 <codecell>
12 <input>from IPython.parallel import Client</input>
13 <language>python</language>
14 <prompt_number>4</prompt_number>
15 <collapsed>1</collapsed>
16 <outputs />
17 </codecell>
18 <codecell>
19 <input>rc = Client()
20 v = rc.load_balanced_view()</input>
21 <language>python</language>
22 <prompt_number>5</prompt_number>
23 <collapsed>1</collapsed>
24 <outputs />
25 </codecell>
26 <codecell>
27 <input>result = v.map(lambda x: 2*x, range(10))
28 print "Simple, default map: ", list(result)</input>
29 <language>python</language>
30 <prompt_number>6</prompt_number>
31 <collapsed>0</collapsed>
32 <outputs>
33 <output>
34 <output_type>stream</output_type>
35 <text>Simple, default map: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]</text>
36 </output>
37 </outputs>
38 </codecell>
39 <codecell>
40 <input>ar = v.map_async(lambda x: 2*x, range(10))
41 print "Submitted tasks, got ids: ", ar.msg_ids
42 result = ar.get()
43 print "Using a mapper: ", result</input>
44 <language>python</language>
45 <prompt_number>7</prompt_number>
46 <collapsed>0</collapsed>
47 <outputs>
48 <output>
49 <output_type>stream</output_type>
50 <text>Submitted tasks, got ids: [&amp;apos;2a25ff3f-f0d0-4428-909a-3fe808ca61f9&amp;apos;, &amp;apos;edd42168-fac2-4b3f-a696-ce61b37aa71d&amp;apos;, &amp;apos;8a548908-7812-44e6-a8b1-68e941bee608&amp;apos;, &amp;apos;26435a77-fe86-49b6-b59f-de864d59c99f&amp;apos;, &amp;apos;6750c7b4-2168-49ec-bcc4-feb1e17c5e53&amp;apos;, &amp;apos;117240d1-5dfc-4783-948f-e9523b2b2f6a&amp;apos;, &amp;apos;6de16d46-f2e2-49bd-8180-e43d1d875529&amp;apos;, &amp;apos;3d372b84-0c68-4315-92c8-a080c68478b7&amp;apos;, &amp;apos;43acedae-e35c-4a17-87f0-9e5e672500f7&amp;apos;, &amp;apos;eb71dd1f-9500-4375-875d-c2c42999848c&amp;apos;]
51 Using a mapper: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]</text>
52 </output>
53 </outputs>
54 </codecell>
55 <codecell>
56 <input>@v.parallel(block=True)
57 def f(x): return 2*x
58
59 result = f.map(range(10))
60 print "Using a parallel function: ", result</input>
61 <language>python</language>
62 <prompt_number>8</prompt_number>
63 <collapsed>0</collapsed>
64 <outputs>
65 <output>
66 <output_type>stream</output_type>
67 <text>Using a parallel function: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]</text>
68 </output>
69 </outputs>
70 </codecell>
71 </cells>
72 </worksheet>
73 </worksheets>
74 </notebook>
@@ -3530,7 +3530,6 b' Defaulting color scheme to \'NoColor\'"""'
3530 formats include (xml/ipynb, json, py).
3530 formats include (xml/ipynb, json, py).
3531 """
3531 """
3532 args = magic_arguments.parse_argstring(self.magic_notebook, s)
3532 args = magic_arguments.parse_argstring(self.magic_notebook, s)
3533 print args
3534
3533
3535 from IPython.nbformat import current
3534 from IPython.nbformat import current
3536 if args.export:
3535 if args.export:
@@ -1,19 +1,34 b''
1 """
1 # <nbformat>2</nbformat>
2 A Distributed Hello world
2
3 Ken Kinder <ken@kenkinder.com>
3 # <markdowncell>
4 """
4
5 # # Distributed hello world
6 #
7 # Originally by Ken Kinder (ken at kenkinder dom com)
8
9 # <codecell>
10
5 from IPython.parallel import Client
11 from IPython.parallel import Client
6
12
13 # <codecell>
14
7 rc = Client()
15 rc = Client()
16 view = rc.load_balanced_view()
17
18 # <codecell>
8
19
9 def sleep_and_echo(t, msg):
20 def sleep_and_echo(t, msg):
10 import time
21 import time
11 time.sleep(t)
22 time.sleep(t)
12 return msg
23 return msg
13
24
14 view = rc.load_balanced_view()
25 # <codecell>
15
26
16 world = view.apply_async(sleep_and_echo, 3, 'World!')
