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README.md
浏览文件 @ dfbf7660
[TOC]
# 概述
PaddlePaddle是公司开源的机器学习框架，广泛支持各种深度学习模型的定制化开发;
Paddle cloud是基于PaddlePaddle框架实现的一整套云平台，对外提供全流程的AI开发平台，对内托管集团内各产品线的机器学习云服务。
Paddle serving是Paddle cloud的在线预测部分，与Paddle cloud模型训练环节无缝衔接，对外提供机器学习预测共有云服务，对内为公司各业务线提供统一的模型预测开发框架和云服务。
# Getting Started
## 运行示例
说明：Imagenet图像分类模型，默认采用CPU模式（GPU模式请修改BCLOUD配置项，并用Dockerfile构建运行环境，[Docker部署请参考Wiki](http://agroup.baidu.com/share/md/044f552e866f4078900be503784e2468)）。
Step1：启动Server端：
```shell
git clone ssh://icode.baidu.com:8235/baidu/paddle-serving/serving ~/my_paddle_serving/baidu/paddle-serving/serving && cd ~/my_paddle_serving/baidu/paddle-serving/serving && bcloud build && ./output/bin/image_class &
```
Step2：启动Client端：
```shell
git clone ssh://icode.baidu.com:8235/baidu/paddle-serving/sdk-cpp ~/my_paddle_serving/baidu/paddle-serving/sdk-cpp && cd ~/my_paddle_serving/baidu/paddle-serving/sdk-cpp && bcloud build && ./output/bin/ximage && pkill image_class
```
## 示例说明
### 预测接口定义
```c++
syntax="proto2";
package baidu.paddle_serving.predictor.image_class;
option cc_generic_services = true;
// x-image request相关（批量接口）
message XImageReqInstance {
required bytes image_binary = 1;
required uint32 image_length = 2;
};
message Request {
repeated XImageReqInstance instances = 1;
};
// x-image response相关（批量接口）
message DensePrediction {
repeated float categories = 1;
};
message ClassResponse {
repeated DensePrediction predictions = 1;
};
message XImageResInstance {
required string response_json = 1;
};
message Response {
// Each json string is serialized from ClassResponse
repeated XImageResInstance predictions = 1;
};
// Service/method相关
service ImageClassifyService {
rpc inference(Request) returns (Response);
rpc debug(Request) returns (Response);
};
```
### Server端实现
用户只需定制或配置以下三类信息的实现，即可快速搭建完整的Paddle-Serving预测模块。
#### 接口改造([proto目录](http://icode.baidu.com/repos/baidu/paddle-serving/serving/tree/master:proto/))
Server端需对预测接口作如下修改即可：
```c++
// 改动1：依赖paddle-serving option接口文件
import "pds_option.proto";
...
service ClassService {
rpc inference(Request) returns (Response);
rpc debug(Request) returns (Response);
// 改动2：打开generate_impl开关(以支持配置驱动)
option (pds.options).generate_impl = true;
};
```
#### 示例配置([conf目录](http://icode.baidu.com/repos/baidu/paddle-serving/serving/tree/master:conf/))
- gflags配置项
| name | 默认值 | 含义 |
|------|--------|------|
| workflow_path | ./conf | workflow配置目录名 |
|workflow_file|workflow.conf|workflow配置文件名|
|inferservice_path|./conf|service配置目录名|
|inferservice_file|service.conf|service配置文件名|
|logger_path|./conf|日志配置目录名|
|logger_file|log.conf|日志配置文件名|
|resource_path|./conf|资源管理器目录名|
|resource_file|resource.conf|资源管理器文件名|
|reload_interval_s|10|重载线程间隔时间(s)|
- 配置文件实例(Image图像分类demo)
```shell
# >>> service.conf
[@Service]
name: ImageClassifyService
@workflow: workflow_image_classification
# >>> workflow.conf
[@Workflow]
name: workflow_image_classification
path: ./conf
file: imagec_dag.conf
# >>> imagec_dag.conf
workflow_type: Sequence
[@Node]
name: image_reader_op
type: ImageReaderOp
[@Node]
name: image_classify_op
type: ImageClassifyOp
[.@Depend]
name: image_reader_op
mode: RO
[@Node]
name: write_json_op
type: WriteJsonOp
[.@Depend]
name: image_classify_op
mode: RO
# >>> resource.conf
model_manager_path: ./conf
model_manager_file: model_toolkit.conf
```
#### 定制Op算子([op目录](http://icode.baidu.com/repos/baidu/paddle-serving/serving/tree/master:op/))
- 预处理算子(ImageReaderOp)：从Request中读取图像字节流，通过opencv解码，填充tensor对象并输出到channel；
- 预测调用算子(ImageClassifyOp)：从ImageReaderOp的channel获得输入tensor，临时申请输出tensor，调用ModelToolkit进行预测，并将输出tensor写入channel
- 后处理算子(WriteJsonOp)：从ImageClassifyop的channel获得输出tensor，将其序列化为json字符串，写入作为rpc的output；
### Client端实现
用户只需定制或配置以下三类信息，即可方便的接入预估请求，并在本地配置多套服务连接：
#### 接口改造([proto目录](http://icode.baidu.com/repos/baidu/paddle-serving/sdk-cpp/tree/master:proto))
Client端接口只需对预测接口作如下修改即可：
```c++
// 改动1：依赖paddle-serving option接口文件
import "pds_option.proto";
...
