fix online learnning on ctr-dnn

f81ecb77 · seiriosPlus · 9c0b0a06 · 9c0b0a06 · f81ecb77 · 9c0b0a06
13 changed file
--- a/doc/online_learning.md
+++ b/doc/online_learning.md
-# PaddleRec 流式训练（OnlineLearning）任务启动及配置流程
-## 流式训练简介
-流式训练是按照一定顺序进行数据的接收和处理，每接收一个数据，模型会对它进行预测并对当前模型进行更新，然后处理下一个数据。 像信息流、小视频、电商等场景，每天都会新增大量的数据， 让每天(每一刻)新增的数据基于上一天(上一刻)的模型进行新的预测和模型更新。
-在大规模流式训练场景下， 需要使用的深度学习框架有对应的能力支持， 即：
-* 支持大规模分布式训练的能力， 数据量巨大， 需要有良好的分布式训练及扩展能力，才能满足训练的时效要求
-* 支持超大规模的Embedding， 能够支持十亿甚至千亿级别的Embedding, 拥有合理的参数输出的能力，能够快速输出模型参数并和线上其他系统进行对接
-* Embedding的特征ID需要支持HASH映射，不要求ID的编码，能够自动增长及控制特征的准入(原先不存在的特征可以以适当的条件创建)， 能够定期淘汰(能够以一定的策略进行过期的特征的清理) 并拥有准入及淘汰策略
-* 最后就是要基于框架开发一套完备的流式训练的 trainer.py， 能够拥有完善的流式训练流程
-## 使用PaddleRec内置的 online learning 进行模型的训练
-目前，PaddleRec基于飞桨分布式训练框架的能力，实现了这套流式训练的流程。 供大家参考和使用。我们在`models/online_learning`目录下提供了一个ctr-dnn的online_training的版本，供大家更好的理解和参考。
-**注意**
-1. 使用online learning 需要安装目前Paddle最新的开发者版本， 你可以从 https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev 此处获得它，需要先卸载当前已经安装的飞桨版本，根据自己的Python环境下载相应的安装包。
-2. 使用流式训练及大规模稀疏的能力，需要对模型做一些微调， 因此需要你修改部分代码。
-3. 当前只有参数服务器的分布式训练是支持带大规模稀疏的流式训练的，因此运行时，请直接选择参数服务器本地训练或集群训练方法。
-## 启动方法
-### 1. 启动内置模型的默认配置训练
-在安装好`paddlepaddle`及`paddlerec`后，可以直接使用一行命令快速启动内置模型的默认配置训练，命令如下;
-```shell
-python -m paddlerec.run -m paddlerec.models.xxx.yyy
-```
-注意事项：
-1. 请确保调用的是安装了paddlerec的`python`环境
-2. `xxx`为paddlerec.models下有多个大类，如：`recall`/`rank`/`rerank`等
-3. `yyy`为每个类别下又有多个模型，如`recall`下有：`gnn`/`grup4rec`/`ncf`等
-例如启动`recall`下的`word2vec`模型的默认配置;
-```shell
-python -m paddlerec.run -m models/recall/word2vec
-```
-### 2. 启动内置模型的个性化配置训练
-如果我们修改了默认模型的config.yaml文件，怎么运行修改后的模型呢？
- **没有改动模型组网**
-  假如你将paddlerec代码库克隆在了`/home/PaddleRec`，并修改了`/home/PaddleRec/models/rank/dnn/config.yaml`，则如下启动训练
-  ```shell
-  python -m paddlerec.run -m /home/PaddleRec/models/rank/dnn/config.yaml
-  ```
-  paddlerec 运行的是在paddlerec库安装目录下的组网文件(model.py)，但个性化配置`config.yaml`是用的是指定路径下的yaml文件。
- **改动了模型组网**
-  假如你将paddlerec代码库克隆在了`/home/PaddleRec`，并修改了`/home/PaddleRec/models/rank/dnn/model.py`， 以及`/home/PaddleRec/models/rank/dnn/config.yaml`，则首先需要更改`yaml`中的`workspace`的设置：
-  ```yaml
-  workspace: /home/PaddleRec/models/rank/dnn/
-  ```
-  再执行：
-  ```shell
-  python -m paddlerec.run -m /home/PaddleRec/models/rank/dnn/config.yaml
-  ```
-  paddlerec 运行的是绝对路径下的组网文件(model.py)以及个性化配置文件(config.yaml)
-## yaml训练配置
-### yaml中训练相关的概念
-`config.yaml`中训练流程相关有两个重要的逻辑概念，`runner`与`phase`：
- **`runner`** : runner是训练的引擎，亦可称之为运行器，在runner中定义执行设备（cpu、gpu），执行的模式（训练、预测、单机、多机等），以及运行的超参，例如训练轮数，模型保存地址等。
- **`phase`** : phase是训练中的阶段的概念，是引擎具体执行的内容，该内容是指：具体运行哪个模型文件，使用哪个reader。
-PaddleRec每次运行时，会执行一个或多个运行器，通过`mode`指定`runner`的名字。每个运行器可以执行一个或多个`phase`，所以PaddleRec支持一键启动多阶段的训练。
-### 单机CPU训练
-下面我们开始定义一个单机CPU训练的`runner`:
-```yaml
