部署在线服务

示例代码

在ModelArts notebook平台，Session鉴权无需输入鉴权参数。其它平台的Session鉴权请参见Session鉴权。

• 方式1：已部署为在线服务predictor的初始化

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  from modelarts.session import Session from modelarts.model import Predictor session = Session() predictor_instance = Predictor(session, service_id="your_service_id")

• 方式2：部署在线服务predictor
  • 部署服务到公共资源池

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    from modelarts.session import Session from modelarts.model import Model from modelarts.config.model_config import ServiceConfig, TransformerConfig, Schedule session = Session() model_instance = Model(session, model_id='your_model_id') vpc_id = None # （可选）在线服务实例部署的虚拟私有云ID，默认为空 subnet_network_id = None # （可选）子网的网络ID，默认为空 security_group_id = None # （可选）安全组，默认为空 configs = [ServiceConfig(model_id=model_instance.model_id, weight="100", instance_count=1, specification="modelarts.vm.cpu.2u")] # 参考表3中specification字段 predictor_instance = model_instance.deploy_predictor( service_name="service_predictor_name", infer_type="real-time", vpc_id=vpc_id, subnet_network_id=subnet_network_id, security_group_id=security_group_id, configs=configs, # predictor配置参数, 参考下文configs参数格式说明 schedule = [Schedule(op_type='stop', time_unit='HOURS', duration=1)] # （可选）设置在线服务运行时间 )

    参数“model_id”代表将部署成在线服务的模型。“model_id”可以通过查询模型列表或者ModelArts管理控制台获取。

  • 部署服务到专属资源池

    from modelarts.config.model_config import ServiceConfig
    
    configs = [ServiceConfig(model_id=model_instance.model_id, weight="100", instance_count=1, 
    						 specification="modelarts.vm.cpu.2u")]
    predictor_instance = model_instance.deploy_predictor( 
                                                          service_name="your_service_name",
                                                          infer_type="real-time",
                                                          configs=configs,
                                                          cluster_id="your dedicated pool id"
                                                        )

  configs参数格式说明：SDK提供了ServiceConfig类对其定义，configs为list，list中的元组对象是ServiceConfig。定义代码如下：

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  configs = [] envs = {"model_name":"mxnet-model-1", "load_epoch":"0"} service_config1 = ServiceConfig( model_id="model_id1", # model_id1和model_id2必须是同一个模型的不同版本对应的model_id weight="70", specification="modelarts.vm.cpu.2u", # 参考表3中specification字段 instance_count=2, envs=envs) # （可选）设置环境变量的值，如：envs = {"model_name":"mxnet-model-1", "load_epoch":"0"} service_config2 = ServiceConfig( model_id='model_id2', weight="30", specification="modelarts.vm.cpu.2u", # 参考表3中specification字段 instance_count=2, envs=envs) # （可选）设置环境变量的值，如：envs = {"model_name":"mxnet-model-1", "load_epoch":"0"} configs.append(service_config1) configs.append(service_config2)

• 方式3：部署批量服务transformer

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  from modelarts.session import Session from modelarts.model import Model from modelarts.config.model_config import TransformerConfig session = Session() model_instance = Model(session, model_id='your_model_id') vpc_id = None # （可选）批量服务实例部署的虚拟私有云ID，默认为空 subnet_network_id = None # （可选）子网的网络ID，默认为空 security_group_id = None # （可选）安全组，默认为空 transformer = model_instance.deploy_transformer( service_name="service_transformer_name", infer_type="batch", vpc_id=vpc_id, subnet_network_id=subnet_network_id, security_group_id=security_group_id, configs=configs # transformer配置参数, 参考下文configs参数格式说明 )

  configs参数格式说明：SDK提供了TransformerConfig类对其定义，configs都是list，list中的元组对象是TransformerConfig。定义代码如下：

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  configs = [] mapping_rule = None # （可选）输入参数与csv数据的映射关系 mapping_type= "file" # file或者csv envs = {"model_name":"mxnet-model-1", "load_epoch":"0"} transformer_config1 = TransformerConfig( model_id="model_id", specification="modelarts.vm.cpu.2u", # 参考表3中specification字段 instance_count=2, src_path="/shp-cn4/sdk-demo/", # 批量任务输入数据的OBS路径，如："/your_obs_bucket/src_path" dest_path="/shp-cn4/data-out/", # 批量任务输出结果的OBS路径，如："/your_obs_bucket/dest_path" req_uri="/", mapping_type=mapping_type, mapping_rule=mapping_rule, envs=envs) # （可选）设置环境变量的值，如：envs = {"model_name":"mxnet-model-1", "load_epoch":"0"} configs.append(transformer_config1)