service ImageClassifyService {
rpc inference(Request) returns (Response);
rpc debug(Request) returns (Response);
// 改动2：打开generate_stub开关(以支持配置驱动)
option (pds.options).generate_stub = true;
};
```
#### 连接配置([conf目录](http://icode.baidu.com/repos/baidu/paddle-serving/sdk-cpp/tree/master:conf))
```shell
# predictions.conf
## 默认配置共享
[DefaultVariantInfo]
Tag : default
[.Connection]
ConnectTimeoutMicroSec : 200
ReadTimeoutMicroSec : 2000
WriteTimeoutMicroSec : 500
ConnectRetryCount : 2
MaxConnectionPerHost : 100
HedgeRequestTimeoutMicroSec : -1
HedgeFetchRetryCount : 2
BnsReloadIntervalSeconds : 10
ConnectionType : pooled
[.NamingInfo]
ClusterFilterStrategy : Default
LoadBalanceStrategy : la
[.RpcParameter]
# 0-NONE, 1-SNAPPY, 2-GZIP, 3-ZLIB, 4-LZ4
CompressType : 0
Protocol : baidu_std
MaxChannelPerRequest : 3
[@Predictor]
name : ximage
service_name : baidu.paddle_serving.predictor.image_class.ImageClassifyService
endpoint_router : WeightedRandomRender
[.WeightedRandomRender]
VariantWeightList : 30|70 # 30% vs 70% pvs
[.@VariantInfo]
Tag : var1 # 变体版本标识，提供上游辨识
[..NamingInfo]
Cluster : list://127.0.0.1:8010
[.@VariantInfo]
Tag : var2
[..NamingInfo]
Cluster : list://127.0.0.1:8011
```
#### 请求逻辑([demo/ximage.cpp](http://icode.baidu.com/repos/baidu/paddle-serving/sdk-cpp/blob/master:demo/ximage.cpp))
```c++
// 进程级初始化
assert(PredictorAPI::instance().create("./conf/predictions.conf") == 0);
// 线程级预测调用：
Request req;
// fill request
// ...
Response res;
Predictor* ximage = PredictorAPI::instance().fetch_predictor("ximage");
assert(ximage != NULL);
ximage->inference(req, res);
// parse response
// ...
assert(PredictorAPI::instance().free_predictor(ximage) == 0);
// 进程级销毁
assert(PredictorAPI::instance().destroy() == 0);
```
## 凤巢协议兼容
Paddle Serving由凤巢观星框架发展而来，而之前框架的通信协议是nshead+compack+idl，为方便新老接口的兼容，Paddle Serving的server和client均支持向后兼容：
- 老API访问新Server，为适配老观星客户端数据包格式，新Server需通过mcpack2pb生成能解析idl格式的pb对象，详见：[wtitleq server实现](http://icode.baidu.com/repos/baidu/paddle-serving/lr-model/tree/master)
- 新SDK访问老Server，为能够访问老观星server服务，SDK需通过mcpack2pb插件生成基于idl格式的序列化逻辑；详见：[wtitleq api实现](http://icode.baidu.com/repos/baidu/infinite-inference/as-wtitleq-demo/tree/master)。
凤巢广告拆包支持：Paddle Serving的SDK-Cpp为用户提供了简单易用的拆包功能，通过修改proto/conf文件开启：
```c++
// interface.proto文件
message PredictorRequest {
message AdvRequest {
// 广告级别字段
repeated uint32 ideaid = 1;
repeated string title = 2;
}
// query级别字段
required uint64 sid = 1;
required string query = 2;
// ...
// 广告级别字段
repeated AdvRequest advs = 3 [(pds.pack_on)=true]; // 改动1：对advs字段进行拆包
}
// ...
service WtitleqService {
rpc ...
rpc ...
option (pds.options).package_size = 10; // 改动2：限制单包大小
}
```
[wtitleq sdk的proto实例](http://icode.baidu.com/repos/baidu/infinite-inference/as-wtitleq-demo/blob/master:proto/predictor_api.proto)。
```bash
# predictions.conf文件
[@Predictor]
# ...
[.@VariantInfo]
#...
[..RpcParameter]
Protocol : itp # 改动3：修改rpc请求参数为itp协议
```
[wtitleq sdk的conf实例](http://icode.baidu.com/repos/baidu/infinite-inference/as-wtitleq-demo/blob/master:conf/predictors.conf)。
PaddlePaddle是公司开源的机器学习框架，广泛支持各种深度学习模型的定制化开发; Paddle serving是Paddle的在线预测部分，与Paddle模型训练环节无缝衔接，提供机器学习预测云服务。
# 框架简介
......@@ -270,7 +13,7 @@ Protocol : itp # 改动3：修改rpc请求参数为itp协议
- 用户接口：搭建服务=定义proto文件+实现/复用Op+撰写配置，支持sdk/http请求；
## 名词解释
- 预测引擎：对PaddlePaddle/Abacus/Tensorflow等各种推理计算Lib的封装，屏蔽预测模型动态Reload细节，对上层暴露统一的预测接口；
- 预测引擎：对PaddlePaddle预测Lib的封装，屏蔽预测模型动态Reload细节，对上层暴露统一的预测接口；
- 预测模型：由离线训练框架生成、在线预测引擎加载的数据文件或目录，以PaddleFluid模型为例，通常包括拓扑文件和参数文件；
- Op 算子：Paddle-serving对在线(预处理/后处理等)业务逻辑的最小粒度封装，框架提供OpWithChannel和OpWithChannelAndConf这两种常用的Op基类；框架默认实现通用Op算子；
- Node：由某个Op算子类结合参数配置组成的Op算子实例，也是Workflow中的一个执行单元；
......@@ -284,8 +27,8 @@ Protocol : itp # 改动3：修改rpc请求参数为itp协议
Paddle serving框架为策略工程师提供以下三层面的功能性扩展：
### 模型
- 预测引擎：集成PaddlePaddle、Abacus、Tensorrt、Anakin、Tensorflow等常用机器学习框架的预测Lib；
- 模型种类：支持PaddlePaddle(V1、V2、Fluid)、TensorrtUFF、Anakin、Tensorflow、Caffe等常见模型格式；
- 预测引擎：集成PaddlePaddle深度学习框架的预测Lib；
- 模型种类：支持Paddle Fluid模型格式；
- 用户接口：支持模型加载、重载的配置化驱动，不同种类模型的预测接口完全一致；
- 模型调度：支持基于异步线程模型的多模型预估调度，实现异构资源的优先级调度；
......@@ -298,28 +41,11 @@ Paddle serving框架为策略工程师提供以下三层面的功能性扩展：
- RPC：底层通过Baidu-rpc封装网络交互，Server端可配置化启动多个独立Service，框架会搜集Service粒度的详细业务指标，并按照BVar接口对接到Noah等监控平台；