-mode: single_cpu_train # 执行名为 single_cpu_train 的运行器
-# mode 也支持多个runner的执行，此处可以改为 mode: [single_cpu_train, single_cpu_infer]
-runner:
- name: single_cpu_train # 定义 runner 名为 single_cpu_train
-  class: train # 执行单机训练
-  device: cpu # 执行在 cpu 上
-  epochs: 10 # 训练轮数
-  save_checkpoint_interval: 2 # 每隔2轮保存一次checkpoint
-  save_inference_interval: 4 # 每隔4轮保存一次inference model
-  save_checkpoint_path: "increment" # checkpoint 的保存地址
-  save_inference_path: "inference" # inference model 的保存地址
-  save_inference_feed_varnames: [] # inference model 的feed参数的名字
-  save_inference_fetch_varnames: [] # inference model 的fetch参数的名字
-  init_model_path: "" # 如果是加载模型热启，则可以指定初始化模型的地址
-  print_interval: 10 # 训练信息的打印间隔，以batch为单位
-  phases: [phase_train] # 若没有指定phases，则会默认运行所有phase
-  # phase 也支持自定多个phase的执行，此处可以改为 phases: [phase_train, phase_infer]
-```
-再定义具体的执行内容：
-```yaml
-phase:
- name: phase_train # 该阶段名为 phase1 
-  model: "{workspace}/model.py" # 模型文件为workspace下的model.py
-  dataset_name: dataset_train # reader的名字
-dataset:
- name: dataset_train
-  type: DataLoader # 使用DataLoader的数据读取方式
-  batch_size: 2
-  data_path: "{workspace}/train_data" # 数据地址
-  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" # sparse 输入的位置定义
-  dense_slots: "dense_var:13"  # dense参数的维度定义
-```
-### 单机单卡GPU训练
-具体执行内容与reader与前述相同，下面介绍需要改动的地方
-```yaml
-mode: single_gpu_train # 执行名为 single_gpu_train 的运行器
-runner:
- name: single_gpu_train # 定义 runner 名为 single_gpu_train
-  class: train # 执行单机训练
-  device: gpu # 执行在 gpu 上
-  selected_gpus: "0" # 默认选择在id=0的卡上执行训练
-  epochs: 10 # 训练轮数
-```
-### 单机多卡GPU训练
-具体执行内容与reader与前述相同，下面介绍需要改动的地方
-```yaml
-mode: single_multi_gpu_train # 执行名为 single_multi_gpu_train 的运行器
-runner:
- name: single_multi_gpu_train # 定义 runner 名为 single_multi_gpu_train
-  class: train # 执行单机训练
-  device: gpu # 执行在 gpu 上
-  selected_gpus: "0,1,2,3" # 选择多卡执行训练
-  epochs: 10 # 训练轮数
-```
-### 本地模拟参数服务器训练
-具体执行内容与reader与前述相同，下面介绍需要改动的地方
-```yaml
-mode: local_cluster_cpu_train # 执行名为 local_cluster_cpu_train 的运行器
-runner:
- name: local_cluster_cpu_train # 定义 runner 名为 runner_train
-  class: local_cluster_train # 执行本地模拟分布式——参数服务器训练
-  device: cpu # 执行在 cpu 上（paddle后续版本会支持PS-GPU）
-  worker_num: 1 # (可选)worker进程数量，默认1
-  server_num: 1 # (可选)server进程数量，默认1
-  epochs: 10 # 训练轮数
-```
--- a/doc/train.md
+++ b/doc/train.md
@@ -20,7 +20,7 @@ python -m paddlerec.run -m paddlerec.models.xxx.yyy
 例如启动`recall`下的`word2vec`模型的默认配置;
 ```shell
-python -m paddlerec.run -m models/recall/word2vec
+python -m paddlerec.run -m models/recall/word2vec/config.yaml
 ```
 ### 2. 启动内置模型的个性化配置训练

--- a/models/demo/online_learning/__init__.py
+++ b/models/demo/online_learning/__init__.py
-# 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.
