OpenMLDB + OneFlow：手把手教你快速链接特征工程到模型训练

Posted 2022-08-18 OneFlow深度学习框架

如何从历史数据中找出规律，去预测用户未来的购买需求，让最合适的商品遇见最需要的人，是大数据应用在精准营销中的关键问题，也是所有电商平台在做智能化升级时所需要的核心技术。

京东作为中国最大的自营式电商，沉淀了数亿的忠实用户，积累了海量的真实数据。本案例以京东商城真实的用户、商品和行为数据（脱敏后）为基础，通过数据挖掘的技术和机器学习的算法，构建用户购买商品的预测模型，输出高潜用户和目标商品的匹配结果，为精准营销提供高质量的目标群体，挖掘数据背后潜在的意义，为电商用户提供更简单、快捷、省心的购物体验。

本案例以京东高潜用户购买意向预测问题为例，使用 OpenMLDB 进行数据挖掘，使用 OneFlow 中的 DeepFM 模型进行高性能训练推理，提供精准的商品推荐，从而打造一个完整的机器学习应用。

本案例基于 OpenMLDB 集群版进行教程演示。注意，本文档使用的是预编译好的 docker 镜像。如果希望在自己编译和搭建的 OpenMLDB 环境下进行测试，推荐针对 OpenMLDB 优化的 Spark 发行版。（参考章节：

https://openmldb.ai/docs/zh/main/deploy/compile.html）

1

环境准备和预备知识

OneFlow 工具包安装

OneFlow 工具依赖 GPU 的强大算力，所以请确保部署机器具备 Nvidia GPU，并且保证驱动版本 >=460.X.X 驱动版本需支持 CUDA 11.0。

使用以下指令安装 OneFlow：

conda activate oneflow
python3 -m pip install --pre oneflow -f https://staging.oneflow.info/branch/support_oneembedding_serving/cu102

还需要安装以下 Python 工具包：

pip install psutil petastorm pandas sklearn

拉取和启动 OpenMLDB Docker 镜像

• 注意，请确保 Docker Engine 版本号 >= 18.03

• 拉取 OpenMLDB docker 镜像，并且运行相应容器

• 映射demo文件夹至/root/project，这里我们使用的路径为demodir=/home/gtest/demo

• export demodir=/home/gtest/demo
  docker run -dit --name=demo --network=host -v $demodir:/root/project 4pdosc/openmldb:0.5.2 bash
  docker exec -it demo bash

• 上述镜像预装了OpenMLDB的工具等，我们需要进一步安装Oneflow推理所需依赖。

因为我们将在OpenMLDB的服务中嵌入OneFlow模型推理的预处理及调用，需要安装以下的依赖。

pip install tritonclient[all] xxhash geventhttpclient

注意，本教程以下的 OpenMLDB 部分的演示命令默认均在该已经启动的 docker 容器内运行。OneFlow 命令默认在 1.1 安装的 OneFlow 环境下运行。

初始化环境

./init.sh

我们在镜像内提供了init.sh脚本帮助用户快速初始化环境，包括：

• 配置 zookeeper

• 启动集群版 OpenMLDB

启动 OpenMLDB CLI 客户端

/work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client

注意，本教程大部分命令在 OpenMLDB CLI 下执行，为了跟普通 shell 环境做区分，在 OpenMLDB CLI 下执行的命令均使用特殊的提示符  `>`  。

预备知识：集群版的非阻塞任务

集群版的部分命令是非阻塞任务，包括在线模式的 LOAD DATA，以及离线模式的 LOAD DATA ，SELECT，SELECT INTO 命令。提交任务以后可以使用相关的命令如 SHOW JOBS, SHOW JOB 来查看任务进度，详情参见离线任务管理文档。

