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PaddleRec
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.travis.yml
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......@@ -23,6 +23,8 @@ before_install:
- sudo pip install pylint pytest astroid isort pre-commit
- sudo pip install kiwisolver
- sudo pip install paddlepaddle==1.7.2 --ignore-installed urllib3
- sudo pip uninstall -y rarfile
- sudo pip install rarfile==3.0
- sudo python setup.py install
- |
function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; }
......
README.md
浏览文件 @ 164937ce
([简体中文](./README_CN.md)|English)
(简体中文|[English](./README_EN.md))
<p align="center">
<img align="center" src="doc/imgs/logo.png">
<p>
<p align="center">
<img align="center" src="doc/imgs/overview_en.png">
<img align="center" src="doc/imgs/structure.png">
<p>
<p align="center">
<img align="center" src="doc/imgs/overview.png">
<p>
<h2 align="center">What is recommendation system ?</h2>
<h2 align="center">什么是推荐系统?</h2>
<p align="center">
<img align="center" src="doc/imgs/rec-overview-en.png">
<img align="center" src="doc/imgs/rec-overview.png">
<p>
- Recommendation system helps users quickly find useful and interesting information from massive data.
- Recommendation system is also a silver bullet to attract users, retain users, increase users' stickness or conversionn.
> Who can better use the recommendation system, who can gain more advantage in the fierce competition.
>
> At the same time, there are many problems in the process of using the recommendation system, such as: huge data, complex model, inefficient distributed training, and so on.
<h2 align="center">What is PaddleRec ?</h2>
- A quick start tool of search & recommendation algorithm based on [PaddlePaddle](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html)
- A complete solution of recommendation system for beginners, developers and researchers.
- Recommendation algorithm library including content-understanding, match, recall, rank, multi-task, re-rank etc.
| Type | Algorithm | CPU | GPU | Parameter-Server | Multi-GPU | Paper |
| :-------------------: | :-----------------------------------------------------------------------: | :---: | :-----: | :--------------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Content-Understanding | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
| Content-Understanding | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) |
| Match | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | x | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) |
| Match | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | x | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) |
| Recall | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) |
| Recall | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) |
| Recall | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | x | [NIPS 2013][Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) |
| Recall | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) |
| Recall | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) |
| Recall | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) |
| Recall | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| Recall | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| Rank | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / |
| Rank | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| Rank | [FM](models/rank/fm/model.py) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| Rank | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| Rank | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) |
| Rank | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) |
| Rank | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) |
| Rank | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) |
| Rank | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) |
| Rank | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) |
| Rank | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) |
| Rank | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) |
| Rank | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) |
| Rank | [DIEN](models/rank/dien/model.py) | ✓ | x | ✓ | x | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) |
| Rank | [BST](models/rank/BST/model.py) | ✓ | x | ✓ | x | [DLP-KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf) |
| Rank | [AutoInt](models/rank/AutoInt/model.py) | ✓ | x | ✓ | x | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf) |
| Rank | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
| Rank | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
| Rank | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
| Rank | [Flen](models/rank/flen/model.py) | ✓ | ✓ | ✓ | ✓ | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
| Multi-Task | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| Multi-Task | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
| Multi-Task | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| Re-Rank | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |
<h2 align="center">Getting Started</h2>
### Environmental requirements
- 推荐系统是在互联网信息爆炸式增长的时代背景下，帮助用户高效获得感兴趣信息的关键；
- 推荐系统也是帮助产品最大限度吸引用户、留存用户、增加用户粘性、提高用户转化率的银弹。
- 有无数优秀的产品依靠用户可感知的推荐系统建立了良好的口碑，也有无数的公司依靠直击用户痛点的推荐系统在行业中占领了一席之地。
> 可以说，谁能掌握和利用好推荐系统，谁就能在信息分发的激烈竞争中抢得先机。
> 但与此同时，有着许多问题困扰着推荐系统的开发者，比如：庞大的数据量，复杂的模型结构，低效的分布式训练环境，波动的在离线一致性，苛刻的上线部署要求，以上种种，不胜枚举。
<h2 align="center">什么是PaddleRec?</h2>
- 源于飞桨生态的搜索推荐模型 **一站式开箱即用工具**
- 适合初学者，开发者，研究者的推荐系统全流程解决方案
- 包含内容理解、匹配、召回、排序、 多任务、重排序等多个任务的完整推荐搜索算法库
| 方向 | 模型 | 单机CPU | 单机GPU | 分布式CPU | 分布式GPU | 论文 |
| :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
| 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) |
| 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | x | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) |
| 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | x | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) |
| 召回 | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) |
| 召回 | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) |
| 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | x | [NIPS 2013][Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) |
| 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) |
| 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) |
| 召回 | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) |
| 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| 召回 | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [FM](models/rank/fm/model.py) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| 排序 | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) |
| 排序 | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) |
| 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) |
| 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) |
| 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) |
| 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) |
| 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) |
| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) |
| 排序 | [DIEN](models/rank/dien/model.py) | ✓ | x | ✓ | x | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) |
| 排序 | [BST](models/rank/BST/model.py) | ✓ | x | ✓ | x | [DLP_KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf) |
| 排序 | [AutoInt](models/rank/AutoInt/model.py) | ✓ | x | ✓ | x | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf) |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
| 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
| 排序 | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
| 排序 | [Flen](models/rank/flen/model.py) | ✓ | ✓ | ✓ | ✓ | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| 重排序 | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |
<h2 align="center">快速安装</h2>
### 环境要求
* Python 2.7/ 3.5 / 3.6 / 3.7
* PaddlePaddle >= 1.7.2
* operating system: Windows/Mac/Linux
* 操作系统: Windows/Mac/Linux
> Linux is recommended for distributed training
> Windows下PaddleRec目前仅支持单机训练，分布式训练建议使用Linux环境
### Installation
### 安装命令
1. **Install by pip**
- 安装方法一 **PIP源直接安装**
```bash
python -m pip install paddle-rec
```
> This method will download and install `paddlepaddle-v1.7.2-cpu`. If `PaddlePaddle` can not be installed automatically，You need to install `PaddlePaddle` manually，and then install `PaddleRec` again：
> - Download [PaddlePaddle](https://pypi.org/project/paddlepaddle/1.7.2/#files) and install by pip.
> - Install `PaddlePaddle` by pip，`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple`
> - Other installation problems can be raised in [Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues) or [PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues)
> 该方法会默认下载安装`paddlepaddle v1.7.2 cpu版本`，若提示`PaddlePaddle`无法安装，则依照下述方法首先安装`PaddlePaddle`，再安装`PaddleRec`：
> - 可以在[该地址](https://pypi.org/project/paddlepaddle/1.7.2/#files)，下载PaddlePaddle后手动安装whl包
> - 可以先pip安装`PaddlePaddle`，`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple`
> - 其他安装问题可以在[Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues)或[PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues)提出，会有工程师及时解答
2. **Install by source code**
- Install PaddlePaddle
- 安装方法二 **源码编译安装**
- 安装飞桨 **注：需要用户安装版本 == 1.7.2 的飞桨**
```shell
python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple
```
- Install PaddleRec by source code
- 源码安装PaddleRec
```
git clone https://github.com/PaddlePaddle/PaddleRec/
......@@ -107,54 +112,54 @@
python setup.py install
```
- Install PaddleRec-GPU
- PaddleRec-GPU安装方法
After installing `PaddleRec`，please install the appropriate version of `paddlepaddle-gpu` according to your environment (CUDA / cudnn)，refer to the installation tutorial [Installation Manuals](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html)
在使用方法一或方法二完成PaddleRec安装后，需再手动安装`paddlepaddle-gpu`，并根据自身环境(Cuda/Cudnn)选择合适的版本，安装教程请查阅[飞桨-开始使用](https://www.paddlepaddle.org.cn/install/quick)
<h2 align="center">Quick Start</h2>
<h2 align="center">一键启动</h2>
We take the `dnn` algorithm as an example to get start of `PaddleRec`, and we take 100 pieces of training data from [Criteo Dataset](https://www.kaggle.com/c/criteo-display-ad-challenge/):
我们以排序模型中的`dnn`模型为例介绍PaddleRec的一键启动。训练数据来源为[Criteo数据集](https://www.kaggle.com/c/criteo-display-ad-challenge/)，我们从中截取了100条数据：
```bash
# Training with cpu
# 使用CPU进行单机训练
python -m paddlerec.run -m paddlerec.models.rank.dnn
```
<h2 align="center">Documentation</h2>
<h2 align="center">帮助文档</h2>
### Background
* [Recommendation System](doc/rec_background.md)
* [Distributed deep learning](doc/ps_background.md)
### 项目背景
* [推荐系统介绍](doc/rec_background.md)
* [分布式深度学习介绍](doc/ps_background.md)
### Introductory Project
* [Get start of PaddleRec in ten minutes](https://aistudio.baidu.com/aistudio/projectdetail/559336)
### 快速开始
* [十分钟上手PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336)
### Introductory tutorial
* [Data](doc/slot_reader.md)
* [Model](doc/model.md)
* [Loacl Train](doc/train.md)
* [Distributed Train](doc/distributed_train.md)
* [Predict](doc/predict.md)
* [Serving](doc/serving.md)
### 入门教程
* [数据准备](doc/slot_reader.md)
* [模型调参](doc/model.md)
* [启动单机训练](doc/train.md)
* [启动分布式训练](doc/distributed_train.md)
* [启动预测](doc/predict.md)
* [快速部署](doc/serving.md)
### Advanced tutorial
* [Custom Reader](doc/custom_reader.md)
* [Custom Model](doc/model_develop.md)
* [Custom Training Process](doc/trainer_develop.md)
* [Configuration description of yaml](doc/yaml.md)
* [Design document of PaddleRec](doc/design.md)
### 进阶教程
* [自定义Reader](doc/custom_reader.md)
* [自定义模型](doc/model_develop.md)
* [自定义流程](doc/trainer_develop.md)
* [yaml配置说明](doc/yaml.md)
* [PaddleRec设计文档](doc/design.md)
### Benchmark
* [Benchmark](doc/benchmark.md)
### FAQ
* [Common Problem FAQ](doc/faq.md)
* [常见问题FAQ](doc/faq.md)
<h2 align="center">Community</h2>
<h2 align="center">社区</h2>
<p align="center">
<br>
......@@ -164,22 +169,22 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn
<br>
<p>
### Version history
### 版本历史
- 2020.06.17 - PaddleRec v0.1.0
- 2020.06.03 - PaddleRec v0.0.2
- 2020.05.14 - PaddleRec v0.0.1
### License
[Apache 2.0 license](LICENSE)
### 许可证书
本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
### Contact us
### 联系我们
For any feedback, please propose a [GitHub Issue](https://github.com/PaddlePaddle/PaddleRec/issues)
如有意见、建议及使用中的BUG，欢迎在[GitHub Issue](https://github.com/PaddlePaddle/PaddleRec/issues)提交
You can also communicate with us in the following ways：
亦可通过以下方式与我们沟通交流：
- QQ group id：`861717190`
- Wechat account：`paddlerec2020`
- QQ群号码：`861717190`
- 微信小助手微信号：`paddlerec2020`
<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/QQ_group.png"/>&#8194;&#8194;&#8194;&#8194;&#8194<img width="200" height="200" src="doc/imgs/weixin_supporter.png"/></p>
