# 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. # pylint: disable=doc-string-missing 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 socket import paddle_serving_server_gpu as paddle_serving_server import time from .version import serving_server_version from contextlib import closing import argparse def serve_args(): parser = argparse.ArgumentParser("serve") parser.add_argument( "--thread", type=int, default=10, help="Concurrency of server") parser.add_argument( "--model", type=str, default="", help="Model for serving") parser.add_argument( "--port", type=int, default=9292, help="Port of the starting gpu") parser.add_argument( "--workdir", type=str, default="workdir", help="Working dir of current service") parser.add_argument( "--device", type=str, default="gpu", help="Type of device") parser.add_argument("--gpu_ids", type=str, default="", help="gpu ids") parser.add_argument( "--name", type=str, default="None", help="Default service name") parser.add_argument( "--mem_optim", type=bool, default=False, help="Memory optimize") return parser.parse_args() 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_infer": "GeneralDistKVInferOp", "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.cube_config_fn = "cube.conf" self.workdir = "" self.max_concurrency = 0 self.num_threads = 4 self.port = 8080 self.reload_interval_s = 10 self.module_path = os.path.dirname(paddle_serving_server.__file__) self.cur_path = os.getcwd() self.check_cuda() self.use_local_bin = False self.gpuid = 0 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 check_local_bin(self): if "SERVING_BIN" in os.environ: self.use_local_bin = True self.bin_path = os.environ["SERVING_BIN"] def check_cuda(self): r = os.system("cat /usr/local/cuda/version.txt") if r != 0: raise SystemExit( "CUDA not found, please check your environment or use cpu version by \"pip install paddle_serving_server\"" ) def set_gpuid(self, gpuid=0): self.gpuid = gpuid 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" self.engine.reloadable_meta = self.workdir + "/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): self.workdir = 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() for workflow in self.workflow_conf.workflows: for node in workflow.nodes: if "dist_kv" in node.name: self.resource_conf.cube_config_path = workdir self.resource_conf.cube_config_file = self.cube_config_fn 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 download_bin(self): os.chdir(self.module_path) need_download = False device_version = "serving-gpu-" folder_name = device_version + serving_server_version tar_name = folder_name + ".tar.gz" bin_url = "https://paddle-serving.bj.bcebos.com/bin/" + tar_name self.server_path = os.path.join(self.module_path, folder_name) download_flag = "{}/{}.is_download".format(self.module_path, folder_name) if os.path.exists(download_flag): os.chdir(self.cur_path) self.bin_path = self.server_path + "/serving" return if not os.path.exists(self.server_path): os.system("touch {}/{}.is_download".format(self.module_path, folder_name)) print('Frist time run, downloading PaddleServing components ...') r = os.system('wget ' + bin_url + ' --no-check-certificate') if r != 0: if os.path.exists(tar_name): os.remove(tar_name) raise SystemExit( 'Download failed, please check your network or permission of {}.'. format(self.module_path)) 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) raise SystemExit( 'Decompressing failed, please check your permission of {} or disk space left.'. format(self.module_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)) if not self.port_is_available(port): raise SystemExit("Prot {} is already used".format(port)) self.set_port(port) 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 port_is_available(self, port): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: sock.settimeout(2) result = sock.connect_ex(('0.0.0.0', port)) if result != 0: return True else: return False def run_server(self): # just run server with system command # currently we do not load cube self.check_local_bin() if not self.use_local_bin: self.download_bin() # wait for other process to download server bin while not os.path.exists(self.server_path): time.sleep(1) else: print("Use local bin : {}".format(self.bin_path)) 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 {} " \ "-gpuid {} ".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, self.gpuid,) print("Going to Run Comand") print(command) os.system(command)