参数说明

{
    "service_name": "mnist",
    "description": "mnist service",
    "infer_type": "real-time",
    "config": [
        {
            "model_id": "xxxmodel-idxxx",
            "weight": "70",
            "specification": "modelarts.vm.cpu.2u",
            "instance_count": 1,
            "envs":
                {
                    "model_name": "mxnet-model-1",
                    "load_epoch": "0"
                }
        },
        {
            "model_id": "xxxxxx",
            "weight": "30",
            "specification": "modelarts.vm.cpu.2u",
            "instance_count": 1
        }
    ]
}

{
"service_name": "batchservicetest",
"description": "",
"infer_type": "batch",
"config": [{
"model_id": "598b913a-af3e-41ba-a1b5-bf065320f1e2",
"specification": "modelarts.vm.cpu.2u",
"instance_count": 1,
"src_path": "https://infers-data.obs.xxx.com/xgboosterdata/",
"dest_path": "https://infers-data.obs.xxx.com/output/",
"req_uri": "/",
"mapping_type": "file"
}]
}

{
    "service_name": "batchservicetest",
    "description": "",
    "infer_type": "batch",
    "config": [{
        "model_id": "598b913a-af3e-41ba-a1b5-bf065320f1e2",
        "specification": "modelarts.vm.cpu.2u",
        "instance_count": 1,
        "src_path": "https://infers-data.obs.xxx.com/xgboosterdata/",
        "dest_path": "https://infers-data.obs.xxx.com/output/",
        "req_uri": "/",
        "mapping_type": "csv",
        "mapping_rule": {
            "type": "object",
            "properties": {
                "data": {
                    "type": "object",
                    "properties": {
                        "req_data": {
                            "type": "array",
                            "items": [{
                                "type": "object",
                                "properties": {
                                    "input5": {
                                        "type": "number",
                                        "index": 0
                                    },
                                    "input4": {
                                        "type": "number",
                                        "index": 1
                                    },
                                    "input3": {
                                        "type": "number",
                                        "index": 2
                                    },
                                    "input2": {
                                        "type": "number",
                                        "index": 3
                                    },
                                    "input1": {
                                        "type": "number",
                                        "index": 4
                                    }
                                }
                            }]
                        }
                    }
                }
            }
        }
    }]
}

{
    "data": {
        "req_data": [{
            "input1": 1,
            "input2": 2,
            "input3": 3,
            "input4": 4,
            "input5": 5
        }]
    }
}

• 给出MXNet实现手写数字识别项目中部署在线predictror实例：

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  from modelarts.session import Session from modelarts.model import Model from modelarts.config.model_config import ServiceConfig, TransformerConfig model_instance = Model(session, model_id = "you_model_id") configs = [] config1 = ServiceConfig(model_id="you_model_id", weight="100", instance_count=1, specification="modelarts.vm.cpu.2u", envs={"input_data_name":"images", "input_data_shape":"0,1,28,28", "output_data_shape":"0,10"}) configs.append(config1) predictor = model_instance.deploy_predictor(service_name="DigitRecognition", configs=configs)

• 给出MXNet实现手写数字识别项目中部署transformer实例（批量推理）：

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  from modelarts.session import Session from modelarts.model import Model from modelarts.config.model_config import ServiceConfig, TransformerConfig model_instance = Model(session, model_id = "your_model_id") configs = [] config1 = TransformerConfig(model_id="your_model_id", specification="modelarts.vm.cpu.2u", instance_count=1, envs={"input_data_name":"images","input_data_shape":"0,1,28,28","output_data_shape":"0,10"}, src_path="/w0403/testdigitrecognition/inferimages/", dest_path="/w0403/testdigitrecognition/" , req_uri = "/", mapping_type = "file") configs.append(config1) predictor = model_instance.deploy_transformer(service_name="DigitRecognition", infer_type="batch", configs=configs)