<p align="center">PaddleRec QQ Group&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;PaddleRec Wechat account</p>
<p align="center">PaddleRec交流QQ群&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;PaddleRec微信小助手</p>
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(简体中文|[English](./README.md))
<p align="center">
<img align="center" src="doc/imgs/logo.png">
<p>
<p align="center">
<img align="center" src="doc/imgs/structure.png">
<p>
<p align="center">
<img align="center" src="doc/imgs/overview.png">
<p>
<h2 align="center">什么是推荐系统?</h2>
<p align="center">
<img align="center" src="doc/imgs/rec-overview.png">
<p>
- 推荐系统是在互联网信息爆炸式增长的时代背景下，帮助用户高效获得感兴趣信息的关键；
- 推荐系统也是帮助产品最大限度吸引用户、留存用户、增加用户粘性、提高用户转化率的银弹。
- 有无数优秀的产品依靠用户可感知的推荐系统建立了良好的口碑，也有无数的公司依靠直击用户痛点的推荐系统在行业中占领了一席之地。
> 可以说，谁能掌握和利用好推荐系统，谁就能在信息分发的激烈竞争中抢得先机。
> 但与此同时，有着许多问题困扰着推荐系统的开发者，比如：庞大的数据量，复杂的模型结构，低效的分布式训练环境，波动的在离线一致性，苛刻的上线部署要求，以上种种，不胜枚举。
<h2 align="center">什么是PaddleRec?</h2>
- 源于飞桨生态的搜索推荐模型 **一站式开箱即用工具**
- 适合初学者，开发者，研究者的推荐系统全流程解决方案
- 包含内容理解、匹配、召回、排序、 多任务、重排序等多个任务的完整推荐搜索算法库
| 方向 | 模型 | 单机CPU | 单机GPU | 分布式CPU | 分布式GPU | 论文 |
| :------: | :-----------------------------------------------------------------------: | :-----: | :-----: | :-------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
| 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) |
| 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | x | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) |
| 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | x | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) |
| 召回 | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) |
| 召回 | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) |
| 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | x | [NIPS 2013][Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) |
| 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) |
| 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) |
| 召回 | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) |
| 召回 | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| 召回 | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| 排序 | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| 排序 | [FM](models/rank/fm/model.py) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| 排序 | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| 排序 | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) |
| 排序 | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) |
| 排序 | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) |
| 排序 | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) |
| 排序 | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) |
| 排序 | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) |
| 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) |
| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) |
| 排序 | [DIEN](models/rank/dien/model.py) | ✓ | x | ✓ | x | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) |
| 排序 | [BST](models/rank/BST/model.py) | ✓ | x | ✓ | x | [DLP_KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf) |
| 排序 | [AutoInt](models/rank/AutoInt/model.py) | ✓ | x | ✓ | x | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf) |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
| 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
| 排序 | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
| 排序 | [Flen](models/rank/flen/model.py) | ✓ | ✓ | ✓ | ✓ | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| 重排序 | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |
<h2 align="center">快速安装</h2>
### 环境要求
* Python 2.7/ 3.5 / 3.6 / 3.7
* PaddlePaddle >= 1.7.2
* 操作系统: Windows/Mac/Linux
> Windows下PaddleRec目前仅支持单机训练，分布式训练建议使用Linux环境
### 安装命令
- 安装方法一 **PIP源直接安装**
```bash
python -m pip install paddle-rec
```
> 该方法会默认下载安装`paddlepaddle v1.7.2 cpu版本`，若提示`PaddlePaddle`无法安装，则依照下述方法首先安装`PaddlePaddle`，再安装`PaddleRec`：
> - 可以在[该地址](https://pypi.org/project/paddlepaddle/1.7.2/#files)，下载PaddlePaddle后手动安装whl包
> - 可以先pip安装`PaddlePaddle`，`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple`
> - 其他安装问题可以在[Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues)或[PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues)提出，会有工程师及时解答
- 安装方法二 **源码编译安装**
- 安装飞桨 **注：需要用户安装版本 == 1.7.2 的飞桨**
```shell
python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple
```
- 源码安装PaddleRec
```
git clone https://github.com/PaddlePaddle/PaddleRec/
cd PaddleRec
python setup.py install
```
- PaddleRec-GPU安装方法
在使用方法一或方法二完成PaddleRec安装后，需再手动安装`paddlepaddle-gpu`，并根据自身环境(Cuda/Cudnn)选择合适的版本，安装教程请查阅[飞桨-开始使用](https://www.paddlepaddle.org.cn/install/quick)
<h2 align="center">一键启动</h2>
我们以排序模型中的`dnn`模型为例介绍PaddleRec的一键启动。训练数据来源为[Criteo数据集](https://www.kaggle.com/c/criteo-display-ad-challenge/)，我们从中截取了100条数据：
```bash
# 使用CPU进行单机训练
python -m paddlerec.run -m paddlerec.models.rank.dnn
```
<h2 align="center">帮助文档</h2>
### 项目背景
* [推荐系统介绍](doc/rec_background.md)
* [分布式深度学习介绍](doc/ps_background.md)
### 快速开始
* [十分钟上手PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336)
### 入门教程
* [数据准备](doc/slot_reader.md)
* [模型调参](doc/model.md)
* [启动单机训练](doc/train.md)
* [启动分布式训练](doc/distributed_train.md)
* [启动预测](doc/predict.md)
* [快速部署](doc/serving.md)
### 进阶教程
* [自定义Reader](doc/custom_reader.md)
* [自定义模型](doc/model_develop.md)
* [自定义流程](doc/trainer_develop.md)
* [yaml配置说明](doc/yaml.md)
* [PaddleRec设计文档](doc/design.md)
### Benchmark
* [Benchmark](doc/benchmark.md)
### FAQ
* [常见问题FAQ](doc/faq.md)
<h2 align="center">社区</h2>
<p align="center">
<br>
<img alt="Release" src="https://img.shields.io/badge/Release-0.1.0-yellowgreen">
<img alt="License" src="https://img.shields.io/github/license/PaddlePaddle/PaddleRec">
<img alt="Slack" src="https://img.shields.io/badge/Join-Slack-green">
<br>
<p>
### 版本历史
- 2020.06.17 - PaddleRec v0.1.0
- 2020.06.03 - PaddleRec v0.0.2
- 2020.05.14 - PaddleRec v0.0.1
### 许可证书
本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
### 联系我们
如有意见、建议及使用中的BUG，欢迎在[GitHub Issue](https://github.com/PaddlePaddle/PaddleRec/issues)提交
亦可通过以下方式与我们沟通交流：
- QQ群号码：`861717190`
- 微信小助手微信号：`paddlerec2020`
<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/QQ_group.png"/>&#8194;&#8194;&#8194;&#8194;&#8194<img width="200" height="200" src="doc/imgs/weixin_supporter.png"/></p>
<p align="center">PaddleRec交流QQ群&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;PaddleRec微信小助手</p>
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([简体中文](./README.md)|English)
<p align="center">
<img align="center" src="doc/imgs/logo.png">
<p>
<p align="center">
<img align="center" src="doc/imgs/overview_en.png">
<p>
<h2 align="center">What is recommendation system ?</h2>
<p align="center">
<img align="center" src="doc/imgs/rec-overview-en.png">
<p>
- Recommendation system helps users quickly find useful and interesting information from massive data.
- Recommendation system is also a silver bullet to attract users, retain users, increase users' stickness or conversionn.
> Who can better use the recommendation system, who can gain more advantage in the fierce competition.
>
> At the same time, there are many problems in the process of using the recommendation system, such as: huge data, complex model, inefficient distributed training, and so on.
<h2 align="center">What is PaddleRec ?</h2>
- A quick start tool of search & recommendation algorithm based on [PaddlePaddle](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html)
- A complete solution of recommendation system for beginners, developers and researchers.
- Recommendation algorithm library including content-understanding, match, recall, rank, multi-task, re-rank etc.
| Type | Algorithm | CPU | GPU | Parameter-Server | Multi-GPU | Paper |
| :-------------------: | :-----------------------------------------------------------------------: | :---: | :-----: | :--------------: | :-------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Content-Understanding | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
| Content-Understanding | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | ✓ | ✓ | x | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) |
| Match | [DSSM](models/match/dssm/model.py) | ✓ | ✓ | ✓ | x | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) |
| Match | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | ✓ | ✓ | x | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) |
| Recall | [TDM](models/treebased/tdm/model.py) | ✓ | >=1.8.0 | ✓ | >=1.8.0 | [KDD 2018][Learning Tree-based Deep Model for Recommender Systems](https://arxiv.org/pdf/1801.02294.pdf) |
| Recall | [fasttext](models/recall/fasttext/model.py) | ✓ | ✓ | x | x | [EACL 2017][Bag of Tricks for Efficient Text Classification](https://www.aclweb.org/anthology/E17-2068.pdf) |
| Recall | [Word2Vec](models/recall/word2vec/model.py) | ✓ | ✓ | ✓ | x | [NIPS 2013][Distributed Representations of Words and Phrases and their Compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) |
| Recall | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2016][Multi-Rate Deep Learning for Temporal Recommendation](http://sonyis.me/paperpdf/spr209-song_sigir16.pdf) |
| Recall | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ | ✓ | [2015][Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) |
| Recall | [Youtube_dnn](models/recall/youtube_dnn/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys 2016][Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) |
| Recall | [NCF](models/recall/ncf/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2017][Neural Collaborative Filtering](https://arxiv.org/pdf/1708.05031.pdf) |
| Recall | [GNN](models/recall/gnn/model.py) | ✓ | ✓ | ✓ | ✓ | [AAAI 2019][Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) |
| Rank | [Logistic Regression](models/rank/logistic_regression/model.py) | ✓ | x | ✓ | x | / |
| Rank | [Dnn](models/rank/dnn/model.py) | ✓ | ✓ | ✓ | ✓ | / |
| Rank | [FM](models/rank/fm/model.py) | ✓ | x | ✓ | x | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| Rank | [FFM](models/rank/ffm/model.py) | ✓ | x | ✓ | x | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| Rank | [FNN](models/rank/fnn/model.py) | ✓ | x | ✓ | x | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) |
| Rank | [Deep Crossing](models/rank/deep_crossing/model.py) | ✓ | x | ✓ | x | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) |
| Rank | [Pnn](models/rank/pnn/model.py) | ✓ | x | ✓ | x | [ICDM 2016][Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) |
| Rank | [DCN](models/rank/dcn/model.py) | ✓ | x | ✓ | x | [KDD 2017][Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754) |
| Rank | [NFM](models/rank/nfm/model.py) | ✓ | x | ✓ | x | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777) |
| Rank | [AFM](models/rank/afm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf) |
| Rank | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ | x | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) |
| Rank | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) |
| Rank | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) |
| Rank | [DIEN](models/rank/dien/model.py) | ✓ | x | ✓ | x | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) |
| Rank | [BST](models/rank/BST/model.py) | ✓ | x | ✓ | x | [DLP-KDD 2019][Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/pdf/1905.06874v1.pdf) |
| Rank | [AutoInt](models/rank/AutoInt/model.py) | ✓ | x | ✓ | x | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf) |
| Rank | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) |
| Rank | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) |
| Rank | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) |
| Rank | [Flen](models/rank/flen/model.py) | ✓ | ✓ | ✓ | ✓ | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf) |
| Multi-Task | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ | ✓ | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |
| Multi-Task | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ | ✓ | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
| Multi-Task | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ | ✓ | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf) |
| Re-Rank | [Listwise](models/rerank/listwise/model.py) | ✓ | ✓ | ✓ | x | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf) |
<h2 align="center">Getting Started</h2>
### Environmental requirements
* Python 2.7/ 3.5 / 3.6 / 3.7
* PaddlePaddle >= 1.7.2
* operating system: Windows/Mac/Linux
> Linux is recommended for distributed training
### Installation
1. **Install by pip**
```bash
python -m pip install paddle-rec
```
> This method will download and install `paddlepaddle-v1.7.2-cpu`. If `PaddlePaddle` can not be installed automatically，You need to install `PaddlePaddle` manually，and then install `PaddleRec` again：
> - Download [PaddlePaddle](https://pypi.org/project/paddlepaddle/1.7.2/#files) and install by pip.
