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1 | 1 | .. _parallel_task: |
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2 | 2 | |
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3 | 3 | ========================== |
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4 | 4 | The IPython task interface |
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5 | 5 | ========================== |
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6 | 6 | |
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7 | 7 | The task interface to the cluster presents the engines as a fault tolerant, |
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8 | 8 | dynamic load-balanced system of workers. Unlike the multiengine interface, in |
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9 | 9 | the task interface the user have no direct access to individual engines. By |
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10 | 10 | allowing the IPython scheduler to assign work, this interface is simultaneously |
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11 | 11 | simpler and more powerful. |
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12 | 12 | |
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13 | 13 | Best of all, the user can use both of these interfaces running at the same time |
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14 | 14 | to take advantage of their respective strengths. When the user can break up |
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15 | 15 | the user's work into segments that do not depend on previous execution, the |
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16 | 16 | task interface is ideal. But it also has more power and flexibility, allowing |
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17 | 17 | the user to guide the distribution of jobs, without having to assign tasks to |
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18 | 18 | engines explicitly. |
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19 | 19 | |
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20 | 20 | Starting the IPython controller and engines |
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21 | 21 | =========================================== |
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22 | 22 | |
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23 | 23 | To follow along with this tutorial, you will need to start the IPython |
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24 | 24 | controller and four IPython engines. The simplest way of doing this is to use |
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25 | 25 | the :command:`ipcluster` command:: |
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26 | 26 | |
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27 | 27 | $ ipcluster start -n 4 |
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28 | 28 | |
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29 | 29 | For more detailed information about starting the controller and engines, see |
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30 | 30 | our :ref:`introduction <parallel_overview>` to using IPython for parallel computing. |
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31 | 31 | |
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32 | 32 | Creating a ``LoadBalancedView`` instance |
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33 | 33 | ======================================== |
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34 | 34 | |
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35 | 35 | The first step is to import the IPython :mod:`IPython.parallel` |
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36 | 36 | module and then create a :class:`.Client` instance, and we will also be using |
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37 | 37 | a :class:`LoadBalancedView`, here called `lview`: |
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38 | 38 | |
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39 | 39 | .. sourcecode:: ipython |
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40 | 40 | |
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41 | 41 | In [1]: from IPython.parallel import Client |
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42 | 42 | |
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43 | 43 | In [2]: rc = Client() |
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44 | 44 | |
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45 | 45 | |
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46 | 46 | This form assumes that the controller was started on localhost with default |
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47 | 47 | configuration. If not, the location of the controller must be given as an |
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48 | 48 | argument to the constructor: |
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49 | 49 | |
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50 | 50 | .. sourcecode:: ipython |
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51 | 51 | |
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52 | 52 | # for a visible LAN controller listening on an external port: |
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53 | 53 | In [2]: rc = Client('tcp://192.168.1.16:10101') |
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54 | 54 | # or to connect with a specific profile you have set up: |
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55 | 55 | In [3]: rc = Client(profile='mpi') |
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56 | 56 | |
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57 | 57 | For load-balanced execution, we will make use of a :class:`LoadBalancedView` object, which can |
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58 | 58 | be constructed via the client's :meth:`load_balanced_view` method: |
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59 | 59 | |
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60 | 60 | .. sourcecode:: ipython |
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61 | 61 | |
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62 | 62 | In [4]: lview = rc.load_balanced_view() # default load-balanced view |
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63 | 63 | |
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64 | 64 | .. seealso:: |
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65 | 65 | |
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66 | 66 | For more information, see the in-depth explanation of :ref:`Views <parallel_details>`. |
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67 | 67 | |
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68 | 68 | |
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69 | 69 | Quick and easy parallelism |
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70 | 70 | ========================== |
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71 | 71 | |
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72 | 72 | In many cases, you simply want to apply a Python function to a sequence of |
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73 | 73 | objects, but *in parallel*. Like the multiengine interface, these can be |
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74 | 74 | implemented via the task interface. The exact same tools can perform these |
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75 | 75 | actions in load-balanced ways as well as multiplexed ways: a parallel version |
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76 | 76 | of :func:`map` and :func:`@parallel` function decorator. If one specifies the |
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77 | 77 | argument `balanced=True`, then they are dynamically load balanced. Thus, if the |
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78 | 78 | execution time per item varies significantly, you should use the versions in |
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79 | 79 | the task interface. |
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80 | 80 | |
