Ejemplo n.º 1
0
    def train_slave(self,
                    instances,
                    host,
                    port,
                    slaves=(),
                    minibatch_size=1,
                    timeout=None,
                    limit=65536,
                    **kwargs):
        """Run the slave process of a round-robin distributed learner. This
        stores all the instances and model parameters (including large
        initialized models) as object members and must therefore NEVER
        be saved to disk.
        """
        # Note the inversion here compared to the other methods. Slave models
        # will never be saved to disk and need to store all their state,
        # including runtime parameters, as class members.
        self.params.update(kwargs)
        self.params['timeout'] = timeout
        self.instances = instances

        if minibatch_size == 1:
            self.machine_init_serial(instances, slaves=slaves, **self.params)
        else:
            self.machine_init_minibatch(instances,
                                        minibatch_size=minibatch_size,
                                        slaves=slaves,
                                        **self.params)

        srv_timeout = 3 * timeout if timeout is not None else 1000
        server = jsonrpc.Server(
            jsonrpc.JsonRpc20(),
            jsonrpc.TransportTcpIp(addr=(host, int(port)),
                                   limit=limit,
                                   timeout=srv_timeout))
        server.register_function(self.slave_init_weights)
        server.register_function(self.slave_receive_weights)
        server.register_function(self.slave_decode_instance)
        server.register_function(self.slave_fetch_weight_update)
        print "Serving at %s:%s with timeout %ds" % (host, port, srv_timeout)
        server.serve()
Ejemplo n.º 2
0
    parser = argparse.ArgumentParser(
            description='Start an LM server')
    parser.add_argument('--ngram_order', action='store', type=int,
            help="order of n-grams to serve",
            default=3)
    parser.add_argument('--lm_path', action='store',
            help="path to the trained SRILM language model",
            default='/path/to/project/resources/LMs/sample.lm')
    parser.add_argument('--host', action='store',
            help="host to serve on (default localhost; 0.0.0.0 for public)",
            default=socket.gethostname()) #os.environ['HOSTNAME']
    parser.add_argument('--port', action='store', type=int,
            help="port to serve on (default 8081)",
            default=8081)
    parser.add_argument('--timeout', action='store', type=int,
            help="time limit for responses",
            default=200)
    args = parser.parse_args()

    server = jsonrpc.Server(jsonrpc.JsonRpc20(),
                            jsonrpc.TransportTcpIp(addr=(args.host,
                                                         args.port),
                                                   timeout=args.timeout))
    lm = LangModel(args.ngram_order, lm_path=args.lm_path)
    server.register_function(lm.score_ngram)
    server.register_function(lm.score_ngrams)
    server.register_function(lm.score_sent)
    print 'Serving %s-grams from %s on http://%s:%s' % (args.ngram_order,
            args.lm_path, args.host, args.port)
    server.serve()