27 world = view.apply_async(sleep_and_echo, 3, 'World!')
17 hello = view.apply_async(sleep_and_echo, 2, 'Hello')
28 hello = view.apply_async(sleep_and_echo, 2, 'Hello')
29
30 # <codecell>
31
18 print "Submitted tasks:", hello.msg_ids, world.msg_ids
32 print "Submitted tasks:", hello.msg_ids, world.msg_ids
19 print hello.get(), world.get()
33 print hello.get(), world.get()
34
1 NO CONTENT: file renamed from docs/examples/newparallel/mcdriver.py to docs/examples/newparallel/options/mcdriver.py
NO CONTENT: file renamed from docs/examples/newparallel/mcdriver.py to docs/examples/newparallel/options/mcdriver.py
@@ -1,13 +1,52 b''
1 # <nbformat>2</nbformat>
2
3 # <markdowncell>
4
5 # # Simple task farming example
6
7 # <codecell>
8
1 from IPython.parallel import Client
9 from IPython.parallel import Client
2
10
11 # <markdowncell>
12
13 # A `Client.load_balanced_view` is used to get the object used for working with load balanced tasks.
14
15 # <codecell>
16
3 rc = Client()
17 rc = Client()
4 v = rc.load_balanced_view()
18 v = rc.load_balanced_view()
5
19
20 # <markdowncell>
21
22 # Set the variable `d` on all engines:
23
24 # <codecell>
25
6 rc[:]['d'] = 30
26 rc[:]['d'] = 30
7
27
28 # <markdowncell>
29
30 # Define a function that will be our task:
31
32 # <codecell>
33
8 def task(a):
34 def task(a):
9 return a, 10*d, a*10*d
35 return a, 10*d, a*10*d
10
36
37 # <markdowncell>
38
39 # Run the task once:
40
41 # <codecell>
42
11 ar = v.apply(task, 5)
43 ar = v.apply(task, 5)
12
44
13 print "a, b, c: ", ar.get() No newline at end of file
45 # <markdowncell>
46
47 # Print the results:
48
49 # <codecell>
50
51 print "a, b, c: ", ar.get()
52
@@ -1,18 +1,35 b''
1 # <nbformat>2</nbformat>
2
3 # <markdowncell>
4
5 # # Load balanced map and parallel function decorator
6
7 # <codecell>
8
1 from IPython.parallel import Client
9 from IPython.parallel import Client
2
10
11 # <codecell>
12
3 rc = Client()
13 rc = Client()
4 v = rc.load_balanced_view()
14 v = rc.load_balanced_view()
5
15
16 # <codecell>
17
6 result = v.map(lambda x: 2*x, range(10))
18 result = v.map(lambda x: 2*x, range(10))
7 print "Simple, default map: ", list(result)
19 print "Simple, default map: ", list(result)
8
20
21 # <codecell>
22
9 ar = v.map_async(lambda x: 2*x, range(10))
23 ar = v.map_async(lambda x: 2*x, range(10))
10 print "Submitted tasks, got ids: ", ar.msg_ids
24 print "Submitted tasks, got ids: ", ar.msg_ids
11 result = ar.get()
25 result = ar.get()
12 print "Using a mapper: ", result
26 print "Using a mapper: ", result
13
27
28 # <codecell>
29
14 @v.parallel(block=True)
30 @v.parallel(block=True)
15 def f(x): return 2*x
31 def f(x): return 2*x
16
32
17 result = f.map(range(10))
33 result = f.map(range(10))
18 print "Using a parallel function: ", result No newline at end of file
34 print "Using a parallel function: ", result
35
1 NO CONTENT: file was removed
NO CONTENT: file was removed
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