2

机器学习训练流程

使用OpenMLDB+OneFlow进行机器学习训练可总结为以下大致步骤。

接下来会介绍每一个步骤的具体操作细节。

使用OpenMLDB进行离线特征抽取

创建数据库和数据表

以下命令均在 OpenMLDB CLI 环境下执行。

> CREATE DATABASE JD_db;
> USE JD_db;
> CREATE TABLE action(reqId string, eventTime timestamp, ingestionTime timestamp, actionValue int);
> CREATE TABLE flattenRequest(reqId string, eventTime timestamp, main_id string, pair_id string, user_id string, sku_id string, time bigint, split_id int, time1 string);
> CREATE TABLE bo_user(ingestionTime timestamp, user_id string, age string, sex string, user_lv_cd string, user_reg_tm bigint);
> CREATE TABLE bo_action(ingestionTime timestamp, pair_id string, time bigint, model_id string, type string, cate string, br string);
> CREATE TABLE bo_product(ingestionTime timestamp, sku_id string, a1 string, a2 string, a3 string, cate string, br string);
> CREATE TABLE bo_comment(ingestionTime timestamp, dt bigint, sku_id string, comment_num int, has_bad_comment string, bad_comment_rate float);

也可使用 sql 脚本(/root/project/create_tables.sql)运行：

/work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client < /root/project/create_tables.sql

离线数据准备

首先，切换到离线执行模式。接着，导入数据作为离线数据，用于离线特征计算。

以下命令均在 OpenMLDB CLI 下执行。

> USE JD_db;> SET @@execute_mode='offline';> LOAD DATA INFILE '/root/project/data/JD_data/action/*.parquet' INTO TABLE action options(format='parquet', header=true, mode='append');> LOAD DATA INFILE '/root/project/data/JD_data/flattenRequest_clean/*.parquet' INTO TABLE flattenRequest options(format='parquet', header=true, mode='append');> LOAD DATA INFILE '/root/project/data/JD_data/bo_user/*.parquet' INTO TABLE bo_user options(format='parquet', header=true, mode='append');> LOAD DATA INFILE '/root/project/data/JD_data/bo_action/*.parquet' INTO TABLE bo_action options(format='parquet', header=true, mode='append');> LOAD DATA INFILE '/root/project/data/JD_data/bo_product/*.parquet' INTO TABLE bo_product options(format='parquet', header=true, mode='append');> LOAD DATA INFILE '/root/project/data/JD_data/bo_comment/*.parquet' INTO TABLE bo_comment options(format='parquet', header=true, mode='append');

或使用脚本执行：

/work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client < /root/project/load_data.sql

并通过以下命令快速查询 jobs 状态：

echo "show jobs;" | /work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client

注意，集群版 `LOAD  DATA` 为非阻塞任务，可以使用命令 `SHOW  JOBS` 查看任务运行状态，请等待任务运行成功（ `state` 转至 `FINISHED` 状态），再进行下一步操作 。

特征设计

通常在设计特征前，用户需要根据机器学习的目标对数据进行分析，然后根据分析设计和调研特征。机器学习的数据分析和特征研究不是本文讨论的范畴，我们将不作展开。本文假定用户具备机器学习的基本理论知识，有解决机器学习问题的能力，能够理解 SQL 语法，并能够使用 SQL 语法构建特征。针对本案例，用户经过分析和调研设计了若干特征。

请注意，在实际的机器学习特征调研过程中，科学家对特征进行反复试验，寻求模型效果最好的特征集。所以会不断的重复多次特征设计->离线特征抽取->模型训练过程，并不断调整特征以达到预期效果。

离线特征抽取

用户在离线模式下，进行特征抽取，并将特征结果输出到'/root/project/out/1'目录下保存（对应映射为$demodir/out/1），以供后续的模型训练。 SELECT 命令对应了基于上述特征设计所产生的 SQL 特征计算脚本。以下命令均在 OpenMLDB CLI 下执行。