--- a/models/demo/online_learning/config.yaml
+++ b/models/demo/online_learning/config.yaml
-# 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.
-# workspace
-workspace: "models/demo/online_learning"
-# list of dataset
-dataset:
- name: dataloader_train # name of dataset to distinguish different datasets
-  batch_size: 2
-  type: DataLoader # or QueueDataset 
-  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"
- name: dataset_train # name of dataset to distinguish different datasets
-  batch_size: 2
-  type: QueueDataset # or 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"
- name: dataset_infer # name
-  batch_size: 2
-  type: DataLoader # or QueueDataset
-  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"
-# hyper parameters of user-defined network
-hyper_parameters:
-  # optimizer config
-  optimizer:
-    class: Adam
-    learning_rate: 0.001
-    strategy: async
-  # user-defined <key, value> pairs
-  sparse_inputs_slots: 27
-  sparse_feature_dim: 9
-  dense_input_dim: 13
-  fc_sizes: [512, 256, 128, 32]
-# select runner by name
-mode: [ps_cluster, single_cpu_infer]
-# config of each runner.
-# runner is a kind of paddle training class, which wraps the train/infer process.
-runner:
- name: single_cpu_infer
-  class: infer
-  # num of epochs
-  epochs: 1
-  # device to run training or infer
-  device: cpu
-  init_model_path: "increment_dnn" # load model path
-  phases: [phase2]
- name: ps_cluster
-  class: cluster_train
-  runner_class_path: "{workspace}/online_learning_runner.py"
-  epochs: 2
-  device: cpu
-  fleet_mode: ps
-  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]
-# runner will run all the phase in each epoch
-phase:
- name: phase1
-  model: "{workspace}/model.py" # user-defined model
-  dataset_name: dataloader_train # select dataset by name
-  thread_num: 1
- name: phase2
-  model: "{workspace}/model.py" # user-defined model
-  dataset_name: dataset_infer # select dataset by name
-  thread_num: 1
--- a/models/demo/online_learning/data/download.sh
+++ b/models/demo/online_learning/data/download.sh
-wget --no-check-certificate https://fleet.bj.bcebos.com/ctr_data.tar.gz
-tar -zxvf ctr_data.tar.gz
-mv ./raw_data ./train_data_full
-mkdir train_data && cd train_data
-cp ../train_data_full/part-0 ../train_data_full/part-1 ./ && cd ..
-mv ./test_data ./test_data_full
-mkdir test_data && cd test_data
-cp ../test_data_full/part-220 ./  && cd ..
-echo "Complete data download."
