将部署终结点升级到 SDK v2
使用 SDK/CLI v1,可以在 ACI 或 AKS 上部署模型作为 Web 服务。 现有的 v1 模型部署和 Web 服务将继续正常运行,但使用 SDK/CLI v1 在 ACI 或 AKS 上部署模型作为 Web 服务现在已被视为旧版。 对于新的模型部署,建议升级到 v2。
在 v2 中,我们提供托管终结点或 Kubernetes 终结点。 有关 v1 和 v2 的比较,请参阅终结点和部署。
有多个部署漏斗,如 v2 中的托管联机终结点、kubernetes 联机终结点(包括 Azure Kubernetes 服务),v1 中的 Azure 容器实例 (ACI) 和 Kubernetes 服务 (AKS) Web 服务。 本文重点介绍部署到 ACI Web 服务 (v1) 和托管联机终结点 (v2) 的过程比较。
本文中的示例演示如何执行以下操作:
- 将模型部署到 Azure
- 使用终结点进行评分
- 删除 Web 服务/终结点
创建推理资源
- SDK v1
配置模型、环境和评分脚本:
# configure a model. example for registering a model from azureml.core.model import Model model = Model.register(ws, model_name="bidaf_onnx", model_path="./model.onnx") # configure an environment from azureml.core import Environment env = Environment(name='myenv') python_packages = ['nltk', 'numpy', 'onnxruntime'] for package in python_packages: env.python.conda_dependencies.add_pip_package(package) # configure an inference configuration with a scoring script from azureml.core.model import InferenceConfig inference_config = InferenceConfig( environment=env, source_directory="./source_dir", entry_script="./score.py", )
配置和部署 ACI Web 服务:
from azureml.core.webservice import AciWebservice # defince compute resources for ACI deployment_config = AciWebservice.deploy_configuration( cpu_cores=0.5, memory_gb=1, auth_enabled=True ) # define an ACI webservice service = Model.deploy( ws, "myservice", [model], inference_config, deployment_config, overwrite=True, ) # create the service service.wait_for_deployment(show_output=True)
有关注册模型的详细信息,请参阅从本地文件注册模型。
SDK v2
配置模型、环境和评分脚本:
from azure.ai.ml.entities import Model # configure a model model = Model(path="../model-1/model/sklearn_regression_model.pkl") # configure an environment from azure.ai.ml.entities import Environment env = Environment( conda_file="../model-1/environment/conda.yml", image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1", ) # configure an inference configuration with a scoring script from azure.ai.ml.entities import CodeConfiguration code_config = CodeConfiguration( code="../model-1/onlinescoring", scoring_script="score.py" )
配置和创建联机终结点:
import datetime from azure.ai.ml.entities import ManagedOnlineEndpoint # create a unique endpoint name with current datetime to avoid conflicts online_endpoint_name = "endpoint-" + datetime.datetime.now().strftime("%m%d%H%M%f") # define an online endpoint endpoint = ManagedOnlineEndpoint( name=online_endpoint_name, description="this is a sample online endpoint", auth_mode="key", tags={"foo": "bar"}, ) # create the endpoint: ml_client.begin_create_or_update(endpoint)
配置和创建联机部署:
from azure.ai.ml.entities import ManagedOnlineDeployment # define a deployment blue_deployment = ManagedOnlineDeployment( name="blue", endpoint_name=online_endpoint_name, model=model, environment=env, code_configuration=code_config, instance_type="Standard_F2s_v2", instance_count=1, ) # create the deployment: ml_client.begin_create_or_update(blue_deployment) # blue deployment takes 100 traffic endpoint.traffic = {"blue": 100} ml_client.begin_create_or_update(endpoint)
有关终结点和部署概念的详细信息,请参阅什么是联机终结点?
提交请求
SDK v1
import json data = { "query": "What color is the fox", "context": "The quick brown fox jumped over the lazy dog.", } data = json.dumps(data) predictions = service.run(input_data=data) print(predictions)
SDK v2
# test the endpoint (the request will route to blue deployment as set above) ml_client.online_endpoints.invoke( endpoint_name=online_endpoint_name, request_file="../model-1/sample-request.json", ) # test the specific (blue) deployment ml_client.online_endpoints.invoke( endpoint_name=online_endpoint_name, deployment_name="blue", request_file="../model-1/sample-request.json", )
删除资源
SDK v1
service.delete()
SDK v2
ml_client.online_endpoints.begin_delete(name=online_endpoint_name)
SDK v1 和 SDK v2 中关键功能的映射
相关文档
有关详细信息,请参阅
v2 文档:
v1 文档: