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regenerate example notebooks to remove transformed output
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@@ -40,7 +40,7 b''
40 40 "output_type": "pyout",
41 41 "prompt_number": 1,
42 42 "text": [
43 "u'/home/fperez/ipython/ipython/docs/examples/notebooks'"
43 "u'/home/fperez/ipython/ipython/docs/examples/notebooks'"
44 44 ]
45 45 }
46 46 ],
@@ -157,7 +157,7 b''
157 157 "output_type": "stream",
158 158 "stream": "stderr",
159 159 "text": [
160 "ERROR: File `non_existent_file.py` not found."
160 "ERROR: File `non_existent_file.py` not found."
161 161 ]
162 162 }
163 163 ],
@@ -178,9 +178,9 b''
178 178 "evalue": "integer division or modulo by zero",
179 179 "output_type": "pyerr",
180 180 "traceback": [
181 "<span class=\"ansired\">---------------------------------------------------------------------------</span>\n<span class=\"ansired\">ZeroDivisionError</span> Traceback (most recent call last)",
182 "<span class=\"ansigreen\">/home/fperez/ipython/ipython/docs/examples/notebooks/&lt;ipython-input-7-dc39888fd1d2&gt;</span> in <span class=\"ansicyan\">&lt;module&gt;</span><span class=\"ansiblue\">()</span>\n<span class=\"ansigreen\"> 1</span> x <span class=\"ansiyellow\">=</span> <span class=\"ansicyan\">1</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\"> 2</span> y <span class=\"ansiyellow\">=</span> <span class=\"ansicyan\">4</span><span class=\"ansiyellow\"></span>\n<span class=\"ansigreen\">----&gt; 3</span><span class=\"ansiyellow\"> </span>z <span class=\"ansiyellow\">=</span> y<span class=\"ansiyellow\">/</span><span class=\"ansiyellow\">(</span><span class=\"ansicyan\">1</span><span class=\"ansiyellow\">-</span>x<span class=\"ansiyellow\">)</span><span class=\"ansiyellow\"></span>\n",
183 "<span class=\"ansired\">ZeroDivisionError</span>: integer division or modulo by zero"
181 "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
182 "\u001b[0;32m/home/fperez/ipython/ipython/docs/examples/notebooks/<ipython-input-7-dc39888fd1d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
183 "\u001b[0;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
184 184 ]
185 185 }
186 186 ],
@@ -915,8 +915,7 b''
915 915 "collapsed": true,
916 916 "input": [],
917 917 "language": "python",
918 "outputs": [],
919 "prompt_number": "&nbsp;"
918 "outputs": []
920 919 }
921 920 ]
922 921 }
Show file before | Show file after | Hide comments
@@ -43,13 +43,13 b''
43 43 "outputs": [
44 44 {
45 45 "output_type": "pyout",
46 "prompt_number": 4,
46 "prompt_number": 1,
47 47 "text": [
48 "&apos;This is the new IPython notebook&apos;"
48 "'This is the new IPython notebook'"
49 49 ]
50 50 }
51 51 ],
52 "prompt_number": 4
52 "prompt_number": 1
53 53 },
54 54 {
55 55 "cell_type": "markdown",
@@ -82,7 +82,7 b''
82 82 ]
83 83 }
84 84 ],
85 "prompt_number": 3
85 "prompt_number": 2
86 86 },
87 87 {
88 88 "cell_type": "markdown",
@@ -113,7 +113,7 b''
113 113 ]
114 114 }
115 115 ],
116 "prompt_number": 11
116 "prompt_number": 3
117 117 },
118 118 {
119 119 "cell_type": "markdown",
@@ -237,8 +237,7 b''
237 237 "list("
238 238 ],
239 239 "language": "python",
240 "outputs": [],
241 "prompt_number": "&nbsp;"
240 "outputs": []
242 241 },
243 242 {
244 243 "cell_type": "markdown",
@@ -277,25 +276,25 b''
277 276 "stream": "stdout",
278 277 "text": [
279 278 "{",
280 " &quot;stdin_port&quot;: 39725, ",
281 " &quot;ip&quot;: &quot;127.0.0.1&quot;, ",
282 " &quot;hb_port&quot;: 52883, ",
283 " &quot;key&quot;: &quot;e7b658da-b60b-42f6-b6b0-5098f5d2e533&quot;, ",
284 " &quot;shell_port&quot;: 51742, ",
285 " &quot;iopub_port&quot;: 41869",
279 " \"stdin_port\": 53970, ",
280 " \"ip\": \"127.0.0.1\", ",
281 " \"hb_port\": 53971, ",
282 " \"key\": \"30daac61-6b73-4bae-a7d9-9dca538794d5\", ",
283 " \"shell_port\": 53968, ",
284 " \"iopub_port\": 53969",
286 285 "}",
287 286 "",
288 287 "Paste the above JSON into a file, and connect with:",
289 " $&gt; ipython &lt;app&gt; --existing &lt;file&gt;",
288 " $> ipython <app> --existing <file>",
290 289 "or, if you are local, you can connect with just:",
291 " $&gt; ipython &lt;app&gt; --existing kernel-faac4917-d0e0-467a-8467-d3c4d86a3ecc.json ",
290 " $> ipython <app> --existing kernel-dd85d1cc-c335-44f4-bed8-f1a2173a819a.json ",
292 291 "or even just:",
293 " $&gt; ipython &lt;app&gt; --existing ",
292 " $> ipython <app> --existing ",
294 293 "if this is the most recent IPython session you have started."