USE JD_db;
select * from(select
`reqId` as reqId_1,
`eventTime` as flattenRequest_eventTime_original_0,
`reqId` as flattenRequest_reqId_original_1,
`pair_id` as flattenRequest_pair_id_original_24,
`sku_id` as flattenRequest_sku_id_original_25,
`user_id` as flattenRequest_user_id_original_26,
    distinct_count(`pair_id`) over flattenRequest_user_id_eventTime_0_10_ as flattenRequest_pair_id_window_unique_count_27,
    fz_top1_ratio(`pair_id`) over flattenRequest_user_id_eventTime_0_10_ as flattenRequest_pair_id_window_top1_ratio_28,
    fz_top1_ratio(`pair_id`) over flattenRequest_user_id_eventTime_0s_14d_200 as flattenRequest_pair_id_window_top1_ratio_29,
    distinct_count(`pair_id`) over flattenRequest_user_id_eventTime_0s_14d_200 as flattenRequest_pair_id_window_unique_count_32,
case when !isnull(at(`pair_id`, 0)) over flattenRequest_user_id_eventTime_0_10_ then count_where(`pair_id`, `pair_id` = at(`pair_id`, 0)) over flattenRequest_user_id_eventTime_0_10_ else null end as flattenRequest_pair_id_window_count_35,
dayofweek(timestamp(`eventTime`)) as flattenRequest_eventTime_dayofweek_41,
case when 1 < dayofweek(timestamp(`eventTime`)) and dayofweek(timestamp(`eventTime`)) < 7 then 1 else 0 end as flattenRequest_eventTime_isweekday_43from
`flattenRequest`
window flattenRequest_user_id_eventTime_0_10_ as (partition by `user_id` order by `eventTime` rows between 10 preceding and 0 preceding),
    flattenRequest_user_id_eventTime_0s_14d_200 as (partition by `user_id` order by `eventTime` rows_range between 14d preceding and 0s preceding MAXSIZE 200))as out0last join(select
`flattenRequest`.`reqId` as reqId_3,
`action_reqId`.`actionValue` as action_actionValue_multi_direct_2,
`bo_product_sku_id`.`a1` as bo_product_a1_multi_direct_3,
`bo_product_sku_id`.`a2` as bo_product_a2_multi_direct_4,
`bo_product_sku_id`.`a3` as bo_product_a3_multi_direct_5,
`bo_product_sku_id`.`br` as bo_product_br_multi_direct_6,
`bo_product_sku_id`.`cate` as bo_product_cate_multi_direct_7,
`bo_product_sku_id`.`ingestionTime` as bo_product_ingestionTime_multi_direct_8,
`bo_user_user_id`.`age` as bo_user_age_multi_direct_9,
`bo_user_user_id`.`ingestionTime` as bo_user_ingestionTime_multi_direct_10,
`bo_user_user_id`.`sex` as bo_user_sex_multi_direct_11,
`bo_user_user_id`.`user_lv_cd` as bo_user_user_lv_cd_multi_direct_12from
`flattenRequest`
last join `action` as `action_reqId` on `flattenRequest`.`reqId` = `action_reqId`.`reqId`
last join `bo_product` as `bo_product_sku_id` on `flattenRequest`.`sku_id` = `bo_product_sku_id`.`sku_id`
last join `bo_user` as `bo_user_user_id` on `flattenRequest`.`user_id` = `bo_user_user_id`.`user_id`)as out1on out0.reqId_1 = out1.reqId_3last join(select
`reqId` as reqId_14,
max(`bad_comment_rate`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_bad_comment_rate_multi_max_13,
min(`bad_comment_rate`) over bo_comment_sku_id_ingestionTime_0_10_ as bo_comment_bad_comment_rate_multi_min_14,
min(`bad_comment_rate`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_bad_comment_rate_multi_min_15,
    distinct_count(`comment_num`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_comment_num_multi_unique_count_22,
    distinct_count(`has_bad_comment`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_has_bad_comment_multi_unique_count_23,
    fz_topn_frequency(`has_bad_comment`, 3) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_has_bad_comment_multi_top3frequency_30,
    fz_topn_frequency(`comment_num`, 3) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_comment_num_multi_top3frequency_33from
    (select `eventTime` as `ingestionTime`, bigint(0) as `dt`, `sku_id` as `sku_id`, int(0) as `comment_num`, '' as `has_bad_comment`, float(0) as `bad_comment_rate`, reqId from `flattenRequest`)
window bo_comment_sku_id_ingestionTime_0s_64d_100 as (UNION (select `ingestionTime`, `dt`, `sku_id`, `comment_num`, `has_bad_comment`, `bad_comment_rate`, '' as reqId from `bo_comment`) partition by `sku_id` order by `ingestionTime` rows_range between 64d preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW),
    bo_comment_sku_id_ingestionTime_0_10_ as (UNION (select `ingestionTime`, `dt`, `sku_id`, `comment_num`, `has_bad_comment`, `bad_comment_rate`, '' as reqId from `bo_comment`) partition by `sku_id` order by `ingestionTime` rows between 10 preceding and 0 preceding INSTANCE_NOT_IN_WINDOW))as out2on out0.reqId_1 = out2.reqId_14last join(select
`reqId` as reqId_17,
    fz_topn_frequency(`br`, 3) over bo_action_pair_id_ingestionTime_0s_10h_100 as bo_action_br_multi_top3frequency_16,
    fz_topn_frequency(`cate`, 3) over bo_action_pair_id_ingestionTime_0s_10h_100 as bo_action_cate_multi_top3frequency_17,
    fz_topn_frequency(`model_id`, 3) over bo_action_pair_id_ingestionTime_0s_7d_100 as bo_action_model_id_multi_top3frequency_18,
    distinct_count(`model_id`) over bo_action_pair_id_ingestionTime_0s_14d_100 as bo_action_model_id_multi_unique_count_19,
    distinct_count(`model_id`) over bo_action_pair_id_ingestionTime_0s_7d_100 as bo_action_model_id_multi_unique_count_20,
    distinct_count(`type`) over bo_action_pair_id_ingestionTime_0s_14d_100 as bo_action_type_multi_unique_count_21,
    fz_topn_frequency(`type`, 3) over bo_action_pair_id_ingestionTime_0s_7d_100 as bo_action_type_multi_top3frequency_40,
    fz_topn_frequency(`type`, 3) over bo_action_pair_id_ingestionTime_0s_14d_100 as bo_action_type_multi_top3frequency_42from
    (select `eventTime` as `ingestionTime`, `pair_id` as `pair_id`, bigint(0) as `time`, '' as `model_id`, '' as `type`, '' as `cate`, '' as `br`, reqId from `flattenRequest`)
window bo_action_pair_id_ingestionTime_0s_10h_100 as (UNION (select `ingestionTime`, `pair_id`, `time`, `model_id`, `type`, `cate`, `br`, '' as reqId from `bo_action`) partition by `pair_id` order by `ingestionTime` rows_range between 10h preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW),
    bo_action_pair_id_ingestionTime_0s_7d_100 as (UNION (select `ingestionTime`, `pair_id`, `time`, `model_id`, `type`, `cate`, `br`, '' as reqId from `bo_action`) partition by `pair_id` order by `ingestionTime` rows_range between 7d preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW),
    bo_action_pair_id_ingestionTime_0s_14d_100 as (UNION (select `ingestionTime`, `pair_id`, `time`, `model_id`, `type`, `cate`, `br`, '' as reqId from `bo_action`) partition by `pair_id` order by `ingestionTime` rows_range between 14d preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW))as out3on out0.reqId_1 = out3.reqId_17INTO OUTFILE '/root/project/out/1';

