add DeepFM

example/deepfm_criteo/README.md

+# DeepFM Description
+
+This is an example of training DeepFM with Criteo dataset in MindSpore.
+
+[Paper](https://arxiv.org/pdf/1703.04247.pdf) Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He
+
+
+# Model architecture
+
+The overall network architecture of DeepFM is show below:
+
+[Link](https://arxiv.org/pdf/1703.04247.pdf)
+
+
+# Requirements
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+- Download the criteo dataset for pre-training. Extract and clean text in the dataset with [WikiExtractor](https://github.com/attardi/wikiextractor). Convert the dataset to TFRecord format and move the files to a specified path.
+- For more information, please check the resources below：
+  - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
+  - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
+
+# Script description
+
+## Script and sample code
+
+```python
+├── deepfm       
+  ├── README.md                      
+  ├── scripts 
+  │   ├──run_train.sh                  
+  │   ├──run_eval.sh                    
+  ├── src                              
+  │   ├──config.py                     
+  │   ├──dataset.py
+  │   ├──callback.py                                    
+  │   ├──deepfm.py
+  ├── train.py
+  ├── eval.py
+```
+
+## Training process
+
+### Usage
+
+- sh run_train.sh [DEVICE_NUM] [DATASET_PATH] [MINDSPORE_HCCL_CONFIG_PAHT]
+- python train.py --dataset_path [DATASET_PATH]
+
+### Launch
+
+``` 
+# distribute training example
+  sh scripts/run_distribute_train.sh 8 /opt/dataset/criteo /opt/mindspore_hccl_file.json
+# standalone training example
+  sh scripts/run_standalone_train.sh 0 /opt/dataset/criteo
+  or
+  python train.py --dataset_path /opt/dataset/criteo > output.log 2>&1 &
+```
+
+### Result
+
+Training result will be stored in the example path. 
+Checkpoints will be stored at `./checkpoint` by default, 
+and training log  will be redirected to `./output.log` by default,
+and loss log will be redirected to `./loss.log` by default,
+and eval log will be redirected to `./auc.log` by default. 
+
+
+## Eval process
+
+### Usage
+
+- sh run_eval.sh [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
+
+### Launch
+
+``` 
+# infer example
+    sh scripts/run_eval.sh 0 ~/criteo/eval/ ~/train/deepfm-15_41257.ckpt
+```
+
+> checkpoint can be produced in training process. 
+
+### Result
+
+Inference result will be stored in the example path, you can find result like the followings in `auc.log`. 
+
+``` 
+2020-05-27 20:51:35 AUC: 0.80577889065281, eval time: 35.55999s.
+```
+
+# Model description
+
+## Performance
+
+### Training Performance
+
+| Parameters                 | DeepFM                                                |
+| -------------------------- | ------------------------------------------------------|
+| Model Version              |                                                       |
+| Resource                   | Ascend 910, cpu:2.60GHz 96cores, memory:1.5T          |
+| uploaded Date              | 05/27/2020                                            |
+| MindSpore Version          | 0.2.0                                                 |
+| Dataset                    | Criteo                                                |
+| Training Parameters        | src/config.py                                         |
+| Optimizer                  | Adam                                                  |
+| Loss Function              | SoftmaxCrossEntropyWithLogits                         |
+| outputs                    |                                                       |
+| Loss                       | 0.4234                                                |
+| Accuracy                   | AUC[0.8055]                                           |
+| Total time                 | 91 min                                                |
+| Params (M)                 |                                                       |
+| Checkpoint for Fine tuning |                                                       |
+| Model for inference        |                                                       |
+
+#### Inference Performance
+
+| Parameters                 |                               |                           |
+| -------------------------- | ----------------------------- | ------------------------- |
+| Model Version              |                               |                           |   
+| Resource                   | Ascend 910                    | Ascend 310                | 
+| uploaded Date              | 05/27/2020                    | 05/27/2020                | 
+| MindSpore Version          | 0.2.0                         | 0.2.0                     |  
+| Dataset                    | Criteo                        |                           |
+| batch_size                 | 1000                          |                           |
+| outputs                    |                               |                           |
+| Accuracy                   | AUC[0.8055]                   |                           |                      
+| Speed                      |                               |                           |                     
+| Total time                 | 35.559s                       |                           |                      
+| Model for inference        |                               |                           |                 
+
+# ModelZoo Homepage  
+ [Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)  

example/deepfm_criteo/__init__.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================

example/deepfm_criteo/eval.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""train_criteo."""
+import os
+import sys
+import time
+import argparse
+
+from mindspore import context
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+from src.deepfm import ModelBuilder, AUCMetric
+from src.config import DataConfig, ModelConfig, TrainConfig
+from src.dataset import create_dataset
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+parser = argparse.ArgumentParser(description='CTR Prediction')
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+
+args_opt, _ = parser.parse_known_args()
+device_id = int(os.getenv('DEVICE_ID'))
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id)
+
+
+def add_write(file_path, print_str):
+    with open(file_path, 'a+', encoding='utf-8') as file_out:
+        file_out.write(print_str + '\n')
+
+
+if __name__ == '__main__':
+    data_config = DataConfig()
+    model_config = ModelConfig()
+    train_config = TrainConfig()
+
+    ds_eval = create_dataset(args_opt.dataset_path, train_mode=False,
+                             epochs=1, batch_size=train_config.batch_size)
+    model_builder = ModelBuilder(ModelConfig, TrainConfig)
+    train_net, eval_net = model_builder.get_train_eval_net()
+    train_net.set_train()
+    eval_net.set_train(False)
+    auc_metric = AUCMetric()
+    model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    load_param_into_net(eval_net, param_dict)
+
+    start = time.time()
+    res = model.eval(ds_eval)
+    eval_time = time.time() - start
+    time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+    out_str = f'{time_str} AUC: {list(res.values())[0]}, eval time: {eval_time}s.'
+    print(out_str)
+    add_write('./auc.log', str(out_str))

