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run_model_server.py | 1 year ago |
该模型使用PaddlePaddle深度学习框架基于卷积神经网络(CNN)和MNIST数据集实现手写数字识别。
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
API接口1:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "predict"
"args": {
"img": <压缩图像的base64编码字符串(或其Data URL表示)>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": <手写数字识别结果>|<错误描述>
}
API接口2:
API端点: /api/file/predict
HTTP方法: POST
HTTP请求体:
<用于HTTP文件上传的XHR格式请求体>
HTTP响应体:
{
"status": "ok"|"err",
"value": <手写数字识别结果>|<错误描述>
}
API接口3:
API端点: /api/stream/predict
HTTP方法: POST
HTTP请求体:
<压缩图像的二进制字节流>
HTTP响应体:
{
"status": "ok"|"err",
"value": <手写数字识别结果>|<错误描述>
}
API接口4:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "gen_demo_img"
"args": {}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": <压缩图像的base64编码字符串>|<错误描述>
}
Paddle手写数字识别
TypeScript Python HTML Shell Text other
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