- SDK：基于Baidu-rpc的client进行封装，提供多下游连接管理、可扩展路由策略、可定制参数实验、自动分包等机制，支持同步、半同步、纯异步等交互模式，以及多种兼容协议，所有连接策略均通过配置驱动
# 平台简介
![图片](http://agroup-bos.cdn.bcebos.com/42a0e34a7c6b36976e3932639209fd823d8f25e0)
- [运维API](http://agroup.baidu.com/share/md/e582f543fb574e9b92445286955a976d)
- [预测API](http://agroup.baidu.com/share/md/eb91a51739514319844ceccdb331564c)
## 名词解释
- 用户（User）：云平台注册用户，可基于平台Dashboard对账户下的端点信息进行增、删、查、改；
- 端点（Endpoit）：对一个预测需求的逻辑抽象，通常包含一到多个服务变体，以方便多版本模型管理；
- 变体（Variant）：一套同质化的Paddle-serving集群服务，每个实例起一个Paddle-serving进程；
- 实验（A/B Test）：支持变体实验和参数化实验两种模式，变体实验根据Endpoint所属变体流量百分比实现流量随机抽样；参数化实验通过对pv绑定实验参数、由Paddle-serving进程解析参数、选择不同的代码分支进行实验；
## 主要功能
在公有云落地场景为Infinite（天衍）云平台，主要为策略工程师提供以下三方面的全流程托管：
- 统一接入代理：提供代理服务，通过zk和云平台实时同步元信息，支持多模型版本管理和A/B测试路由策略，提供统一入口和标准预测API；
- 自动化部署：对接K8S/Opera等常见PaaS部署平台，支持服务的一键部署、回滚、下线等运维操作，支持endpoint/variant/model等维度的资源管理；
- 可视化运维：对接console、notebook、dashboard等前端工具和页面，满足可视化运维需求；
# 设计文档
- [总体设计文档](http://agroup.baidu.com/paddleserving/view/office/895070)
- [框架详设文档](http://agroup.baidu.com:8964/static/a3/e40876e464ba08ae5de14aa7710cf326456751.pdf?filename=PaddleServing%E6%9C%8D%E5%8A%A1%E6%A1%86%E6%9E%B6%E8%AF%A6%E7%BB%86%E8%AE%BE%E8%AE%A1%E6%96%87%E6%A1%A3v0_1.pdf)
- [平台详设文档](http://agroup.baidu.com/share/office/042a0941579e49adb8c255c8b5e92d51)
# FAQ
1. 如何修改端口配置？
......@@ -327,12 +53,3 @@ Paddle serving框架为策略工程师提供以下三层面的功能性扩展：
- 如果在inferservice_file里指定了port:xxx，那么就去申请该端口号；
- 否则，如果在gflags.conf里指定了--port:xxx，那就去申请该端口号；
- 否则，使用程序里指定的默认端口号：8010。
2. 如何在部署的时候配置动态端口？
- 如果使用FCCI部署协议(凤巢检索端内部的部署协议)，需要(1)通过inferservice_file指定端口号；(2)修改[Rakefile.opera](http://wiki.baidu.com/pages/viewpage.action?pageId=399979183#id-%E4%BB%8E%E9%9B%B6%E5%BC%80%E5%A7%8B%E5%86%99production-%E7%BC%96%E5%86%99Rakefile)的dynamic_port_config配置
- `@dynamic_port_config为动态端口配置，向Opera申请名为:name的动态端口，其端口号会被写到:conf文件中的:target配置项。`例子如下：
```
@dynamic_port_config = [
{:name => 'main', :conf => 'framework/service.conf', :target => 'port'}, // 部署时自动向Opera申请端口，服务将会监听这个端口
{:name => 'main', :conf => 'predictor_valid.conf', :target => 'port'}, // valid工具向这个端口发送测试请求，确保服务已正常启动
]
```
cmake/generic.cmake
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......@@ -839,7 +839,7 @@ function(PROTOBUF_GENERATE_SERVING_CPP SRCS HDRS)
ARGS --cpp_out=${CMAKE_CURRENT_BINARY_DIR}
--pdcodegen_out=${CMAKE_CURRENT_BINARY_DIR}
--plugin=protoc-gen-pdcodegen=${CMAKE_BINARY_DIR}/predictor/pdcodegen
# --proto_path=${CMAKE_SOURCE_DIR}/predictor/proto
--proto_path=${CMAKE_SOURCE_DIR}/predictor/proto
${_protobuf_include_path} ${ABS_FIL}
DEPENDS ${ABS_FIL} ${Protobuf_PROTOC_EXECUTABLE}
COMMENT "Running Paddle-serving C++ protocol buffer compiler on ${FIL}"
......
configure/proto/sdk_configure.proto
浏览文件 @ dfbf7660
......@@ -16,12 +16,12 @@ syntax = "proto2";
package baidu.paddle_serving.configure;
message ConnectionConf {
required uint32 connect_timeout_ms = 1;
required uint32 rpc_timeout_ms = 2;
required uint32 connect_retry_count = 3;
required uint32 max_connection_per_host = 4;
required uint32 hedge_request_timeout_ms = 5;
required uint32 hedge_fetch_retry_count = 6;
required int32 connect_timeout_ms = 1;
required int32 rpc_timeout_ms = 2;
required int32 connect_retry_count = 3;
required int32 max_connection_per_host = 4;
required int32 hedge_request_timeout_ms = 5;
required int32 hedge_fetch_retry_count = 6;
required string connection_type = 7;
};
......@@ -33,10 +33,10 @@ message NamingConf {
message RpcParameter {
// 0-NONE, 1-SNAPPY, 2-GZIP, 3-ZLIB, 4-LZ4
required uint32 compress_type = 1;
required uint32 package_size = 2;
required int32 compress_type = 1;
required int32 package_size = 2;
required string protocol = 3;
required uint32 max_channel_per_request = 4;
required int32 max_channel_per_request = 4;
};
message SplitConf {
......
configure/proto/server_configure.proto
浏览文件 @ dfbf7660
......@@ -21,9 +21,9 @@ message EngineDesc {
required string reloadable_meta = 3;
required string reloadable_type = 4;
required string model_data_path = 5;
required uint32 runtime_thread_num = 6;
required uint32 batch_infer_size = 7;
required uint32 enable_batch_align = 8;
required int32 runtime_thread_num = 6;
required int32 batch_infer_size = 7;
required int32 enable_batch_align = 8;
optional string version_file = 9;
optional string version_type = 10;
};
......