295 294 ]
296 295 }
297 296 ],
298 "prompt_number": 8
297 "prompt_number": 4
299 298 },
300 299 {
301 300 "cell_type": "markdown",
@@ -361,14 +360,14 b''
361 360 "text": [
362 361 "",
363 362 "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].",
364 "For more information, type &apos;help(pylab)&apos;."
363 "For more information, type 'help(pylab)'."
365 364 ]
366 365 },
367 366 {
368 367 "output_type": "pyout",
369 "prompt_number": 12,
368 "prompt_number": 5,
370 369 "text": [
371 "[&lt;matplotlib.lines.Line2D at 0x43a2890&gt;]"
370 "[<matplotlib.lines.Line2D at 0x11165bcd0>]"
372 371 ]
373 372 },
374 373 {
@@ -376,7 +375,7 b''
376 375 "png": 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KENI/dkzdg4/aoskrBQ+Ya9OYpuCp/w5kB1k7O0nQMmkFr0Lwfgp+dJTc1GgKm/szBwYI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\n"
377 376 }
378 377 ],
379 "prompt_number": 12
378 "prompt_number": 5
380 379 },
381 380 {
382 381 "cell_type": "markdown",
@@ -412,10 +411,9 b''
412 411 "collapsed": true,
413 412 "input": [],
414 413 "language": "python",
415 "outputs": [],
416 "prompt_number": "&nbsp;"
414 "outputs": []
417 415 }
418 416 ]
419 417 }
420 418 ]
421 }
419 } No newline at end of file
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@@ -117,7 +117,7 b''
117 117 "collapsed": true,
118 118 "input": [],
119 119 "language": "python",
120 "outputs": [],
120 "outputs": [],
121 121 "prompt_number": "&nbsp;"
122 122 }
123 123 ]
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@@ -40,7 +40,7 b''
40 40 "text": [
41 41 "",
42 42 "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].",
43 "For more information, type &apos;help(pylab)&apos;."
43 "For more information, type 'help(pylab)'."
