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annotate doc/v2_planning/plugin.txt @ 1419:cff305ad9f60

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TensorFnDataset - added x_ attribute that caches the dataset function return value, but does not get pickled.
author James Bergstra <bergstrj@iro.umontreal.ca>
date Fri, 04 Feb 2011 16:05:22 -0500
parents a1957faecc9b
children
rev   line source
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1
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2 ======================================
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3 Plugin system for iterative algorithms
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4 ======================================
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5
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6 I would like to propose a plugin system for iterative algorithms in
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7 Pylearn. Basically, it would be useful to be able to sandwich
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8 arbitrary behavior in-between two training iterations of an algorithm
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9 (whenever applicable). I believe many mechanisms are best implemented
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10 this way: early stopping, saving checkpoints, tracking statistics,
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11 real time visualization, remote control of the process, or even
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12 interlacing the training of several models and making them interact
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13 with each other.
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14
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15 So here is the proposal: essentially, a plugin would be a (schedule,
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16 timeline, function) tuple.
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17
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18 Schedule
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19 ========
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20
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21 The schedule is some function that takes two "times", t1 and t2, and
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22 returns True if the plugin should be run in-between these times. The
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23 indices refer to a "timeline" unit described below (e.g. "real time" or
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24 "iterations"). The reason why we check a time range [t1, t2] rather than
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25 some discrete time t is that we do not necessarily want to schedule plugins
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26 on iteration numbers. For instance, we could want to run a plugin every
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27 second, or every minute, and then [t1, t2] would be the start time and end
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28 time of the last iteration - and then we run the plugin whenever a new
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29 second started in that range (but still on training iteration
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30 boundaries). Alternatively, we could want to run a plugin every n examples
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31 seen - but if we use mini-batches, the nth example might be square in the
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32 middle of a batch.
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33
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34 I've implemented a somewhat elaborate schedule system. `each(10)`
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35 produces a schedule that returns true whenever a multiple of 10 is in
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36 the time range. `at(17, 153)` produces one that returns true when 17
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37 or 143 is in the time range. Schedules can be combined and negated,
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38 e.g. `each(10) & ~at(20, 30)` (execute at each 10, except at 20 and
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39 30). So that gives a lot of flexibility as to when you want to do
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40 things.
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41
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42 Timeline
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43 ========
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44
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45 This would be a string indicating on what "timeline" the schedule is
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46 supposed to operate. For instance, there could be a "real time"
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47 timeline, an "algorithm time" timeline, an "iterations" timeline, a
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48 "number of examples" timeline, and so on. This means you can schedule
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49 some action to be executed every actual second, or every second of
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50 training time (ignoring time spent executing plugins), or every
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51 discrete iteration, or every n examples processed. This might be a
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52 bloat feature (it was an afterthought to my original design, anyway),
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53 but I think that there are circumstances where each of these options
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54 is the best one.
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55
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56 Function
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57 ========
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58
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59 The plugin function would receive some object containing the time
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60 range, a flag indicating whether the training has started, a flag
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61 indicating whether the training is done (which they can set in order
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62 to stop training), as well as anything pertinent about the model.
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63
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64 Implementation
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65 ==============
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66
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67 I have implemented the feature in plugin.py, in this directory. Simply
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68 run python plugin.py to test it.
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69
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70
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71
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72 ===============
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73 Revised version
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74 ===============
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75
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76 Taking into account ideas thrown around during the September 16
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77 meeting I (OB) have made the following modifications to my original
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78 proposal:
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79
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80 Event objects
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81 =============
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82
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83 In the revised framework, an Event is a generic object which can
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84 contain any attributes you want, with one privileged attribute, the
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85 'type' attribute, which is a string. I expect the following attributes
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86 to be used widely:
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87
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88 * type: this is a string describing the abstract semantics of this
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89 event ("tick", "second", "millisecond", "batch", etc.)
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90
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91 * issuer: a pointer to the plugin that issued this event. This allows
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92 for fine grained filtering in the case where several plugins can
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93 fire the same event type
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94
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95 * time: an integer or float index on an abstract timeline. For
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96 instance, the "tick" event would have a "time" field, which would be
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97 increased by one every time the event is fired. Pretty much all
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98 recurrent events should include this.
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99
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100 * data: some data associated to the event. presumably it doesn't have
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101 to be named "data", and more than one data field could be given.
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102
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103 The basic idea is that it should be possible to say: "I want this
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104 plugin to be executed every tenth time an event of this type is fired
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105 by this plugin", or any subset of these conditions.
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106
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107 Matching events
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108 ===============
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109
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110 When registering a plugin, you specify a sort of "abstract event" that
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111 an event must "match" in order to be fed to the plugin. This can be
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112 done by simply instantiating an event with the fields you want to
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113 match. I think examples would explain best my idea
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114 (sch.schedule_plugin = add a plugin to the scheduler):
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115
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116 # Print the error on every parameter update (learner given in the event)
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117 sch.schedule_plugin(Event("parameter_update"), PrintError())
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118 # Print the reconstruction error of daa0 whenever it does a parameter update
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119 sch.schedule_plugin(Event("parameter_update", issuer = daa0), PrintReconstructionError())
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120 # Save the learner every 10 minutes
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121 sch.schedule_plugin(Event("minute", time = each(10)), Save(learner))
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122
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123 The events given as first argument to schedule_plugin are not real
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124 events: they are "template events" meant to be *matched* against the
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125 real events that will be fired. If the terminology is confusing, it
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126 would not be a problem to use another class with a better name (for
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127 example, On("minute", time = each(10)) could be clearer than
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128 Event(...), I don't know).
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129
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130 Note that fields in these Event objects can be a special kind of
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131 object, a Matcher, which allows to filter events based on arbitrary
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132 conditions. My Schedule objects (each, at, etc.) now inherit from
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133 Matcher. You could easily have a matcher that allows you to match
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134 issuers that are instances of a certain class, or matches every single
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135 event (I have an example of the latter in plugin.py).
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136
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137 Plugins
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138 =======
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139
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140 The plugin class would have the following methods:
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141
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142 * attach(scheduler): tell the plugin that it is being scheduled by the
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143 scheduler, store the scheduler in self. The method can return self,
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144 or a copy of itself.
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145
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146 * fire(type, **attributes): adds Event(type, issuer = self, **attributes)
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147 to the event queue of self.scheduler
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148
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149 Scheduler
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150 =========
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151
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152 A Scheduler would have a schedule_plugin(event_template, plugin)
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153 method to add plugins, a queue(event) method to queue a new event, and
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154 it would be callable.
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155
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156 My current version proceeds as follows:
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157
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158 * Fire Event("begin"). Somewhat equivalent to "tick" at time 0, but I
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159 find it cleaner to have a special event to mark the beginning of the
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160 event loop.
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161 * Infinite loop
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162 * Fire Event("tick", time = <iteration#>)
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163 * Loop until the queue is empty
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164 * Pop event, execute all plugins that respond to it
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165 * Check if event.type == "terminate". If so, stop.
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166
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167 Varia
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168 =====
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169
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170 I've made a very simple implementation of a DispatchPlugin which, upon
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171 reception of an event, dispatches it to its "on_<event.type>" method
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172 (or calls a fallback). It seems nice. However, in order for it to work
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173 reliably, it has to be registered on all events, and I'm not sure it
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174 can scale well to more complex problems where the source of events is
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175 important.
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176
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177 Implementation
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178 ==============
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179
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180 See plugin.py.
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181