doc/CLIENT_CONFIGURE.md 0 → 100644
浏览文件 @ dfbf7660
# Client side configuration
Paddle-serving C++ client SDK主配置文件为conf/predictors.prototxt。其中一个示例如下：
## Sample conf
```shell
default_variant_conf {
tag: "default"
connection_conf {
connect_timeout_ms: 2000
rpc_timeout_ms: 20000
connect_retry_count: 2
max_connection_per_host: 100
hedge_request_timeout_ms: -1
hedge_fetch_retry_count: 2
connection_type: "pooled"
}
naming_conf {
cluster_filter_strategy: "Default"
load_balance_strategy: "la"
}
rpc_parameter {
compress_type: 0
package_size: 20
protocol: "baidu_std"
max_channel_per_request: 3
}
}
predictors {
name: "ximage"
service_name: "baidu.paddle_serving.predictor.image_classification.ImageClassifyService"
endpoint_router: "WeightedRandomRender"
weighted_random_render_conf {
variant_weight_list: "50|50"
}
variants {
tag: "var1"
naming_conf {
cluster: "list://127.0.0.1:8010"
}
}
variants {
tag: "var2"
naming_conf {
cluster: "list://127.0.0.1:8011"
}
}
}
predictors {
name: "echo_service"
service_name: "baidu.paddle_serving.predictor.echo_service.BuiltinTestEchoService"
endpoint_router: "WeightedRandomRender"
weighted_random_render_conf {
variant_weight_list: "50"
}
variants {
tag: "var1"
naming_conf {
cluster: "list://127.0.0.1:8010,127.0.0.1:8011"
}
}
}
```
## 名词解释
- 预测服务 (Predictor)：对一个Paddle预测服务的封装
- 端点（Endpoit）：对一个预测需求的逻辑抽象，通常包含一到多个服务变体，以方便多版本模型管理；
- 变体（Variant）：一套同质化的Paddle-serving集群服务，每个实例起一个Paddle-serving进程；
## 配置项解释
### default_variant_conf
```shell
default_variant_conf {
tag: "default"
connection_conf {
connect_timeout_ms: 2000
rpc_timeout_ms: 20000
connect_retry_count: 2
max_connection_per_host: 100
hedge_request_timeout_ms: -1
hedge_fetch_retry_count: 2
connection_type: "pooled"
}
naming_conf {
cluster_filter_strategy: "Default" # Not used for now
load_balance_strategy: "la"
}
rpc_parameter {
compress_type: 0
package_size: 20
protocol: "baidu_std"
max_channel_per_request: 3
}
}
```
其中：
connection_type: Maybe single/short/pooled, see [BRPC DOC: connection_type](https://github.com/apache/incubator-brpc/blob/master/docs/cn/client.md#%E8%BF%9E%E6%8E%A5%E6%96%B9%E5%BC%8F)
cluster_filter_strategy: 暂时未用
load_balance_strategy: Maybe rr/wrr/random/la/c_murmurhash/c_md5, see [BRPC DOC: load_balance](https://github.com/apache/incubator-brpc/blob/master/docs/cn/client.md#%E8%B4%9F%E8%BD%BD%E5%9D%87%E8%A1%A1)
compress_type: 0-None, 1-Snappy, 2-gzip, 3-zlib, 4-lz4, see [BRPC DOC: compress_type](https://github.com/apache/incubator-brpc/blob/master/docs/cn/client.md#%E5%8E%8B%E7%BC%A9)
protocol: Maybe baidu_std/http/h2/h2:grpc/thrift/memcache/redis... see [BRPC DOC: protocol](https://github.com/apache/incubator-brpc/blob/master/docs/cn/client.md#%E5%8D%8F%E8%AE%AE)
### Predictors
可以为客户端配置多个predictor，每个predictor代表一个要访问的预测服务
```shell
predictors {
name: "ximage"
service_name: "baidu.paddle_serving.predictor.image_classification.ImageClassifyService"
endpoint_router: "WeightedRandomRender"
weighted_random_render_conf {
variant_weight_list: "50|50"
}
variants {
tag: "var1"
naming_conf {
cluster: "list://127.0.0.1:8010, 127.0.0.1:8011"
}
}
variants {
tag: "var2"
naming_conf {
cluster: "list://127.0.0.1:8011"
}
}
}
predictors {
name: "echo_service"
service_name: "baidu.paddle_serving.predictor.echo_service.BuiltinTestEchoService"
endpoint_router: "WeightedRandomRender"
weighted_random_render_conf {
variant_weight_list: "50"
}
variants {
tag: "var1"
naming_conf {
cluster: "list://127.0.0.1:8010"
}
}
}
```
其中：
service_name: 写sdk-cpp/proto/xx.proto的package name
endpoint_router: 目前只支持WeightedRandomRender
variant_weight_list: 与接下来的variants列表共用，用于表示variants之间相对权重；通过修改此数值可以调整variants调度的比重
cluster: Cluster支持的格式见 [BRPC DOC: naming service](https://github.com/apache/incubator-brpc/blob/master/docs/cn/client.md#%E5%91%BD%E5%90%8D%E6%9C%8D%E5%8A%A1)
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# 从零开始写一个预测服务
## 1. 示例说明
图像分类是根据图像的语义信息将不同类别图像区分开来，是计算机视觉中重要的基本问题，也是图像检测、图像分割、物体跟踪、行为分析等其他高层视觉任务的基础。图像分类在很多领域有广泛应用，包括安防领域的人脸识别和智能视频分析等，交通领域的交通场景识别，互联网领域基于内容的图像检索和相册自动归类，医学领域的图像识别等。
paddle-serving已经提供了一个基于ResNet的模型预测服务，按照INSTALL.md中所述步骤，编译paddle-serving，然后按GETTING_STARTED.md所述步骤启动client端和server端即可看到预测服务运行效果。
本文接下来以图像分类任务为例，介绍从零搭建一个模型预测服务的步骤。
## 2. Serving端
### 2.1 定义预测接口
** 添加文件：serving/proto/image_class.proto **
Paddle-serving服务端与客户端通过brpc进行通信，通信协议和格式可以自定，我们选择baidu_std协议。这是一种以protobuf为基本数据交换格式的协议，其说明可参考[BRPC文档: baidu_std](https://github.com/apache/incubator-brpc/blob/master/docs/cn/baidu_std.md)。
我们编写图像分类任务预测接口的protobuf如下：
```c++
syntax="proto2";
import "pds_option.proto";
import "builtin_format.proto";
package baidu.paddle_serving.predictor.image_classification;
option cc_generic_services = true;
message ClassifyResponse {
repeated baidu.paddle_serving.predictor.format.DensePrediction predictions = 1;
};
message Request {
repeated baidu.paddle_serving.predictor.format.XImageReqInstance instances = 1;
};
message Response {