example/deepfm_criteo/scripts/run_distribute_train.sh

+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+echo "Please run the script as: "
+echo "sh scripts/run_distribute_train.sh DEVICE_NUM DATASET_PATH MINDSPORE_HCCL_CONFIG_PAHT"
+echo "for example: sh scripts/run_distribute_train.sh 8 /dataset_path /rank_table_8p.json"
+echo "After running the script, the network runs in the background, The log will be generated in logx/output.log"
+
+
+export RANK_SIZE=$1
+DATA_URL=$2
+export MINDSPORE_HCCL_CONFIG_PAHT=$3
+
+for ((i=0; i<RANK_SIZE;i++))
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$i
+    rm -rf log$i
+    mkdir ./log$i
+    cp *.py ./log$i
+    cp -r src ./log$i
+    cd ./log$i || exit
+    echo "start training for rank $i, device $DEVICE_ID"
+    env > env.log
+    python -u train.py \
+    --dataset_path=$DATA_URL \
+    --ckpt_path="checkpoint" \
+    --eval_file_name='auc.log' \
+    --loss_file_name='loss.log' \
+    --do_eval=True > output.log 2>&1 &
+    cd ../
+done

example/deepfm_criteo/scripts/run_eval.sh

+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+echo "Please run the script as: "
+echo "sh scripts/run_eval.sh DEVICE_ID DATASET_PATH CHECKPOINT_PATH"
+echo "for example: sh scripts/run_eval.sh 0 /dataset_path /checkpoint_path"
+echo "After running the script, the network runs in the background, The log will be generated in ms_log/eval_output.log"
+
+export DEVICE_ID=$1
+DATA_URL=$2
+CHECKPOINT_PATH=$3
+
+mkdir -p ms_log
+CUR_DIR=`pwd`
+export GLOG_log_dir=${CUR_DIR}/ms_log
+export GLOG_logtostderr=0
+
+python -u eval.py \
+    --dataset_path=$DATA_URL \
+    --checkpoint_path=$CHECKPOINT_PATH > ms_log/eval_output.log 2>&1 &
\ No newline at end of file

example/deepfm_criteo/scripts/run_standalone_train.sh

+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+echo "Please run the script as: "
+echo "sh scripts/run_standalone_train.sh DEVICE_ID DATASET_PATH"
+echo "for example: sh scripts/run_standalone_train.sh 0 /dataset_path"
+echo "After running the script, the network runs in the background, The log will be generated in ms_log/output.log"
+
+export DEVICE_ID=$1
+DATA_URL=$2
+
+mkdir -p ms_log
+CUR_DIR=`pwd`
+export GLOG_log_dir=${CUR_DIR}/ms_log
+export GLOG_logtostderr=0
+
+python -u train.py \
+    --dataset_path=$DATA_URL \
+    --ckpt_path="checkpoint" \
+    --eval_file_name='auc.log' \
+    --loss_file_name='loss.log' \
+    --do_eval=True > ms_log/output.log 2>&1 &

example/deepfm_criteo/src/__init__.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================

example/deepfm_criteo/src/callback.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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
+#
+# httpwww.apache.orglicensesLICENSE-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.
+# ============================================================================
+"""
+Defined callback for DeepFM.
+"""
+import time
+from mindspore.train.callback import Callback
+
+
+def add_write(file_path, out_str):
+    with open(file_path, 'a+', encoding='utf-8') as file_out:
+        file_out.write(out_str + '\n')
+
+
+class EvalCallBack(Callback):
+    """
+    Monitor the loss in training.
+    If the loss is NAN or INF terminating training.
+    Note
+        If per_print_times is 0 do not print loss.
+    """
+    def __init__(self, model, eval_dataset, auc_metric, eval_file_path):
+        super(EvalCallBack, self).__init__()
+        self.model = model
+        self.eval_dataset = eval_dataset
+        self.aucMetric = auc_metric
+        self.aucMetric.clear()
+        self.eval_file_path = eval_file_path
+
+    def epoch_end(self, run_context):
+        start_time = time.time()
+        out = self.model.eval(self.eval_dataset)
+        eval_time = int(time.time() - start_time)
+        time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        out_str = "{} EvalCallBack metric{}; eval_time{}s".format(
+            time_str, out.values(), eval_time)
+        print(out_str)
+        add_write(self.eval_file_path, out_str)
+
+
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+    If the loss is NAN or INF terminating training.
+    Note
+        If per_print_times is 0 do not print loss.
+    Args
+        loss_file_path (str) The file absolute path, to save as loss_file;
+        per_print_times (int) Print loss every times. Default 1.
+    """
+    def __init__(self, loss_file_path, per_print_times=1):
+        super(LossCallBack, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0.")
+        self.loss_file_path = loss_file_path
+        self._per_print_times = per_print_times
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        loss = cb_params.net_outputs.asnumpy()
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+        cur_num = cb_params.cur_step_num
+        if self._per_print_times != 0 and cur_num % self._per_print_times == 0:
+            with open(self.loss_file_path, "a+") as loss_file:
+                time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+                loss_file.write("{} epoch: {} step: {}, loss is {}\n".format(
+                    time_str, cb_params.cur_epoch_num, cur_step_in_epoch, loss))
+            print("epoch: {} step: {}, loss is {}\n".format(
+                cb_params.cur_epoch_num, cur_step_in_epoch, loss))
+
+
+class TimeMonitor(Callback):
+    """
+    Time monitor for calculating cost of each epoch.
+    Args
+        data_size (int) step size of an epoch.
+    """
+    def __init__(self, data_size):
+        super(TimeMonitor, self).__init__()
+        self.data_size = data_size
+
+    def epoch_begin(self, run_context):
+        self.epoch_time = time.time()
+
+    def epoch_end(self, run_context):
+        epoch_mseconds = (time.time() - self.epoch_time) * 1000
+        per_step_mseconds = epoch_mseconds / self.data_size
+        print("epoch time: {0}, per step time: {1}".format(epoch_mseconds, per_step_mseconds), flush=True)
+
+    def step_begin(self, run_context):
+        self.step_time = time.time()
+
+    def step_end(self, run_context):
+        step_mseconds = (time.time() - self.step_time) * 1000
+        print(f"step time {step_mseconds}", flush=True)