-echo "Full Train data stored in ./train_data_full "
-echo "Full Test data stored in ./test_data_full "
-echo "Rapid Verification train data stored in ./train_data "
-echo "Rapid Verification test data stored in ./test_data "
--- a/models/demo/online_learning/data/get_slot_data.py
+++ b/models/demo/online_learning/data/get_slot_data.py
-#   Copyright (c) 2019 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 paddle.fluid.incubate.data_generator as dg
-cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
-cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
-hash_dim_ = 1000001
-continuous_range_ = range(1, 14)
-categorical_range_ = range(14, 40)
-class CriteoDataset(dg.MultiSlotDataGenerator):
-    """
-    DacDataset: inheritance MultiSlotDataGeneratior, Implement data reading
-    Help document: http://wiki.baidu.com/pages/viewpage.action?pageId=728820675
-    """
-    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.rstrip('\n').split('\t')
-            dense_feature = []
-            sparse_feature = []
-            for idx in continuous_range_:
-                if features[idx] == "":
-                    dense_feature.append(0.0)
-                else:
-                    dense_feature.append(
-                        (float(features[idx]) - cont_min_[idx - 1]) /
-                        cont_diff_[idx - 1])
-            for idx in categorical_range_:
-                sparse_feature.append(
-                    [hash(str(idx) + features[idx]) % hash_dim_])
-            label = [int(features[0])]
-            process_line = dense_feature, sparse_feature, label
-            feature_name = ["dense_feature"]
-            for idx in categorical_range_:
-                feature_name.append("C" + str(idx - 13))
-            feature_name.append("label")
-            s = "click:" + str(label[0])
-            for i in dense_feature:
-                s += " dense_feature:" + str(i)
-            for i in range(1, 1 + len(categorical_range_)):
-                s += " " + str(i) + ":" + str(sparse_feature[i - 1][0])
-            print(s.strip())
-            yield None
-        return reader
-d = CriteoDataset()
-d.run_from_stdin()
--- a/models/demo/online_learning/data/run.sh
+++ b/models/demo/online_learning/data/run.sh
-sh download.sh
-mkdir slot_train_data_full
-for i in `ls ./train_data_full`
-do
-    cat train_data_full/$i | python get_slot_data.py > slot_train_data_full/$i
-done
-mkdir slot_test_data_full
-for i in `ls ./test_data_full`
-do
-    cat test_data_full/$i | python get_slot_data.py > slot_test_data_full/$i
-done
-mkdir slot_train_data
-for i in `ls ./train_data`
-do
-    cat train_data/$i | python get_slot_data.py > slot_train_data/$i
-done
-mkdir slot_test_data
-for i in `ls ./test_data`
-do
-    cat test_data/$i | python get_slot_data.py > slot_test_data/$i
-done
--- a/models/demo/online_learning/data/sample_data/train/sample_train.txt
+++ b/models/demo/online_learning/data/sample_data/train/sample_train.txt
-click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.08 dense_feature:0.207421875 dense_feature:0.028 dense_feature:0.35 dense_feature:0.08 dense_feature:0.082 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.08 1:737395 2:210498 3:903564 4:286224 5:286835 6:906818 7:906116 8:67180 9:27346 10:51086 11:142177 12:95024 13:157883 14:873363 15:600281 16:812592 17:228085 18:35900 19:880474 20:984402 21:100885 22:26235 23:410878 24:798162 25:499868 26:306163
-click:1 dense_feature:0.0 dense_feature:0.932006633499 dense_feature:0.02 dense_feature:0.14 dense_feature:0.0395625 dense_feature:0.328 dense_feature:0.98 dense_feature:0.12 dense_feature:1.886 dense_feature:0.0 dense_feature:1.8 dense_feature:0.0 dense_feature:0.14 1:715353 2:761523 3:432904 4:892267 5:515218 6:948614 7:266726 8:67180 9:27346 10:266081 11:286126 12:789480 13:49621 14:255651 15:47663 16:79797 17:342789 18:616331 19:880474 20:984402 21:242209 22:26235 23:669531 24:26284 25:269955 26:187951