// Each json string is serialized from ClassifyResponse predictions
repeated baidu.paddle_serving.predictor.format.XImageResInstance predictions = 1;
};
service ImageClassifyService {
rpc inference(Request) returns (Response);
rpc debug(Request) returns (Response);
option (pds.options).generate_impl = true;
};
```
其中：
`service ImageClassifiyService`定义一个RPC Service，并声明2个RPC接口：`inference`和`debug`，分别接受`Reqeust`类型请求参数，并返回`Response`类型结果。
`DensePrediction`, `XImageReqInstance`和`XImageResInstance`类型的消息分别在其他.proto文件中定义，因此要通过`import 'builtin_format.proto'`语句将需要的类型引入。
`generate_impl = true`: 告诉protobuf编译器，生成RPC service的实现 (在client端，此处为`generate_stub = true`，告诉protobuf编译器生成RPC的stub)
### 2.2 Server端实现
图像分类任务的处理，设计分为3个阶段，对应3个OP
- 读请求：从Request消息解出请求样例数据
- 调用Paddle预测lib的接口，对样例进行预测，并保存
- 预测结果写到Response
此后，框架将负责将Response回传给client端
#### 2.2.1 定制Op算子
** 在serving/op/目录下添加reader_op.cpp, classify_op.cpp, write_json_op.cpp **
- 预处理算子(ReaderOp, serving/op/reader_op.cpp)：从Request中读取图像字节流，通过opencv解码，填充tensor对象并输出到channel；
- 预测调用算子(ClassifyOp, serving/op/classify_op.cpp)：从ImageReaderOp的channel获得输入tensor，临时申请输出tensor，调用ModelToolkit进行预测，并将输出tensor写入channel
- 后处理算子(WriteJsonOp, serving/op/write_json.cpp)：从ImageClassifyop的channel获得输出tensor，将其序列化为json字符串，写入作为rpc的output
具体实现可参考demo中的源代码
#### 2.2.2 示例配置([conf目录](http://icode.baidu.com/repos/baidu/personal-code/paddle-serving/tree/master:serving/conf))
以下配置文件将ReaderOP, ClassifyOP和WriteJsonOP串联成一个workflow (关于OP/workflow等概念，可参考[设计文档](DESIGN.md))
- 配置文件示例：
** 添加文件 serving/conf/service.prototxt **
```shell
services {
name: "ImageClassifyService"
workflows: "workflow1"
}
```
** 添加文件 serving/conf/workflow.prototxt **
```shell
workflows {
name: "workflow1"
workflow_type: "Sequence"
nodes {
name: "image_reader_op"
type: "ReaderOp"
}
nodes {
name: "image_classify_op"
type: "ClassifyOp"
dependencies {
name: "image_reader_op"
mode: "RO"
}
}
nodes {
name: "write_json_op"
type: "WriteJsonOp"
dependencies {
name: "image_classify_op"
mode: "RO"
}
}
}
```
以下配置文件为模型加载配置
** 添加文件 serving/conf/resource.prototxt **
```shell
model_manager_path: ./conf
model_manager_file: model_toolkit.prototxt
```
** 添加文件 serving/conf/model_toolkit.prototxt **
```shell
engines {
name: "image_classification_resnet"
type: "FLUID_CPU_NATIVE_DIR"
reloadable_meta: "./data/model/paddle/fluid_time_file"
reloadable_type: "timestamp_ne"
model_data_path: "./data/model/paddle/fluid/SE_ResNeXt50_32x4d"
runtime_thread_num: 0
batch_infer_size: 0
enable_batch_align: 0
}
```
#### 2.2.3 代码编译
Serving端代码包含如下部分：
- protobuf接口文件，需要编译成.pb.cc及.pb.h文件并链接到最终可执行文件
- OP算子实现，需要链接到最终可执行文件
- Paddle-serving框架代码，封装在libpdserving.a中，需要链接到最终可执行文件
- Paddle-serving封装paddle-fluid预测库的代码，在inferencer-fluid-cpu/目录产出的libfluid_cpu_engine.a中
- 其他第三方依赖库：paddle预测库，brpc, opencv等
1) protobuf接口文件编译: 不能用protoc默认插件编译，需要编译成paddle-serving定制的.pb.cc及.pb.h文件。具体命令是
```shell
$ protoc --cpp_out=/path/to/paddle-serving/build/serving/ --pdcodegen_out=/path/to/paddle-serving/ --plugin=protoc-gen-pdcodegen=/path/to/paddle-serving/build/predictor/pdcodegen --proto_path=/path/to/paddle-serving/predictor/proto
```
其中
`pdcodegen`是由predictor/src/pdcodegen.cpp编译成的protobuf编译插件, --proto_path用来指定去哪里寻找`import`语句需要的protobuf文件
predictor/proto目录下有serving端和client端都要包含的builtin_format.proto和pds_option.proto
**NOTE**
上述protoc命令在paddle-serving编译系统中被封装成一个CMake函数了，在cmake/generic.cmake::PROTOBUF_GENERATE_SERVING_CPP
CMakeLists.txt中调用函数的方法为：
```shell
PROTOBUF_GENERATE_SERVING_CPP(PROTO_SRCS PROTO_HDRS xxx.proto)
```
2) OP
serving/op/目录下OP对应的.cpp文件
3) Paddle-serving框架代码，封装在predictor目录产出的libpdserving.a中
4) Paddle-serving封装paddle-fluid预测库的代码，在inference-fluid-cpu/目录产出的libfluid_cpu_engine.a中
5) serving端main函数
为简化用户编写初始化代码的工作量，serving端必须的初始化过程已经由paddle-serving框架提供，请参考predictor/src/pdserving.cpp。该文件中包含了完整的初始化过程，用户只需提供合适的配置文件列表即可(请参考2.2.2节)，不必编写main函数
6) 第三方依赖库
brpc, paddle-fluid, opencv等第三方库，
7) 链接
整个链接过程在CMakeLists.txt中写法如下：
```shell
target_link_libraries(serving opencv_imgcodecs
${opencv_depend_libs} -Wl,--whole-archive fluid_cpu_engine
-Wl,--no-whole-archive pdserving paddle_fluid ${paddle_depend_libs}
${MKLML_LIB} ${MKLML_IOMP_LIB} -lpthread -lcrypto -lm -lrt -lssl -ldl -lz)
```
### 2.3 gflags配置项
以下是serving端支持的gflag配置选项列表，并提供了默认值。
| name | 默认值 | 含义 |
|------|--------|------|
|workflow_path|./conf|workflow配置目录名|
|workflow_file|workflow.prototxt|workflow配置文件名|
|inferservice_path|./conf|service配置目录名|
|inferservice_file|service.prototxt|service配置文件名|
|resource_path|./conf|资源管理器目录名|
|resource_file|resource.prototxt|资源管理器文件名|
|reload_interval_s|10|重载线程间隔时间(s)|
|enable_model_toolkit|true|模型管理|
|enable_protocol_list|baidu_std|brpc 通信协议列表|
|log_dir|./log|log dir|
可以通过在serving/conf/gflags.conf覆盖默认值，例如
```
--log_dir=./serving_log/
```
将指定日志目录到./serving_log目录下
## 3. Client端
### 3.1 定义预测接口
** 在sdk-cpp/proto添加image_class.proto **
与serving端预测接口protobuf文件基本一致，只要将`generate_impl=true`改为`generate_stub=true`
```c++
import "pds_option.proto";
...