example/deepfm_criteo/src/config.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+network config setting, will be used in train.py and eval.py
+"""
+
+
+class DataConfig:
+    """
+    Define parameters of dataset.
+    """
+    data_vocab_size = 184965
+    train_num_of_parts = 21
+    test_num_of_parts = 3
+    batch_size = 1000
+    data_field_size = 39
+    # dataset format, 1: mindrecord, 2: tfrecord, 3: h5
+    data_format = 2
+
+
+class ModelConfig:
+    """
+    Define parameters of model.
+    """
+    batch_size = DataConfig.batch_size
+    data_field_size = DataConfig.data_field_size
+    data_vocab_size = DataConfig.data_vocab_size
+    data_emb_dim = 80
+    deep_layer_args = [[400, 400, 512], "relu"]
+    init_args = [-0.01, 0.01]
+    weight_bias_init = ['normal', 'normal']
+    keep_prob = 0.9
+
+
+class TrainConfig:
+    """
+    Define parameters of training.
+    """
+    batch_size = DataConfig.batch_size
+    l2_coef = 1e-6
+    learning_rate = 1e-5
+    epsilon = 1e-8
+    loss_scale = 1024.0
+    train_epochs = 15
+    save_checkpoint = True
+    ckpt_file_name_prefix = "deepfm"
+    save_checkpoint_steps = 1
+    keep_checkpoint_max = 15
+    eval_callback = True
+    loss_callback = True

