test_train.py

import paddle.v2 as paddle
import paddle.v2.dataset.uci_housing as uci_housing
import paddle.v2.master as master
import os
import cPickle as pickle

etcd_ip = os.getenv("MASTER_IP", "127.0.0.1")
etcd_endpoint = "http://" + etcd_ip + ":2379"
print "connecting to master, etcd endpoints: ", etcd_endpoint
master_client = master.client(etcd_endpoint, 5, 64)


def cloud_reader():
    global master_client
    master_client.set_dataset(
        ["/pfs/dlnel/public/dataset/uci_housing/uci_housing-*"], passes=30)
    while 1:
        r, e = master_client.next_record()
        if not r:
            if e != -2:  # other errors
                print "get record error:", e
            break
        yield pickle.loads(r)


def main():
    # init
    paddle.init(use_gpu=False, trainer_count=1)

    # network config
    x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
    y_predict = paddle.layer.fc(input=x,
                                param_attr=paddle.attr.Param(
                                    name='w', learning_rate=1e-3),
                                size=1,
                                act=paddle.activation.Linear(),
                                bias_attr=paddle.attr.Param(
                                    name='b', learning_rate=1e-3))
    y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
    cost = paddle.layer.mse_cost(input=y_predict, label=y)

    # create parameters
    parameters = paddle.parameters.create(cost)

    # create optimizer of new remote updater to pserver
    optimizer = paddle.optimizer.Momentum(momentum=0, learning_rate=1e-3)

    trainer = paddle.trainer.SGD(cost=cost,
                                 parameters=parameters,
                                 update_equation=optimizer,
                                 is_local=False,
                                 pserver_spec=etcd_endpoint,
                                 use_etcd=True)

    # event_handler to print training and testing info
    def event_handler(event):
        if isinstance(event, paddle.event.EndIteration):
            # FIXME: for cloud data reader, pass number is managed by master
            # should print the server side pass number
            if event.batch_id % 100 == 0:
                print "Pass %d, Batch %d, Cost %f" % (
                    event.pass_id, event.batch_id, event.cost)

        if isinstance(event, paddle.event.EndPass):
            if (event.pass_id + 1) % 10 == 0:
                result = trainer.test(
                    reader=paddle.batch(
                        uci_housing.test(), batch_size=2),
                    feeding={'x': 0,
                             'y': 1})
                print "Test %d, %.2f" % (event.pass_id, result.cost)

    # training
    # NOTE: use uci_housing.train() as reader for non-paddlecloud training
    trainer.train(
        reader=paddle.batch(
            paddle.reader.shuffle(
                cloud_reader, buf_size=500), batch_size=2),
        feeding={'x': 0,
                 'y': 1},
        event_handler=event_handler,
        num_passes=30)


if __name__ == '__main__':
    main()