-click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.08 dense_feature:0.06 dense_feature:0.14125 dense_feature:0.076 dense_feature:0.05 dense_feature:0.22 dense_feature:0.208 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.06 1:737395 2:952384 3:511141 4:271077 5:286835 6:948614 7:903547 8:507110 9:27346 10:56047 11:612953 12:747707 13:977426 14:671506 15:158148 16:833738 17:342789 18:427155 19:880474 20:537425 21:916237 22:26235 23:468277 24:676936 25:751788 26:363967
-click:0 dense_feature:0.0 dense_feature:0.124378109453 dense_feature:0.02 dense_feature:0.04 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.08 dense_feature:0.024 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.04 1:210127 2:286436 3:183920 4:507656 5:286835 6:906818 7:199553 8:67180 9:502607 10:708281 11:809876 12:888238 13:375164 14:202774 15:459895 16:475933 17:555571 18:847163 19:26230 20:26229 21:808836 22:191474 23:410878 24:315120 25:26224 26:26223
-click:0 dense_feature:0.1 dense_feature:0.0149253731343 dense_feature:0.34 dense_feature:0.32 dense_feature:0.016421875 dense_feature:0.098 dense_feature:0.04 dense_feature:0.96 dense_feature:0.202 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.32 1:230803 2:817085 3:539110 4:388629 5:286835 6:948614 7:586040 8:67180 9:27346 10:271155 11:176640 12:827381 13:36881 14:202774 15:397299 16:411672 17:342789 18:474060 19:880474 20:984402 21:216871 22:26235 23:761351 24:787115 25:884722 26:904135
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-click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.0 dense_feature:0.16 dense_feature:4.790078125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.28 dense_feature:0.016 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.2 1:737395 2:532829 3:158777 4:112926 5:286835 6:948614 7:277312 8:67180 9:502607 10:708281 11:755513 12:524443 13:4029 14:873363 15:503814 16:146094 17:644532 18:121590 19:26230 20:26229 21:901094 22:191474 23:744389 24:259263 25:26224 26:26223
-click:0 dense_feature:0.0 dense_feature:3.30845771144 dense_feature:0.0 dense_feature:0.04 dense_feature:0.022671875 dense_feature:0.062 dense_feature:0.01 dense_feature:0.4 dense_feature:0.062 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:663372 2:529436 3:511823 4:942782 5:286835 6:906818 7:190054 8:67180 9:27346 10:708281 11:32527 12:494263 13:652478 14:873363 15:616057 16:17325 17:342789 18:325238 19:26230 20:26229 21:256747 22:179327 23:410878 24:169709 25:26224 26:26223
-click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.01 dense_feature:0.16 dense_feature:0.206765625 dense_feature:0.328 dense_feature:0.13 dense_feature:0.16 dense_feature:0.176 dense_feature:0.0 dense_feature:0.7 dense_feature:0.0 dense_feature:0.16 1:737395 2:552854 3:606082 4:267619 5:286835 6:948614 7:918889 8:67180 9:27346 10:708281 11:400024 12:972010 13:66330 14:255651 15:432931 16:650209 17:506108 18:212910 19:26230 20:26229 21:107726 22:26235 23:410878 24:718419 25:26224 26:26223
--- a/models/demo/online_learning/model.py
+++ b/models/demo/online_learning/model.py
-# 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 math
-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.is_distributed = True if envs.get_fleet_mode().upper(
-        ) == "PSLIB" else False
-        self.sparse_feature_number = envs.get_global_env(
-            "hyper_parameters.sparse_feature_number")
-        self.sparse_feature_dim = envs.get_global_env(
-            "hyper_parameters.sparse_feature_dim")
-        self.learning_rate = envs.get_global_env(
-            "hyper_parameters.optimizer.learning_rate")
-    def net(self, input, is_infer=False):
-        self.sparse_inputs = self._sparse_data_var[1:]
-        self.dense_input = self._dense_data_var[0]
-        self.label_input = self._sparse_data_var[0]
-        def embedding_layer(input):
-            emb = fluid.contrib.layers.sparse_embedding(
-                input=input,
-                is_test=False,
-                # for distributed sparse embedding, dim0 just fake.
-                size=[1024, self.sparse_feature_dim],
-                param_attr=fluid.ParamAttr(
-                    name="SparseFeatFactors",
-                    initializer=fluid.initializer.Uniform()), )
-            emb_sum = fluid.layers.sequence_pool(input=emb, pool_type='sum')
-            return emb_sum