example/deepfm_criteo/src/dataset.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+Create train or eval dataset.
+"""
+import os
+import math
+from enum import Enum
+
+import pandas as pd
+import numpy as np
+import mindspore.dataset.engine as de
+import mindspore.common.dtype as mstype
+
+from .config import DataConfig
+
+
+class DataType(Enum):
+    """
+    Enumerate supported dataset format.
+    """
+    MINDRECORD = 1
+    TFRECORD = 2
+    H5 = 3
+
+
+class H5Dataset():
+    """
+    Create dataset with H5 format.
+
+    Args:
+        data_path (str): Dataset directory.
+        train_mode (bool): Whether dataset is used for train or eval (default=True).
+        train_num_of_parts (int): The number of train data file (default=21).
+        test_num_of_parts (int): The number of test data file (default=3).
+    """
+    max_length = 39
+
+    def __init__(self, data_path, train_mode=True,
+                 train_num_of_parts=DataConfig.train_num_of_parts,
+                 test_num_of_parts=DataConfig.test_num_of_parts):
+        self._hdf_data_dir = data_path
+        self._is_training = train_mode
+        if self._is_training:
+            self._file_prefix = 'train'
+            self._num_of_parts = train_num_of_parts
+        else:
+            self._file_prefix = 'test'
+            self._num_of_parts = test_num_of_parts
+        self.data_size = self._bin_count(self._hdf_data_dir, self._file_prefix, self._num_of_parts)
+        print("data_size: {}".format(self.data_size))
+
+    def _bin_count(self, hdf_data_dir, file_prefix, num_of_parts):
+        size = 0
+        for part in range(num_of_parts):
+            _y = pd.read_hdf(os.path.join(hdf_data_dir, f'{file_prefix}_output_part_{str(part)}.h5'))
+            size += _y.shape[0]
+        return size
+
+    def _iterate_hdf_files_(self, num_of_parts=None,
+                            shuffle_block=False):
+        """
+        iterate among hdf files(blocks). when the whole data set is finished, the iterator restarts
+            from the beginning, thus the data stream will never stop
+        :param train_mode: True or false,false is eval_mode,
+            this file iterator will go through the train set
+        :param num_of_parts: number of files
+        :param shuffle_block: shuffle block files at every round
+        :return: input_hdf_file_name, output_hdf_file_name, finish_flag
+        """
+        parts = np.arange(num_of_parts)
+        while True:
+            if shuffle_block:
+                for _ in range(int(shuffle_block)):
+                    np.random.shuffle(parts)
+            for i, p in enumerate(parts):
+                yield os.path.join(self._hdf_data_dir, f'{self._file_prefix}_input_part_{str(p)}.h5'), \
+                      os.path.join(self._hdf_data_dir, f'{self._file_prefix}_output_part_{str(p)}.h5'), \
+                      i + 1 == len(parts)
+
+    def _generator(self, X, y, batch_size, shuffle=True):
+        """
+        should be accessed only in private
+        :param X:
+        :param y:
+        :param batch_size:
+        :param shuffle:
+        :return:
+        """
+        number_of_batches = np.ceil(1. * X.shape[0] / batch_size)
+        counter = 0
+        finished = False
+        sample_index = np.arange(X.shape[0])
+        if shuffle:
+            for _ in range(int(shuffle)):
+                np.random.shuffle(sample_index)
+        assert X.shape[0] > 0
+        while True:
+            batch_index = sample_index[batch_size * counter: batch_size * (counter + 1)]
+            X_batch = X[batch_index]
+            y_batch = y[batch_index]
+            counter += 1
+            yield X_batch, y_batch, finished
+            if counter == number_of_batches:
+                counter = 0
+                finished = True
+
+    def batch_generator(self, batch_size=1000,
+                        random_sample=False, shuffle_block=False):
+        """
+        :param train_mode: True or false,false is eval_mode,
+        :param batch_size
+        :param num_of_parts: number of files
+        :param random_sample: if True, will shuffle
+        :param shuffle_block: shuffle file blocks at every round
+        :return:
+        """
+
+        for hdf_in, hdf_out, _ in self._iterate_hdf_files_(self._num_of_parts,
+                                                           shuffle_block):
+            start = stop = None
+            X_all = pd.read_hdf(hdf_in, start=start, stop=stop).values
+            y_all = pd.read_hdf(hdf_out, start=start, stop=stop).values
+            data_gen = self._generator(X_all, y_all, batch_size,
+                                       shuffle=random_sample)
+            finished = False
+
+            while not finished:
+                X, y, finished = data_gen.__next__()
+                X_id = X[:, 0:self.max_length]
+                X_va = X[:, self.max_length:]
+                yield np.array(X_id.astype(dtype=np.int32)), \
+                    np.array(X_va.astype(dtype=np.float32)), \
+                    np.array(y.astype(dtype=np.float32))
+
+
+def _get_h5_dataset(directory, train_mode=True, epochs=1, batch_size=1000):
+    """
+    Get dataset with h5 format.
+
+    Args:
+        directory (str): Dataset directory.
+        train_mode (bool): Whether dataset is use for train or eval (default=True).
+        epochs (int): Dataset epoch size (default=1).
+        batch_size (int): Dataset batch size (default=1000)
+
+    Returns:
+        Dataset.
+    """
+    data_para = {'batch_size': batch_size}
+    if train_mode:
+        data_para['random_sample'] = True
+        data_para['shuffle_block'] = True
+
+    h5_dataset = H5Dataset(data_path=directory, train_mode=train_mode)
+    numbers_of_batch = math.ceil(h5_dataset.data_size / batch_size)
+
+    def _iter_h5_data():
+        train_eval_gen = h5_dataset.batch_generator(**data_para)
+        for _ in range(0, numbers_of_batch, 1):
+            yield train_eval_gen.__next__()
+
+    ds = de.GeneratorDataset(_iter_h5_data, ["ids", "weights", "labels"])