> - Install `PaddlePaddle` by pip，`python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple`
> - Other installation problems can be raised in [Paddle Issue](https://github.com/PaddlePaddle/Paddle/issues) or [PaddleRec Issue](https://github.com/PaddlePaddle/PaddleRec/issues)
2. **Install by source code**
- Install PaddlePaddle
```shell
python -m pip install paddlepaddle==1.7.2 -i https://mirror.baidu.com/pypi/simple
```
- Install PaddleRec by source code
```
git clone https://github.com/PaddlePaddle/PaddleRec/
cd PaddleRec
python setup.py install
```
- Install PaddleRec-GPU
After installing `PaddleRec`，please install the appropriate version of `paddlepaddle-gpu` according to your environment (CUDA / cudnn)，refer to the installation tutorial [Installation Manuals](https://www.paddlepaddle.org.cn/documentation/docs/en/install/index_en.html)
<h2 align="center">Quick Start</h2>
We take the `dnn` algorithm as an example to get start of `PaddleRec`, and we take 100 pieces of training data from [Criteo Dataset](https://www.kaggle.com/c/criteo-display-ad-challenge/):
```bash
# Training with cpu
python -m paddlerec.run -m paddlerec.models.rank.dnn
```
<h2 align="center">Documentation</h2>
### Background
* [Recommendation System](doc/rec_background.md)
* [Distributed deep learning](doc/ps_background.md)
### Introductory Project
* [Get start of PaddleRec in ten minutes](https://aistudio.baidu.com/aistudio/projectdetail/559336)
### Introductory tutorial
* [Data](doc/slot_reader.md)
* [Model](doc/model.md)
* [Loacl Train](doc/train.md)
* [Distributed Train](doc/distributed_train.md)
* [Predict](doc/predict.md)
* [Serving](doc/serving.md)
### Advanced tutorial
* [Custom Reader](doc/custom_reader.md)
* [Custom Model](doc/model_develop.md)
* [Custom Training Process](doc/trainer_develop.md)
* [Configuration description of yaml](doc/yaml.md)
* [Design document of PaddleRec](doc/design.md)
### Benchmark
* [Benchmark](doc/benchmark.md)
### FAQ
* [Common Problem FAQ](doc/faq.md)
<h2 align="center">Community</h2>
<p align="center">
<br>
<img alt="Release" src="https://img.shields.io/badge/Release-0.1.0-yellowgreen">
<img alt="License" src="https://img.shields.io/github/license/PaddlePaddle/PaddleRec">
<img alt="Slack" src="https://img.shields.io/badge/Join-Slack-green">
<br>
<p>
### Version history
- 2020.06.17 - PaddleRec v0.1.0
- 2020.06.03 - PaddleRec v0.0.2
- 2020.05.14 - PaddleRec v0.0.1
### License
[Apache 2.0 license](LICENSE)
### Contact us
For any feedback, please propose a [GitHub Issue](https://github.com/PaddlePaddle/PaddleRec/issues)
You can also communicate with us in the following ways：
- QQ group id：`861717190`
- Wechat account：`paddlerec2020`
<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/QQ_group.png"/>&#8194;&#8194;&#8194;&#8194;&#8194<img width="200" height="200" src="doc/imgs/weixin_supporter.png"/></p>
<p align="center">PaddleRec QQ Group&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;PaddleRec Wechat account</p>
core/engine/cluster/cloud/before_hook_cpu.sh.template
浏览文件 @ 164937ce
echo "Run before_hook.sh ..."
wget https://paddlerec.bj.bcebos.com/whl/PaddleRec.tar.gz
wget https://paddlerec.bj.bcebos.com/whl/PaddleRec.tar.gz --no-check-certificate
tar -xf PaddleRec.tar.gz
......@@ -10,6 +10,6 @@ python setup.py install
pip uninstall -y paddlepaddle
pip install paddlepaddle-gpu==<$ PADDLEPADDLE_VERSION $> --index-url=http://pip.baidu.com/pypi/simple --trusted-host pip.baidu.com
pip install paddlepaddle==<$ PADDLEPADDLE_VERSION $> --index-url=http://pip.baidu.com/pypi/simple --trusted-host pip.baidu.com
echo "End before_hook.sh ..."
core/engine/cluster/cloud/before_hook_gpu.sh.template
浏览文件 @ 164937ce
echo "Run before_hook.sh ..."
wget https://paddlerec.bj.bcebos.com/whl/PaddleRec.tar.gz
wget https://paddlerec.bj.bcebos.com/whl/PaddleRec.tar.gz --no-check-certificate
tar -xf PaddleRec.tar.gz
......
core/engine/cluster/cloud/cluster.sh
浏览文件 @ 164937ce
......@@ -39,7 +39,12 @@ function _before_submit() {
elif [ ${DISTRIBUTE_MODE} == "COLLECTIVE_GPU_K8S" ]; then
_gen_gpu_before_hook
_gen_k8s_config
_gen_k8s_job
_gen_k8s_gpu_job
_gen_end_hook
elif [ ${DISTRIBUTE_MODE} == "PS_CPU_K8S" ]; then
_gen_cpu_before_hook
_gen_k8s_config
_gen_k8s_cpu_job
_gen_end_hook
fi
......@@ -54,6 +59,7 @@ function _gen_mpi_config() {
-e "s#<$ OUTPUT_PATH $>#$OUTPUT_PATH#g" \
-e "s#<$ THIRDPARTY_PATH $>#$THIRDPARTY_PATH#g" \
-e "s#<$ CPU_NUM $>#$max_thread_num#g" \
-e "s#<$ USE_PYTHON3 $>#$USE_PYTHON3#g" \
-e "s#<$ FLAGS_communicator_is_sgd_optimizer $>#$FLAGS_communicator_is_sgd_optimizer#g" \
-e "s#<$ FLAGS_communicator_send_queue_size $>#$FLAGS_communicator_send_queue_size#g" \
-e "s#<$ FLAGS_communicator_thread_pool_size $>#$FLAGS_communicator_thread_pool_size#g" \
......@@ -71,6 +77,7 @@ function _gen_k8s_config() {
-e "s#<$ AFS_REMOTE_MOUNT_POINT $>#$AFS_REMOTE_MOUNT_POINT#g" \
-e "s#<$ OUTPUT_PATH $>#$OUTPUT_PATH#g" \
-e "s#<$ CPU_NUM $>#$max_thread_num#g" \
-e "s#<$ USE_PYTHON3 $>#$USE_PYTHON3#g" \
-e "s#<$ FLAGS_communicator_is_sgd_optimizer $>#$FLAGS_communicator_is_sgd_optimizer#g" \
-e "s#<$ FLAGS_communicator_send_queue_size $>#$FLAGS_communicator_send_queue_size#g" \
-e "s#<$ FLAGS_communicator_thread_pool_size $>#$FLAGS_communicator_thread_pool_size#g" \
......@@ -101,6 +108,7 @@ function _gen_end_hook() {
function _gen_mpi_job() {
echo "gen mpi_job.sh"
sed -e "s#<$ GROUP_NAME $>#$GROUP_NAME#g" \
-e "s#<$ JOB_NAME $>#$OLD_JOB_NAME#g" \
-e "s#<$ AK $>#$AK#g" \
-e "s#<$ SK $>#$SK#g" \
-e "s#<$ MPI_PRIORITY $>#$PRIORITY#g" \
......@@ -109,18 +117,34 @@ function _gen_mpi_job() {
${abs_dir}/cloud/mpi_job.sh.template >${PWD}/job.sh
}
function _gen_k8s_job() {
function _gen_k8s_gpu_job() {
echo "gen k8s_job.sh"
sed -e "s#<$ GROUP_NAME $>#$GROUP_NAME#g" \
-e "s#<$ JOB_NAME $>#$OLD_JOB_NAME#g" \
-e "s#<$ AK $>#$AK#g" \
-e "s#<$ SK $>#$SK#g" \
-e "s#<$ K8S_PRIORITY $>#$PRIORITY#g" \
-e "s#<$ K8S_TRAINERS $>#$K8S_TRAINERS#g" \
-e "s#<$ K8S_CPU_CORES $>#$K8S_CPU_CORES#g" \
-e "s#<$ K8S_GPU_CARD $>#$K8S_GPU_CARD#g" \
-e "s#<$ START_CMD $>#$START_CMD#g" \
${abs_dir}/cloud/k8s_job.sh.template >${PWD}/job.sh
}
function _gen_k8s_cpu_job() {
echo "gen k8s_job.sh"
sed -e "s#<$ GROUP_NAME $>#$GROUP_NAME#g" \
-e "s#<$ JOB_NAME $>#$OLD_JOB_NAME#g" \
-e "s#<$ AK $>#$AK#g" \
-e "s#<$ SK $>#$SK#g" \
-e "s#<$ K8S_PRIORITY $>#$PRIORITY#g" \
-e "s#<$ K8S_TRAINERS $>#$K8S_TRAINERS#g" \
-e "s#<$ K8S_PS_NUM $>#$K8S_PS_NUM#g" \
-e "s#<$ K8S_PS_CORES $>#$K8S_PS_CORES#g" \
-e "s#<$ K8S_CPU_CORES $>#$K8S_CPU_CORES#g" \
-e "s#<$ START_CMD $>#$START_CMD#g" \
${abs_dir}/cloud/k8s_cpu_job.sh.template >${PWD}/job.sh
}
#-----------------------------------------------------------------------------------------------------------------
......@@ -145,6 +169,7 @@ function _submit() {
function package_hook() {
cur_time=`date +"%Y%m%d%H%M"`
new_job_name="${JOB_NAME}_${cur_time}"
export OLD_JOB_NAME=${JOB_NAME}
export JOB_NAME=${new_job_name}
export job_file_path="${PWD}/${new_job_name}"
mkdir ${job_file_path}
......