-        sparse_embed_seq = list(map(embedding_layer, self.sparse_inputs))
-        concated = fluid.layers.concat(
-            sparse_embed_seq + [self.dense_input], axis=1)
-        fcs = [concated]
-        hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes")
-        for size in hidden_layers:
-            output = fluid.layers.fc(
-                input=fcs[-1],
-                size=size,
-                act='relu',
-                param_attr=fluid.ParamAttr(
-                    initializer=fluid.initializer.Normal(
-                        scale=1.0 / math.sqrt(fcs[-1].shape[1]))))
-            fcs.append(output)
-        predict = fluid.layers.fc(
-            input=fcs[-1],
-            size=2,
-            act="softmax",
-            param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
-                scale=1 / math.sqrt(fcs[-1].shape[1]))))
-        self.predict = predict
-        auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
-                                             label=self.label_input,
-                                             num_thresholds=2**12,
-                                             slide_steps=20)
-        if is_infer:
-            self._infer_results["AUC"] = auc
-            self._infer_results["BATCH_AUC"] = batch_auc
-            return
-        self._metrics["AUC"] = auc
-        self._metrics["BATCH_AUC"] = batch_auc
-        cost = fluid.layers.cross_entropy(
-            input=self.predict, label=self.label_input)
-        avg_cost = fluid.layers.reduce_mean(cost)
-        self._cost = avg_cost
-    def optimizer(self):
-        optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)
-        return optimizer
-    def infer_net(self):
-        pass
--- a/models/rank/dnn/README.md
+++ b/models/rank/dnn/README.md
@@ -259,3 +259,133 @@ auc_var, batch_auc_var, auc_states = fluid.layers.auc(
 ```
 完成上述组网后，我们最终可以通过训练拿到`avg_cost`与`auc`两个重要指标。
+## 流式训练（OnlineLearning）任务启动及配置流程
+### 流式训练简介
+流式训练是按照一定顺序进行数据的接收和处理，每接收一个数据，模型会对它进行预测并对当前模型进行更新，然后处理下一个数据。 像信息流、小视频、电商等场景，每天都会新增大量的数据， 让每天(每一刻)新增的数据基于上一天(上一刻)的模型进行新的预测和模型更新。
+在大规模流式训练场景下， 需要使用的深度学习框架有对应的能力支持， 即：
+* 支持大规模分布式训练的能力， 数据量巨大， 需要有良好的分布式训练及扩展能力，才能满足训练的时效要求
+* 支持超大规模的Embedding， 能够支持十亿甚至千亿级别的Embedding, 拥有合理的参数输出的能力，能够快速输出模型参数并和线上其他系统进行对接
+* Embedding的特征ID需要支持HASH映射，不要求ID的编码，能够自动增长及控制特征的准入(原先不存在的特征可以以适当的条件创建)， 能够定期淘汰(能够以一定的策略进行过期的特征的清理) 并拥有准入及淘汰策略
+* 最后就是要基于框架开发一套完备的流式训练的 trainer.py， 能够拥有完善的流式训练流程
+### 使用ctr-dnn online learning 进行模型的训练
+目前，PaddleRec基于飞桨分布式训练框架的能力，实现了这套流式训练的流程。 供大家参考和使用。我们基于`models/rank/ctr-dnn`修改了一个online_training的版本，供大家更好的理解和参考。
+**注意**
+1. 使用online learning 需要安装目前Paddle最新的开发者版本， 你可以从 https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev 此处获得它，需要先卸载当前已经安装的飞桨版本，根据自己的Python环境下载相应的安装包。
+2. 使用online learning 需要安装目前PaddleRec最新的开发者版本， 你可以通过 git clone https://github.com/PaddlePaddle/PaddleRec.git 得到最新版的PaddleRec并自行安装
+### 启动方法
+1. 修改config.yaml中的 hyper_parameters.distributed_embedding=1，表示打开大规模稀疏的模式
+2. 修改config.yaml中的 mode: [single_cpu_train, single_cpu_infer] 中的 `single_cpu_train` 为online_learning_cluster，表示使用online learning对应的运行模式
+3. 准备训练数据， ctr-dnn中使用的online learning对应的训练模式为 天级别训练， 每天又分为24个小时， 因此训练数据需要 天--小时的目录结构进行整理。
+    以 2020年08月10日 到 2020年08月11日 2天的训练数据举例， 用户需要准备的数据的目录结构如下：
+    ```
+    train_data/
+    |-- 20200810
+    |   |-- 00
+    |   |   `-- train.txt
+    |   |-- 01
+    |   |   `-- train.txt
+    |   |-- 02
+    |   |   `-- train.txt
+    |   |-- 03
+    |   |   `-- train.txt
+    |   |-- 04
+    |   |   `-- train.txt
+    |   |-- 05
+    |   |   `-- train.txt
+    |   |-- 06
+    |   |   `-- train.txt
+    |   |-- 07
+    |   |   `-- train.txt
+    |   |-- 08
+    |   |   `-- train.txt
+    |   |-- 09
+    |   |   `-- train.txt
+    |   |-- 10
+    |   |   `-- train.txt
+    |   |-- 11
+    |   |   `-- train.txt
+    |   |-- 12
+    |   |   `-- train.txt
+    |   |-- 13
+    |   |   `-- train.txt
+    |   |-- 14
+    |   |   `-- train.txt
+    |   |-- 15
+    |   |   `-- train.txt
+    |   |-- 16
+    |   |   `-- train.txt
+    |   |-- 17
+    |   |   `-- train.txt
+    |   |-- 18
+    |   |   `-- train.txt
+    |   |-- 19
+    |   |   `-- train.txt
+    |   |-- 20