44 44 ]
45 45 }
46 46 ],
@@ -123,7 +123,7 b''
123 123 "output_type": "pyout",
124 124 "prompt_number": 6,
125 125 "text": [
126 "Add(Symbol(&apos;x&apos;), Mul(Integer(2), Symbol(&apos;y&apos;)))"
126 "Add(Symbol('x'), Mul(Integer(2), Symbol('y')))"
127 127 ]
128 128 }
129 129 ],
@@ -317,10 +317,11 b''
317 317 "",
318 318 " b ",
319 319 " ___ ",
320 " \\ &#96; ",
321 " \\ \u239b n 2\u239e",
322 " / \u239d2 + 6\u22c5n \u23a0",
323 " /__, ",
320 " \u2572 ",
321 " \u2572 \u239b n 2\u239e",
322 " \u2571 \u239d2 + 6\u22c5n \u23a0",
323 " \u2571 ",
324 " \u203e\u203e\u203e ",
324 325 "n = a "
325 326 ]
326 327 }
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@@ -110,8 +110,7 b''
110 110 "collapsed": true,
111 111 "input": [],
112 112 "language": "python",
113 "outputs": [],
114 "prompt_number": "&nbsp;"
113 "outputs": []
115 114 }
116 115 ]
117 116 }
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@@ -1,65 +1,92 b''
1 1 {
2 "nbformat": 2,
3 "metadata": {
4 "name": "helloworld"
2 "metadata": {
3 "name": "helloworld"
4 },
5 "nbformat": 2,
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "cell_type": "markdown",
11 "source": [
12 "# Distributed hello world",
13 "",
14 "Originally by Ken Kinder (ken at kenkinder dom com)"
15 ]
5 16 },
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "source": "# Distributed hello world\n\nOriginally by Ken Kinder (ken at kenkinder dom com)",
11 "cell_type": "markdown"
12 },
13 {
14 "cell_type": "code",
15 "language": "python",
16 "outputs": [],
17 "collapsed": true,
18 "prompt_number": 3,
19 "input": "from IPython.parallel import Client"
20 },
21 {
22 "cell_type": "code",
23 "language": "python",
24 "outputs": [],
25 "collapsed": true,
26 "prompt_number": 4,
27 "input": "rc = Client()\nview = rc.load_balanced_view()"
28 },
29 {
30 "cell_type": "code",
31 "language": "python",
32 "outputs": [],
33 "collapsed": true,
34 "prompt_number": 5,
35 "input": "def sleep_and_echo(t, msg):\n import time\n time.sleep(t)\n return msg"
36 },
37 {
38 "cell_type": "code",
39 "language": "python",
40 "outputs": [],
41 "collapsed": true,
42 "prompt_number": 6,
43 "input": "world = view.apply_async(sleep_and_echo, 3, 'World!')\nhello = view.apply_async(sleep_and_echo, 2, 'Hello')\n"
44 },
45 {
46 "cell_type": "code",
47 "language": "python",
48 "outputs": [
49 {
50 "output_type": "stream",
51 "text": "Submitted tasks: [&apos;9e533683-d54e-4588-929e-984dd3eb6dc4&apos;] [&apos;90395f15-723f-44df-a743-a5d88cdeb6a0&apos;]\nHello"
52 },
53 {
54 "output_type": "stream",
55 "text": "World!"
56 }
57 ],
58 "collapsed": false,
59 "prompt_number": 7,
60 "input": "print \"Submitted tasks:\", hello.msg_ids, world.msg_ids\nprint hello.get(), world.get()"
61 }
62 ]
63 }
64 ]
17 {
18 "cell_type": "code",
19 "collapsed": true,
20 "input": [
21 "from IPython.parallel import Client"
22 ],
23 "language": "python",
24 "outputs": [],
25 "prompt_number": 1
26 },
27 {
28 "cell_type": "code",
29 "collapsed": true,
30 "input": [
31 "rc = Client()",
32 "view = rc.load_balanced_view()"
33 ],
34 "language": "python",
35 "outputs": [],
36 "prompt_number": 2
37 },
38 {
39 "cell_type": "code",
40 "collapsed": true,
41 "input": [
42 "def sleep_and_echo(t, msg):",
43 " import time",
44 " time.sleep(t)",
45 " return msg"
46 ],
47 "language": "python",
48 "outputs": [],
49 "prompt_number": 3
50 },
51 {
52 "cell_type": "code",
53 "collapsed": true,
54 "input": [
55 "world = view.apply_async(sleep_and_echo, 3, 'World!')",
56 "hello = view.apply_async(sleep_and_echo, 2, 'Hello')"
57 ],
58 "language": "python",
59 "outputs": [],
60 "prompt_number": 4
61 },
62 {
63 "cell_type": "code",
64 "collapsed": false,
65 "input": [
66 "print \"Submitted tasks:\", hello.msg_ids, world.msg_ids",
67 "print hello.get(), world.get()"
68 ],
69 "language": "python",
70 "outputs": [
71 {
72 "output_type": "stream",
73 "stream": "stdout",
74 "text": [
75 "Submitted tasks: ['dd1052e0-aa75-4b25-9d35-ecbdaf6e3ed7'] ['1b46aa21-20d1-459c-bc36-2d8d03336f74']",
76 "Hello"
77 ]
78 },
79 {
80 "output_type": "stream",
81 "stream": "stdout",
82 "text": [
83 " World!"