core/engine/cluster/cloud/k8s_config.ini.template
浏览文件 @ 164937ce
......@@ -19,6 +19,7 @@ afs_local_mount_point="/root/paddlejob/workspace/env_run/afs/"
# 新k8s afs挂载帮助文档: http://wiki.baidu.com/pages/viewpage.action?pageId=906443193
PADDLE_PADDLEREC_ROLE=WORKER
use_python3=<$ USE_PYTHON3 $>
CPU_NUM=<$ CPU_NUM $>
GLOG_v=0
......
core/engine/cluster/cloud/k8s_cpu_job.sh.template 0 → 100644
浏览文件 @ 164937ce
#!/bin/bash
###############################################################
## 注意-- 注意--注意 ##
## K8S PS-CPU多机作业作业示例 ##
###############################################################
job_name=<$ JOB_NAME $>
# 作业参数
group_name="<$ GROUP_NAME $>"
job_version="paddle-fluid-v1.7.1"
start_cmd="<$ START_CMD $>"
wall_time="2000:00:00"
k8s_priority=<$ K8S_PRIORITY $>
k8s_trainers=<$ K8S_TRAINERS $>
k8s_cpu_cores=<$ K8S_CPU_CORES $>
k8s_ps_num=<$ K8S_PS_NUM $>
k8s_ps_cores=<$ K8S_PS_CORES $>
# 你的ak/sk（可在paddlecloud web页面【个人中心】处获取）
ak=<$ AK $>
sk=<$ SK $>
paddlecloud job --ak ${ak} --sk ${sk} \
train --job-name ${job_name} \
--group-name ${group_name} \
--job-conf config.ini \
--start-cmd "${start_cmd}" \
--files ./* \
--job-version ${job_version} \
--k8s-priority ${k8s_priority} \
--wall-time ${wall_time} \
--k8s-trainers ${k8s_trainers} \
--k8s-cpu-cores ${k8s_cpu_cores} \
--k8s-ps-num ${k8s_ps_num} \
--k8s-ps-cores ${k8s_ps_cores} \
--is-standalone 0 \
--distribute-job-type "PSERVER" \
--json
\ No newline at end of file
core/engine/cluster/cloud/k8s_job.sh.template
浏览文件 @ 164937ce
......@@ -3,18 +3,30 @@
## 注意-- 注意--注意 ##
## K8S NCCL2多机作业作业示例 ##
###############################################################
job_name=${JOB_NAME}
job_name=<$ JOB_NAME $>
# 作业参数
group_name="<$ GROUP_NAME $>"
job_version="paddle-fluid-v1.7.1"
start_cmd="<$ START_CMD $>"
wall_time="10:00:00"
wall_time="2000:00:00"
k8s_priority=<$ K8S_PRIORITY $>
k8s_trainers=<$ K8S_TRAINERS $>
k8s_cpu_cores=<$ K8S_CPU_CORES $>
k8s_gpu_cards=<$ K8S_GPU_CARD $>
is_stand_alone=0
nccl="--distribute-job-type "NCCL2""
if [ ${k8s_trainers} == 1 ];then
is_stand_alone=1
nccl="--job-remark single-trainer"
if [ ${k8s_gpu_cards} == 1];then
nccl="--job-remark single-gpu"
echo "Attention: Use single GPU card for PaddleRec distributed training, please set runner class from 'cluster_train' to 'train' in config.yaml."
fi
fi
# 你的ak/sk（可在paddlecloud web页面【个人中心】处获取）
ak=<$ AK $>
sk=<$ SK $>
......@@ -27,9 +39,11 @@ paddlecloud job --ak ${ak} --sk ${sk} \
--files ./* \
--job-version ${job_version} \
--k8s-trainers ${k8s_trainers} \
--k8s-cpu-cores ${k8s_cpu_cores} \
--k8s-gpu-cards ${k8s_gpu_cards} \
--k8s-priority ${k8s_priority} \
--wall-time ${wall_time} \
--is-standalone 0 \
--distribute-job-type "NCCL2" \
--json
\ No newline at end of file
--is-standalone ${is_stand_alone} \
--json \
${nccl}
\ No newline at end of file
core/engine/cluster/cloud/mpi_config.ini.template
浏览文件 @ 164937ce
......@@ -17,6 +17,7 @@ output_path=<$ OUTPUT_PATH $>
thirdparty_path=<$ THIRDPARTY_PATH $>
PADDLE_PADDLEREC_ROLE=WORKER
use_python3=<$ USE_PYTHON3 $>
CPU_NUM=<$ CPU_NUM $>
GLOG_v=0
......
core/engine/cluster/cloud/mpi_job.sh.template
浏览文件 @ 164937ce
......@@ -3,13 +3,13 @@
## 注意--注意--注意 ##
## MPI 类型作业演示 ##
###############################################################
job_name=${JOB_NAME}
job_name=<$ JOB_NAME $>
# 作业参数
group_name=<$ GROUP_NAME $>
job_version="paddle-fluid-v1.7.1"
start_cmd="<$ START_CMD $>"
wall_time="2:00:00"
wall_time="2000:00:00"
# 你的ak/sk（可在paddlecloud web页面【个人中心】处获取）
ak=<$ AK $>
......
core/engine/cluster/cluster.py
浏览文件 @ 164937ce
......@@ -67,10 +67,10 @@ class ClusterEngine(Engine):
@staticmethod
def workspace_replace():
workspace = envs.get_runtime_environ("workspace")
remote_workspace = envs.get_runtime_environ("remote_workspace")
for k, v in os.environ.items():
v = v.replace("{workspace}", workspace)
v = v.replace("{workspace}", remote_workspace)
os.environ[k] = str(v)
def run(self):
......@@ -98,14 +98,12 @@ class ClusterEngine(Engine):
cluster_env_check_tool = PaddleCloudMpiEnv()
else:
raise ValueError(
"Paddlecloud with Mpi don't support GPU training, check your config"
"Paddlecloud with Mpi don't support GPU training, check your config.yaml & backend.yaml"
)
elif cluster_type.upper() == "K8S":
if fleet_mode == "PS":
if device == "CPU":
raise ValueError(
"PS-CPU on paddlecloud is not supported at this time, comming soon"
)
cluster_env_check_tool = CloudPsCpuEnv()
elif device == "GPU":
raise ValueError(
"PS-GPU on paddlecloud is not supported at this time, comming soon"
......@@ -115,7 +113,7 @@ class ClusterEngine(Engine):
cluster_env_check_tool = CloudCollectiveEnv()
elif device == "CPU":
raise ValueError(
"Unexpected config -> device: CPU with fleet_mode: Collective, check your config"
"Unexpected config -> device: CPU with fleet_mode: Collective, check your config.yaml"
)
else:
raise ValueError("cluster_type {} error, must in MPI/K8S".format(
......@@ -161,23 +159,30 @@ class ClusterEnvBase(object):
self.cluster_env["PADDLE_VERSION"] = self.backend_env.get(
"config.paddle_version", "1.7.2")
# python_version
self.cluster_env["USE_PYTHON3"] = self.backend_env.get(
"config.use_python3", "0")
# communicator
max_thread_num = int(envs.get_runtime_environ("max_thread_num"))
self.cluster_env[
"FLAGS_communicator_is_sgd_optimizer"] = self.backend_env.get(
"config.communicator.FLAGS_communicator_is_sgd_optimizer", 0)
self.cluster_env[
"FLAGS_communicator_send_queue_size"] = self.backend_env.get(
"config.communicator.FLAGS_communicator_send_queue_size", 5)
"config.communicator.FLAGS_communicator_send_queue_size",
max_thread_num)
self.cluster_env[
"FLAGS_communicator_thread_pool_size"] = self.backend_env.get(
"config.communicator.FLAGS_communicator_thread_pool_size", 32)
self.cluster_env[
"FLAGS_communicator_max_merge_var_num"] = self.backend_env.get(
"config.communicator.FLAGS_communicator_max_merge_var_num", 5)
"config.communicator.FLAGS_communicator_max_merge_var_num",
max_thread_num)
self.cluster_env[
"FLAGS_communicator_max_send_grad_num_before_recv"] = self.backend_env.get(
"config.communicator.FLAGS_communicator_max_send_grad_num_before_recv",
5)
max_thread_num)
self.cluster_env["FLAGS_communicator_fake_rpc"] = self.backend_env.get(
"config.communicator.FLAGS_communicator_fake_rpc", 0)
self.cluster_env["FLAGS_rpc_retry_times"] = self.backend_env.get(
......@@ -234,7 +239,7 @@ class PaddleCloudMpiEnv(ClusterEnvBase):
"config.train_data_path", "")
if self.cluster_env["TRAIN_DATA_PATH"] == "":
raise ValueError(
"No -- TRAIN_DATA_PATH -- found in your backend.yaml, please check."
"No -- TRAIN_DATA_PATH -- found in your backend.yaml, please add train_data_path in your backend yaml."