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81 | 81 | Parallel map |
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82 | 82 | ------------ |
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83 | 83 | |
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84 | 84 | To load-balance :meth:`map`,simply use a LoadBalancedView: |
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85 | 85 | |
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86 | 86 | .. sourcecode:: ipython |
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87 | 87 | |
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88 | 88 | In [62]: lview.block = True |
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89 | 89 | |
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90 | 90 | In [63]: serial_result = map(lambda x:x**10, range(32)) |
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91 | 91 | |
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92 | 92 | In [64]: parallel_result = lview.map(lambda x:x**10, range(32)) |
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93 | 93 | |
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94 | 94 | In [65]: serial_result==parallel_result |
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95 | 95 | Out[65]: True |
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96 | 96 | |
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97 | 97 | Parallel function decorator |
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98 | 98 | --------------------------- |
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99 | 99 | |
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100 | 100 | Parallel functions are just like normal function, but they can be called on |
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101 | 101 | sequences and *in parallel*. The multiengine interface provides a decorator |
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102 | 102 | that turns any Python function into a parallel function: |
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103 | 103 | |
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104 | 104 | .. sourcecode:: ipython |
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105 | 105 | |
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106 | 106 | In [10]: @lview.parallel() |
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107 | 107 | ....: def f(x): |
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108 | 108 | ....: return 10.0*x**4 |
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109 | 109 | ....: |
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110 | 110 | |
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111 | 111 | In [11]: f.map(range(32)) # this is done in parallel |
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112 | 112 | Out[11]: [0.0,10.0,160.0,...] |
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113 | 113 | |
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114 | 114 | .. _parallel_dependencies: |
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115 | 115 | |
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116 | 116 | Dependencies |
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117 | 117 | ============ |
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118 | 118 | |
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119 | 119 | Often, pure atomic load-balancing is too primitive for your work. In these cases, you |
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120 | 120 | may want to associate some kind of `Dependency` that describes when, where, or whether |
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121 | 121 | a task can be run. In IPython, we provide two types of dependencies: |
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122 | 122 | `Functional Dependencies`_ and `Graph Dependencies`_ |
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123 | 123 | |
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124 | 124 | .. note:: |
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125 | 125 | |
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126 | 126 | It is important to note that the pure ZeroMQ scheduler does not support dependencies, |
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127 | 127 | and you will see errors or warnings if you try to use dependencies with the pure |
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128 | 128 | scheduler. |
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129 | 129 | |
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130 | 130 | Functional Dependencies |
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131 | 131 | ----------------------- |
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132 | 132 | |
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133 | 133 | Functional dependencies are used to determine whether a given engine is capable of running |
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134 | 134 | a particular task. This is implemented via a special :class:`Exception` class, |
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135 | 135 | :class:`UnmetDependency`, found in `IPython.parallel.error`. Its use is very simple: |
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136 | 136 | if a task fails with an UnmetDependency exception, then the scheduler, instead of relaying |
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137 | 137 | the error up to the client like any other error, catches the error, and submits the task |
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138 | 138 | to a different engine. This will repeat indefinitely, and a task will never be submitted |
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139 | 139 | to a given engine a second time. |
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140 | 140 | |
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141 | 141 | You can manually raise the :class:`UnmetDependency` yourself, but IPython has provided |
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142 | 142 | some decorators for facilitating this behavior. |
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143 | 143 | |
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144 | 144 | There are two decorators and a class used for functional dependencies: |
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145 | 145 | |
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146 | 146 | .. sourcecode:: ipython |
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147 | 147 | |
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148 | 148 | In [9]: from IPython.parallel import depend, require, dependent |
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149 | 149 | |
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150 | 150 | @require |
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151 | 151 | ******** |
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152 | 152 | |
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153 | 153 | The simplest sort of dependency is requiring that a Python module is available. The |
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154 | 154 | ``@require`` decorator lets you define a function that will only run on engines where names |
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155 | 155 | you specify are importable: |
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156 | 156 | |
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157 | 157 | .. sourcecode:: ipython |
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158 | 158 | |
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159 | 159 | In [10]: @require('numpy', 'zmq') |
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160 | 160 | ....: def myfunc(): |
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161 | 161 | ....: return dostuff() |
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162 | 162 | |
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163 | 163 | Now, any time you apply :func:`myfunc`, the task will only run on a machine that has |
