model.py

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import errno
import uuid

import paddle.v2.master

__all__ = ["save_model", "load_model"]

trainer_id = str(uuid.uuid4())


def mkdir_p(path):
    try:
        os.makedirs(path)
    except OSError as exc:
        if exc.errno == errno.EEXIST and os.path.isdir(path):
            pass
        else:
            raise


def save_model(parameters, path):
    need_request = "KUBERNETES_SERVICE_HOST" in os.environ.keys()

    if need_request:
        # TODO(helin): figure out how MPI trains, since MPI only save
        # model when trainer_id == "0", we can consolidate the logic
        # here.

        # TODO(helin): change this environment variable name from
        # MASTER_IP to ETCD_IP
        etcd_name = "MASTER_IP"
        if etcd_name not in os.environ.keys():
            raise Exception('not find ' + etcd_name +
                            ' in environment variable.')

        etcd_ip = os.environ.get(etcd_name)
        client = paddle.v2.master.client("http://" + etcd_ip + ":2379", 5, 0)
        r = client.request_save_model(trainer_id, 5000)
        if r == 0:
            # do not need to save
            return
        elif r < 0:
            # error
            return
        else:
            # save model
            path = os.path.join(path, trainer_id)
            path = os.path.join(path, "model.tar")

    mkdir_p(path)

    with open(path, 'wb') as f:
        parameters.to_tar(f)


def load_model(parameters, path):
    with open(path, 'rb') as f:
        parameters.from_tar(f)