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model_zoo/official/recommend/deepfm/README.md
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# DeepFM Description
# Contents
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
- [DeepFM Description](#deepfm-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Environment Requirements](#environment-requirements)
- [Quick Start](#quick-start)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
- [Training Process](#training-process)
- [Training](#training)
- [Distributed Training](#distributed-training)
- [Evaluation Process](#evaluation-process)
- [Evaluation](#evaluation)
- [Model Description](#model-description)
- [Performance](#performance)
- [Evaluation Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
# Model architecture
# [DeepFM Description](#contents)
The overall network architecture of DeepFM is show below:
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
[Link](https://arxiv.org/pdf/1703.04247.pdf)
[Paper](https://arxiv.org/abs/1703.04247): Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
# [Model Architecture](#contents)
# 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.
DeepFM consists of two components. The FM component is a factorization machine, which is proposed in to learn feature interactions for recommendation. The deep component is a feed-forward neural network, which is used to learn high-order feature interactions.
The FM and deep component share the same input raw feature vector, which enables DeepFM to learn low- and high-order feature interactions simultaneously from the input raw features.
# [Dataset](#contents)
- [1] A dataset used in Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[J]. 2017.
# [Environment Requirements](#contents)
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Framework
- [MindSpore](https://www.mindspore.cn/install/en)
- 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
```shell
├── deepfm
├── README.md
├── scripts
│ ├──run_distribute_train.sh
│ ├──run_distribute_train_gpu.sh
│ ├──run_standalone_train.sh
│ ├──run_eval.sh
├── src
│ ├──__init__.py
│ ├──config.py
│ ├──dataset.py
│ ├──callback.py
│ ├──deepfm.py
├── train.py
├── eval.py
# [Quick Start](#contents)
After installing MindSpore via the official website, you can start training and evaluation as follows:
- runing on Ascend
```
# run training example
python train.py \
--dataset_path='dataset/train' \
--ckpt_path='./checkpoint' \
--eval_file_name='auc.log' \
--loss_file_name='loss.log' \
--device_target='Ascend' \
--do_eval=True > ms_log/output.log 2>&1 &
# run distributed training example
sh scripts/run_distribute_train.sh 8 /dataset_path /rank_table_8p.json
# run evaluation example
python eval.py \
--dataset_path='dataset/test' \
--checkpoint_path='./checkpoint/deepfm.ckpt' \
--device_target='Ascend' > ms_log/eval_output.log 2>&1 &
OR
sh scripts/run_eval.sh 0 Ascend /dataset_path /checkpoint_path/deepfm.ckpt
```
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
Please follow the instructions in the link below:
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
- running on GPU
For running on GPU, please change `device_target` from `Ascend` to `GPU` in configuration file src/config.py
```
# run training example
python train.py \
--dataset_path='dataset/train' \
--ckpt_path='./checkpoint' \
--eval_file_name='auc.log' \
--loss_file_name='loss.log' \
--device_target='GPU' \
--do_eval=True > ms_log/output.log 2>&1 &
# run distributed training example
sh scripts/run_distribute_train.sh 8 /dataset_path
# run evaluation example
python eval.py \
--dataset_path='dataset/test' \
--checkpoint_path='./checkpoint/deepfm.ckpt' \
--device_target='GPU' > ms_log/eval_output.log 2>&1 &
OR
sh scripts/run_eval.sh 0 GPU /dataset_path /checkpoint_path/deepfm.ckpt
```
# [Script Description](#contents)
## [Script and Sample Code](#contents)
```
.
└─deepfm
├─README.md
├─scripts
├─run_standalone_train.sh # launch standalone training(1p) in Ascend or GPU
├─run_distribute_train.sh # launch distributed training(8p) in Ascend
├─run_distribute_train_gpu.sh # launch distributed training(8p) in GPU
└─run_eval.sh # launch evaluating in Ascend or GPU
├─src
├─__init__.py # python init file
├─config.py # parameter configuration
├─callback.py # define callback function
├─deepfm.py # deepfm network
├─dataset.py # create dataset for deepfm
├─eval.py # eval net
└─train.py # train net
```
## Training process
## [Script Parameters](#contents)
### Usage
Parameters for both training and evaluation can be set in config.py
- sh run_distribute_train.sh [DEVICE_NUM] [DATASET_PATH] [RANK_TABLE_FILE]
- sh run_distribute_train_gpu.sh [DEVICE_NUM] [DATASET_PATH]
- sh run_standalone_train.sh [DEVICE_ID] [DEVICE_TARGET] [DATASET_PATH]
- python train.py --dataset_path [DATASET_PATH] --device_target [DEVICE_TARGET]
### Launch
```
# distribute training example
sh scripts/run_distribute_train.sh 8 /opt/dataset/criteo /opt/mindspore_hccl_file.json
sh scripts/run_distribute_train_gpu.sh 8 /opt/dataset/criteo
# standalone training example
sh scripts/run_standalone_train.sh 0 Ascend /opt/dataset/criteo
or
python train.py --dataset_path /opt/dataset/criteo --device_target Ascend > 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.
