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- # 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 __future__ import print_function
- from utils.static_ps.reader_helper import get_reader, get_example_num, get_file_list, get_word_num
- from utils.static_ps.program_helper import get_model, get_strategy
- from utils.static_ps.common import YamlHelper, is_distributed_env
- import argparse
- import time
- import sys
- import paddle.distributed.fleet as fleet
- import paddle.distributed.fleet.base.role_maker as role_maker
- import paddle
- import os
- import warnings
- import logging
- import paddle.fluid as fluid
-
- __dir__ = os.path.dirname(os.path.abspath(__file__))
- sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
-
- logging.basicConfig(
- format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)
- logger = logging.getLogger(__name__)
-
-
- def parse_args():
- parser = argparse.ArgumentParser("PaddleRec train script")
- parser.add_argument(
- '-m',
- '--config_yaml',
- type=str,
- required=True,
- help='config file path')
- args = parser.parse_args()
- args.abs_dir = os.path.dirname(os.path.abspath(args.config_yaml))
- yaml_helper = YamlHelper()
- config = yaml_helper.load_yaml(args.config_yaml)
- config["yaml_path"] = args.config_yaml
- config["config_abs_dir"] = args.abs_dir
- yaml_helper.print_yaml(config)
- return config
-
-
- class Main(object):
- def __init__(self, config):
- self.metrics = {}
- self.config = config
- self.input_data = None
- self.reader = None
- self.exe = None
- self.train_result_dict = {}
- self.train_result_dict["speed"] = []
-
- def run(self):
- fleet.init()
- self.network()
- if fleet.is_server():
- self.run_server()
- elif fleet.is_worker():
- self.run_online_worker()
- fleet.stop_worker()
- self.record_result()
- logger.info("Run Success, Exit.")
-
- def network(self):
- model = get_model(self.config)
- self.input_data = model.create_feeds()
- self.metrics = model.net(self.input_data)
- self.inference_target_var = model.inference_target_var
- logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
- model.create_optimizer(get_strategy(self.config))
-
- def run_server(self):
- logger.info("Run Server Begin")
- fleet.init_server(config.get("runner.warmup_model_path"))
- fleet.run_server()
-
- def wait_and_prepare_dataset(self, day, pass_index):
- train_data_dir = self.config.get("runner.train_data_dir", [])
-
- dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
- dataset.set_use_var(self.input_data)
- dataset.set_batch_size(self.config.get('runner.train_batch_size'))
- dataset.set_thread(self.config.get('runner.train_thread_num'))
-
- # may you need define your dataset_filelist for day/pass_index
- filelist = []
- for path in train_data_dir:
- filelist += [path + "/%s" % x for x in os.listdir(path)]
-
- dataset.set_filelist(filelist)
- dataset.set_pipe_command(self.config.get("runner.pipe_command"))
- dataset.load_into_memory()
- return dataset
-
- def run_online_worker(self):
- logger.info("Run Online Worker Begin")
- use_cuda = int(config.get("runner.use_gpu"))
- place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
- self.exe = paddle.static.Executor(place)
-
- with open("./{}_worker_main_program.prototxt".format(
- fleet.worker_index()), 'w+') as f:
- f.write(str(paddle.static.default_main_program()))
- with open("./{}_worker_startup_program.prototxt".format(
- fleet.worker_index()), 'w+') as f:
- f.write(str(paddle.static.default_startup_program()))
-
- self.exe.run(paddle.static.default_startup_program())
- fleet.init_worker()
-
- save_model_path = self.config.get("runner.model_save_path")
- if save_model_path and (not os.path.exists(save_model_path)):
- os.makedirs(save_model_path)
-
- days = os.popen("echo -n " + self.config.get("runner.days")).read().split(" ")
- pass_per_day = int(self.config.get("runner.pass_per_day"))
-
- for day_index in range(len(days)):
- day = days[day_index]
- for pass_index in range(1, pass_per_day + 1):
- logger.info("Day: {} Pass: {} Begin.".format(day, pass_index))
-
- prepare_data_start_time = time.time()
- dataset = self.wait_and_prepare_dataset(day, pass_index)
- prepare_data_end_time = time.time()
- logger.info(
- "Prepare Dataset Done, using time {} second.".format(prepare_data_end_time - prepare_data_start_time))
-
- train_start_time = time.time()
- self.dataset_train_loop(dataset, day, pass_index)
- train_end_time = time.time()
- logger.info(
- "Train Dataset Done, using time {} second.".format(train_end_time - train_start_time))
-
- model_dir = "{}/{}/{}".format(save_model_path, day, pass_index)
-
- if fleet.is_first_worker() and save_model_path and is_distributed_env():
- fleet.save_inference_model(
- self.exe, model_dir,
- [feed.name for feed in self.input_data],
- self.inference_target_var,
- mode=2)
-
- if fleet.is_first_worker() and save_model_path and is_distributed_env():
- fleet.save_inference_model(
- self.exe, model_dir,
- [feed.name for feed in self.input_data],
- self.inference_target_var,
- mode=0)
-
- def dataset_train_loop(self, cur_dataset, day, pass_index):
- logger.info("Day: {} Pass: {}, Running Dataset Begin.".format(day, pass_index))
- fetch_info = [
- "Day: {} Pass: {} Var {}".format(day, pass_index, var_name)
- for var_name in self.metrics
- ]
- fetch_vars = [var for _, var in self.metrics.items()]
- print_step = int(config.get("runner.print_interval"))
- self.exe.train_from_dataset(
- program=paddle.static.default_main_program(),
- dataset=cur_dataset,
- fetch_list=fetch_vars,
- fetch_info=fetch_info,
- print_period=print_step,
- debug=config.get("runner.dataset_debug"))
- cur_dataset.release_memory()
-
- if __name__ == "__main__":
- paddle.enable_static()
- config = parse_args()
- # os.environ["CPU_NUM"] = str(config.get("runner.thread_num"))
- benchmark_main = Main(config)
- benchmark_main.run()
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