+    ds.set_dataset_size(numbers_of_batch)
+    ds = ds.repeat(epochs)
+    return ds
+
+
+def _get_mindrecord_dataset(directory, train_mode=True, epochs=1, batch_size=1000,
+                            line_per_sample=1000, rank_size=None, rank_id=None):
+    """
+    Get dataset with mindrecord format.
+
+    Args:
+        directory (str): Dataset directory.
+        train_mode (bool): Whether dataset is use for train or eval (default=True).
+        epochs (int): Dataset epoch size (default=1).
+        batch_size (int): Dataset batch size (default=1000).
+        line_per_sample (int): The number of sample per line (default=1000).
+        rank_size (int): The number of device, not necessary for single device (default=None).
+        rank_id (int): Id of device, not necessary for single device (default=None).
+
+    Returns:
+        Dataset.
+    """
+    file_prefix_name = 'train_input_part.mindrecord' if train_mode else 'test_input_part.mindrecord'
+    file_suffix_name = '00' if train_mode else '0'
+    shuffle = train_mode
+
+    if rank_size is not None and rank_id is not None:
+        ds = de.MindDataset(os.path.join(directory, file_prefix_name + file_suffix_name),
+                            columns_list=['feat_ids', 'feat_vals', 'label'],
+                            num_shards=rank_size, shard_id=rank_id, shuffle=shuffle,
+                            num_parallel_workers=8)
+    else:
+        ds = de.MindDataset(os.path.join(directory, file_prefix_name + file_suffix_name),
+                            columns_list=['feat_ids', 'feat_vals', 'label'],
+                            shuffle=shuffle, num_parallel_workers=8)
+    ds = ds.batch(int(batch_size / line_per_sample), drop_remainder=True)
+    ds = ds.map(operations=(lambda x, y, z: (np.array(x).flatten().reshape(batch_size, 39),
+                                             np.array(y).flatten().reshape(batch_size, 39),
+                                             np.array(z).flatten().reshape(batch_size, 1))),
+                input_columns=['feat_ids', 'feat_vals', 'label'],
+                columns_order=['feat_ids', 'feat_vals', 'label'],
+                num_parallel_workers=8)
+    ds = ds.repeat(epochs)
+    return ds
+
+
+def _get_tf_dataset(directory, train_mode=True, epochs=1, batch_size=1000,
+                    line_per_sample=1000, rank_size=None, rank_id=None):
+    """
+    Get dataset with tfrecord format.
+
+    Args:
+        directory (str): Dataset directory.
+        train_mode (bool): Whether dataset is use for train or eval (default=True).
+        epochs (int): Dataset epoch size (default=1).
+        batch_size (int): Dataset batch size (default=1000).
+        line_per_sample (int): The number of sample per line (default=1000).
+        rank_size (int): The number of device, not necessary for single device (default=None).
+        rank_id (int): Id of device, not necessary for single device (default=None).
+
+    Returns:
+        Dataset.
+    """
+    dataset_files = []
+    file_prefixt_name = 'train' if train_mode else 'test'
+    shuffle = train_mode
+    for (dir_path, _, filenames) in os.walk(directory):
+        for filename in filenames:
+            if file_prefixt_name in filename and 'tfrecord' in filename:
+                dataset_files.append(os.path.join(dir_path, filename))
+    schema = de.Schema()
+    schema.add_column('feat_ids', de_type=mstype.int32)
+    schema.add_column('feat_vals', de_type=mstype.float32)
+    schema.add_column('label', de_type=mstype.float32)
+    if rank_size is not None and rank_id is not None:
+        ds = de.TFRecordDataset(dataset_files=dataset_files, shuffle=shuffle,
+                                schema=schema, num_parallel_workers=8,
+                                num_shards=rank_size, shard_id=rank_id,
+                                shard_equal_rows=True)
+    else:
+        ds = de.TFRecordDataset(dataset_files=dataset_files, shuffle=shuffle,
+                                schema=schema, num_parallel_workers=8)
+    ds = ds.batch(int(batch_size / line_per_sample), drop_remainder=True)
+    ds = ds.map(operations=(lambda x, y, z: (
+        np.array(x).flatten().reshape(batch_size, 39),
+        np.array(y).flatten().reshape(batch_size, 39),
+        np.array(z).flatten().reshape(batch_size, 1))),
+                input_columns=['feat_ids', 'feat_vals', 'label'],
+                columns_order=['feat_ids', 'feat_vals', 'label'],
+                num_parallel_workers=8)
+    ds = ds.repeat(epochs)
+    return ds
+
+
+def create_dataset(directory, train_mode=True, epochs=1, batch_size=1000,
+                   data_type=DataType.TFRECORD, line_per_sample=1000,
+                   rank_size=None, rank_id=None):
+    """
+    Get dataset.
+
+    Args:
+        directory (str): Dataset directory.
+        train_mode (bool): Whether dataset is use for train or eval (default=True).
+        epochs (int): Dataset epoch size (default=1).
+        batch_size (int): Dataset batch size (default=1000).
+        data_type (DataType): The type of dataset which is one of H5, TFRECORE, MINDRECORD (default=TFRECORD).
+        line_per_sample (int): The number of sample per line (default=1000).
+        rank_size (int): The number of device, not necessary for single device (default=None).
+        rank_id (int): Id of device, not necessary for single device (default=None).
+
+    Returns:
+        Dataset.
+    """
+    if data_type == DataType.MINDRECORD:
+        return _get_mindrecord_dataset(directory, train_mode, epochs,
+                                       batch_size, line_per_sample,
+                                       rank_size, rank_id)
+    if data_type == DataType.TFRECORD:
+        return _get_tf_dataset(directory, train_mode, epochs, batch_size,
+                               line_per_sample, rank_size=rank_size, rank_id=rank_id)
+
+    if rank_size is not None and rank_size > 1:
+        raise ValueError('Please use mindrecord dataset.')
+    return _get_h5_dataset(directory, train_mode, epochs, batch_size)

