__init__.py

#   Copyright (c) 2020 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.

from .serving_client import PredictorClient
from ..proto import sdk_configure_pb2 as sdk
import time

class SDKConfig(object):
    def __init__(self):
        self.sdk_desc = sdk.SDKConf()
        self.endpoints = []

    def set_server_endpoints(self, endpoints):
        self.endpoints = endpoints

    def gen_desc(self):
        predictor_desc = sdk.Predictor()
        predictor_desc.name = "general_model"
        predictor_desc.service_name = \
            "baidu.paddle_serving.predictor.general_model.GeneralModelService"
        predictor_desc.endpoint_router = "WeightedRandomRender"
        predictor_desc.weighted_random_render_conf.variant_weight_list = "30"

        variant_desc = sdk.VariantConf()
        variant_desc.tag = "var1"
        variant_desc.naming_conf.cluster = "list://%s".format(":".join(self.endpoints))

        predictor_desc.variants.extend([variant_desc])

        self.sdk_desc.predictors.extend([predictor_desc])
        self.sdk_desc.default_variant_conf.tag = "default"
        self.sdk_desc.default_variant_conf.connection_conf.connect_timeout_ms = 2000
        self.sdk_desc.default_variant_conf.connection_conf.rpc_timeout_ms = 20000
        self.sdk_desc.default_variant_conf.connection_conf.connect_retry_count = 2
        self.sdk_desc.default_variant_conf.connection_conf.max_connection_per_host = 100
        self.sdk_desc.default_variant_conf.connection_conf.hedge_request_timeout_ms = -1
        self.sdk_desc.default_variant_conf.connection_conf.hedge_fetch_retry_count = 2
        self.sdk_desc.default_variant_conf.connection_conf.connection_type = "pooled"
        
        self.sdk_desc.default_variant_conf.naming_conf.cluster_filter_strategy = "Default"
        self.sdk_desc.default_variant_conf.naming_conf.load_balance_strategy = "la"

        self.sdk_desc.default_variant_conf.rpc_parameter.compress_type = 0
        self.sdk_desc.default_variant_conf.rpc_parameter.package_size = 20
        self.sdk_desc.default_variant_conf.rpc_parameter.protocol = "baidu_std"
        self.sdk_desc.default_variant_conf.rpc_parameter.max_channel_per_request = 3

        return str(self.sdk_desc)


class Client(object):
    def __init__(self):
        self.feed_names_ = []
        self.fetch_names_ = []
        self.client_handle_ = None
        self.feed_shapes_ = []
        self.feed_types_ = []
        self.feed_names_to_idx_ = {}

    def load_client_config(self, path):
        # load configuraion here
        # get feed vars, fetch vars
        # get feed shapes, feed types
        # map feed names to index
        self.client_handle_ = PredictorClient()
        self.client_handle_.init(path)
        return

    def connect(self, endpoints):
        # check whether current endpoint is available
        # init from client config
        # create predictor here
        predictor_sdk = SDKConfig()
        predictor_sdk.set_server_endpoints(endpoints)
        sdk_desc = predictor_sdk.gen_desc()
        print(sdk_desc)
        timestamp = time.asctime(time.localtime(time.time()))
        predictor_path = "/tmp/"
        predictor_file = "%s_predictor.conf" % timestamp
        with open(predictor_path + predictor_file, "w") as fout:
            fout.write(sdk_desc)
        self.client_handle_.set_predictor_conf(
            predictor_path, predictor_file)
        self.client_handle_.create_predictor()

    def get_feed_names(self):
        return self.feed_names_

    def get_fetch_names(self):
        return self.fetch_names_

    def predict(self, feed={}, fetch={}):
        int_slot = []
        float_slot = []
        int_feed_names = []
        float_feed_names = []
        fetch_names = []
        for key in feed:
            if key not in self.feed_names_:
                continue
            if self.feed_types_[key] == int_type:
                int_feed_names.append(key)
                int_slot.append(feed_map[key])
            elif self.feed_types_[key] == float_type:
                float_feed_names.append(key)
                float_slot.append(feed_map[key])

        for key in fetch:
            if key in self.fetch_names_:
                fetch_names.append(key)

        result = self.client_handle_.predict(
            float_slot, float_feed_names,
            int_slot, int_feed_names,
            fetch_names)
            
        result_map = {}
        for i, name in enumerate(fetch_names):
            result_map[name] = result[i]
            
        return result_map