service ImageClassifyService {
rpc inference(Request) returns (Response);
rpc debug(Request) returns (Response);
// 改动：打开generate_stub开关(以支持配置驱动)
option (pds.options).generate_stub = true;
};
```
### 3.2 Client端逻辑
Paddle-serving提供的C++ SDK在sdk-cpp/目录中，入口为sdk-cpp/include/predictor_sdk.h中的`class PredictorApi`类。
该类的主要接口：
```C++
class PredictroApi {
// 创建PredictorApi句柄，输入为client端配置文件predictor.prototxt的目录和文件名
int create(const char *path, const char *file);
// 线程级初始化
int thrd_initialize();
// 根据名称获取Predictor句柄; ep_name对应predictor.prototxt中predictors的name字段
Predictor *fetch_predictor(std::string ep_name);
};
class Predictor {
// 预测
int inference(google::protobuf::Message *req, google::protobuf::Message *res);
// Debug模式
int debug(google::protobuf::Message *req,
google::protobuf::Message *res,
buitl::IOBufBuilder *debug_os);
};
```
#### 3.2.1 请求逻辑
** 增加sdk-cpp/demo/ximage.cpp **
```c++
// 进程级初始化
PredictorApi api;
if (api.create("./conf/", "predictors.prototxt") == 0) {
return -1;
}
// 线程级预测调用：
Request req;
Response res;
api.thrd_initialize();
// Call this before every request
api.thrd_clear();
create_req(&req);
Predictor* predictor = api.fetch_predictor("ximage");
if (predictor == NULL) {
return -1;
}
if (predictor->inference(req, res) != 0) {
return -1;
}
// parse response
print_res(res);
// 线程级销毁
api.thrd_finalize();
// 进程级销毁
api.destroy();
```
具体实现可参考paddle-serving提供的例子sdk-cpp/demo/ximage.cpp
### 3.3 链接
Client端可执行文件包含的代码有：
- protobuf接口文件，需要编译成.pb.cc及.pb.h文件并链接到最终可执行文件
- main函数，以及调用SDK接口访问预测服务的逻辑，见3.2.1节
- Client端读取并维护predictor信息列表的代码，在sdk-cpp/目录产出的libsdk-cpp.a
- 因为protobuf接口文件用到了predictor/proto/目录下的builtin_format.proto和pds_option.proto，因此还需要联编libpdserving.a
1) protobuf接口文件，同serving端，需要用predictor/src/pdcodegen.cpp产出的pdcodegen插件，配合protoc使用，具体命令为
```shell
$ protoc --cpp_out=/path/to/paddle-serving/build/serving/ --pdcodegen_out=/path/to/paddle-serving/ --plugin=protoc-gen-pdcodegen=/path/to/paddle-serving/build/predictor/pdcodegen --proto_path=/path/to/paddle-serving/predictor/proto
```
其中
`pdcodegen`是由predictor/src/pdcodegen.cpp编译成的protobuf编译插件, --proto_path用来指定去哪里寻找`import`语句需要的protobuf文件
** NOTE **
上述protoc命令在paddle-serving编译系统中被封装成一个CMake函数了，在cmake/generic.cmake::PROTOBUF_GENERATE_SERVING_CPP
CMakeLists.txt中调用函数的方法为：
```shell
PROTOBUF_GENERATE_SERVING_CPP(PROTO_SRCS PROTO_HDRS xxx.proto)
```
2) main函数，以及调用SDK接口访问预测服务的逻辑
3) Client端读取并维护predictor信息列表的代码，在sdk-cpp/目录产出的libsdk-cpp.a
4) predictor/目录产出的libpdserving.a
最终链接命令如下：
```shell
add_executable(ximage ${CMAKE_CURRENT_LIST_DIR}/demo/ximage.cpp)
target_link_libraries(ximage -Wl,--whole-archive sdk-cpp
-Wl,--no-whole-archive pdserving -lpthread -lcrypto -lm -lrt -lssl -ldl
-lz)
```
### 3.4 连接配置
** 增加配置文件sdk/conf/predictors.prototxt **
```shell
## 默认配置共享
default_variant_conf {
tag: "default"
connection_conf {
connect_timeout_ms: 2000
rpc_timeout_ms: 20000
connect_retry_count: 2
max_connection_per_host: 100
hedge_request_timeout_ms: -1
hedge_fetch_retry_count: 2
connection_type: "pooled"
}
naming_conf {
cluster_filter_strategy: "Default"
load_balance_strategy: "la"
}
rpc_parameter {
compress_type: 0
package_size: 20
protocol: "baidu_std"
max_channel_per_request: 3
}
}
predictors {
name: "ximage"
service_name: "baidu.paddle_serving.predictor.image_classification.ImageClassifyService"
endpoint_router: "WeightedRandomRender"
weighted_random_render_conf {
variant_weight_list: "50"
}
variants {
tag: "var1"
naming_conf {
cluster: "list://127.0.0.1:8010"
}
}
}
```
关于客户端的详细配置选项，可参考[CLIENT CONFIGURATION](CLIENT_CONFIGURE.md)
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# 设计文档
# 项目背景
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# Getting Started
## 运行示例
说明：Imagenet图像分类模型，默认采用CPU模式（GPU模式当前版本暂未提供支持）
Step1：启动Server端：
```shell
cd paddle-serving/output/demo/serving/ && ./bin/serving &
```
默认启动后日志写在./log/下，可tail日志查看serving端接收请求的日志：
```shell
tail -f log/serving.INFO
```
Step2：启动Client端：
```shell
cd paddle-serving/output/demo/client/image_class && ./bin/ximage &
```
默认启动后日志写在./log/下，可tail日志查看分类结果：
```shell
tail -f log/ximage.INFO
```
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[Client Configure](CLIENT_CONFIGURE.md)
[Creating a Prediction Service](CREATING.md)
[Design](DESIGN.md)
[Getting Started](GETTING_STARTED.md)
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# Install
## 系统需求
OS: Linux
CMake: 3.2
python
## 编译
```shell
$ git clone ssh://wangguibao@icode.baidu.com:8235/baidu/personal-code/paddle-serving
$ cd paddle-serving
$ mkdir build
$ cd build
$ cmake ..