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164 | 164 | numpy and pyzmq available, and when :func:`myfunc` is called, numpy and zmq will be imported. |
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165 | 165 | |
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166 | 166 | @depend |
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167 | 167 | ******* |
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168 | 168 | |
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169 | 169 | The ``@depend`` decorator lets you decorate any function with any *other* function to |
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170 | 170 | evaluate the dependency. The dependency function will be called at the start of the task, |
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171 | 171 | and if it returns ``False``, then the dependency will be considered unmet, and the task |
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172 | 172 | will be assigned to another engine. If the dependency returns *anything other than |
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173 | 173 | ``False``*, the rest of the task will continue. |
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174 | 174 | |
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175 | 175 | .. sourcecode:: ipython |
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176 | 176 | |
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177 | 177 | In [10]: def platform_specific(plat): |
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178 | 178 | ....: import sys |
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179 | 179 | ....: return sys.platform == plat |
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180 | 180 | |
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181 | 181 | In [11]: @depend(platform_specific, 'darwin') |
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182 | 182 | ....: def mactask(): |
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183 | 183 | ....: do_mac_stuff() |
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184 | 184 | |
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185 | 185 | In [12]: @depend(platform_specific, 'nt') |
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186 | 186 | ....: def wintask(): |
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187 | 187 | ....: do_windows_stuff() |
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188 | 188 | |
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189 | 189 | In this case, any time you apply ``mytask``, it will only run on an OSX machine. |
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190 | 190 | ``@depend`` is just like ``apply``, in that it has a ``@depend(f,*args,**kwargs)`` |
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191 | 191 | signature. |
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192 | 192 | |
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193 | 193 | dependents |
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194 | 194 | ********** |
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195 | 195 | |
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196 | 196 | You don't have to use the decorators on your tasks, if for instance you may want |
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197 | 197 | to run tasks with a single function but varying dependencies, you can directly construct |
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198 | 198 | the :class:`dependent` object that the decorators use: |
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199 | 199 | |
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200 | 200 | .. sourcecode::ipython |
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201 | 201 | |
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202 | 202 | In [13]: def mytask(*args): |
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203 | 203 | ....: dostuff() |
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204 | 204 | |
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205 | 205 | In [14]: mactask = dependent(mytask, platform_specific, 'darwin') |
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206 | 206 | # this is the same as decorating the declaration of mytask with @depend |
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207 | 207 | # but you can do it again: |
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208 | 208 | |
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209 | 209 | In [15]: wintask = dependent(mytask, platform_specific, 'nt') |
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210 | 210 | |
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211 | 211 | # in general: |
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212 | 212 | In [16]: t = dependent(f, g, *dargs, **dkwargs) |
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213 | 213 | |
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214 | 214 | # is equivalent to: |
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215 | 215 | In [17]: @depend(g, *dargs, **dkwargs) |
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216 | 216 | ....: def t(a,b,c): |
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217 | 217 | ....: # contents of f |
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218 | 218 | |
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219 | 219 | Graph Dependencies |
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220 | 220 | ------------------ |
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221 | 221 | |
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222 | 222 | Sometimes you want to restrict the time and/or location to run a given task as a function |
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223 | 223 | of the time and/or location of other tasks. This is implemented via a subclass of |
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224 | 224 | :class:`set`, called a :class:`Dependency`. A Dependency is just a set of `msg_ids` |
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225 | 225 | corresponding to tasks, and a few attributes to guide how to decide when the Dependency |
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226 | 226 | has been met. |
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227 | 227 | |
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228 | 228 | The switches we provide for interpreting whether a given dependency set has been met: |
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229 | 229 | |
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230 | 230 | any|all |
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231 | 231 | Whether the dependency is considered met if *any* of the dependencies are done, or |
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232 | 232 | only after *all* of them have finished. This is set by a Dependency's :attr:`all` |
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233 | 233 | boolean attribute, which defaults to ``True``. |
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234 | 234 | |
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235 | 235 | success [default: True] |
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236 | 236 | Whether to consider tasks that succeeded as fulfilling dependencies. |
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237 | 237 | |
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238 | 238 | failure [default : False] |
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239 | 239 | Whether to consider tasks that failed as fulfilling dependencies. |
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240 | 240 | using `failure=True,success=False` is useful for setting up cleanup tasks, to be run |
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241 | 241 | only when tasks have failed. |
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242 | 242 | |
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243 | 243 | Sometimes you want to run a task after another, but only if that task succeeded. In this case, |
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244 | 244 | ``success`` should be ``True`` and ``failure`` should be ``False``. However sometimes you may |