84 ]
85 }
86 ],
87 "prompt_number": 5
88 }
89 ]
90 }
91 ]
65 92 } No newline at end of file
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@@ -1,139 +1,224 b''
1 1 {
2 "worksheets": [
3 {
4 "cells": [
5 {
6 "source": "# Simple usage of a set of MPI engines\n\nThis example assumes you've started a cluster of N engines (4 in this example) as part\nof an MPI world. \n\nOur documentation describes [how to create an MPI profile](http://ipython.org/ipython-doc/dev/parallel/parallel_process.html#using-ipcluster-in-mpiexec-mpirun-mode)\nand explains [basic MPI usage of the IPython cluster](http://ipython.org/ipython-doc/dev/parallel/parallel_mpi.html).\n\n\nFor the simplest possible way to start 4 engines that belong to the same MPI world, \nyou can run this in a terminal or antoher notebook:\n\n<pre>\nipcluster start --engines=MPIExecEngineSetLauncher -n 4\n</pre>\n\nNote: to run the above in a notebook, use a *new* notebook and prepend the command with `!`, but do not run\nit in *this* notebook, as this command will block until you shut down the cluster. To stop the cluster, use \nthe 'Interrupt' button on the left, which is the equivalent of sending `Ctrl-C` to the kernel.\n\nOnce the cluster is running, we can connect to it and open a view into it:",
7 "cell_type": "markdown"
8 },
9 {
10 "cell_type": "code",
11 "language": "python",
12 "outputs": [],
13 "collapsed": true,
14 "prompt_number": 21,
15 "input": "from IPython.parallel import Client\nc = Client()\nview = c[:]"
16 },
17 {
18 "source": "Let's define a simple function that ",
19 "cell_type": "markdown"
20 },
21 {
22 "cell_type": "code",
23 "language": "python",
24 "outputs": [],
25 "collapsed": true,
26 "prompt_number": 22,
27 "input": "@view.remote(block=True)\ndef mpi_rank():\n from mpi4py import MPI\n comm = MPI.COMM_WORLD\n return comm.Get_rank()"
28 },
29 {
30 "cell_type": "code",
31 "language": "python",
32 "outputs": [
33 {
34 "output_type": "pyout",
35 "prompt_number": 23,
36 "text": "[3, 0, 2, 1]"
37 }
38 ],
39 "collapsed": false,
40 "prompt_number": 23,
41 "input": "mpi_rank()"
42 },
43 {
44 "source": "For interactive convenience, we load the parallel magic extensions and make this view\nthe active one for the automatic parallelism magics.\n\nThis is not necessary and in production codes likely won't be used, as the engines will \nload their own MPI codes separately. But it makes it easy to illustrate everything from\nwithin a single notebook here.",
45 "cell_type": "markdown"
46 },
47 {
48 "cell_type": "code",
49 "language": "python",
50 "outputs": [],
51 "collapsed": true,
52 "prompt_number": 4,
53 "input": "%load_ext parallelmagic\nview.activate()"
54 },
55 {
56 "source": "Use the autopx magic to make the rest of this cell execute on the engines instead\nof locally",
57 "cell_type": "markdown"
58 },
59 {
60 "cell_type": "code",
61 "language": "python",
62 "outputs": [],
63 "collapsed": true,
64 "prompt_number": 24,
65 "input": "view.block = True"
66 },
67 {
68 "cell_type": "code",
69 "language": "python",
70 "outputs": [
71 {
72 "output_type": "stream",
73 "stream": "stdout",
74 "text": "%autopx enabled\n\n"
75 }
76 ],
77 "collapsed": false,
78 "prompt_number": 32,
79 "input": "%autopx"
80 },
81 {
82 "source": "With autopx enabled, the next cell will actually execute *entirely on each engine*:",
83 "cell_type": "markdown"
84 },
85 {
86 "cell_type": "code",
87 "language": "python",
88 "outputs": [],
89 "collapsed": true,
90 "prompt_number": 29,
91 "input": "from mpi4py import MPI\n\ncomm = MPI.COMM_WORLD\nsize = comm.Get_size()\nrank = comm.Get_rank()\n\nif rank == 0:\n data = [(i+1)**2 for i in range(size)]\nelse:\n data = None\ndata = comm.scatter(data, root=0)\n\nassert data == (rank+1)**2, 'data=%s, rank=%s' % (data, rank)"