example/deepfm_criteo/src/deepfm.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+""" test_training """
+import os
+
+import numpy as np
+from sklearn.metrics import roc_auc_score
+import mindspore.common.dtype as mstype
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.nn import Dropout
+from mindspore.nn.optim import Adam
+from mindspore.nn.metrics import Metric
+from mindspore import nn, ParameterTuple, Parameter
+from mindspore.common.initializer import Uniform, initializer, Normal
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
+
+from .callback import EvalCallBack, LossCallBack
+
+
+np_type = np.float32
+ms_type = mstype.float32
+
+
+class AUCMetric(Metric):
+    """AUC metric for DeepFM model."""
+    def __init__(self):
+        super(AUCMetric, self).__init__()
+        self.pred_probs = []
+        self.true_labels = []
+
+    def clear(self):
+        """Clear the internal evaluation result."""
+        self.pred_probs = []
+        self.true_labels = []
+
+    def update(self, *inputs):
+        batch_predict = inputs[1].asnumpy()
+        batch_label = inputs[2].asnumpy()
+        self.pred_probs.extend(batch_predict.flatten().tolist())
+        self.true_labels.extend(batch_label.flatten().tolist())
+
+    def eval(self):
+        if len(self.true_labels) != len(self.pred_probs):
+            raise RuntimeError('true_labels.size() is not equal to pred_probs.size()')
+        auc = roc_auc_score(self.true_labels, self.pred_probs)
+        return auc
+
+
+def init_method(method, shape, name, max_val=0.01):
+    """
+    The method of init parameters.
+
+    Args:
+        method (str): The method uses to initialize parameter.
+        shape (list): The shape of parameter.
+        name (str): The name of parameter.
+        max_val (float): Max value in parameter when uses 'random' or 'uniform' to initialize parameter.
+
+    Returns:
+        Parameter.
+    """
+    if method in ['random', 'uniform']:
+        params = Parameter(initializer(Uniform(max_val), shape, ms_type), name=name)
+    elif method == "one":
+        params = Parameter(initializer("ones", shape, ms_type), name=name)
+    elif method == 'zero':
+        params = Parameter(initializer("zeros", shape, ms_type), name=name)
+    elif method == "normal":
+        params = Parameter(initializer(Normal(max_val), shape, ms_type), name=name)
+    return params
+
+
+def init_var_dict(init_args, values):
+    """
+    Init parameter.
+
+    Args:
+        init_args (list): Define max and min value of parameters.
+        values (list): Define name, shape and init method of parameters.
+
+    Returns:
+        dict, a dict ot Parameter.
+    """
+    var_map = {}
+    _, _max_val = init_args
+    for key, shape, init_flag in values:
+        if key not in var_map.keys():
+            if init_flag in ['random', 'uniform']:
+                var_map[key] = Parameter(initializer(Uniform(_max_val), shape, ms_type), name=key)
+            elif init_flag == "one":
+                var_map[key] = Parameter(initializer("ones", shape, ms_type), name=key)
+            elif init_flag == "zero":
+                var_map[key] = Parameter(initializer("zeros", shape, ms_type), name=key)
+            elif init_flag == 'normal':
+                var_map[key] = Parameter(initializer(Normal(_max_val), shape, ms_type), name=key)
+    return var_map
+
+
+class DenseLayer(nn.Cell):
+    """
+    Dense Layer for Deep Layer of DeepFM Model;
+    Containing: activation, matmul, bias_add;
+    Args:
+        input_dim (int): the shape of weight at 0-aixs;
+        output_dim (int): the shape of weight at 1-aixs, and shape of bias
+        weight_bias_init (list): weight and bias init method, "random", "uniform", "one", "zero", "normal";
+        act_str (str): activation function method, "relu", "sigmoid", "tanh";
+        keep_prob (float): Dropout Layer keep_prob_rate;
+        scale_coef (float): input scale coefficient;
+    """
+    def __init__(self, input_dim, output_dim, weight_bias_init, act_str, keep_prob=0.9, scale_coef=1.0):
+        super(DenseLayer, self).__init__()
+        weight_init, bias_init = weight_bias_init
+        self.weight = init_method(weight_init, [input_dim, output_dim], name="weight")
+        self.bias = init_method(bias_init, [output_dim], name="bias")
+        self.act_func = self._init_activation(act_str)
+        self.matmul = P.MatMul(transpose_b=False)
+        self.bias_add = P.BiasAdd()
+        self.cast = P.Cast()
+        self.dropout = Dropout(keep_prob=keep_prob)
+        self.mul = P.Mul()
+        self.realDiv = P.RealDiv()
+        self.scale_coef = scale_coef
+
+    def _init_activation(self, act_str):
+        act_str = act_str.lower()
+        if act_str == "relu":
+            act_func = P.ReLU()
+        elif act_str == "sigmoid":
+            act_func = P.Sigmoid()
+        elif act_str == "tanh":
+            act_func = P.Tanh()
+        return act_func
+
+    def construct(self, x):
+        x = self.act_func(x)
+        if self.training:
+            x = self.dropout(x)
+        x = self.mul(x, self.scale_coef)
+        x = self.cast(x, mstype.float16)
+        weight = self.cast(self.weight, mstype.float16)
+        wx = self.matmul(x, weight)
+        wx = self.cast(wx, mstype.float32)
+        wx = self.realDiv(wx, self.scale_coef)
+        output = self.bias_add(wx, self.bias)
+        return output
+
+
+class DeepFMModel(nn.Cell):
+    """
+    From paper: "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction"
+
+    Args:
+        batch_size (int):  smaple_number of per step in training; (int, batch_size=128)
+        filed_size (int):  input filed number, or called id_feature number; (int, filed_size=39)
+        vocab_size (int):  id_feature vocab size, id dict size;  (int, vocab_size=200000)
+        emb_dim (int):  id embedding vector dim, id mapped to embedding vector; (int, emb_dim=100)
+        deep_layer_args (list):  Deep Layer args, layer_dim_list, layer_activator;
+                             (int, deep_layer_args=[[100, 100, 100], "relu"])
+        init_args (list): init args for Parameter init; (list, init_args=[min, max, seeds])
+        weight_bias_init (list): weight, bias init method for deep layers;
+                            (list[str], weight_bias_init=['random', 'zero'])
+        keep_prob (float): if dropout_flag is True, keep_prob rate to keep connect; (float, keep_prob=0.8)
+    """
+    def __init__(self, config):
+        super(DeepFMModel, self).__init__()
+
+        self.batch_size = config.batch_size
+        self.field_size = config.data_field_size
+        self.vocab_size = config.data_vocab_size
+        self.emb_dim = config.data_emb_dim
+        self.deep_layer_dims_list, self.deep_layer_act = config.deep_layer_args
+        self.init_args = config.init_args
+        self.weight_bias_init = config.weight_bias_init
+        self.keep_prob = config.keep_prob
+        init_acts = [('W_l2', [self.vocab_size, 1], 'normal'),
+                     ('V_l2', [self.vocab_size, self.emb_dim], 'normal'),
+                     ('b', [1], 'normal')]
+        var_map = init_var_dict(self.init_args, init_acts)
+        self.fm_w = var_map["W_l2"]
+        self.fm_b = var_map["b"]
+        self.embedding_table = var_map["V_l2"]
+        # Deep Layers
+        self.deep_input_dims = self.field_size * self.emb_dim + 1
+        self.all_dim_list = [self.deep_input_dims] + self.deep_layer_dims_list + [1]
+        self.dense_layer_1 = DenseLayer(self.all_dim_list[0], self.all_dim_list[1],