)
# test_data_path
self.cluster_env["TEST_DATA_PATH"] = self.backend_env.get(
......@@ -274,7 +279,7 @@ class PaddleCloudK8sEnv(ClusterEnvBase):
category=UserWarning,
stacklevel=2)
warnings.warn(
"The remote mount point will be mounted to the ./afs/",
"The remote afs path will be mounted to the ./afs/",
category=UserWarning,
stacklevel=2)
......@@ -293,3 +298,21 @@ class CloudCollectiveEnv(PaddleCloudK8sEnv):
"submit.k8s_gpu_card", 1)
self.cluster_env["K8S_CPU_CORES"] = self.backend_env.get(
"submit.k8s_cpu_cores", 1)
class CloudPsCpuEnv(PaddleCloudK8sEnv):
def __init__(self):
super(CloudPsCpuEnv, self).__init__()
def env_check(self):
super(CloudPsCpuEnv, self).env_check()
self.cluster_env["DISTRIBUTE_MODE"] = "PS_CPU_K8S"
self.cluster_env["K8S_TRAINERS"] = self.backend_env.get(
"submit.k8s_trainers", 1)
self.cluster_env["K8S_CPU_CORES"] = self.backend_env.get(
"submit.k8s_cpu_cores", 2)
self.cluster_env["K8S_PS_NUM"] = self.backend_env.get(
"submit.k8s_ps_num", 1)
self.cluster_env["K8S_PS_CORES"] = self.backend_env.get(
"submit.k8s_ps_cores", 2)
doc/distributed_train.md
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......@@ -9,6 +9,7 @@
- [第三步：增加集群运行`backend.yaml`配置](#第三步增加集群运行backendyaml配置)
- [MPI集群的Parameter Server模式配置](#mpi集群的parameter-server模式配置)
- [K8S集群的Collective模式配置](#k8s集群的collective模式配置)
- [K8S集群的PS-CPU模式配置](#k8s集群的ps-cpu模式配置)
- [第四步：任务提交](#第四步任务提交)
- [使用PaddleCloud Client提交](#使用paddlecloud-client提交)
- [第一步：在`before_hook.sh`里手动安装PaddleRec](#第一步在before_hooksh里手动安装paddlerec)
......@@ -34,10 +35,10 @@
分布式运行首先需要更改`config.yaml`，主要调整以下内容：
- workspace: 调整为在节点运行时的工作目录
- runner_class: 从单机的"train"调整为"cluster_train"
- fleet_mode: 选则参数服务器模式，抑或GPU Collective模式
- distribute_strategy: 可选项，选择分布式训练的策略
- workspace: 调整为在远程节点运行时的工作目录，一般设置为`"./"`即可
- runner_class: 从单机的"train"调整为"cluster_train"，单机训练->分布式训练（例外情况，k8s上单机单卡训练仍然为train，后续支持）
- fleet_mode: 选择参数服务器模式(ps)，或者GPU的all-reduce模式(collective)
- distribute_strategy: 可选项，选择分布式训练的策略，目前只在参数服务器模式下生效，可选项:`sync、asycn、half_async、geo`
配置选项具体参数，可以参考[yaml配置说明](./yaml.md)
......@@ -50,47 +51,69 @@
workspace: "paddlerec.models.rank.dnn"
mode: [single_cpu_train]
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: single_cpu_train
class: train
# num of epochs
epochs: 4
# device to run training or infer
device: cpu
save_checkpoint_interval: 2 # save model interval of epochs
save_checkpoint_path: "increment_dnn" # save checkpoint path
init_model_path: "" # load model path
save_checkpoint_interval: 2
save_checkpoint_path: "increment_dnn"
init_model_path: ""
print_interval: 10
phases: [phase1]
dataset:
- name: dataloader_train
batch_size: 2
type: DataLoader
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: dataloader_train
thread_num: 1
```
分布式的训练配置可以改为：
```yaml
# workspace
# 改变一：代码上传至节点后，与运行shell同在一个默认目录下
# 改变一：代码上传至节点后，在默认目录下
workspace: "./"
mode: [ps_cluster]
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: ps_cluster
# 改变二：调整runner的class
class: cluster_train
# num of epochs
epochs: 4
# device to run training or infer
device: cpu
# 改变三 & 四： 指定fleet_mode 与 distribute_strategy
fleet_mode: ps
distribute_strategy: async
save_checkpoint_interval: 2 # save model interval of epochs
save_checkpoint_path: "increment_dnn" # save checkpoint path
init_model_path: "" # load model path
save_checkpoint_interval: 2
save_checkpoint_path: "increment_dnn"
init_model_path: ""
print_interval: 10
phases: [phase1]
dataset:
- name: dataloader_train
batch_size: 2
type: DataLoader
# 改变五： 改变数据的读取目录
# 通常而言，mpi模式下，数据会下载到远程节点执行目录的'./train_data'下， k8s则与挂载位置有关
data_path: "{workspace}/train_data"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: dataloader_train
# 分布式训练节点的CPU_NUM环境变量与thread_num相等，多个phase时，取最大的thread_num
thread_num: 1
```
除此之外，还需关注数据及模型加载的路径，一般而言：
......@@ -110,6 +133,8 @@ cluster_type: mpi # k8s 可选
config:
# 填写任务运行的paddle官方版本号 >= 1.7.2， 默认1.7.2
paddle_version: "1.7.2"
# 是否使用PaddleCloud运行环境下的Python3，默认使用python2
use_python3: 1
# hdfs/afs的配置信息填写
fs_name: "afs://xxx.com"
......@@ -130,11 +155,13 @@ config:
# paddle参数服务器分布式底层超参，无特殊需求不理不改
communicator:
# 使用SGD优化器时，建议设置为1
FLAGS_communicator_is_sgd_optimizer: 0
# 以下三个变量默认都等于训练时的线程数：CPU_NUM
FLAGS_communicator_send_queue_size: 5
FLAGS_communicator_thread_pool_size: 32
FLAGS_communicator_max_merge_var_num: 5
FLAGS_communicator_max_send_grad_num_before_recv: 5
FLAGS_communicator_thread_pool_size: 32
FLAGS_communicator_fake_rpc: 0
FLAGS_rpc_retry_times: 3
......@@ -165,7 +192,14 @@ submit:
# for k8s gpu
# k8s gpu 模式下，训练节点数，及每个节点上的GPU卡数
k8s_trainers: 2
k8s_cpu_cores: 4
k8s_gpu_card: 1
# for k8s ps-cpu
k8s_trainers: 2
k8s_cpu_cores: 4
k8s_ps_num: 2
k8s_ps_cores: 4
```
......@@ -173,18 +207,51 @@ submit:
除此之外，我们还需要关注上传到工作目录的文件(`files选项`)的路径问题，在示例中是`./*.py`，说明我们执行任务提交时，与这些py文件在同一目录。若不在同一目录，则需要适当调整files路径，或改为这些文件的绝对路径。
不建议利用`files`上传数据文件，可以通过指定`train_data_path`自动下载，或指定`afs_remote_mount_point`挂载实现数据到节点的转移。
不建议利用`files`上传过大的数据文件，可以通过指定`train_data_path`自动下载，或在k8s模式下指定`afs_remote_mount_point`挂载实现数据到节点的转移。
#### MPI集群的Parameter Server模式配置
下面是一个利用PaddleCloud提交MPI参数服务器模式任务的`backend.yaml`示例
首先调整`config.yaml`:
```yaml
workspace: "./"
mode: [ps_cluster]
dataset:
- name: dataloader_train
batch_size: 2
type: DataLoader
data_path: "{workspace}/train_data"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
runner:
- name: ps_cluster
class: cluster_train
epochs: 2
device: cpu
fleet_mode: ps
save_checkpoint_interval: 1
save_checkpoint_path: "increment_dnn"
init_model_path: ""
print_interval: 1
phases: [phase1]
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: dataloader_train
thread_num: 1
```
再新增`backend.yaml`
```yaml
backend: "PaddleCloud"
cluster_type: mpi # k8s 可选
cluster_type: mpi # k8s可选
config:
# 填写任务运行的paddle官方版本号 >= 1.7.2， 默认1.7.2
paddle_version: "1.7.2"
# hdfs/afs的配置信息填写
......@@ -229,9 +296,45 @@ submit:
下面是一个利用PaddleCloud提交K8S集群进行GPU训练的`backend.yaml`示例
首先调整`config.yaml`
```yaml
workspace: "./"
mode: [collective_cluster]
dataset:
- name: dataloader_train
batch_size: 2
type: DataLoader
data_path: "{workspace}/afs/挂载数据文件夹的路径"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
runner:
- name: collective_cluster
class: cluster_train
epochs: 2
device: gpu
fleet_mode: collective
save_checkpoint_interval: 1 # save model interval of epochs
save_checkpoint_path: "increment_dnn" # save checkpoint path
init_model_path: "" # load model path
print_interval: 1
phases: [phase1]
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: dataloader_train
thread_num: 1
```
再增加`backend.yaml`
```yaml
backend: "PaddleCloud"
cluster_type: mpi # k8s 可选
cluster_type: k8s # mpi 可选
config:
# 填写任务运行的paddle官方版本号 >= 1.7.2， 默认1.7.2
......@@ -271,9 +374,93 @@ submit:
# for k8s gpu
# k8s gpu 模式下，训练节点数，及每个节点上的GPU卡数
k8s_trainers: 2
k8s_cpu_cores: 4
k8s_gpu_card: 1
```
#### K8S集群的PS-CPU模式配置
下面是一个利用PaddleCloud提交K8S集群进行参数服务器CPU训练的`backend.yaml`示例
首先调整`config.yaml`:
```yaml
workspace: "./"
mode: [ps_cluster]
dataset:
- name: dataloader_train
batch_size: 2
type: DataLoader
data_path: "{workspace}/afs/挂载数据文件夹的路径"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
runner:
- name: ps_cluster
class: cluster_train
epochs: 2
device: cpu
fleet_mode: ps
save_checkpoint_interval: 1
save_checkpoint_path: "increment_dnn"
init_model_path: ""
print_interval: 1
phases: [phase1]
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: dataloader_train
thread_num: 1
```
再新增`backend.yaml`
```yaml
backend: "PaddleCloud"
cluster_type: k8s # mpi 可选
config:
# 填写任务运行的paddle官方版本号 >= 1.7.2， 默认1.7.2
paddle_version: "1.7.2"
# hdfs/afs的配置信息填写
fs_name: "afs://xxx.com"
fs_ugi: "usr,pwd"
# 填任务输出目录的远程地址，如afs:/user/your/path/ 则此处填 /user/your/path
output_path: ""
# for k8s
# 填远程挂载地址，如afs:/user/your/path/ 则此处填 /user/your/path
afs_remote_mount_point: ""
submit:
# PaddleCloud 个人信息 AK 及 SK
ak: ""
sk: ""
# 任务运行优先级，默认high
priority: "high"
# 任务名称
job_name: "PaddleRec_CTR"
# 训练资源所在组
group: ""
# 节点上的任务启动命令
start_cmd: "python -m paddlerec.run -m ./config.yaml"
# 本地需要上传到节点工作目录的文件
files: ./*.py ./*.yaml
# for k8s gpu
# k8s ps-cpu 模式下，训练节点数，参数服务器节点数，及每个节点上的cpu核心数及内存限制
k8s_trainers: 2
k8s_cpu_cores: 4
k8s_ps_num: 2
k8s_ps_cores: 4
```
### 第四步：任务提交
当我们准备好`config.yaml`与`backend.yaml`，便可以进行一键任务提交，命令为：
......