+    |   |   `-- train.txt
+    |   |-- 21
+    |   |   `-- train.txt
+    |   |-- 22
+    |   |   `-- train.txt
+    |   `-- 23
+    |       `-- train.txt
+    `-- 20200811
+        |-- 00
+        |   `-- train.txt
+        |-- 01
+        |   `-- train.txt
+        |-- 02
+        |   `-- train.txt
+        |-- 03
+        |   `-- train.txt
+        |-- 04
+        |   `-- train.txt
+        |-- 05
+        |   `-- train.txt
+        |-- 06
+        |   `-- train.txt
+        |-- 07
+        |   `-- train.txt
+        |-- 08
+        |   `-- train.txt
+        |-- 09
+        |   `-- train.txt
+        |-- 10
+        |   `-- train.txt
+        |-- 11
+        |   `-- train.txt
+        |-- 12
+        |   `-- train.txt
+        |-- 13
+        |   `-- train.txt
+        |-- 14
+        |   `-- train.txt
+        |-- 15
+        |   `-- train.txt
+        |-- 16
+        |   `-- train.txt
+        |-- 17
+        |   `-- train.txt
+        |-- 18
+        |   `-- train.txt
+        |-- 19
+        |   `-- train.txt
+        |-- 20
+        |   `-- train.txt
+        |-- 21
+        |   `-- train.txt
+        |-- 22
+        |   `-- train.txt
+        `-- 23
+            `-- train.txt
+    ```    
+4. 准备好数据后， 即可按照标准的训练流程进行流式训练了
+    ```shell
+    python -m paddlerec.run -m models/rerank/ctr-dnn/config.yaml
+    ```
--- a/models/rank/dnn/config.yaml
+++ b/models/rank/dnn/config.yaml
@@ -49,6 +49,7 @@ hyper_parameters:
  sparse_feature_dim: 9
  dense_input_dim: 13
  fc_sizes: [512, 256, 128, 32]
+  distributed_embedding: 0
 # select runner by name
 mode: [single_cpu_train, single_cpu_infer]
@@ -90,6 +91,18 @@ runner:
  print_interval: 1
  phases: [phase1]
+- name: online_learning_cluster
+  class: cluster_train
+  runner_class_path: "{workspace}/online_learning_runner.py"
+  epochs: 2
+  device: cpu
+  fleet_mode: ps
+  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]
 - name: collective_cluster
  class: cluster_train
  epochs: 2

--- a/models/rank/dnn/model.py
+++ b/models/rank/dnn/model.py
@@ -25,8 +25,16 @@ class Model(ModelBase):
        ModelBase.__init__(self, config)
    def _init_hyper_parameters(self):
-        self.is_distributed = True if envs.get_fleet_mode().upper(
+        self.is_distributed = False
-        ) == "PSLIB" else False
+        self.distributed_embedding = False
+        if envs.get_fleet_mode().upper() == "PSLIB":
+            self.is_distributed = True
+        if envs.get_global_env("hyper_parameters.distributed_embedding",
+                               0) == 1:
+            self.distributed_embedding = True
        self.sparse_feature_number = envs.get_global_env(
            "hyper_parameters.sparse_feature_number")
        self.sparse_feature_dim = envs.get_global_env(
@@ -40,14 +48,26 @@ class Model(ModelBase):
        self.label_input = self._sparse_data_var[0]
        def embedding_layer(input):
+            if self.distributed_embedding:
+                emb = fluid.contrib.layers.sparse_embedding(
+                    input=input,
+                    size=[
+                        self.sparse_feature_number, self.sparse_feature_dim
+                    ],
+                    param_attr=fluid.ParamAttr(
+                        name="SparseFeatFactors",
+                        initializer=fluid.initializer.Uniform()))
+            else:
                emb = fluid.layers.embedding(
                    input=input,
                    is_sparse=True,
                    is_distributed=self.is_distributed,
-                size=[self.sparse_feature_number, self.sparse_feature_dim],
+                    size=[
+                        self.sparse_feature_number, self.sparse_feature_dim
+                    ],
                    param_attr=fluid.ParamAttr(
                        name="SparseFeatFactors",
-                    initializer=fluid.initializer.Uniform()), )
+                        initializer=fluid.initializer.Uniform()))
            emb_sum = fluid.layers.sequence_pool(input=emb, pool_type='sum')
            return emb_sum

--- a/models/demo/online_learning/online_learning_runner.py
+++ b/models/demo/online_learning/online_learning_runner.py