- train parameters
```
optional arguments:
-h, --help show this help message and exit
--dataset_path DATASET_PATH
Dataset path
--ckpt_path CKPT_PATH
Checkpoint path
--eval_file_name EVAL_FILE_NAME
Auc log file path. Default: "./auc.log"
--loss_file_name LOSS_FILE_NAME
Loss log file path. Default: "./loss.log"
--do_eval DO_EVAL Do evaluation or not. Default: True
--device_target DEVICE_TARGET
Ascend or GPU. Default: Ascend
```
- eval parameters
```
optional arguments:
-h, --help show this help message and exit
--checkpoint_path CHECKPOINT_PATH
Checkpoint file path
--dataset_path DATASET_PATH
Dataset path
--device_target DEVICE_TARGET
Ascend or GPU. Default: Ascend
```
## Eval process
## [Training Process](#contents)
### Usage
### Training
- sh run_eval.sh [DEVICE_ID] [DEVICE_TARGET] [DATASET_PATH] [CHECKPOINT_PATH]
- running on Ascend
### Launch
```
python train.py \
--dataset_path='dataset/train' \
--ckpt_path='./checkpoint' \
--eval_file_name='auc.log' \
--loss_file_name='loss.log' \
--device_target='Ascend' \
--do_eval=True > ms_log/output.log 2>&1 &
```
The python command above will run in the background, you can view the results through the file `ms_log/output.log`.
After training, you'll get some checkpoint files under `./checkpoint` folder by default. The loss value are saved in loss.log file.
```
2020-05-27 15:26:29 epoch: 1 step: 41257, loss is 0.498953253030777
2020-05-27 15:32:32 epoch: 2 step: 41257, loss is 0.45545706152915955
...
```
The model checkpoint will be saved in the current directory.
```
# infer example
sh scripts/run_eval.sh 0 Ascend ~/criteo/eval/ ~/train/deepfm-15_41257.ckpt
```
- running on GPU
To do.
> checkpoint can be produced in training process.
### Distributed Training
### Result
- running on Ascend
Inference result will be stored in the example path, you can find result like the followings in `auc.log`.
```
sh scripts/run_distribute_train.sh 8 /dataset_path /rank_table_8p.json
```
The above shell script will run distribute training in the background. You can view the results through the file `log[X]/output.log`. The loss value are saved in loss.log file.
```
2020-05-27 20:51:35 AUC: 0.80577889065281, eval time: 35.55999s.
```
- running on GPU
To do.
# Model description
## Learning Rate
## [Evaluation Process](#contents)
| Number of Devices | Learning Rate |
| ---------------------- | ------------------ |
| 1 | 1e-5 |
| 8 | 1e-4 |
### Evaluation
> Change the learning rate at src/config.py accordingly.
- evaluation on dataset when running on Ascend
## Performance
Before running the command below, please check the checkpoint path used for evaluation.
```
python eval.py \
--dataset_path='dataset/test' \
--checkpoint_path='./checkpoint/deepfm.ckpt' \
--device_target='Ascend' > ms_log/eval_output.log 2>&1 &
OR
sh scripts/run_eval.sh 0 Ascend /dataset_path /checkpoint_path/deepfm.ckpt
```
The above python command will run in the background. You can view the results through the file "eval_output.log". The accuracy is saved in auc.log file.
```
{'result': {'AUC': 0.8057789065281104, 'eval_time': 35.64779996871948}}
```
### 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 | |
- evaluation on dataset when running on GPU
To do.
#### 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 | | |
# [Model Description](#contents)
## [Performance](#contents)
### Evaluation Performance
| Parameters | Ascend | GPU |
| -------------------------- | ----------------------------------------------------------- | ---------------------- |
| Model Version | DeepFM | To do |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 314G | To do |
| uploaded Date | 05/17/2020 (month/day/year) | To do |
| MindSpore Version | 0.3.0-alpha | To do |
| Dataset | [1] | To do |
| Training Parameters | epoch=15, batch_size=1000, lr=1e-5 | To do |
| Optimizer | Adam | To do |
| Loss Function | Sigmoid Cross Entropy With Logits | To do |
| outputs | Accuracy | To do |
| Loss | 0.45 | To do |
| Speed | 1pc: 8.16 ms/step; | To do |
| Total time | 1pc: 90 mins; | To do |
| Parameters (M) | 16.5 | To do |
| Checkpoint for Fine tuning | 190M (.ckpt file) | To do |
| Scripts | [deepfm script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/deepfm) | To do |
### Inference Performance
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --------------------------- |
| Model Version | DeepFM | To do |
| Resource | Ascend 910 | To do |
| Uploaded Date | 05/27/2020 (month/day/year) | To do |
| MindSpore Version | 0.3.0-alpha | To do |
| Dataset | [1] | To do |
| batch_size | 1000 | To do |
| outputs | accuracy | To do |
| Accuracy | 1pc: 80.55%; | To do |
| Model for inference | 190M (.ckpt file) | To do |
# [Description of Random Situation](#contents)
We set the random seed before training in train.py.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
# ModelZoo Homepage
[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)

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model_zoo/official/recommend/deepfm/eval.py
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@ -30,7 +30,7 @@ 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')
parser.add_argument('--device_target', type=str, default="Ascend", help='Ascend, GPU, or CPU')
parser.add_argument('--device_target', type=str, default="Ascend", help='Ascend or GPU. Default: Ascend')
args_opt, _ = parser.parse_known_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=device_id)

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model_zoo/official/recommend/deepfm/train.py
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@ -34,11 +34,15 @@ 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.')
parser.add_argument('--device_target', type=str, default="Ascend", help='Ascend, GPU, or CPU')
parser.add_argument('--eval_file_name', type=str, default="./auc.log",
help='Auc log file path. Default: "./auc.log"')
parser.add_argument('--loss_file_name', type=str, default="./loss.log",
help='Loss log file path. Default: "./loss.log"')
parser.add_argument('--do_eval', type=str, default='True',
help='Do evaluation or not, only support "True" or "False". Default: "True"')
parser.add_argument('--device_target', type=str, default="Ascend", help='Ascend or GPU. Default: Ascend')
args_opt, _ = parser.parse_known_args()
args_opt.do_eval = args_opt.do_eval == 'True'
rank_size = int(os.environ.get("RANK_SIZE", 1))
random.seed(1)