$ make -j4
$ make install
```
predictor/src/pdserving.cpp
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......@@ -107,15 +107,18 @@ int main(int argc, char** argv) {
g_change_server_port();
// initialize logger instance
FLAGS_log_dir = "./log";
if (FLAGS_log_dir == "") {
FLAGS_log_dir = "./log";
}
struct stat st_buf;
int ret = 0;
if ((ret = stat("./log", &st_buf)) != 0) {
mkdir("./log", 0777);
ret = stat("./log", &st_buf);
if ((ret = stat(FLAGS_log_dir.c_str(), &st_buf)) != 0) {
mkdir(FLAGS_log_dir.c_str(), 0777);
ret = stat(FLAGS_log_dir.c_str(), &st_buf);
if (ret != 0) {
LOG(WARNING) << "Log path ./log not exist, and create fail";
LOG(WARNING) << "Log path " << FLAGS_log_dir
<< " not exist, and create fail";
return -1;
}
}
......
sdk-cpp/CMakeLists.txt
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......@@ -3,26 +3,29 @@ include(proto/CMakeLists.txt)
add_library(sdk-cpp ${sdk_cpp_srcs})
add_dependencies(sdk-cpp pdcodegen configure)
target_link_libraries(sdk-cpp brpc configure protobuf leveldb)
target_include_directories(sdk-cpp PUBLIC
${CMAKE_BINARY_DIR}/predictor/)
add_executable(ximage ${CMAKE_CURRENT_LIST_DIR}/demo/ximage.cpp)
target_link_libraries(ximage -Wl,--whole-archive sdk-cpp
-Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
-Wl,--no-whole-archive pdserving -lpthread -lcrypto -lm -lrt -lssl -ldl
-lz)
add_executable(echo ${CMAKE_CURRENT_LIST_DIR}/demo/echo.cpp)
target_link_libraries(echo -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
target_link_libraries(echo -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive
pdserving -lpthread -lcrypto -lm -lrt -lssl -ldl
-lz)
add_executable(dense_format ${CMAKE_CURRENT_LIST_DIR}/demo/dense_format.cpp)
target_link_libraries(dense_format -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
target_link_libraries(dense_format pdserving -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
-lz)
add_executable(sparse_format ${CMAKE_CURRENT_LIST_DIR}/demo/sparse_format.cpp)
target_link_libraries(sparse_format -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
target_link_libraries(sparse_format pdserving -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
-lz)
add_executable(int64tensor_format ${CMAKE_CURRENT_LIST_DIR}/demo/int64tensor_format.cpp)
target_link_libraries(int64tensor_format -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
target_link_libraries(int64tensor_format pdserving -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
-lz)
# install
......
sdk-cpp/conf/predictors.prototxt
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......@@ -5,7 +5,7 @@ default_variant_conf {
rpc_timeout_ms: 20000
connect_retry_count: 2
max_connection_per_host: 100
hedge_request_timeout_ms: 4294967295
hedge_request_timeout_ms: -1
hedge_fetch_retry_count: 2
connection_type: "pooled"
}
......
sdk-cpp/demo/dense_format.cpp
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......@@ -17,7 +17,7 @@
#include <unistd.h>
#include <fstream>
#include "sdk-cpp/builtin_format.pb.h"
#include "predictor/builtin_format.pb.h"
#include "sdk-cpp/dense_service.pb.h"
#include "sdk-cpp/include/common.h"
#include "sdk-cpp/include/predictor_sdk.h"
......
sdk-cpp/demo/echo.cpp
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......@@ -17,7 +17,7 @@
#include <unistd.h>
#include <fstream>
#include "sdk-cpp/builtin_format.pb.h"
#include "predictor/builtin_format.pb.h"
#include "sdk-cpp/echo_service.pb.h"
#include "sdk-cpp/include/common.h"
#include "sdk-cpp/include/predictor_sdk.h"
......
sdk-cpp/demo/int64tensor_format.cpp
浏览文件 @ dfbf7660
......@@ -17,7 +17,7 @@
#include <unistd.h>
#include <fstream>
#include "sdk-cpp/builtin_format.pb.h"
#include "predictor/builtin_format.pb.h"
#include "sdk-cpp/include/common.h"
#include "sdk-cpp/include/predictor_sdk.h"
#include "sdk-cpp/int64tensor_service.pb.h"
......
sdk-cpp/demo/sparse_format.cpp
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......@@ -17,7 +17,7 @@
#include <unistd.h>
#include <fstream>
#include "sdk-cpp/builtin_format.pb.h"
#include "predictor/builtin_format.pb.h"
#include "sdk-cpp/include/common.h"
#include "sdk-cpp/include/predictor_sdk.h"
#include "sdk-cpp/sparse_service.pb.h"
......