此处仅一个命令，可以使用阻塞式LOAD DATA，直接运行sql脚本sync_select_out.sql:

/work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client < /root/project/sync_select_out.sql

注意，集群版 `LOAD  DATA` 为非阻塞任务，可以使用命令 `SHOW  JOBS` 查看任务运行状态，请等待任务运行成功（ `state` 转至 `FINISHED` 状态），再进行下一步操作 。

预处理数据集以配合 DeepFM 模型要求

注意，以下命令在 docker 外执行，使用安装了 1.1 所描述的 OneFlow 运行环境

根据 DeepFM 论文, 类别特征和连续特征都被当作稀疏特征对待。

χ may include categorical fields (e.g., gender, location) and continuous fields (e.g., age). Each categorical field is represented as a vector of one-hot encoding, and each continuous field is represented as the value itself, or a vector of one-hot encoding after discretization.

进入demo文件夹，运行以下指令进行数据处理

cd $demodir/openmldb_process/
sh process_JD_out_full.sh $demodir/out/1

After generating parquet dataset, dataset information will also be printed. It contains the information about the number of samples and table size array, which is needed when training.

对应生成parquet数据集将生成在 $demodir/openmldb_process/out。数据信息将被打印如下，该信息将被输入为训练的配置文件。

train samples = 4007924
val samples = 504398
test samples = 530059
table size array:
11,42,1105,200,11,1295,1,1,5,3,23,23,7,5042381,3127923,5042381,3649642,28350,105180,7,2,5042381,5,4,4,41,2,2,8,3456,4,5,5042381,10,60,5042381,843,17,1276,101,100

启动 OneFlow 进行模型训练

注意，以下命令在安装 1.1 所描述的 OneFlow 运行环境中运行

修改对应 train-deepfm.sh 配置文件

cd $demodir/oneflow_process/

#!/bin/bash
DEVICE_NUM_PER_NODE=1
DATA_DIR=$demodir/openmldb_process/out
PERSISTENT_PATH=/$demodir/oneflow_process/persistent
MODEL_SAVE_DIR=$demodir/oneflow_process/model_out
MODEL_SERVING_PATH=$demodir/oneflow_process/model/embedding/1/model


python3 -m oneflow.distributed.launch \\
--nproc_per_node $DEVICE_NUM_PER_NODE \\
--nnodes 1 \\--node_rank 0 \\
--master_addr 127.0.0.1 \\
deepfm_train_eval_JD.py \\
--disable_fusedmlp \\
--data_dir $DATA_DIR \\
--persistent_path $PERSISTENT_PATH \\
--table_size_array "11,42,1105,200,11,1295,1,1,5,3,23,23,7,5042381,3127923,5042381,3649642,28350,105180,7,2,5042381,5,4,4,41,2,2,8,3456,4,5,5042381,10,60,5042381,843,17,1276,101,100" \\
--store_type 'cached_host_mem' \\
--cache_memory_budget_mb 1024 \\
--batch_size 10000 \\
--train_batches 75000 \\
--loss_print_interval 100 \\
--dnn "1000,1000,1000,1000,1000" \\
--net_dropout 0.2 \\
--learning_rate 0.001 \\
--embedding_vec_size 16 \\
--num_train_samples 4007924 \\
--num_val_samples 504398 \\
--num_test_samples 530059 \\
--model_save_dir $MODEL_SAVE_DIR \\
--save_best_model \\
--save_graph_for_serving \\
--model_serving_path $MODEL_SERVING_PATH \\
--save_model_after_each_eval

‍

开始模型训练

bash train_deepfm.sh

生成模型将存放在$demodir/oneflow_process/model_out，用来 serving 的模型存放在$demodir/oneflow_process/model/embedding/1/model

3

模型上线流程

使用 OpenMLDB+OneFlow 进行模型 serving 可总结为以下大致步骤。接下来会介绍每一个步骤的具体操作细节。

配置 OpenMLDB 进行在线特征抽取

特征抽取 SQL 脚本上线 

假定 2.4 节中所设计的特征在上一步的模型训练中产出的模型符合预期，那么下一步就是将该特征抽取SQL脚本部署到线上去，以提供在线的特征抽取。

重新启动 OpenMLDB CLI，以进行 SQL 上线部署

docker exec -it demo bash
/work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client

