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annotate doc/v2_planning/plugin.txt @ 1119:81ea57c6716d

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clarification to plugin.txt
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Tue, 14 Sep 2010 17:22:25 -0400
parents 8cc324f388ba
children a1957faecc9b
rev   line source
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8cc324f388ba proposal for a plugin system
Olivier Breuleux <breuleuo@iro.umontreal.ca>
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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
81ea57c6716d clarification to plugin.txt
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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