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245 | 245 | not care whether the task succeeds, and always want the second task to run, in which case you |
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246 | 246 | should use `success=failure=True`. The default behavior is to only use successes. |
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247 | 247 | |
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248 | 248 | There are other switches for interpretation that are made at the *task* level. These are |
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249 | 249 | specified via keyword arguments to the client's :meth:`apply` method. |
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250 | 250 | |
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251 | 251 | after,follow |
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252 | 252 | You may want to run a task *after* a given set of dependencies have been run and/or |
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253 | 253 | run it *where* another set of dependencies are met. To support this, every task has an |
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254 | 254 | `after` dependency to restrict time, and a `follow` dependency to restrict |
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255 | 255 | destination. |
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256 | 256 | |
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257 | 257 | timeout |
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258 | 258 | You may also want to set a time-limit for how long the scheduler should wait before a |
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259 | 259 | task's dependencies are met. This is done via a `timeout`, which defaults to 0, which |
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260 | 260 | indicates that the task should never timeout. If the timeout is reached, and the |
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261 | 261 | scheduler still hasn't been able to assign the task to an engine, the task will fail |
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262 | 262 | with a :class:`DependencyTimeout`. |
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263 | 263 | |
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264 | 264 | .. note:: |
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265 | 265 | |
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266 | 266 | Dependencies only work within the task scheduler. You cannot instruct a load-balanced |
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267 | 267 | task to run after a job submitted via the MUX interface. |
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268 | 268 | |
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269 | 269 | The simplest form of Dependencies is with `all=True,success=True,failure=False`. In these cases, |
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270 | 270 | you can skip using Dependency objects, and just pass msg_ids or AsyncResult objects as the |
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271 | 271 | `follow` and `after` keywords to :meth:`client.apply`: |
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272 | 272 | |
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273 | 273 | .. sourcecode:: ipython |
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274 | 274 | |
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275 | 275 | In [14]: client.block=False |
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276 | 276 | |
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277 | 277 | In [15]: ar = lview.apply(f, args, kwargs) |
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278 | 278 | |
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279 | 279 | In [16]: ar2 = lview.apply(f2) |
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280 | 280 | |
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281 | 281 | In [17]: with lview.temp_flags(after=[ar,ar2]): |
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282 | 282 | ....: ar3 = lview.apply(f3) |
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283 | 283 | |
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284 | 284 | In [18]: with lview.temp_flags(follow=[ar], timeout=2.5) |
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285 | 285 | ....: ar4 = lview.apply(f3) |
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286 | 286 | |
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287 | 287 | .. seealso:: |
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288 | 288 | |
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289 | 289 | Some parallel workloads can be described as a `Directed Acyclic Graph |
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290 | 290 | <http://en.wikipedia.org/wiki/Directed_acyclic_graph>`_, or DAG. See :ref:`DAG |
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291 | 291 | Dependencies <dag_dependencies>` for an example demonstrating how to use map a NetworkX DAG |
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292 | 292 | onto task dependencies. |
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293 | 293 | |
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294 | 294 | |
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295 | 295 | Impossible Dependencies |
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296 | 296 | *********************** |
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297 | 297 | |
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298 | 298 | The schedulers do perform some analysis on graph dependencies to determine whether they |
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299 | 299 | are not possible to be met. If the scheduler does discover that a dependency cannot be |
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300 | 300 | met, then the task will fail with an :class:`ImpossibleDependency` error. This way, if the |
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301 | 301 | scheduler realized that a task can never be run, it won't sit indefinitely in the |
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302 | 302 | scheduler clogging the pipeline. |
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303 | 303 | |
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304 | 304 | The basic cases that are checked: |
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305 | 305 | |
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306 | 306 | * depending on nonexistent messages |
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307 | 307 | * `follow` dependencies were run on more than one machine and `all=True` |
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308 | 308 | * any dependencies failed and `all=True,success=True,failures=False` |
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309 | 309 | * all dependencies failed and `all=False,success=True,failure=False` |
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310 | 310 | |
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311 | 311 | .. warning:: |
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312 | 312 | |
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313 | 313 | This analysis has not been proven to be rigorous, so it is likely possible for tasks |
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314 | 314 | to become impossible to run in obscure situations, so a timeout may be a good choice. |
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315 | 315 | |
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316 | 316 | |
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317 | 317 | Retries and Resubmit |
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318 | 318 | ==================== |
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319 | 319 | |
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320 | 320 | Retries |
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321 | 321 | ------- |
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322 | 322 | |
