7b15f72516
* [FlyCV] Bump up FlyCV -> official release 1.0.0 * add seg models for XPU * add ocr model for XPU * add matting * add matting python * fix infer.cc * add keypointdetection support for XPU * Add adaface support for XPU * add ernie-3.0 * fix doc Co-authored-by: DefTruth <qiustudent_r@163.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
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Example of ERNIE 3.0 Models Python Deployment
Before deployment, two steps require confirmation.
-
- Environment of software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
-
- FastDeploy Python whl package should be installed. Please refer to FastDeploy Python Installation
This directory provides deployment examples that seq_cls_inferve.py fast finish text classification tasks on CPU/GPU.
Dependency Installation
The Python Predictor in this project uses AutoTokenizer provided by PaddleNLP to conduct word segmentation and fast_tokenizer to speed up word segmentation. Run the following command to install it.
pip install -r requirements.txt
Text Classification Tasks
A Quick Start
The following example shows how to employ FastDeploy library to complete Python predictive deployment of ERNIE 3.0 Medium model on AFQMC Datasetof CLUE Benchmark for text classification tasks.
# Download the deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/text/ernie-3.0/python
# Download fine-tuned ERNIE 3.0 models for AFQMC dataset
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
python seq_cls_infer.py --device cpu --model_dir ernie-3.0-medium-zh-afqmc
# GPU Inference
python seq_cls_infer.py --device gpu --model_dir ernie-3.0-medium-zh-afqmc
# XPU Inference
python seq_cls_infer.py --device xpu --model_dir ernie-3.0-medium-zh-afqmc
The result returned after running is as follows:
[INFO] fastdeploy/runtime.cc(469)::Init Runtime initialized with Backend::ORT in Device::CPU.
Batch id:0, example id:0, sentence1:花呗收款额度限制, sentence2:收钱码，对花呗支付的金额有限制吗, label:1, similarity:0.5819
Batch id:1, example id:0, sentence1:花呗支持高铁票支付吗, sentence2:为什么友付宝不支持花呗付款, label:0, similarity:0.9979
Parameter Description
seq_cls_infer.pysupports 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 |
| --use_fast | Whether to use FastTokenizer to speed up the tokenization, and default is True |