mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
1135d33dd7
* 第一次提交 * 补充一处漏翻译 * deleted: docs/en/quantize.md * Update one translation * Update en version * Update one translation in code * Standardize one writing * Standardize one writing * Update some en version * Fix a grammer problem * Update en version for api/vision result * Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop * Checkout the link in README in vision_results/ to the en documents * Modify a title * Add link to serving/docs/ * Finish translation of demo.md * Update english version of serving/docs/ * Update title of readme * Update some links * Modify a title * Update some links * Update en version of java android README * Modify some titles * Modify some titles * Modify some titles * modify article to document * update some english version of documents in examples * Add english version of documents in examples/visions * Sync to current branch * Add english version of documents in examples
English | 简体中文
Example of ERNIE 3.0 models C++ Deployment
Before deployment, two steps require confirmation.
-
- Environment of software and hardware should meet the requirements. Please refer toFastDeploy Environment Requirements
-
- Based on the develop environment, download the precompiled deployment library and samples code. Please refer to FastDeploy Precompiled Library
This directory provides deployment examples that seq_cls_inferve.py fast finish text classification tasks on CPU/GPU.
Text Classification Tasks
A Quick Start
The following example shows how to employ FastDeploy library to complete C++ predictive deployment of ERNIE 3.0 Medium model on AFQMC dataset of CLUE Benchmark for text classification tasks.FastDeploy version 0.7.0 or above is required to support this model(x.x.x>=0.7.0) )
mkdir build
cd build
# Download FastDeploy precompiled library. Uses can choose proper version in the `FastDeploy Precompiled Library`mentioned above.
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# Download the fine-tuned ERNIE 3.0 models for the AFQMC dataset and the word lists.
wget https://bj.bcebos.com/fastdeploy/models/ernie-3.0/ernie-3.0-medium-zh-afqmc.tgz
tar xvfz ernie-3.0-medium-zh-afqmc.tgz
# CPU Inference
./seq_cls_infer_demo --device cpu --model_dir ernie-3.0-medium-zh-afqmc
# GPU Inference
./seq_cls_infer_demo --device gpu --model_dir ernie-3.0-medium-zh-afqmc
# KunlunXin XPU Inference
./seq_cls_infer_demo --device kunlunxin --model_dir ernie-3.0-medium-zh-afqmc
The result returned after running is as follows：
[INFO] /paddle/FastDeploy/examples/text/ernie-3.0/cpp/seq_cls_infer.cc(93)::CreateRuntimeOption model_path = ernie-3.0-medium-zh-afqmc/infer.pdmodel, param_path = ernie-3.0-medium-zh-afqmc/infer.pdiparams
[INFO] fastdeploy/runtime.cc(469)::Init Runtime initialized with Backend::ORT in Device::CPU.
Batch id: 0, example id: 0, sentence 1: 花呗收款额度限制, sentence 2: 收钱码，对花呗支付的金额有限制吗, label: 1, confidence: 0.581852
Batch id: 1, example id: 0, sentence 1: 花呗支持高铁票支付吗, sentence 2: 为什么友付宝不支持花呗付款, label: 0, confidence: 0.997921
Parameter Description
seq_cls_infer_demo supports more command-line arguments in addition to the preceding example. The following is a description of every command-line argument:
| Parameter | Parameter Description |
|---|---|
| --model_dir | Specify the directory where the model is to be deployed |
| --batch_size | Maximum measurable batch size, default 1 |
| --max_length | Maximum sequence length, default 128 |
| --device | Running devices, optional range: ['cpu', 'gpu'], default 'cpu' |
| --backend | Supported inference backend, optional range: ['onnx_runtime', 'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt']，default'onnx_runtime' |
| --use_fp16 | Whether to use FP16 mode for inference. Enabled when using tensorrt and paddle_tensorrt backend, and default is False |