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323 | 323 | Another flag for tasks is `retries`. This is an integer, specifying how many times |
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324 | 324 | a task should be resubmitted after failure. This is useful for tasks that should still run |
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325 | 325 | if their engine was shutdown, or may have some statistical chance of failing. The default |
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326 | 326 | is to not retry tasks. |
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327 | 327 | |
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328 | 328 | Resubmit |
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329 | 329 | -------- |
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330 | 330 | |
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331 | 331 | Sometimes you may want to re-run a task. This could be because it failed for some reason, and |
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332 | 332 | you have fixed the error, or because you want to restore the cluster to an interrupted state. |
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333 | 333 | For this, the :class:`Client` has a :meth:`rc.resubmit` method. This simply takes one or more |
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334 | 334 | msg_ids, and returns an :class:`AsyncHubResult` for the result(s). You cannot resubmit |
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335 | 335 | a task that is pending - only those that have finished, either successful or unsuccessful. |
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336 | 336 | |
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337 | 337 | .. _parallel_schedulers: |
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338 | 338 | |
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339 | 339 | Schedulers |
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340 | 340 | ========== |
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341 | 341 | |
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342 | 342 | There are a variety of valid ways to determine where jobs should be assigned in a |
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343 | 343 | load-balancing situation. In IPython, we support several standard schemes, and |
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344 | 344 | even make it easy to define your own. The scheme can be selected via the ``scheme`` |
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345 | 345 | argument to :command:`ipcontroller`, or in the :attr:`TaskScheduler.schemename` attribute |
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346 | 346 | of a controller config object. |
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347 | 347 | |
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348 | 348 | The built-in routing schemes: |
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349 | 349 | |
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350 | 350 | To select one of these schemes, simply do:: |
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351 | 351 | |
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352 | 352 | $ ipcontroller --scheme=<schemename> |
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353 | 353 | for instance: |
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354 | 354 | $ ipcontroller --scheme=lru |
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355 | 355 | |
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356 | 356 | lru: Least Recently Used |
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357 | 357 | |
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358 | 358 | Always assign work to the least-recently-used engine. A close relative of |
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359 | 359 | round-robin, it will be fair with respect to the number of tasks, agnostic |
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360 | 360 | with respect to runtime of each task. |
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361 | 361 | |
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362 | 362 | plainrandom: Plain Random |
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363 | 363 | |
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364 | 364 | Randomly picks an engine on which to run. |
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365 | 365 | |
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366 | 366 | twobin: Two-Bin Random |
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367 | 367 | |
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368 | 368 | **Requires numpy** |
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369 | 369 | |
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370 | 370 | Pick two engines at random, and use the LRU of the two. This is known to be better |
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371 | 371 | than plain random in many cases, but requires a small amount of computation. |
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372 | 372 | |
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373 | 373 | leastload: Least Load |
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374 | 374 | |
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375 | 375 | **This is the default scheme** |
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376 | 376 | |
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377 | 377 | Always assign tasks to the engine with the fewest outstanding tasks (LRU breaks tie). |
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378 | 378 | |
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379 | 379 | weighted: Weighted Two-Bin Random |
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380 | 380 | |
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381 | 381 | **Requires numpy** |
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382 | 382 | |
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383 | 383 | Pick two engines at random using the number of outstanding tasks as inverse weights, |
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384 | 384 | and use the one with the lower load. |
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385 | 385 | |
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386 | 386 | Greedy Assignment |
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387 | 387 | ----------------- |
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388 | 388 | |
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389 |
Tasks |
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389 | Tasks can be assigned greedily as they are submitted. If their dependencies are | |
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390 | 390 | met, they will be assigned to an engine right away, and multiple tasks can be |
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391 | 391 | assigned to an engine at a given time. This limit is set with the |
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392 |
``TaskScheduler.hwm`` (high water mark) configurable |
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392 | ``TaskScheduler.hwm`` (high water mark) configurable in your | |
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393 | :file:`ipcontroller_config.py` config file, with: | |
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393 | 394 | |
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394 | 395 | .. sourcecode:: python |
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395 | 396 | |
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396 | 397 | # the most common choices are: |
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397 |
c.TaskSheduler.hwm = 0 # (minimal latency, default in IPython |
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398 | c.TaskSheduler.hwm = 0 # (minimal latency, default in IPython < 0.13) | |
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398 | 399 | # or |
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399 |
c.TaskScheduler.hwm = 1 # (most-informed balancing, default in |