+                                        self.weight_bias_init, self.deep_layer_act, self.keep_prob)
+        self.dense_layer_2 = DenseLayer(self.all_dim_list[1], self.all_dim_list[2],
+                                        self.weight_bias_init, self.deep_layer_act, self.keep_prob)
+        self.dense_layer_3 = DenseLayer(self.all_dim_list[2], self.all_dim_list[3],
+                                        self.weight_bias_init, self.deep_layer_act, self.keep_prob)
+        self.dense_layer_4 = DenseLayer(self.all_dim_list[3], self.all_dim_list[4],
+                                        self.weight_bias_init, self.deep_layer_act, self.keep_prob)
+        # FM, linear Layers
+        self.Gatherv2 = P.GatherV2()
+        self.Mul = P.Mul()
+        self.ReduceSum = P.ReduceSum(keep_dims=False)
+        self.Reshape = P.Reshape()
+        self.Square = P.Square()
+        self.Shape = P.Shape()
+        self.Tile = P.Tile()
+        self.Concat = P.Concat(axis=1)
+        self.Cast = P.Cast()
+
+    def construct(self, id_hldr, wt_hldr):
+        """
+        Args:
+            id_hldr: batch ids;   [bs, field_size]
+            wt_hldr: batch weights;   [bs, field_size]
+        """
+
+        mask = self.Reshape(wt_hldr, (self.batch_size, self.field_size, 1))
+        # Linear layer
+        fm_id_weight = self.Gatherv2(self.fm_w, id_hldr, 0)
+        wx = self.Mul(fm_id_weight, mask)
+        linear_out = self.ReduceSum(wx, 1)
+        # FM layer
+        fm_id_embs = self.Gatherv2(self.embedding_table, id_hldr, 0)
+        vx = self.Mul(fm_id_embs, mask)
+        v1 = self.ReduceSum(vx, 1)
+        v1 = self.Square(v1)
+        v2 = self.Square(vx)
+        v2 = self.ReduceSum(v2, 1)
+        fm_out = 0.5 * self.ReduceSum(v1 - v2, 1)
+        fm_out = self.Reshape(fm_out, (-1, 1))
+        #  Deep layer
+        b = self.Reshape(self.fm_b, (1, 1))
+        b = self.Tile(b, (self.batch_size, 1))
+        deep_in = self.Reshape(vx, (-1, self.field_size * self.emb_dim))
+        deep_in = self.Concat((deep_in, b))
+        deep_in = self.dense_layer_1(deep_in)
+        deep_in = self.dense_layer_2(deep_in)
+        deep_in = self.dense_layer_3(deep_in)
+        deep_out = self.dense_layer_4(deep_in)
+        out = linear_out + fm_out + deep_out
+        return out, fm_id_weight, fm_id_embs
+
+
+class NetWithLossClass(nn.Cell):
+    """
+    NetWithLossClass definition.
+    """
+    def __init__(self, network, l2_coef=1e-6):
+        super(NetWithLossClass, self).__init__(auto_prefix=False)
+        self.loss = P.SigmoidCrossEntropyWithLogits()
+        self.network = network
+        self.l2_coef = l2_coef
+        self.Square = P.Square()
+        self.ReduceMean_false = P.ReduceMean(keep_dims=False)
+        self.ReduceSum_false = P.ReduceSum(keep_dims=False)
+
+    def construct(self, batch_ids, batch_wts, label):
+        predict, fm_id_weight, fm_id_embs = self.network(batch_ids, batch_wts)
+        log_loss = self.loss(predict, label)
+        mean_log_loss = self.ReduceMean_false(log_loss)
+        l2_loss_w = self.ReduceSum_false(self.Square(fm_id_weight))
+        l2_loss_v = self.ReduceSum_false(self.Square(fm_id_embs))
+        l2_loss_all = self.l2_coef * (l2_loss_v + l2_loss_w) * 0.5
+        loss = mean_log_loss + l2_loss_all
+        return loss
+
+
+class TrainStepWrap(nn.Cell):
+    """
+    TrainStepWrap definition
+    """
+    def __init__(self, network, lr=5e-8, eps=1e-8, loss_scale=1000.0):
+        super(TrainStepWrap, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.set_train()
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = Adam(self.weights, learning_rate=lr, eps=eps, loss_scale=loss_scale)
+        self.hyper_map = C.HyperMap()
+        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.sens = loss_scale
+
+    def construct(self, batch_ids, batch_wts, label):
+        weights = self.weights
+        loss = self.network(batch_ids, batch_wts, label)
+        sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens) #
+        grads = self.grad(self.network, weights)(batch_ids, batch_wts, label, sens)
+        return F.depend(loss, self.optimizer(grads))
+
+
+class PredictWithSigmoid(nn.Cell):
+    """
+    Eval model with sigmoid.
+    """
+    def __init__(self, network):
+        super(PredictWithSigmoid, self).__init__(auto_prefix=False)
+        self.network = network
+        self.sigmoid = P.Sigmoid()
+
+    def construct(self, batch_ids, batch_wts, labels):
+        logits, _, _, = self.network(batch_ids, batch_wts)
+        pred_probs = self.sigmoid(logits)
+
+        return logits, pred_probs, labels
+
+
+class ModelBuilder:
+    """
+    Model builder for DeepFM.
+
+    Args:
+        model_config (ModelConfig): Model configuration.
+        train_config (TrainConfig): Train configuration.
+    """
+    def __init__(self, model_config, train_config):
+        self.model_config = model_config
+        self.train_config = train_config
+
+    def get_callback_list(self, model=None, eval_dataset=None):
+        """
+        Get callbacks which contains checkpoint callback, eval callback and loss callback.
+
+        Args:
+            model (Cell): The network is added callback (default=None).
+            eval_dataset (Dataset): Dataset for eval (default=None).
+        """
+        callback_list = []
+        if self.train_config.save_checkpoint:
+            config_ck = CheckpointConfig(save_checkpoint_steps=self.train_config.save_checkpoint_steps,
+                                         keep_checkpoint_max=self.train_config.keep_checkpoint_max)
+            ckpt_cb = ModelCheckpoint(prefix=self.train_config.ckpt_file_name_prefix,
+                                      directory=self.train_config.output_path,
+                                      config=config_ck)
+            callback_list.append(ckpt_cb)
+        if self.train_config.eval_callback:
+            if model is None:
+                raise RuntimeError("train_config.eval_callback is {}; get_callback_list() args model is {}".format(
+                                        self.train_config.eval_callback, model))
+            if eval_dataset is None:
+                raise RuntimeError("train_config.eval_callback is {}; get_callback_list() "
+                                   "args eval_dataset is {}".format(self.train_config.eval_callback, eval_dataset))
+            auc_metric = AUCMetric()
+            eval_callback = EvalCallBack(model, eval_dataset, auc_metric,
+                                         eval_file_path=os.path.join(self.train_config.output_path,
+                                                                     self.train_config.eval_file_name))
+            callback_list.append(eval_callback)
+        if self.train_config.loss_callback:
+            loss_callback = LossCallBack(loss_file_path=os.path.join(self.train_config.output_path,
+                                                                     self.train_config.loss_file_name))
+            callback_list.append(loss_callback)
+        if callback_list:
+            return callback_list
+        return None
+
+    def get_train_eval_net(self):
+        deepfm_net = DeepFMModel(self.model_config)
+        loss_net = NetWithLossClass(deepfm_net, l2_coef=self.train_config.l2_coef)
+        train_net = TrainStepWrap(loss_net, lr=self.train_config.learning_rate,
+                                  eps=self.train_config.epsilon,
+                                  loss_scale=self.train_config.loss_scale)
+        eval_net = PredictWithSigmoid(deepfm_net)
+        return train_net, eval_net

