# 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. import os from .proto import server_configure_pb2 as server_sdk from .proto import general_model_config_pb2 as m_config import google.protobuf.text_format import tarfile import paddle_serving_server as paddle_serving_server from version import serving_server_version class OpMaker(object): def __init__(self): self.op_dict = { "general_infer":"GeneralInferOp", "general_reader":"GeneralReaderOp", "general_response":"GeneralResponseOp", "general_text_reader":"GeneralTextReaderOp", "general_text_response":"GeneralTextResponseOp", "general_single_kv":"GeneralSingleKVOp", "general_dist_kv":"GeneralDistKVOp" } # currently, inputs and outputs are not used # when we have OpGraphMaker, inputs and outputs are necessary def create(self, name, inputs=[], outputs=[]): if name not in self.op_dict: raise Exception("Op name {} is not supported right now".format( name)) node = server_sdk.DAGNode() node.name = "{}_op".format(name) node.type = self.op_dict[name] return node class OpSeqMaker(object): def __init__(self): self.workflow = server_sdk.Workflow() self.workflow.name = "workflow1" self.workflow.workflow_type = "Sequence" def add_op(self, node): if len(self.workflow.nodes) >= 1: dep = server_sdk.DAGNodeDependency() dep.name = self.workflow.nodes[-1].name dep.mode = "RO" node.dependencies.extend([dep]) self.workflow.nodes.extend([node]) def get_op_sequence(self): workflow_conf = server_sdk.WorkflowConf() workflow_conf.workflows.extend([self.workflow]) return workflow_conf class Server(object): def __init__(self): self.server_handle_ = None self.infer_service_conf = None self.model_toolkit_conf = None self.resource_conf = None self.engine = None self.memory_optimization = False self.model_conf = None self.workflow_fn = "workflow.prototxt" self.resource_fn = "resource.prototxt" self.infer_service_fn = "infer_service.prototxt" self.model_toolkit_fn = "model_toolkit.prototxt" self.general_model_config_fn = "general_model.prototxt" self.workdir = "" self.max_concurrency = 0 self.num_threads = 0 self.port = 8080 self.reload_interval_s = 10 self.module_path = os.path.dirname(paddle_serving_server.__file__) self.cur_path = os.getcwd() def set_max_concurrency(self, concurrency): self.max_concurrency = concurrency def set_num_threads(self, threads): self.num_threads = threads def set_port(self, port): self.port = port def set_reload_interval(self, interval): self.reload_interval_s = interval def set_op_sequence(self, op_seq): self.workflow_conf = op_seq def set_memory_optimize(self, flag=False): self.memory_optimization = flag def _prepare_engine(self, model_config_path, device): if self.model_toolkit_conf == None: self.model_toolkit_conf = server_sdk.ModelToolkitConf() if self.engine == None: self.engine = server_sdk.EngineDesc() self.model_config_path = model_config_path self.engine.name = "general_model" self.engine.reloadable_meta = model_config_path + "/fluid_time_file" os.system("touch {}".format(self.engine.reloadable_meta)) self.engine.reloadable_type = "timestamp_ne" self.engine.runtime_thread_num = 0 self.engine.batch_infer_size = 0 self.engine.enable_batch_align = 0 self.engine.model_data_path = model_config_path self.engine.enable_memory_optimization = self.memory_optimization self.engine.static_optimization = False self.engine.force_update_static_cache = False if device == "cpu": self.engine.type = "FLUID_CPU_ANALYSIS_DIR" elif device == "gpu": self.engine.type = "FLUID_GPU_ANALYSIS_DIR" self.model_toolkit_conf.engines.extend([self.engine]) def _prepare_infer_service(self, port): if self.infer_service_conf == None: self.infer_service_conf = server_sdk.InferServiceConf() self.infer_service_conf.port = port infer_service = server_sdk.InferService() infer_service.name = "GeneralModelService" infer_service.workflows.extend(["workflow1"]) self.infer_service_conf.services.extend([infer_service]) def _prepare_resource(self, workdir): if self.resource_conf == None: with open("{}/{}".format(workdir, self.general_model_config_fn), "w") as fout: fout.write(str(self.model_conf)) self.resource_conf = server_sdk.ResourceConf() self.resource_conf.model_toolkit_path = workdir self.resource_conf.model_toolkit_file = self.model_toolkit_fn self.resource_conf.general_model_path = workdir self.resource_conf.general_model_file = self.general_model_config_fn def _write_pb_str(self, filepath, pb_obj): with open(filepath, "w") as fout: fout.write(str(pb_obj)) def load_model_config(self, path): self.model_config_path = path self.model_conf = m_config.GeneralModelConfig() f = open("{}/serving_server_conf.prototxt".format(path), 'r') self.model_conf = google.protobuf.text_format.Merge( str(f.read()), self.model_conf) # check config here # print config here def get_device_version(self): avx_flag = False mkl_flag = False openblas_flag = False r = os.system("cat /proc/cpuinfo | grep avx > /dev/null 2>&1") if r == 0: avx_flag = True r = os.system("which mkl") if r == 0: mkl_flag = True if avx_flag: if mkl_flag: device_version = "serving-cpu-avx-mkl-" else: device_version = "serving-cpu-avx-openblas-" else: device_version = "serving-cpu-noavx-openblas-" return device_version def download_bin(self): os.chdir(self.module_path) need_download = False device_version = self.get_device_version() floder_name = device_version + serving_server_version tar_name = floder_name + ".tar.gz" bin_url = "https://paddle-serving.bj.bcebos.com/bin/" + tar_name self.server_path = os.path.join(self.module_path, floder_name) if not os.path.exists(self.server_path): print('Frist time run, downloading PaddleServing components ...') r = os.system('wget ' + bin_url + ' --no-check-certificate') if r != 0: print('Download failed') if os.path.exists(tar_name): os.remove(tar_name) else: try: print('Decompressing files ..') tar = tarfile.open(tar_name) tar.extractall() tar.close() except: if os.path.exists(exe_path): os.remove(exe_path) finally: os.remove(tar_name) os.chdir(self.cur_path) self.bin_path = self.server_path + "/serving" def prepare_server(self, workdir=None, port=9292, device="cpu"): if workdir == None: workdir = "./tmp" os.system("mkdir {}".format(workdir)) else: os.system("mkdir {}".format(workdir)) os.system("touch {}/fluid_time_file".format(workdir)) self._prepare_resource(workdir) self._prepare_engine(self.model_config_path, device) self._prepare_infer_service(port) self.workdir = workdir infer_service_fn = "{}/{}".format(workdir, self.infer_service_fn) workflow_fn = "{}/{}".format(workdir, self.workflow_fn) resource_fn = "{}/{}".format(workdir, self.resource_fn) model_toolkit_fn = "{}/{}".format(workdir, self.model_toolkit_fn) self._write_pb_str(infer_service_fn, self.infer_service_conf) self._write_pb_str(workflow_fn, self.workflow_conf) self._write_pb_str(resource_fn, self.resource_conf) self._write_pb_str(model_toolkit_fn, self.model_toolkit_conf) def run_server(self): # just run server with system command # currently we do not load cube self.download_bin() command = "{} " \ "-enable_model_toolkit " \ "-inferservice_path {} " \ "-inferservice_file {} " \ "-max_concurrency {} " \ "-num_threads {} " \ "-port {} " \ "-reload_interval_s {} " \ "-resource_path {} " \ "-resource_file {} " \ "-workflow_path {} " \ "-workflow_file {} " \ "-bthread_concurrency {} ".format( self.bin_path, self.workdir, self.infer_service_fn, self.max_concurrency, self.num_threads, self.port, self.reload_interval_s, self.workdir, self.resource_fn, self.workdir, self.workflow_fn, self.num_threads,) os.system(command)