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400 | c.TaskScheduler.hwm = 1 # (most-informed balancing, default in β₯ 0.13) | |
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400 | 401 | |
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401 |
In IPython |
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402 | In IPython < 0.13, the default is 0, or no-limit. That is, there is no limit to the number of | |
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402 | 403 | tasks that can be outstanding on a given engine. This greatly benefits the |
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403 | 404 | latency of execution, because network traffic can be hidden behind computation. |
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404 | 405 | However, this means that workload is assigned without knowledge of how long |
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405 | 406 | each task might take, and can result in poor load-balancing, particularly for |
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406 | 407 | submitting a collection of heterogeneous tasks all at once. You can limit this |
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407 | 408 | effect by setting hwm to a positive integer, 1 being maximum load-balancing (a |
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408 | 409 | task will never be waiting if there is an idle engine), and any larger number |
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409 |
being a compromise between load-balanc |
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410 | being a compromise between load-balancing and latency-hiding. | |
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410 | 411 | |
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411 | 412 | In practice, some users have been confused by having this optimization on by |
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412 |
default, |
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413 | default, so the default value has been changed to 1 in IPython 0.13. This can be slower, | |
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413 | 414 | but has more obvious behavior and won't result in assigning too many tasks to |
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414 | 415 | some engines in heterogeneous cases. |
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415 | 416 | |
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416 | 417 | |
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417 | 418 | Pure ZMQ Scheduler |
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418 | 419 | ------------------ |
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419 | 420 | |
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420 | 421 | For maximum throughput, the 'pure' scheme is not Python at all, but a C-level |
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421 | 422 | :class:`MonitoredQueue` from PyZMQ, which uses a ZeroMQ ``DEALER`` socket to perform all |
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422 | 423 | load-balancing. This scheduler does not support any of the advanced features of the Python |
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423 | 424 | :class:`.Scheduler`. |
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424 | 425 | |
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425 | 426 | Disabled features when using the ZMQ Scheduler: |
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426 | 427 | |
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427 | 428 | * Engine unregistration |
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428 | 429 | Task farming will be disabled if an engine unregisters. |
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429 | 430 | Further, if an engine is unregistered during computation, the scheduler may not recover. |
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430 | 431 | * Dependencies |
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431 | 432 | Since there is no Python logic inside the Scheduler, routing decisions cannot be made |
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432 | 433 | based on message content. |
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433 | 434 | * Early destination notification |
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434 | 435 | The Python schedulers know which engine gets which task, and notify the Hub. This |
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435 | 436 | allows graceful handling of Engines coming and going. There is no way to know |
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436 | 437 | where ZeroMQ messages have gone, so there is no way to know what tasks are on which |
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437 | 438 | engine until they *finish*. This makes recovery from engine shutdown very difficult. |
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438 | 439 | |
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439 | 440 | |
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440 | 441 | .. note:: |
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441 | 442 | |
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442 | 443 | TODO: performance comparisons |
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443 | 444 | |
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444 | 445 | |
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445 | 446 | |
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446 | 447 | |
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447 | 448 | More details |
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448 | 449 | ============ |
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449 | 450 | |
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450 | 451 | The :class:`LoadBalancedView` has many more powerful features that allow quite a bit |
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451 | 452 | of flexibility in how tasks are defined and run. The next places to look are |
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452 | 453 | in the following classes: |
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453 | 454 | |
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454 | 455 | * :class:`~IPython.parallel.client.view.LoadBalancedView` |
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455 | 456 | * :class:`~IPython.parallel.client.asyncresult.AsyncResult` |
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456 | 457 | * :meth:`~IPython.parallel.client.view.LoadBalancedView.apply` |
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457 | 458 | * :mod:`~IPython.parallel.controller.dependency` |
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458 | 459 | |
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459 | 460 | The following is an overview of how to use these classes together: |
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460 | 461 | |
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461 | 462 | 1. Create a :class:`Client` and :class:`LoadBalancedView` |
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462 | 463 | 2. Define some functions to be run as tasks |
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463 | 464 | 3. Submit your tasks to using the :meth:`apply` method of your |
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464 | 465 | :class:`LoadBalancedView` instance. |
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465 | 466 | 4. Use :meth:`.Client.get_result` to get the results of the |
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466 | 467 | tasks, or use the :meth:`AsyncResult.get` method of the results to wait |
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467 | 468 | for and then receive the results. |
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468 | 469 | |
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469 | 470 | .. seealso:: |
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470 | 471 | |
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471 | 472 | A demo of :ref:`DAG Dependencies <dag_dependencies>` with NetworkX and IPython. |
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