example/deepfm_criteo/train.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""train_criteo."""
+import os
+import sys
+import argparse
+
+from mindspore import context, ParallelMode
+from mindspore.communication.management import init
+from mindspore.train.model import Model
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
+
+from src.deepfm import ModelBuilder, AUCMetric
+from src.config import DataConfig, ModelConfig, TrainConfig
+from src.dataset import create_dataset, DataType
+from src.callback import EvalCallBack, LossCallBack
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+parser = argparse.ArgumentParser(description='CTR Prediction')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--ckpt_path', type=str, default=None, help='Checkpoint path')
+parser.add_argument('--eval_file_name', type=str, default="./auc.log", help='eval file path')
+parser.add_argument('--loss_file_name', type=str, default="./loss.log", help='loss file path')
+parser.add_argument('--do_eval', type=bool, default=True, help='Do evaluation or not.')
+
+args_opt, _ = parser.parse_known_args()
+device_id = int(os.getenv('DEVICE_ID'))
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id)
+
+
+if __name__ == '__main__':
+    data_config = DataConfig()
+    model_config = ModelConfig()
+    train_config = TrainConfig()
+
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+    if rank_size > 1:
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True)
+        init()
+        rank_id = int(os.environ.get('RANK_ID'))
+    else:
+        rank_size = None
+        rank_id = None
+
+    ds_train = create_dataset(args_opt.dataset_path,
+                              train_mode=True,
+                              epochs=train_config.train_epochs,
+                              batch_size=train_config.batch_size,
+                              data_type=DataType(data_config.data_format),
+                              rank_size=rank_size,
+                              rank_id=rank_id)
+
+    model_builder = ModelBuilder(ModelConfig, TrainConfig)
+    train_net, eval_net = model_builder.get_train_eval_net()
+    auc_metric = AUCMetric()
+    model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
+
+    time_callback = TimeMonitor(data_size=ds_train.get_dataset_size())
+    loss_callback = LossCallBack(loss_file_path=args_opt.loss_file_name)
+    callback_list = [time_callback, loss_callback]
+
+    if train_config.save_checkpoint:
+        config_ck = CheckpointConfig(save_checkpoint_steps=train_config.save_checkpoint_steps,
+                                     keep_checkpoint_max=train_config.keep_checkpoint_max)
+        ckpt_cb = ModelCheckpoint(prefix=train_config.ckpt_file_name_prefix,
+                                  directory=args_opt.ckpt_path,
+                                  config=config_ck)
+        callback_list.append(ckpt_cb)
+
+    if args_opt.do_eval:
+        ds_eval = create_dataset(args_opt.dataset_path, train_mode=False,
+                                 epochs=train_config.train_epochs,
+                                 batch_size=train_config.batch_size,
+                                 data_type=DataType(data_config.data_format))
+        eval_callback = EvalCallBack(model, ds_eval, auc_metric,
+                                     eval_file_path=args_opt.eval_file_name)
+        callback_list.append(eval_callback)
+    model.train(train_config.train_epochs, ds_train, callbacks=callback_list)