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# PaddleRec yaml配置说明
# PaddleRec config.yaml配置说明
## 全局变量
......@@ -12,31 +12,31 @@
## runner变量
| 名称 | 类型 | 取值 | 是否必须 | 作用描述 |
| :---------------------------: | :----------: | :-------------------------------------------: | :------: | :------------------------------------------------------------------: |
| name | string | 任意 | 是 | 指定runner名称 |
| 名称 | 类型 | 取值 | 是否必须 | 作用描述 |
| :---------------------------: | :----------: | :-------------------------------------------------------: | :------: | :------------------------------------------------------------------: |
| name | string | 任意 | 是 | 指定runner名称 |
| class | string | train(默认) / infer / local_cluster_train / cluster_train | 是 | 指定运行runner的类别（单机/分布式， 训练/预测） |
| device | string | cpu(默认) / gpu | 否 | 程序执行设备 |
| fleet_mode | string | ps(默认) / pslib / collective | 否 | 分布式运行模式 |
| selected_gpus | string | "0"(默认) | 否 | 程序运行GPU卡号，若以"0,1"的方式指定多卡，则会默认启用collective模式 |
| worker_num | int | 1(默认) | 否 | 参数服务器模式下worker的数量 |
| server_num | int | 1(默认) | 否 | 参数服务器模式下server的数量 |
| distribute_strategy | string | async(默认)/sync/half_async/geo | 否 | 参数服务器模式下训练模式的选择 |
| epochs | int | >= 1 | 否 | 模型训练迭代轮数 |
| phases | list[string] | 由phase name组成的list | 否 | 当前runner的训练过程列表，顺序执行 |
| init_model_path | string | 路径 | 否 | 初始化模型地址 |
| save_checkpoint_interval | int | >= 1 | 否 | Save参数的轮数间隔 |
| save_checkpoint_path | string | 路径 | 否 | Save参数的地址 |
| save_inference_interval | int | >= 1 | 否 | Save预测模型的轮数间隔 |
| save_inference_path | string | 路径 | 否 | Save预测模型的地址 |
| save_inference_feed_varnames | list[string] | 组网中指定Variable的name | 否 | 预测模型的入口变量name |
| save_inference_fetch_varnames | list[string] | 组网中指定Variable的name | 否 | 预测模型的出口变量name |
| print_interval | int | >= 1 | 否 | 训练指标打印batch间隔 |
| instance_class_path | string | 路径 | 否 | 自定义instance流程实现的地址 |
| network_class_path | string | 路径 | 否 | 自定义network流程实现的地址 |
| startup_class_path | string | 路径 | 否 | 自定义startup流程实现的地址 |
| runner_class_path | string | 路径 | 否 | 自定义runner流程实现的地址 |
| terminal_class_path | string | 路径 | 否 | 自定义terminal流程实现的地址 |
| device | string | cpu(默认) / gpu | 否 | 程序执行设备 |
| fleet_mode | string | ps(默认) / pslib / collective | 否 | 分布式运行模式 |
| selected_gpus | string | "0"(默认) | 否 | 程序运行GPU卡号，若以"0,1"的方式指定多卡，则会默认启用collective模式 |
| worker_num | int | 1(默认) | 否 | 参数服务器模式下worker的数量 |
| server_num | int | 1(默认) | 否 | 参数服务器模式下server的数量 |
| distribute_strategy | string | async(默认)/sync/half_async/geo | 否 | 参数服务器模式下训练模式的选择 |
| epochs | int | >= 1 | 否 | 模型训练迭代轮数 |
| phases | list[string] | 由phase name组成的list | 否 | 当前runner的训练过程列表，顺序执行 |
| init_model_path | string | 路径 | 否 | 初始化模型地址 |
| save_checkpoint_interval | int | >= 1 | 否 | Save参数的轮数间隔 |
| save_checkpoint_path | string | 路径 | 否 | Save参数的地址 |
| save_inference_interval | int | >= 1 | 否 | Save预测模型的轮数间隔 |
| save_inference_path | string | 路径 | 否 | Save预测模型的地址 |
| save_inference_feed_varnames | list[string] | 组网中指定Variable的name | 否 | 预测模型的入口变量name |
| save_inference_fetch_varnames | list[string] | 组网中指定Variable的name | 否 | 预测模型的出口变量name |
| print_interval | int | >= 1 | 否 | 训练指标打印batch间隔 |
| instance_class_path | string | 路径 | 否 | 自定义instance流程实现的地址 |
| network_class_path | string | 路径 | 否 | 自定义network流程实现的地址 |
| startup_class_path | string | 路径 | 否 | 自定义startup流程实现的地址 |
| runner_class_path | string | 路径 | 否 | 自定义runner流程实现的地址 |
| terminal_class_path | string | 路径 | 否 | 自定义terminal流程实现的地址 |
......@@ -70,3 +70,55 @@
| optimizer.learning_rate | float | > 0 | 否 | 指定学习率 |
| reg | float | > 0 | 否 | L2正则化参数，只在SGD下生效 |
| others | / | / | / | 由各个模型组网独立指定 |
# PaddleRec backend.yaml配置说明
## 全局变量
| 名称 | 类型 | 取值 | 是否必须 | 作用描述 |
| :----------: | :----: | :-------------: | :------: | :----------------------------------------------: |
| backend | string | paddlecloud/k8s | 是 | 使用PaddleCloud平台提交，还是在公有云K8S集群提交 |
| cluster_type | string | mpi/k8s | 是 | 指定运行的计算集群： mpi 还是 k8s |
## config
| 名称 | 类型 | 取值 | 是否必须 | 作用描述 |
| :--------------------: | :----: | :-------------------------------------: | :------: | :------------------------------------------------------------------------------------------: |
| paddle_version | string | paddle官方版本号，如1.7.2/1.8.0/1.8.3等 | 否 | 指定运行训练使用的Paddle版本，默认1.7.2 |
| use_python3 | int | 0（默认）/1 | 否 | 指定是否使用python3进行训练 |
| fs_name | string | "afs://xxx.com" | 是 | hdfs/afs集群名称所需配置 |
| fs_ugi | string | "usr,pwd" | 是 | hdfs/afs集群密钥所需配置 |
| output_path | string | "/user/your/path" | 否 | 任务输出的远程目录 |
| train_data_path | string | "/user/your/path" | 是 | mpi集群下指定训练数据路径，paddlecloud会自动将数据分片并下载到工作目录的`./train_data`文件夹 |
| test_data_path | string | "/user/your/path" | 否 | mpi集群下指定测试数据路径，会自动下载到工作目录的`./test_data`文件夹 |
| thirdparty_path | string | "/user/your/path" | 否 | mpi集群下指定thirdparty路径，会自动下载到工作目录的`./thirdparty`文件夹 |
| afs_remote_mount_point | string | "/user/your/path" | 是 | k8s集群下指定远程路径的地址，会挂载到工作目录的`./afs/下` |
### config.communicator
| 名称 | 类型 | 取值 | 是否必须 | 作用描述 |
| :----------------------------------------------: | :---: | :------------: | :------: | :----------------------------------------------------: |
| FLAGS_communicator_is_sgd_optimizer | int | 0（默认）/1 | 否 | 异步分布式训练时的多线程的梯度融合方式是否使用SGD模式 |
| FLAGS_communicator_send_queue_size | int | 线程数（默认） | 否 | 分布式训练时发送队列的大小 |
| FLAGS_communicator_max_merge_var_num | int | 线程数（默认） | 否 | 分布式训练多线程梯度融合时，线程数的配置 |
| FLAGS_communicator_max_send_grad_num_before_recv | int | 线程数（默认） | 否 | 分布式训练使用独立recv参数线程时，与send的步调配置超参 |
| FLAGS_communicator_thread_pool_size | int | 32（默认） | 否 | 分布式训练时，多线程发送参数的线程池大小 |
| FLAGS_communicator_fake_rpc | int | 0（默认）/1 | 否 | 分布式训练时，选择不进行通信 |
| FLAGS_rpc_retry_times | int | 3(默认) | 否 | 分布式训练时，GRPC的失败重试次数 |
## submit
| 名称 | 类型 | 取值 | 是否必须 | 作用描述 |
| :-----------: | :----: | :-------------------------: | :------: | :------------------------------------------------------: |
| ak | string | PaddleCloud平台提供的ak密钥 | 是 | paddlecloud用户配置 |
| sk | string | PaddleCloud平台提供的sk密钥 | 否 | paddlecloud用户配置 |
| priority | string | normal/high/very_high | 否 | 任务优先级 |
| job_name | string | 任意 | 是 | 任务名称 |
| group | string | 计算资源所在组名称 | 是 | 组名称 |
| start_cmd | string | 任意 | 是 | 启动命令，默认`python -m paddlerec.run -m ./config.yaml` |
| files | string | 任意 | 是 | 随任务提交上传的文件，给出相对或绝对路径 |
| nodes | int | >=1（默认1） | 否 | mpi集群下的节点数 |
| k8s_trainers | int | >=1（默认1） | 否 | k8s集群下worker的节点数 |
| k8s_cpu_cores | int | >=1（默认1） | 否 | k8s集群下worker的CPU核数 |
| k8s_gpu_card | int | >=1（默认1） | 否 | k8s集群下worker的GPU卡数 |
| k8s_ps_num | int | >=1（默认1） | 否 | k8s集群下server的节点数 |
| k8s_ps_cores | int | >=1（默认1） | 否 | k8s集群下server的CPU核数 |
models/match/match-pyramid/__init__.py 0 → 100644
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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.
models/match/match-pyramid/config.yaml 0 → 100644
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# Copyrigh t(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.
workspace: "paddlerec.models.match.match-pyramid"
dataset:
- name: dataset_train
batch_size: 128
type: DataLoader
data_path: "{workspace}/data/train"
data_converter: "{workspace}/train_reader.py"
- name: dataset_infer
batch_size: 1
type: DataLoader
data_path: "{workspace}/data/test"
data_converter: "{workspace}/test_reader.py"
hyper_parameters:
optimizer:
class: adam
learning_rate: 0.001
strategy: async
emb_path: "./data/embedding.npy"
sentence_left_size: 20
sentence_right_size: 500
vocab_size: 193368
emb_size: 50
kernel_num: 8
hidden_size: 20
hidden_act: "relu"
out_size: 1
channels: 1
conv_filter: [2,10]
conv_act: "relu"
pool_size: [6,50]
pool_stride: [6,50]
pool_type: "max"
pool_padding: "VALID"
mode: [train_runner , infer_runner]
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: train_runner
class: train
# num of epochs
epochs: 2
# device to run training or infer
device: cpu
save_checkpoint_interval: 1 # save model interval of epochs
save_inference_interval: 1 # save inference
save_checkpoint_path: "inference" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
print_interval: 2
phases: phase_train
- name: infer_runner
class: infer
# device to run training or infer
device: cpu
print_interval: 1
init_model_path: "inference/1" # load model path
phases: phase_infer
# runner will run all the phase in each epoch
phase:
- name: phase_train
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_train # select dataset by name
thread_num: 1
- name: phase_infer
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_infer # select dataset by name