sdk-cpp/demo/ximage.cpp
浏览文件 @ dfbf7660
......@@ -17,7 +17,7 @@
#include <unistd.h>
#include <fstream>
#include "sdk-cpp/builtin_format.pb.h"
#include "predictor/builtin_format.pb.h"
#include "sdk-cpp/image_class.pb.h"
#include "sdk-cpp/include/common.h"
#include "sdk-cpp/include/predictor_sdk.h"
......@@ -150,7 +150,7 @@ int main(int argc, char** argv) {
Predictor* predictor = api.fetch_predictor("ximage");
if (!predictor) {
LOG(ERROR) << "Failed fetch predictor: wasq";
LOG(ERROR) << "Failed fetch predictor: ximage";
return -1;
}
......
sdk-cpp/include/endpoint.h
浏览文件 @ dfbf7660
......@@ -16,6 +16,7 @@
#include <string>
#include <vector>
#include "sdk-cpp/include/abtest.h"
#include "sdk-cpp/include/common.h"
#include "sdk-cpp/include/endpoint_config.h"
#include "sdk-cpp/include/predictor.h"
......@@ -59,6 +60,7 @@ class Endpoint {
private:
std::string _endpoint_name;
std::vector<Variant*> _variant_list;
EndpointRouterBase* _abtest_router;
};
} // namespace sdk_cpp
......
sdk-cpp/proto/builtin_format.proto 已删除 100644 → 0
浏览文件 @ 666fb7c5
// 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.
syntax = "proto2";
package baidu.paddle_serving.predictor.format;
// dense format
message DenseInstance { repeated float features = 1; };
message DensePrediction { repeated float categories = 1; };
// sparse format
message SparseInstance {
repeated uint32 keys = 1;
repeated uint32 shape = 2;
repeated float values = 3;
};
message SparsePrediction { repeated float categories = 1; };
// int64-tensor format
message Int64TensorInstance {
repeated int64 data = 1;
repeated uint32 shape = 2;
};
message Float32TensorPredictor {
repeated float data = 1;
repeated uint32 shape = 2;
};
// x-image format
message XImageReqInstance {
required bytes image_binary = 1;
required uint32 image_length = 2;
};
message XImageResInstance { required string response_json = 1; };
// x-record format
message XRecordInstance {
// TODO
required bytes data = 1;
};
sdk-cpp/proto/pds_option.proto 已删除 100644 → 0
浏览文件 @ 666fb7c5
// 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.
syntax = "proto2";
import "google/protobuf/descriptor.proto";
package pds;
extend google.protobuf.FieldOptions {
optional bool pack_on = 70000 [ default = false ];
};
extend google.protobuf.ServiceOptions {
optional PaddleServiceOption options = 80000;
};
message PaddleServiceOption {
optional bool generate_impl = 1 [ default = false ];
optional bool generate_stub = 2 [ default = false ];
};
sdk-cpp/src/endpoint.cpp
浏览文件 @ dfbf7660
......@@ -22,6 +22,7 @@ namespace sdk_cpp {
int Endpoint::initialize(const EndpointInfo& ep_info) {
_variant_list.clear();
_endpoint_name = ep_info.endpoint_name;
_abtest_router = static_cast<EndpointRouterBase*>(ep_info.ab_test);
uint32_t var_size = ep_info.vars.size();
for (uint32_t vi = 0; vi < var_size; ++vi) {
const VariantInfo& var_info = ep_info.vars[vi];
......@@ -80,25 +81,7 @@ int Endpoint::thrd_finalize() {
return 0;
}
// 带全流量分层实验路由信息
Predictor* Endpoint::get_predictor(const void* params) {
Variant* var = NULL;
if (_variant_list.size() == 1) {
var = _variant_list[0];
}
if (!var) {
LOG(ERROR) << "get null var from endpoint.";
return NULL;
}
return var->get_predictor(params);
}
Predictor* Endpoint::get_predictor() {
#if 1
LOG(INFO) << "Endpoint::get_predictor";
#endif
if (_variant_list.size() == 1) {
if (_variant_list[0] == NULL) {
LOG(ERROR) << "Not valid variant info";
......@@ -107,7 +90,18 @@ Predictor* Endpoint::get_predictor() {
return _variant_list[0]->get_predictor();
}
return NULL;
if (_abtest_router == NULL) {
LOG(FATAL) << "Not valid abtest_router!";
return NULL;
}
Variant* var = _abtest_router->route(_variant_list);
if (!var) {
LOG(FATAL) << "get null var from endpoint";
return NULL;
}
return var->get_predictor();
}
int Endpoint::ret_predictor(Predictor* predictor) {
......
serving/conf/workflow.prototxt
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......@@ -44,7 +44,7 @@ workflows {
}
nodes {
name: "write_json_op"
type: "WriteOp"
type: "WriteJsonOp"
dependencies {
name: "image_classify_op"
mode: "RO"
......
serving/proto/builtin_format.proto 已删除 100644 → 0
浏览文件 @ 666fb7c5
// 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.
syntax = "proto2";
import "pds_option.proto";
package baidu.paddle_serving.predictor.format;
// dense format
message DenseInstance { repeated float features = 1; };
message DensePrediction { repeated float categories = 1; };
// sparse format
message SparseInstance {
repeated uint32 keys = 1;
repeated uint32 shape = 2;
repeated float values = 3;
};
message SparsePrediction { repeated float categories = 1; };
// int64-tensor format
message Int64TensorInstance {
repeated int64 data = 1;
repeated uint32 shape = 2;
};
message Float32TensorPredictor {
repeated float data = 1;
repeated uint32 shape = 2;
};
// x-image format
message XImageReqInstance {
required bytes image_binary = 1;
required uint32 image_length = 2;
};
message XImageResInstance { required string response_json = 1; };
// x-record format
message XRecordInstance {
// TODO
required bytes data = 1;
};
serving/proto/pds_option.proto 已删除 100644 → 0
浏览文件 @ 666fb7c5
// 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.
syntax = "proto2";
import "google/protobuf/descriptor.proto";
package pds;
extend google.protobuf.FieldOptions {
optional bool pack_on = 70000 [ default = false ];
};
extend google.protobuf.ServiceOptions {
optional PaddleServiceOption options = 80000;
};
message PaddleServiceOption {
optional bool generate_impl = 1 [ default = false ];
optional bool generate_stub = 2 [ default = false ];
};
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