执行上线部署，以下命令在 OpenMLDB CLI 内执行。

> USE JD_db;
> SET @@execute_mode='online';
> deploy demo select * from(select
`reqId` as reqId_1,
`eventTime` as flattenRequest_eventTime_original_0,
`reqId` as flattenRequest_reqId_original_1,
`pair_id` as flattenRequest_pair_id_original_24,
`sku_id` as flattenRequest_sku_id_original_25,
`user_id` as flattenRequest_user_id_original_26,
    distinct_count(`pair_id`) over flattenRequest_user_id_eventTime_0_10_ as flattenRequest_pair_id_window_unique_count_27,
    fz_top1_ratio(`pair_id`) over flattenRequest_user_id_eventTime_0_10_ as flattenRequest_pair_id_window_top1_ratio_28,
    fz_top1_ratio(`pair_id`) over flattenRequest_user_id_eventTime_0s_14d_200 as flattenRequest_pair_id_window_top1_ratio_29,
    distinct_count(`pair_id`) over flattenRequest_user_id_eventTime_0s_14d_200 as flattenRequest_pair_id_window_unique_count_32,
case when !isnull(at(`pair_id`, 0)) over flattenRequest_user_id_eventTime_0_10_ then count_where(`pair_id`, `pair_id` = at(`pair_id`, 0)) over flattenRequest_user_id_eventTime_0_10_ else null end as flattenRequest_pair_id_window_count_35,
    dayofweek(timestamp(`eventTime`)) as flattenRequest_eventTime_dayofweek_41,
case when 1 < dayofweek(timestamp(`eventTime`)) and dayofweek(timestamp(`eventTime`)) < 7 then 1 else 0 end as flattenRequest_eventTime_isweekday_43from
`flattenRequest`
window flattenRequest_user_id_eventTime_0_10_ as (partition by `user_id` order by `eventTime` rows between 10 preceding and 0 preceding),
    flattenRequest_user_id_eventTime_0s_14d_200 as (partition by `user_id` order by `eventTime` rows_range between 14d preceding and 0s preceding MAXSIZE 200))as out0last join(select
`flattenRequest`.`reqId` as reqId_3,
`action_reqId`.`actionValue` as action_actionValue_multi_direct_2,
`bo_product_sku_id`.`a1` as bo_product_a1_multi_direct_3,
`bo_product_sku_id`.`a2` as bo_product_a2_multi_direct_4,
`bo_product_sku_id`.`a3` as bo_product_a3_multi_direct_5,
`bo_product_sku_id`.`br` as bo_product_br_multi_direct_6,
`bo_product_sku_id`.`cate` as bo_product_cate_multi_direct_7,
`bo_product_sku_id`.`ingestionTime` as bo_product_ingestionTime_multi_direct_8,
`bo_user_user_id`.`age` as bo_user_age_multi_direct_9,
`bo_user_user_id`.`ingestionTime` as bo_user_ingestionTime_multi_direct_10,
`bo_user_user_id`.`sex` as bo_user_sex_multi_direct_11,
`bo_user_user_id`.`user_lv_cd` as bo_user_user_lv_cd_multi_direct_12from
`flattenRequest`
    last join `action` as `action_reqId` on `flattenRequest`.`reqId` = `action_reqId`.`reqId`
    last join `bo_product` as `bo_product_sku_id` on `flattenRequest`.`sku_id` = `bo_product_sku_id`.`sku_id`
    last join `bo_user` as `bo_user_user_id` on `flattenRequest`.`user_id` = `bo_user_user_id`.`user_id`)as out1on out0.reqId_1 = out1.reqId_3last join(select
`reqId` as reqId_14,
    max(`bad_comment_rate`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_bad_comment_rate_multi_max_13,
    min(`bad_comment_rate`) over bo_comment_sku_id_ingestionTime_0_10_ as bo_comment_bad_comment_rate_multi_min_14,
    min(`bad_comment_rate`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_bad_comment_rate_multi_min_15,
    distinct_count(`comment_num`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_comment_num_multi_unique_count_22,