thread_num: 1
models/match/match-pyramid/data/process.py 0 → 100644
浏览文件 @ 164937ce
# 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.
import os
import numpy as np
import random
# Read Word Dict and Inverse Word Dict
def read_word_dict(filename):
word_dict = {}
for line in open(filename):
line = line.strip().split()
word_dict[int(line[1])] = line[0]
print('[%s]\n\tWord dict size: %d' % (filename, len(word_dict)))
return word_dict
# Read Embedding File
def read_embedding(filename):
embed = {}
for line in open(filename):
line = line.strip().split()
embed[int(line[0])] = list(map(float, line[1:]))
print('[%s]\n\tEmbedding size: %d' % (filename, len(embed)))
return embed
# Convert Embedding Dict 2 numpy array
def convert_embed_2_numpy(embed_dict, embed=None):
for k in embed_dict:
embed[k] = np.array(embed_dict[k])
print('Generate numpy embed:', embed.shape)
return embed
# Read Data
def read_data(filename):
data = {}
for line in open(filename):
line = line.strip().split()
data[line[0]] = list(map(int, line[2:]))
print('[%s]\n\tData size: %s' % (filename, len(data)))
return data
# Read Relation Data
def read_relation(filename):
data = []
for line in open(filename):
line = line.strip().split()
data.append((int(line[0]), line[1], line[2]))
print('[%s]\n\tInstance size: %s' % (filename, len(data)))
return data
Letor07Path = "./data"
word_dict = read_word_dict(filename=os.path.join(Letor07Path, 'word_dict.txt'))
query_data = read_data(filename=os.path.join(Letor07Path, 'qid_query.txt'))
doc_data = read_data(filename=os.path.join(Letor07Path, 'docid_doc.txt'))
embed_dict = read_embedding(filename=os.path.join(Letor07Path,
'embed_wiki-pdc_d50_norm'))
_PAD_ = len(word_dict) #193367
embed_dict[_PAD_] = np.zeros((50, ), dtype=np.float32)
word_dict[_PAD_] = '[PAD]'
W_init_embed = np.float32(np.random.uniform(-0.02, 0.02, [len(word_dict), 50]))
embedding = convert_embed_2_numpy(embed_dict, embed=W_init_embed)
np.save("embedding.npy", embedding)
batch_size = 64
data1_maxlen = 20
data2_maxlen = 500
embed_size = 50
train_iters = 2500
def make_train():
rel_set = {}
pair_list = []
rel = read_relation(filename=os.path.join(Letor07Path,
'relation.train.fold1.txt'))
for label, d1, d2 in rel:
if d1 not in rel_set:
rel_set[d1] = {}
if label not in rel_set[d1]:
rel_set[d1][label] = []
rel_set[d1][label].append(d2)
for d1 in rel_set:
label_list = sorted(rel_set[d1].keys(), reverse=True)
for hidx, high_label in enumerate(label_list[:-1]):
for low_label in label_list[hidx + 1:]:
for high_d2 in rel_set[d1][high_label]:
for low_d2 in rel_set[d1][low_label]:
pair_list.append((d1, high_d2, low_d2))
print('Pair Instance Count:', len(pair_list))
f = open("./data/train/train.txt", "w")
for batch in range(800):
X1 = np.zeros((batch_size * 2, data1_maxlen), dtype=np.int32)
X2 = np.zeros((batch_size * 2, data2_maxlen), dtype=np.int32)
X1[:] = _PAD_
X2[:] = _PAD_
for i in range(batch_size):
d1, d2p, d2n = random.choice(pair_list)
d1_len = min(data1_maxlen, len(query_data[d1]))
d2p_len = min(data2_maxlen, len(doc_data[d2p]))
d2n_len = min(data2_maxlen, len(doc_data[d2n]))
X1[i, :d1_len] = query_data[d1][:d1_len]
X2[i, :d2p_len] = doc_data[d2p][:d2p_len]
X1[i + batch_size, :d1_len] = query_data[d1][:d1_len]
X2[i + batch_size, :d2n_len] = doc_data[d2n][:d2n_len]
for i in range(batch_size * 2):
q = [str(x) for x in list(X1[i])]
d = [str(x) for x in list(X2[i])]
f.write(",".join(q) + "\t" + ",".join(d) + "\n")
f.close()
def make_test():
rel = read_relation(filename=os.path.join(Letor07Path,
'relation.test.fold1.txt'))
f = open("./data/test/test.txt", "w")
for label, d1, d2 in rel:
X1 = np.zeros(data1_maxlen, dtype=np.int32)
X2 = np.zeros(data2_maxlen, dtype=np.int32)
X1[:] = _PAD_
X2[:] = _PAD_
d1_len = min(data1_maxlen, len(query_data[d1]))
d2_len = min(data2_maxlen, len(doc_data[d2]))
X1[:d1_len] = query_data[d1][:d1_len]
X2[:d2_len] = doc_data[d2][:d2_len]
q = [str(x) for x in list(X1)]
d = [str(x) for x in list(X2)]
f.write(",".join(q) + "\t" + ",".join(d) + "\t" + str(label) + "\t" +
d1 + "\n")
f.close()
make_train()
make_test()
models/match/match-pyramid/data/relation.test.fold1.txt 0 → 100644
浏览文件 @ 164937ce
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models/match/match-pyramid/data/test/test.txt 0 → 100644
浏览文件 @ 164937ce
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
models/match/match-pyramid/data/train/train.txt 0 → 100644
浏览文件 @ 164937ce
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
models/match/match-pyramid/data_process.sh 0 → 100644
浏览文件 @ 164937ce
#!/bin/bash
echo "...........load data................."
wget --no-check-certificate 'https://paddlerec.bj.bcebos.com/match_pyramid/match_pyramid_data.tar.gz'
mv ./match_pyramid_data.tar.gz ./data
rm -rf ./data/relation.test.fold1.txt ./data/realtion.train.fold1.txt
tar -xvf ./data/match_pyramid_data.tar.gz
echo "...........data process..............."
python ./data/process.py
models/match/match-pyramid/eval.py 0 → 100644
浏览文件 @ 164937ce
# 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.
import random
import numpy as np
def eval_MAP(pred, gt):
map_value = 0.0
r = 0.0
c = list(zip(pred, gt))
random.shuffle(c)
c = sorted(c, key=lambda x: x[0], reverse=True)
for j, (p, g) in enumerate(c):
if g != 0:
r += 1
map_value += r / (j + 1.0)
if r == 0:
return 0.0
else:
return map_value / r
filename = './data/relation.test.fold1.txt'
gt = []
qid = []
f = open(filename, "r")
f.readline()
num = 0
for line in f.readlines():
num = num + 1
line = line.strip().split()
gt.append(int(line[0]))
qid.append(line[1])
f.close()
print(num)
filename = './result.txt'
pred = []
for line in open(filename):
line = line.strip().split(",")
line[1] = line[1].split(":")
line = line[1][1].strip(" ")
line = line.strip("[")
line = line.strip("]")
pred.append(float(line))
result_dict = {}
for i in range(len(qid)):
if qid[i] not in result_dict:
result_dict[qid[i]] = []
result_dict[qid[i]].append([gt[i], pred[i]])
print(len(result_dict))
map = 0
for qid in result_dict:
gt = np.array(result_dict[qid])[:, 0]
pred = np.array(result_dict[qid])[:, 1]
map += eval_MAP(pred, gt)
map = map / len(result_dict)
print("map=", map)
models/match/match-pyramid/model.py 0 → 100644
浏览文件 @ 164937ce
# 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.
import os
import sys
import random
import numpy as np
import paddle
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.emb_path = envs.get_global_env("hyper_parameters.emb_path")
self.sentence_left_size = envs.get_global_env(
"hyper_parameters.sentence_left_size")
self.sentence_right_size = envs.get_global_env(
"hyper_parameters.sentence_right_size")
self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
self.emb_size = envs.get_global_env("hyper_parameters.emb_size")
self.kernel_num = envs.get_global_env("hyper_parameters.kernel_num")
self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
self.hidden_act = envs.get_global_env("hyper_parameters.hidden_act")
self.out_size = envs.get_global_env("hyper_parameters.out_size")
self.channels = envs.get_global_env("hyper_parameters.channels")
self.conv_filter = envs.get_global_env("hyper_parameters.conv_filter")
self.conv_act = envs.get_global_env("hyper_parameters.conv_act")
self.pool_size = envs.get_global_env("hyper_parameters.pool_size")
self.pool_stride = envs.get_global_env("hyper_parameters.pool_stride")
self.pool_type = envs.get_global_env("hyper_parameters.pool_type")
self.pool_padding = envs.get_global_env(
"hyper_parameters.pool_padding")
def input_data(self, is_infer=False, **kwargs):
sentence_left = fluid.data(
name="sentence_left",
shape=[-1, self.sentence_left_size, 1],
dtype='int64',
lod_level=0)
sentence_right = fluid.data(
name="sentence_right",
shape=[-1, self.sentence_right_size, 1],
dtype='int64',
lod_level=0)
return [sentence_left, sentence_right]
def embedding_layer(self, input):
"""
embedding layer
"""
if os.path.isfile(self.emb_path):
embedding_array = np.load(self.emb_path)
emb = fluid.layers.embedding(
input=input,
size=[self.vocab_size, self.emb_size],
padding_idx=0,
param_attr=fluid.ParamAttr(
name="word_embedding",
initializer=fluid.initializer.NumpyArrayInitializer(
embedding_array)))
else:
emb = fluid.layers.embedding(
input=input,
size=[self.vocab_size, self.emb_size],
padding_idx=0,
param_attr=fluid.ParamAttr(
name="word_embedding",
initializer=fluid.initializer.Xavier()))
return emb
def conv_pool_layer(self, input):
"""
convolution and pool layer
"""
# data format NCHW
# same padding
conv = fluid.layers.conv2d(
input=input,
num_filters=self.kernel_num,
stride=1,
padding="SAME",
filter_size=self.conv_filter,
act=self.conv_act)
pool = fluid.layers.pool2d(
input=conv,
pool_size=self.pool_size,
pool_stride=self.pool_stride,
pool_type=self.pool_type,
pool_padding=self.pool_padding)
return pool
def net(self, inputs, is_infer=False):
left_emb = self.embedding_layer(inputs[0])
right_emb = self.embedding_layer(inputs[1])
cross = fluid.layers.matmul(left_emb, right_emb, transpose_y=True)
cross = fluid.layers.reshape(cross,
[-1, 1, cross.shape[1], cross.shape[2]])
conv_pool = self.conv_pool_layer(input=cross)
relu_hid = fluid.layers.fc(input=conv_pool,
size=self.hidden_size,
act=self.hidden_act)
prediction = fluid.layers.fc(
input=relu_hid,
size=self.out_size, )
if is_infer:
self._infer_results["prediction"] = prediction
return
pos = fluid.layers.slice(
prediction, axes=[0, 1], starts=[0, 0], ends=[64, 1])
neg = fluid.layers.slice(
prediction, axes=[0, 1], starts=[64, 0], ends=[128, 1])
loss_part1 = fluid.layers.elementwise_sub(
fluid.layers.fill_constant(
shape=[64, 1], value=1.0, dtype='float32'),
pos)
loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
loss_part3 = fluid.layers.elementwise_max(
fluid.layers.fill_constant(
shape=[64, 1], value=0.0, dtype='float32'),
loss_part2)
avg_cost = fluid.layers.mean(loss_part3)
self._cost = avg_cost
models/match/match-pyramid/readme.md 0 → 100644
浏览文件 @ 164937ce
# match-pyramid文本匹配模型
## 介绍
在许多自然语言处理任务中，匹配两个文本是一个基本问题。一种有效的方法是从单词，短语和句子中提取有意义的匹配模式以产生匹配分数。受卷积神经网络在图像识别中的成功启发，神经元可以根据提取的基本视觉模式（例如定向的边角和边角）捕获许多复杂的模式，所以我们尝试将文本匹配建模为图像识别问题。本模型对齐原作者庞亮开源的tensorflow代码：https://github.com/pl8787/MatchPyramid-TensorFlow/blob/master/model/model_mp.py， 实现了下述论文中提出的Match-Pyramid模型：
```text
@inproceedings{Pang L , Lan Y , Guo J , et al. Text Matching as Image Recognition[J]. 2016.,
title={Text Matching as Image Recognition},
author={Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, Xueqi Cheng},
year={2016}
}
```
## 数据准备
训练及测试数据集选用Letor07数据集和 embed_wiki-pdc_d50_norm 词向量初始化embedding层。
该数据集包括：
1.词典文件：我们将每个单词映射得到一个唯一的编号wid，并将此映射保存在单词词典文件中。例如：word_dict.txt
2.语料库文件：我们使用字符串标识符的值表示一个句子的编号。第二个数字表示句子的长度。例如：qid_query.txt和docid_doc.txt
3.关系文件：关系文件被用来存储两个句子之间的关系，如query 和document之间的关系。例如：relation.train.fold1.txt, relation.test.fold1.txt
4.嵌入层文件：我们将预训练的词向量存储在嵌入文件中。例如：embed_wiki-pdc_d50_norm
## 数据下载和预处理
本文提供了数据集的下载以及一键生成训练和测试数据的预处理脚本，您可以直接一键运行:bash data_process.sh
执行该脚本，会从国内源的服务器上下载Letor07数据集，删除掉data文件夹中原有的relation.test.fold1.txt和relation.train.fold1.txt，并将完整的数据集解压到data文件夹。随后运行 process.py 将全量训练数据放置于`./data/train`，全量测试数据放置于`./data/test`。并生成用于初始化embedding层的embedding.npy文件
执行该脚本的理想输出为：
```
bash data_process.sh
...........load data...............