92 },
93 {
94 "source": "Though the assertion at the end of the previous block validated the code, we can now \npull the 'data' variable from all the nodes for local inspection.\nFirst, don't forget to toggle off `autopx` mode so code runs again in the notebook:\n",
95 "cell_type": "markdown"
96 },
97 {
98 "cell_type": "code",
99 "language": "python",
100 "outputs": [
101 {
102 "output_type": "stream",
103 "stream": "stdout",
104 "text": "%autopx disabled\n\n"
105 }
106 ],
107 "collapsed": false,
108 "prompt_number": 33,
109 "input": "%autopx"
110 },
111 {
112 "cell_type": "code",
113 "language": "python",
114 "outputs": [
115 {
116 "output_type": "pyout",
117 "prompt_number": 34,
118 "text": "[16, 1, 9, 4]"
119 }
120 ],
121 "collapsed": false,
122 "prompt_number": 34,
123 "input": "view['data']"
124 },
125 {
126 "input": "",
127 "cell_type": "code",
128 "collapsed": true,
129 "language": "python",
130 "outputs": []
131 }
132 ]
133 }
134 ],
135 "metadata": {
136 "name": "parallel_mpi"
2 "metadata": {
3 "name": "parallel_mpi"
4 },
5 "nbformat": 2,
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "cell_type": "markdown",
11 "source": [
12 "# Simple usage of a set of MPI engines",
13 "",
14 "This example assumes you've started a cluster of N engines (4 in this example) as part",
15 "of an MPI world. ",
16 "",
17 "Our documentation describes [how to create an MPI profile](http://ipython.org/ipython-doc/dev/parallel/parallel_process.html#using-ipcluster-in-mpiexec-mpirun-mode)",
18 "and explains [basic MPI usage of the IPython cluster](http://ipython.org/ipython-doc/dev/parallel/parallel_mpi.html).",
19 "",
20 "",
21 "For the simplest possible way to start 4 engines that belong to the same MPI world, ",
22 "you can run this in a terminal or antoher notebook:",
23 "",
24 "<pre>",
25 "ipcluster start --engines=MPI -n 4",
26 "</pre>",
27 "",
28 "Note: to run the above in a notebook, use a *new* notebook and prepend the command with `!`, but do not run",
29 "it in *this* notebook, as this command will block until you shut down the cluster. To stop the cluster, use ",
30 "the 'Interrupt' button on the left, which is the equivalent of sending `Ctrl-C` to the kernel.",
31 "",
32 "Once the cluster is running, we can connect to it and open a view into it:"
33 ]
137 34 },
138 "nbformat": 2
35 {
36 "cell_type": "code",
37 "collapsed": true,
38 "input": [
39 "from IPython.parallel import Client",
40 "c = Client()",
41 "view = c[:]"
42 ],
43 "language": "python",
44 "outputs": [],
45 "prompt_number": 21
46 },
47 {
48 "cell_type": "markdown",
49 "source": [
50 "Let's define a simple function that gets the MPI rank from each engine."
51 ]
52 },
53 {
54 "cell_type": "code",
55 "collapsed": true,
56 "input": [
57 "@view.remote(block=True)",
58 "def mpi_rank():",
59 " from mpi4py import MPI",
60 " comm = MPI.COMM_WORLD",
61 " return comm.Get_rank()"
62 ],
63 "language": "python",
64 "outputs": [],
65 "prompt_number": 22
66 },
67 {
68 "cell_type": "code",
69 "collapsed": false,
70 "input": [
71 "mpi_rank()"
72 ],
73 "language": "python",
74 "outputs": [
75 {
76 "output_type": "pyout",
77 "prompt_number": 23,
78 "text": [
79 "[3, 0, 2, 1]"
80 ]
81 }
82 ],
83 "prompt_number": 23
84 },
85 {
86 "cell_type": "markdown",
87 "source": [
88 "For interactive convenience, we load the parallel magic extensions and make this view",
89 "the active one for the automatic parallelism magics.",
90 "",
91 "This is not necessary and in production codes likely won't be used, as the engines will ",
92 "load their own MPI codes separately. But it makes it easy to illustrate everything from",
93 "within a single notebook here."