    distinct_count(`has_bad_comment`) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_has_bad_comment_multi_unique_count_23,
    fz_topn_frequency(`has_bad_comment`, 3) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_has_bad_comment_multi_top3frequency_30,
    fz_topn_frequency(`comment_num`, 3) over bo_comment_sku_id_ingestionTime_0s_64d_100 as bo_comment_comment_num_multi_top3frequency_33from
    (select `eventTime` as `ingestionTime`, bigint(0) as `dt`, `sku_id` as `sku_id`, int(0) as `comment_num`, '' as `has_bad_comment`, float(0) as `bad_comment_rate`, reqId from `flattenRequest`)
window bo_comment_sku_id_ingestionTime_0s_64d_100 as (UNION (select `ingestionTime`, `dt`, `sku_id`, `comment_num`, `has_bad_comment`, `bad_comment_rate`, '' as reqId from `bo_comment`) partition by `sku_id` order by `ingestionTime` rows_range between 64d preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW),
    bo_comment_sku_id_ingestionTime_0_10_ as (UNION (select `ingestionTime`, `dt`, `sku_id`, `comment_num`, `has_bad_comment`, `bad_comment_rate`, '' as reqId from `bo_comment`) partition by `sku_id` order by `ingestionTime` rows between 10 preceding and 0 preceding INSTANCE_NOT_IN_WINDOW))as out2on out0.reqId_1 = out2.reqId_14last join(select
`reqId` as reqId_17,
    fz_topn_frequency(`br`, 3) over bo_action_pair_id_ingestionTime_0s_10h_100 as bo_action_br_multi_top3frequency_16,
    fz_topn_frequency(`cate`, 3) over bo_action_pair_id_ingestionTime_0s_10h_100 as bo_action_cate_multi_top3frequency_17,
    fz_topn_frequency(`model_id`, 3) over bo_action_pair_id_ingestionTime_0s_7d_100 as bo_action_model_id_multi_top3frequency_18,
    distinct_count(`model_id`) over bo_action_pair_id_ingestionTime_0s_14d_100 as bo_action_model_id_multi_unique_count_19,
    distinct_count(`model_id`) over bo_action_pair_id_ingestionTime_0s_7d_100 as bo_action_model_id_multi_unique_count_20,
    distinct_count(`type`) over bo_action_pair_id_ingestionTime_0s_14d_100 as bo_action_type_multi_unique_count_21,
    fz_topn_frequency(`type`, 3) over bo_action_pair_id_ingestionTime_0s_7d_100 as bo_action_type_multi_top3frequency_40,
    fz_topn_frequency(`type`, 3) over bo_action_pair_id_ingestionTime_0s_14d_100 as bo_action_type_multi_top3frequency_42from
    (select `eventTime` as `ingestionTime`, `pair_id` as `pair_id`, bigint(0) as `time`, '' as `model_id`, '' as `type`, '' as `cate`, '' as `br`, reqId from `flattenRequest`)
window bo_action_pair_id_ingestionTime_0s_10h_100 as (UNION (select `ingestionTime`, `pair_id`, `time`, `model_id`, `type`, `cate`, `br`, '' as reqId from `bo_action`) partition by `pair_id` order by `ingestionTime` rows_range between 10h preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW),
    bo_action_pair_id_ingestionTime_0s_7d_100 as (UNION (select `ingestionTime`, `pair_id`, `time`, `model_id`, `type`, `cate`, `br`, '' as reqId from `bo_action`) partition by `pair_id` order by `ingestionTime` rows_range between 7d preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW),
    bo_action_pair_id_ingestionTime_0s_14d_100 as (UNION (select `ingestionTime`, `pair_id`, `time`, `model_id`, `type`, `cate`, `br`, '' as reqId from `bo_action`) partition by `pair_id` order by `ingestionTime` rows_range between 14d preceding and 0s preceding MAXSIZE 100 INSTANCE_NOT_IN_WINDOW))as out3on out0.reqId_1 = out3.reqId_17;