--2020-07-13 13:24:50-- https://paddlerec.bj.bcebos.com/match_pyramid/match_pyramid_data.tar.gz
Resolving paddlerec.bj.bcebos.com... 10.70.0.165
Connecting to paddlerec.bj.bcebos.com|10.70.0.165|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 214449643 (205M) [application/x-gzip]
Saving to: “match_pyramid_data.tar.gz”
100%[==========================================================================================================>] 214,449,643 114M/s in 1.8s
2020-07-13 13:24:52 (114 MB/s) - “match_pyramid_data.tar.gz” saved [214449643/214449643]
data/
data/relation.test.fold1.txt
data/relation.test.fold2.txt
data/relation.test.fold3.txt
data/relation.test.fold4.txt
data/relation.test.fold5.txt
data/relation.train.fold1.txt
data/relation.train.fold2.txt
data/relation.train.fold3.txt
data/relation.train.fold4.txt
data/relation.train.fold5.txt
data/relation.txt
data/docid_doc.txt
data/qid_query.txt
data/word_dict.txt
data/embed_wiki-pdc_d50_norm
...........data process...............
[./data/word_dict.txt]
Word dict size: 193367
[./data/qid_query.txt]
Data size: 1692
[./data/docid_doc.txt]
Data size: 65323
[./data/embed_wiki-pdc_d50_norm]
Embedding size: 109282
('Generate numpy embed:', (193368, 50))
[./data/relation.train.fold1.txt]
Instance size: 47828
('Pair Instance Count:', 325439)
[./data/relation.test.fold1.txt]
Instance size: 13652
```
## 一键训练并测试评估
本文提供了一键执行训练，测试和评估的脚本，您可以直接一键运行：bash run.sh
执行该脚本后，会执行python -m paddlerec.run -m ./config.yaml 命令开始训练并测试模型，将测试的结果保存到result.txt文件，最后通过执行eval.py进行评估得到数据的map指标
执行该脚本的理想输出为：
```
..............test.................
13651
336
('map=', 0.420878322843591)
```
## 每个文件的作用
paddlerec可以：
通过config.yaml规定模型的参数
通过model.py规定模型的组网
使用train_reader.py读取训练集中的数据
使用test_reader.py读取测试集中的数据。
本文额外提供：
data_process.sh用来一键处理数据
run.sh用来一键启动训练，直接得出测试结果
eval.py通过保存的测试结果，计算map指标
如需详细了解paddlerec的使用方法请参考https://github.com/PaddlePaddle/PaddleRec/blob/master/README_CN.md 页面下方的教程。
models/match/match-pyramid/run.sh 0 → 100644
浏览文件 @ 164937ce
#!/bin/bash
echo "................run................."
python -m paddlerec.run -m ./config.yaml >result1.txt
grep -A1 "prediction" ./result1.txt >./result.txt
rm -f result1.txt
python eval.py
models/match/match-pyramid/test_reader.py 0 → 100644
浏览文件 @ 164937ce
# 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 paddlerec.core.reader import ReaderBase
class Reader(ReaderBase):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = line.strip('\n').split('\t')
doc1 = [int(word_id) for word_id in features[0].split(",")]
doc2 = [int(word_id) for word_id in features[1].split(",")]
features_name = ["doc1", "doc2"]
yield zip(features_name, [doc1] + [doc2])
return reader
models/match/match-pyramid/train_reader.py 0 → 100644
浏览文件 @ 164937ce
# 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 paddlerec.core.reader import ReaderBase
class Reader(ReaderBase):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = line.strip('\n').split('\t')
doc1 = [int(word_id) for word_id in features[0].split(",")]
doc2 = [int(word_id) for word_id in features[1].split(",")]
features_name = ["doc1", "doc2"]
yield zip(features_name, [doc1] + [doc2])
return reader
models/rank/dnn/backend.yaml
浏览文件 @ 164937ce
......@@ -11,12 +11,8 @@
# 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.
workspace: "./"
backend: "PaddleCloud"
cluster_type: k8s # k8s 可选
cluster_type: k8s # mpi 可选
config:
fs_name: "afs://xxx.com"
......@@ -56,5 +52,12 @@ submit:
# for k8s gpu
k8s_trainers: 2
k8s_cpu_cores: 2
k8s_gpu_card: 1
# for k8s ps-cpu
k8s_trainers: 2
k8s_cpu_cores: 4
k8s_ps_num: 2
k8s_ps_cores: 4
models/rank/fibinet/README.md
浏览文件 @ 164937ce
......@@ -16,13 +16,35 @@
├── config.yaml #配置文件
```
## 简介
注：在阅读该示例前，建议您先了解以下内容：
[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md)
---
## 内容
- [模型简介](#模型简介)
- [数据准备](#数据准备)
- [运行环境](#运行环境)
- [快速开始](#快速开始)
- [论文复现](#论文复现)
- [进阶使用](#进阶使用)
- [FAQ](#FAQ)
## 模型简介
[《FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction》]( https://arxiv.org/pdf/1905.09433.pdf)是新浪微博机器学习团队发表在RecSys19上的一篇论文，文章指出当前的许多通过特征组合进行CTR预估的工作主要使用特征向量的内积或哈达玛积来计算交叉特征，这种方法忽略了特征本身的重要程度。提出通过使用Squeeze-Excitation network (SENET) 结构动态学习特征的重要性以及使用一个双线性函数来更好的建模交叉特征。
本项目在paddlepaddle上实现FibiNET的网络结构，并在开源数据集Criteo上验证模型效果。
本项目在paddlepaddle上实现FibiNET的网络结构，并在开源数据集Criteo上验证模型效果, 本模型配置默认使用demo数据集，若进行精度验证，请参考[论文复现](#论文复现)部分。
本项目支持功能
训练：单机CPU、单机单卡GPU、单机多卡GPU、本地模拟参数服务器训练、增量训练，配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md)
预测：单机CPU、单机单卡GPU ；配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md)
## 数据下载及预处理
## 数据准备
数据地址：[Criteo]( https://fleet.bj.bcebos.com/ctr_data.tar.gz)
......@@ -36,15 +58,33 @@
sh run.sh
```
## 环境
原始的数据格式为13个dense部分特征+离散化特征，用'\t'切分, 对应的数据是data/train_data_full data/test_data_full
```
0 1 1 5 0 1382 4 15 2 181 1 2 2 68fd1e64 80e26c9b fb936136 7b4723c4 25c83c98 7e0ccccf de7995b8 1f89b562 a73ee510 a8cd5504 b2cb9c98 37c9c164 2824a5f6 1adce6ef 8ba8b39a 891b62e7 e5ba7672 f54016b9 21ddcdc9 b1252a9d 07b5194c 3a171ecb c5c50484 e8b83407 9727dd16
```
经过get_slot_data.py处理后，得到如下数据, dense_feature中的值会merge在一起，对应net.py中的self._dense_data_var, '1:715353'表示net.py中的self._sparse_data_var[1] = 715353, 对应的数据是data/slot_train_data_full, data/slot_test_data_full
```
click:0 dense_feature:0.05 dense_feature:0.00663349917081 dense_feature:0.05 dense_feature:0.0 dense_feature:0.02159375 dense_feature:0.008 dense_feature:0.15 dense_feature:0.04 dense_feature:0.362 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.04 1:715353 2:817085 3:851010 4:833725 5:286835 6:948614 7:881652 8:507110 9:27346 10:646986 11:643076 12:200960 13:18464 14:202774 15:532679 16:729573 17:342789 18:562805 19:880474 20:984402 21:666449 22:26235 23:700326 24:452909 25:884722 26:787527
```
PaddlePaddle 1.7.2
## 运行环境
python3.7
PaddlePaddle>=1.7.2
PaddleRec
python 2.7/3.5/3.6/3.7
## 单机训练
PaddleRec >=0.1
os : windows/linux/macos
## 快速开始
### 单机训练
CPU环境
......@@ -73,7 +113,7 @@ runner:
phases: [phase1]
```
## 单机预测
### 单机预测
CPU环境
......@@ -90,17 +130,15 @@ CPU环境
phases: [phase2]
```
## 运行
### 运行
```
python -m paddlerec.run -m paddlerec.models.rank.fibinet
```
## 模型效果
在样例数据上测试模型
### 结果展示
训练：
样例数据训练结果展示：
```
Running SingleStartup.
......@@ -122,7 +160,7 @@ batch: 1800, AUC: [0.85260467], BATCH_AUC: [0.92847032]
epoch 3 done, use time: 1618.1106688976288
```
预测
样例数据预测结果展示
```
load persistables from increment_model/3
......@@ -136,3 +174,18 @@ batch: 1800, AUC: [0.86633785], BATCH_AUC: [0.96900967]
batch: 1820, AUC: [0.86662365], BATCH_AUC: [0.96759972]
```
## 论文复现
用原论文的完整数据复现论文效果需要在config.yaml中修改batch_size=1000, thread_num=8, epoch_num=4
使用gpu p100 单卡训练 60h 测试auc:0.79
修改后运行方案：修改config.yaml中的'workspace'为config.yaml的目录位置，执行
```
python -m paddlerec.run -m /home/your/dir/config.yaml #调试模式 直接指定本地config的绝对路径
```
## 进阶使用
## FAQ
models/rerank/listwise/config.yaml
浏览文件 @ 164937ce
......@@ -17,29 +17,30 @@ workspace: "paddlerec.models.rerank.listwise"
dataset:
- name: dataset_train
batch_size: 5
type: DataLoader
data_path: "{workspace}/data/train"
data_converter: "{workspace}/random_reader.py"
- name: dataset_infer
batch_size: 5
type: DataLoader
data_path: "{workspace}/data/test"
data_converter: "{workspace}/random_reader.py"
hyper_parameters:
optimizer:
class: sgd
learning_rate: 0.01
strategy: async
hidden_size: 128
user_vocab: 200
item_vocab: 1000
item_len: 5
embed_size: 16
batch_size: 1
optimizer:
class: sgd
learning_rate: 0.01
strategy: async
#use infer_runner mode and modify 'phase' below if infer
mode: train_runner
#mode: infer_runner
mode: [train_runner, infer_runner]
runner:
- name: train_runner
......@@ -48,19 +49,22 @@ runner:
epochs: 3
save_checkpoint_interval: 2
save_inference_interval: 4
save_checkpoint_path: "increment"
save_checkpoint_path: "increment_listwise"
save_inference_path: "inference"
print_interval: 1
phases: [train]
- name: infer_runner
class: infer
init_model_path: "increment/0"
init_model_path: "increment_listwise/2"
device: cpu
phases: [infer]
phase:
- name: train
model: "{workspace}/model.py"
dataset_name: dataset_train
thread_num: 1
#- name: infer
# model: "{workspace}/model.py"
# dataset_name: dataset_infer
# thread_num: 1
- name: infer
model: "{workspace}/model.py"
dataset_name: dataset_infer
thread_num: 1
setup.cfg 0 → 100644
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[easy_install]
index_url=http://pip.baidu.com/pypi/simple
\ No newline at end of file
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