94 ]
95 },
96 {
97 "cell_type": "code",
98 "collapsed": true,
99 "input": [
100 "%load_ext parallelmagic",
101 "view.activate()"
102 ],
103 "language": "python",
104 "outputs": [],
105 "prompt_number": 4
106 },
107 {
108 "cell_type": "markdown",
109 "source": [
110 "Use the autopx magic to make the rest of this cell execute on the engines instead",
111 "of locally"
112 ]
113 },
114 {
115 "cell_type": "code",
116 "collapsed": true,
117 "input": [
118 "view.block = True"
119 ],
120 "language": "python",
121 "outputs": [],
122 "prompt_number": 24
123 },
124 {
125 "cell_type": "code",
126 "collapsed": false,
127 "input": [
128 "%autopx"
129 ],
130 "language": "python",
131 "outputs": [
132 {
133 "output_type": "stream",
134 "stream": "stdout",
135 "text": [
136 "%autopx enabled"
137 ]
138 }
139 ],
140 "prompt_number": 32
141 },
142 {
143 "cell_type": "markdown",
144 "source": [
145 "With autopx enabled, the next cell will actually execute *entirely on each engine*:"
146 ]
147 },
148 {
149 "cell_type": "code",
150 "collapsed": true,
151 "input": [
152 "from mpi4py import MPI",
153 "",
154 "comm = MPI.COMM_WORLD",
155 "size = comm.Get_size()",
156 "rank = comm.Get_rank()",
157 "",
158 "if rank == 0:",
159 " data = [(i+1)**2 for i in range(size)]",
160 "else:",
161 " data = None",
162 "data = comm.scatter(data, root=0)",
163 "",
164 "assert data == (rank+1)**2, 'data=%s, rank=%s' % (data, rank)"
165 ],
166 "language": "python",
167 "outputs": [],
168 "prompt_number": 29
169 },
170 {
171 "cell_type": "markdown",
172 "source": [
173 "Though the assertion at the end of the previous block validated the code, we can now ",
174 "pull the 'data' variable from all the nodes for local inspection.",
175 "First, don't forget to toggle off `autopx` mode so code runs again in the notebook:"
176 ]
177 },
178 {
179 "cell_type": "code",
180 "collapsed": false,
181 "input": [
182 "%autopx"
183 ],
184 "language": "python",
185 "outputs": [
186 {
187 "output_type": "stream",
188 "stream": "stdout",
189 "text": [
190 "%autopx disabled"
191 ]
192 }
193 ],
194 "prompt_number": 33
195 },
196 {
197 "cell_type": "code",
198 "collapsed": false,
199 "input": [
200 "view['data']"
201 ],
202 "language": "python",
203 "outputs": [
204 {
205 "output_type": "pyout",
206 "prompt_number": 34,
207 "text": [
208 "[16, 1, 9, 4]"
209 ]
210 }
211 ],
212 "prompt_number": 34
213 },
214 {
215 "cell_type": "code",
216 "collapsed": true,
217 "input": [],
218 "language": "python",
219 "outputs": []
220 }
221 ]
222 }
223 ]
139 224 } No newline at end of file
Show file before | Show file after | Hide comments
@@ -1,71 +1,109 b''
1 1 {
2 "nbformat": 2,
3 "metadata": {
4 "name": "taskmap"
2 "metadata": {
3 "name": "taskmap"
4 },
5 "nbformat": 2,
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "cell_type": "markdown",
11 "source": [
12 "# Load balanced map and parallel function decorator"
13 ]
5 14 },
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "source": "# Load balanced map and parallel function decorator",
11 "cell_type": "markdown"
12 },
13 {
14 "cell_type": "code",
15 "language": "python",
16 "outputs": [],
17 "collapsed": true,
18 "prompt_number": 4,
19 "input": "from IPython.parallel import Client"
20 },
21 {
22 "cell_type": "code",
23 "language": "python",
24 "outputs": [],
25 "collapsed": true,
26 "prompt_number": 5,
27 "input": "rc = Client()\nv = rc.load_balanced_view()"
28 },
29 {
30 "cell_type": "code",
31 "language": "python",
32 "outputs": [
33 {
34 "output_type": "stream",
35 "text": "Simple, default map: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
36 }
37 ],
38 "collapsed": false,
39 "prompt_number": 6,
40 "input": "result = v.map(lambda x: 2*x, range(10))\nprint \"Simple, default map: \", list(result)"