/work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client < /root/project/deploy.sql

可使用如下命令确认 deploy 信息

show deployment demo;

在线数据准备

首先，请切换到在线执行模式。接着在在线模式下，导入数据作为在线数据，用于在线特征计算。以下命令均在 OpenMLDB CLI 下执行。

> USE JD_db;
> SET @@execute_mode='online';
> LOAD DATA INFILE '/root/project/data/JD_data/action/*.parquet' INTO TABLE action options(format='parquet', header=true, mode='append');
> LOAD DATA INFILE '/root/project/data/JD_data/flattenRequest_remove_last/*.parquet' INTO TABLE flattenRequest options(format='parquet', header=true, mode='append');
> LOAD DATA INFILE '/root/project/data/JD_data/bo_user/*.parquet' INTO TABLE bo_user options(format='parquet', header=true, mode='append');
> LOAD DATA INFILE '/root/project/data/JD_data/bo_action/*.parquet' INTO TABLE bo_action options(format='parquet', header=true, mode='append');
> LOAD DATA INFILE '/root/project/data/JD_data/bo_product/*.parquet' INTO TABLE bo_product options(format='parquet', header=true, mode='append');
> LOAD DATA INFILE '/root/project/data/JD_data/bo_comment/*.parquet' INTO TABLE bo_comment options(format='parquet', header=true, mode='append');

‍

/work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client < /root/project/load_online_data.sql

注意，集群版 `LOAD  DATA` 为非阻塞任务，可以使用命令 `SHOW  JOBS` 查看任务运行状态，请等待任务运行成功（ `state` 转至 `FINISHED` 状态），再进行下一步操作 。

配置 OneFlow 推理服务

OneFlow 的推理服务需要 OneEmbedding 的支持。该支持目前还没有合入主框架中。若需要重新编译，可参考附录 A 进行编译测试。接下来步骤默认相关支持已编译完成，并且存放在/home/gtest/work/oneflow_serving/路径中。

