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114 lines
5.3 KiB
114 lines
5.3 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""train_criteo."""
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import os
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import sys
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import argparse
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import random
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import numpy as np
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from mindspore import context, ParallelMode
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.train.model import Model
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
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import mindspore.dataset.engine as de
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from src.deepfm import ModelBuilder, AUCMetric
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from src.config import DataConfig, ModelConfig, TrainConfig
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from src.dataset import create_dataset, DataType
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from src.callback import EvalCallBack, LossCallBack
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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parser = argparse.ArgumentParser(description='CTR Prediction')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--ckpt_path', type=str, default=None, help='Checkpoint path')
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parser.add_argument('--eval_file_name', type=str, default="./auc.log", help='eval file path')
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parser.add_argument('--loss_file_name', type=str, default="./loss.log", help='loss file path')
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parser.add_argument('--do_eval', type=bool, default=True, help='Do evaluation or not.')
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parser.add_argument('--device_target', type=str, default="Ascend", help='Ascend, GPU, or CPU')
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args_opt, _ = parser.parse_known_args()
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rank_size = int(os.environ.get("RANK_SIZE", 1))
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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if __name__ == '__main__':
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data_config = DataConfig()
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model_config = ModelConfig()
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train_config = TrainConfig()
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if rank_size > 1:
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if args_opt.device_target == "Ascend":
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
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init()
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rank_id = int(os.environ.get('RANK_ID'))
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elif args_opt.device_target == "GPU":
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init("nccl")
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(device_num=get_group_size(),
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parallel_mode=ParallelMode.DATA_PARALLEL,
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mirror_mean=True)
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rank_id = get_rank()
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else:
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print("Unsupported device_target ", args_opt.device_target)
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exit()
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else:
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)
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rank_size = None
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rank_id = None
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ds_train = create_dataset(args_opt.dataset_path,
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train_mode=True,
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epochs=1,
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batch_size=train_config.batch_size,
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data_type=DataType(data_config.data_format),
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rank_size=rank_size,
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rank_id=rank_id)
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model_builder = ModelBuilder(ModelConfig, TrainConfig)
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train_net, eval_net = model_builder.get_train_eval_net()
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auc_metric = AUCMetric()
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model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
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time_callback = TimeMonitor(data_size=ds_train.get_dataset_size())
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loss_callback = LossCallBack(loss_file_path=args_opt.loss_file_name)
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callback_list = [time_callback, loss_callback]
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if train_config.save_checkpoint:
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if rank_size:
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train_config.ckpt_file_name_prefix = train_config.ckpt_file_name_prefix + str(get_rank())
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config_ck = CheckpointConfig(save_checkpoint_steps=train_config.save_checkpoint_steps,
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keep_checkpoint_max=train_config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(prefix=train_config.ckpt_file_name_prefix,
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directory=args_opt.ckpt_path,
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config=config_ck)
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callback_list.append(ckpt_cb)
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if args_opt.do_eval:
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ds_eval = create_dataset(args_opt.dataset_path, train_mode=False,
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epochs=1,
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batch_size=train_config.batch_size,
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data_type=DataType(data_config.data_format))
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eval_callback = EvalCallBack(model, ds_eval, auc_metric,
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eval_file_path=args_opt.eval_file_name)
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callback_list.append(eval_callback)
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model.train(train_config.train_epochs, ds_train, callbacks=callback_list)
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