Mercurial > pylearn
view doc/v2_planning/plugin_architecture_GD.txt @ 1165:42ddbefd1e03
made the API_formulas.txt and removed duplicate stuff from the formulas.txt file
author | Frederic Bastien <nouiz@nouiz.org> |
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date | Fri, 17 Sep 2010 13:57:07 -0400 |
parents | 9f0502f8c7a5 |
children | 9ff2242a817b |
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Overview ======== The "central authority" (CA) is the glue which takes care of interfacing plugins with one another. It has 3 basic roles: * it maintains a list of "registered" or "active" plugins * it receives and queues the various messages sent by the plugins * dispatches the messages to the recipient, based on various "events" Events can take different forms: * the CA can trigger various events based on running time * can be linked to messages emitted by the various plugins. Events can be triggered based on the frequency of such messages. * Once an event is triggered, it is relayed to the appropriate "recipient plugin(s)" It is the responsibility of each plugin to inform the CA of which "events" it cares about. Generic Pseudo-code =================== I'll try to write this in pseudo-python as best I can. I'll do this in traditional OOP, as this is what I'm more comfortable with. I'll leave it up to James and OB to python-ize this :) class MessageX(Message): """ A message is basically a data container. This could very well be replaced by a generic Python object. """ class Plugin(object): """ The base plugin object doesn't do much. It contains a reference to the CA (upon plugin being registered with the CA), provides boilerplate code for storing which "events" this plugin is susceptible to, as well as code for registering callback functions for the various messages. """ CA = None # to be initialized upon plugin registration active_msg = {} # dictionary of messages this plugin is susceptible to callbacks = {} # mapping of message class names --> callback function def listen(msg_class, interval): """ :param msg_class: reference to the "message" class we are interested in. These messages will be forwarded to this plugin, when the trigger condition is met. :param interval: integer. Forward the message to this plugin every 'interval' such messages. """ self.active_msg[msg_class] = interval def check_trigger(msg_class, time): """ Checks whether or not the "trigger" condition associated with message of class 'msg_class' is satisfied or not. This could be the default behavior, and be overridden by the various plugins. """ return time % self.active_msg[msg_class] == 0 def handler(msg_class, callback): """ Decorator which registers a callback function for the given message type. NOTE: I don't think what I wrote would work as a Python decorator. I am not sure how to handle decoraters with multiple parameters (one explicit, and the other as the reference to the function). I'm pretty sure James or OB could figure it out though ! :params msg_class: reference to the message class for which we are registering a callback function :params callback : reference to which function to call for a given message """ self.callbacks[msg_class] = callback def execute(self, message): """ Boiler-plate code which executes the right callback function, for the given message type. """ for (msg_class, callback) in self.callbacks.iteritems(): if message.__class__ == msg_class: callback(message) class ProducerPlugin(Plugin): def dostuff(): """ A typical "producer" plugin. It basically performs an arbitrary action and asks the CA to forward the results (in the form of a message) to other plugins. """ # iteratively do stuff and relay messages to other plugins while(condition): msga = # do something ca.send(msga) # ask CA to forward to other plugins class ConsumerPlugin(Plugin): @handler(MessageA) def func(msga): """ A consumer or "passive plugin" (eg. logger, etc). This function is register as being the callback function for Message A objects. """ # do something with message A class ConsumerProducerPlugin(Plugin): @handler(MessageA) def func(msga): """ Example of a consumer / producer plugin. It receives MessageA messages, processes the data, then asks the CA to send a new message (MessageB) as the result of its computation. The CA will automatically forward to all interested parties. :param msga: MessageA instance """ data = dostuff(msga) # process message msgb = MessageB(data) # generate new message for other plugins ca.send(msgb) # ask CA to forward to other plugins class CentralAuthority(object): active_plugins = [] # contains a list of registered plugins mailmain = {} # dictionary which contains, for each message class, a # list of plugins interested in this message event_count = {} # dictionary of "event" counts for various messages def register(plugin): """ Registers the plugin and adds it as a listener for the various messages it is interested in. :param plugin: plugin instance which we want to "activate" """ # each plugin must have a reference to the CA plugin.ca = self # maintain list of active plugins active_plugins.append(plugin) # remember which messages this plugin cares about for msg in plugin.active_msg.keys(): self.mailman[msg].append(plugin) self.event_count[msg] = 0 def send(msg): """ This function relays the message to the appropriate plugins, based on their "trigger" condition. It also keeps track of the number of times this event was raised. :param msg: message instance """ event_count[msg.__class__] += 1 # for all plugins interested in this message ... for plugin in self.mailman[msg.__class__]: # check if trigger condition is met if plugin.check_trigger(msg, self.event_count[msg.__class__]): # have the plugin execute the message plugin.execute(msg) def run(self): """ This would be the main loop of the program. I won't go into details because its still somewhat blurry in my head :) But basically, the CA could be configured to send out its own messages, independently from all other plugins. These could be "synchronous" messages such as: "5 seconds have passed", or others such as "save state we are about to get killed". NOTE: seems like this would almost have to live in its own thread ... """ # the following would be parametrized obviously while(True): msg = ElapsedTimeMessage(5) self.send(msg) sleep(5) Putting it all-together ======================= def main(): ca = CentralAuthority() producer = ProducerPlugin() ca.register(producer) consumer = ConsumerPlugin() consumer.listen(MessageB, 1) ca.register(consumer)) other = ConsumerProducerPlugin() other.listen(MessageB, 10) ca.register(other) # this is the function call which gets the ball rolling producer.dostuff() DISCUSSION: blocking vs. non-blocking ===================================== In the above example, I used "blocking" sends. However it is not-clear that this is the best option. In the example, the producer basically acts as the main loop. It relinquishes control of the main loop when the CA decides to forward the message to other plugins. Control will only be returned once the cascade of send/receives initiated with MessageA is complete (all subplugins have processed MessageA and any messages sent as a side-effect have also been processed). This definitely imposes constraints on what the plugins can do, and how they do it. For the type of single-processor / linear jobs we tend to run, this might be enough (??). The good news is that going forward, the above plugin architecture can also scale to distributed systems, by changing the sends to be non-blocking. Plugins could then live on different machines and process data as they see fit. Synchronization would be enforced by way of messages. In the above, the "main producer" would thus become a consumer/producer who listens for "done processing MessageA" messages and produces a new MessageA as a result. On single-processor systems, the synchronization overhead might be too costly however. That is something we would have to investigate. On the plus side however, our plugins would be "future proof" and lend themselves well to the type of "massively parallel jobs" we wish to run (i.e. meta-learners, etc.) Logistic Regression =================== TO COME SOON (?)