确认离线特征检查模型路径（$demodir/oneflow_process/model）中模型文件及组织方式是否正确。

$ tree  -L 3 model/
model/
└── embedding
    ├── 1
    │   └── model
    │       ├── model.mlir
    │       ├── module.dnn_layer.linear_layers.0.bias
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.0.weight
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.12.bias
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.12.weight
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.15.bias
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.15.weight
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.3.bias
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.3.weight
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.6.bias
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.6.weight
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.9.bias
    │       │   ├── meta
    │       │   └── out
    │       ├── module.dnn_layer.linear_layers.9.weight
    │       │   ├── meta
    │       │   └── out
    │       ├── module.embedding_layer.one_embedding.shadow
    │       │   ├── meta
    │       │   └── out
    │       └── one_embedding_options.json
    └── config.pbtxt

‍

确认 config.pbtxt 中的配置正确

name: "embedding"
backend: "oneflow"
max_batch_size: 10000


input [

name: "INPUT_0"
data_type: TYPE_INT64
dims: [ 39 ]

]


output [

name: "OUTPUT_0"
data_type: TYPE_FP32
dims: [ 1 ]

]


instance_group [

count: 1
kind: KIND_GPU
gpus: [ 0 ]

]

其中name要和config.pbtxt所在目录的名字保持一致

变更one_embedding_options.json文件中的persistent table路径

"embedding": [
        
"snapshot": "2022-08-03-10-57-56-476258",
"kv_options": 
"name": "sparse_embedding",
"key_type_size": 8,
"value_type_size": 4,
"value_type": "oneflow.float32",
"storage_dim": 51,
"kv_store": 
"caches": [
                        
"policy": "full",
"capacity": 34290906,
"value_memory_kind": "device"
                        
                    ],
"persistent_table": 
"path": "/root/demo/persistent",
"physical_block_size": 512,
"capacity_hint": 34290906
                    
                ,
"parallel_num": 1
            
        
    ]

把 embedding/kv_options/kv_store/persistent_table/path 变更为映射到容器里面的persistent table的位置 /root/demo/persistent

启动推理服务

启动 OneFlow 推理服务

注意，以下命令在安装 1.1 所描述的 OneFlow 运行环境中运行

使用以下命令启动 OneFlow 推理服务：

docker run --runtime=nvidia --rm --network=host \\
  -v $demodir/oneflow_process/model:/models \\
  -v $demodir/oneflow_process/persistent:/root/demo/persistent \\
  oneflowinc/oneflow-serving:nightly \\
  bash -c '/opt/tritonserver/bin/tritonserver --model-repository=/models'

启动在线推理服务 demo

注意，以下命令在 demo docker 中运行

OpenMLDB 的在线特征计算服务已通过 SQL 上线完成，OneFlow 推理服务也已经启动。这个 demo 将串联两者，在收到实时请求后，访问 OpenMLDB 进行特征抽取，再访问 OneFlow 推理服务，进行在线推理，最后返回推理结果。

1. 如果尚未退出 OpenMLDB CLI，请使用 quit 命令退出 OpenMLDB CLI。

2. 在普通命令行下启动预估服务：

cd /root/project/serving/openmldb_serving
./start_predict_server.sh 0.0.0.0:9080

‍

发送预估请求

预估请求可在 OpenMLDB 的容器外执行。容器外部访问的具体信息可参见IP 配置。

在普通命令行下执行内置的 predict.py 脚本。该脚本发送一行请求数据到预估服务，接收返回的预估结果，并打印出来。

python predict.py

范例输出：

----------------ins---------------
['200080_5505_2016-03-15 20:43:04' '1458045784000'
'200080_5505_2016-03-15 20:43:04' '200080_5505' '5505' '200080' '1' '1.0'
'1.0' '1' '1' '3' '1' '200080_5505_2016-03-15 20:43:04' '0' '3' '1' '1'
'214' '8' '1603438960564' '-1' '1453824000000' '2' '1'
'200080_5505_2016-03-15 20:43:04' '0.02879999950528145' '0.0' '0.0' '2'
'2' '1,,NULL' '4,0,NULL' '200080_5505_2016-03-15 20:43:04' ',NULL,NULL'
',NULL,NULL' ',NULL,NULL' '1' '1' '1' ',NULL,NULL' ',NULL,NULL']
---------------predict change of purchase -------------
[[b'0.025186:0']]

希望本文能够帮大家快速理解掌握如何使用 OpenMLDB 和 OneFlow 联合来打造一个完整的机器学习应用，链接特征工程到模型训练的全流程。

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