41 },
42 {
43 "cell_type": "code",
44 "language": "python",
45 "outputs": [
46 {
47 "output_type": "stream",
48 "text": "Submitted tasks, got ids: [&apos;2a25ff3f-f0d0-4428-909a-3fe808ca61f9&apos;, &apos;edd42168-fac2-4b3f-a696-ce61b37aa71d&apos;, &apos;8a548908-7812-44e6-a8b1-68e941bee608&apos;, &apos;26435a77-fe86-49b6-b59f-de864d59c99f&apos;, &apos;6750c7b4-2168-49ec-bcc4-feb1e17c5e53&apos;, &apos;117240d1-5dfc-4783-948f-e9523b2b2f6a&apos;, &apos;6de16d46-f2e2-49bd-8180-e43d1d875529&apos;, &apos;3d372b84-0c68-4315-92c8-a080c68478b7&apos;, &apos;43acedae-e35c-4a17-87f0-9e5e672500f7&apos;, &apos;eb71dd1f-9500-4375-875d-c2c42999848c&apos;]\nUsing a mapper: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
49 }
50 ],
51 "collapsed": false,
52 "prompt_number": 7,
53 "input": "ar = v.map_async(lambda x: 2*x, range(10))\nprint \"Submitted tasks, got ids: \", ar.msg_ids\nresult = ar.get()\nprint \"Using a mapper: \", result"
54 },
55 {
56 "cell_type": "code",
57 "language": "python",
58 "outputs": [
59 {
60 "output_type": "stream",
61 "text": "Using a parallel function: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
62 }
63 ],
64 "collapsed": false,
65 "prompt_number": 8,
66 "input": "@v.parallel(block=True)\ndef f(x): return 2*x\n\nresult = f.map(range(10))\nprint \"Using a parallel function: \", result"
67 }
68 ]
69 }
70 ]
15 {
16 "cell_type": "code",
17 "collapsed": true,
18 "input": [
19 "from IPython.parallel import Client"
20 ],
21 "language": "python",
22 "outputs": [],
23 "prompt_number": 1
24 },
25 {
26 "cell_type": "code",
27 "collapsed": false,
28 "input": [
29 "rc = Client()",
30 "v = rc.load_balanced_view()"
31 ],
32 "language": "python",
33 "outputs": [],
34 "prompt_number": 3
35 },
36 {
37 "cell_type": "code",
38 "collapsed": false,
39 "input": [
40 "result = v.map(lambda x: 2*x, range(10))",
41 "print \"Simple, default map: \", list(result)"
42 ],
43 "language": "python",
44 "outputs": [
45 {
46 "output_type": "stream",
47 "stream": "stdout",
48 "text": [
49 "Simple, default map: "
50 ]
51 },
52 {
53 "output_type": "stream",
54 "stream": "stdout",
55 "text": [
56 "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
57 ]
58 }
59 ],
60 "prompt_number": 4
61 },
62 {
63 "cell_type": "code",
64 "collapsed": false,
65 "input": [
66 "ar = v.map_async(lambda x: 2*x, range(10))",
67 "print \"Submitted tasks, got ids: \", ar.msg_ids",
68 "result = ar.get()",
69 "print \"Using a mapper: \", result"
70 ],
71 "language": "python",
72 "outputs": [
73 {
74 "output_type": "stream",
75 "stream": "stdout",
76 "text": [
77 "Submitted tasks, got ids: ['5100a4c7-73a4-4832-aa91-e774f6f3ede8', 'd0cae1cf-2b32-4092-9eb7-f17b43fb3849', 'e08d3ee2-f221-47fe-9556-ed938e692030', '065585e4-cdf9-4240-a5fe-e44b2ae5d023', 'd2162f23-68e5-4318-ba1e-e34fd03a72ac', '5b3b835f-2099-4a70-9896-d1aa810c77e6', 'e2c2a823-bd44-4f91-8db3-c154d0d86e56', '991e0c25-f98a-44b5-9d9e-889d4180b9a5', '4ad41221-28bd-482f-a300-97c404648161', '5b730eb3-e0bb-4cdd-b228-c3b8d158828a']",
78 "Using a mapper: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
79 ]
80 }
81 ],
82 "prompt_number": 5
83 },
84 {
85 "cell_type": "code",
86 "collapsed": false,
87 "input": [
88 "@v.parallel(block=True)",
89 "def f(x): return 2*x",
90 "",
91 "result = f.map(range(10))",
92 "print \"Using a parallel function: \", result"
93 ],
94 "language": "python",
95 "outputs": [
96 {
97 "output_type": "stream",
98 "stream": "stdout",
99 "text": [
100 "Using a parallel function: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
101 ]
102 }
103 ],
104 "prompt_number": 6
105 }
106 ]
107 }
108 ]
71 109 } No newline at end of file
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