@@ -30,13 +30,14 @@ The goal of Paddle Serving is to provide high-performance, flexible and easy-to-
- Integrate high-performance server-side inference engine paddle Inference and mobile-side engine paddle Lite. Models of other machine learning platforms (Caffe/TensorFlow/ONNX/PyTorch) can be migrated to paddle through [x2paddle](https://github.com/PaddlePaddle/X2Paddle).
- There are two frameworks, namely high-performance C++ Serving and high-easy-to-use Python pipeline. The C++ Serving is based on the bRPC network framework to create a high-throughput, low-latency inference service, and its performance indicators are ahead of competing products. The Python pipeline is based on the gRPC/gRPC-Gateway network framework and the Python language to build a highly easy-to-use and high-throughput inference service. How to choose which one please see [Techinical Selection](doc/Serving_Design_EN.md#21-design-selection).
- Support multiple [protocols](doc/C++_Serving/Inference_Protocols_CN.md) such as HTTP, gRPC, bRPC, and provide C++, Python, Java language SDK.
- Design and implement a high-performance inference service framework for asynchronous pipelines based on directed acyclic graph (DAG), with features such as multi-model combination, asynchronous scheduling, concurrent inference, dynamic batch, multi-card multi-stream inference, etc.
- Design and implement a high-performance inference service framework for asynchronous pipelines based on directed acyclic graph (DAG), with features such as multi-model combination, asynchronous scheduling, concurrent inference, dynamic batch, multi-card multi-stream inference, request cache, etc.
- Adapt to a variety of commonly used computing hardwares, such as x86 (Intel) CPU, ARM CPU, Nvidia GPU, Kunlun XPU, HUAWEI Ascend 310/910, HYGON DCU、Nvidia Jetson etc.
- Integrate acceleration libraries of Intel MKLDNN and Nvidia TensorRT, and low-precision and quantitative inference.
- Provide a model security deployment solution, including encryption model deployment, and authentication mechanism, HTTPs security gateway, which is used in practice.
- Support cloud deployment, provide a deployment case of Baidu Cloud Intelligent Cloud kubernetes cluster.
- Provide more than 40 classic pre-model deployment examples, such as PaddleOCR, PaddleClas, PaddleDetection, PaddleSeg, PaddleNLP, PaddleRec and other suites, and more models continue to expand.
- Supports distributed deployment of large-scale sparse parameter index models, with features such as multiple tables, multiple shards, multiple copies, local high-frequency cache, etc., and can be deployed on a single machine or clouds.
- Support service monitoring, provide prometheus-based performance statistics and port access
<h2align="center">Tutorial</h2>
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@@ -62,7 +63,6 @@ This chapter guides you through the installation and deployment steps. It is str
-[Deploy Paddle Serving on Kubernetes(Chinese)](doc/Run_On_Kubernetes_CN.md)
-[Deploy Paddle Serving with Security gateway(Chinese)](doc/Serving_Auth_Docker_CN.md)
- Deploy on more hardwares[[百度昆仑](doc/Run_On_XPU_CN.md)、[华为昇腾](doc/Run_On_NPU_CN.md)、[海光DCU](doc/Run_On_DCU_CN.md)、[Jetson](doc/Run_On_JETSON_CN.md)]
@@ -105,13 +108,13 @@ For Paddle Serving developers, we provide extended documents such as custom OP,
<h2align="center">Model Zoo</h2>
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 42 models.
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 45 models.
If you use `the command line + configuration file method to start C++ server`, you only need to modify [the configuration file](./Serving_Configure_CN.md), don`t need to change any line of 👆 code.
If you use `the command line + configuration file method to start C++ server`, you only need to modify [the configuration file](../Serving_Configure_CN.md), don`t need to change any line of 👆 code.
For simple series logic, we simplify it and build it with `OpSeqMaker`. You can determine the successor by default according to the order of joining `OpSeqMaker` without specifying the successor of each node.
Running Images is lighter than Develop Images, and Running Images are made up with serving whl and bin, but without develop tools like cmake because of lower image size. If you want to know about it, plese check the document [Paddle Serving on Kubernetes](./Run_On_Kubernetes_CN.md).
| Env | Version | Docker images tag | OS | Gcc Version | Size |
@@ -23,7 +23,7 @@ Arguments are the same as `inference_model_to_serving` API.
| `model_filename` | str | None | The name of file to load the inference program. If it is None, the default filename `__model__` will be used. |
| `params_filename` | str | None | The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. |
**Demo: Convert From Dynamic Graph**
### Convert From Dynamic Graph
PaddlePaddle 2.0 provides a new dynamic graph mode, so here we use imagenet ResNet50 dynamic graph as an example to teach how to export from a saved model and use it for real online inference scenarios.
