我有一个在Azure机器学习服务上的机器学习计算上训练过的模型。注册的模型已经存在于我的工作空间中，我想将其部署到我先前在我的工作空间中预先配置的AKS实例。我能够成功配置和注册容器映像：
# retrieve cloud representations of the models
rf = Model(workspace=ws, name='pumps_rf')
le = Model(workspace=ws, name='pumps_le')
ohc = Model(workspace=ws, name='pumps_ohc')
print(rf); print(le); print(ohc)
<azureml.core.model.Model object at 0x7f66ab3b1f98>
<azureml.core.model.Model object at 0x7f66ab7e49b0>
<azureml.core.model.Model object at 0x7f66ab85e710>
package_list = [
'category-encoders==1.3.0',
'numpy==1.15.0',
'pandas==0.24.1',
'scikit-learn==0.20.2']
# Conda environment configuration
myenv = CondaDependencies.create(pip_packages=package_list)
conda_yml = 'file:'+os.getcwd()+'/myenv.yml'
with open(conda_yml,"w") as f:
f.write(myenv.serialize_to_string())
配置和注册映像的工作原理：
# Image configuration
image_config = ContainerImage.image_configuration(execution_script='score.py',
runtime='python',
conda_file='myenv.yml',
description='Pumps Random Forest model')
# Register the image from the image configuration
# to Azure Container Registry
image = ContainerImage.create(name = Config.IMAGE_NAME,
models = [rf, le, ohc],
image_config = image_config,
workspace = ws)
Creating image
Running....................
SucceededImage creation operation finished for image pumpsrfimage:2, operation "Succeeded"
附加到现有集群也可以：
# Attach the cluster to your workgroup
attach_config = AksCompute.attach_configuration(resource_group = Config.RESOURCE_GROUP,
cluster_name = Config.DEPLOY_COMPUTE)
aks_target = ComputeTarget.attach(workspace=ws,
name=Config.DEPLOY_COMPUTE,
attach_configuration=attach_config)
# Wait for the operation to complete
aks_target.wait_for_completion(True)
SucceededProvisioning operation finished, operation "Succeeded"
但是，当我尝试将映像部署到现有群集时，它会失败，并显示WebserviceException。
# Set configuration and service name
aks_config = AksWebservice.deploy_configuration()
# Deploy from image
service = Webservice.deploy_from_image(workspace = ws,
name = 'pumps-aks-service-1' ,
image = image,
deployment_config = aks_config,
deployment_target = aks_target)
# Wait for the deployment to complete
service.wait_for_deployment(show_output = True)
print(service.state)
WebserviceException: Unable to create service with image pumpsrfimage:1 in non "Succeeded" creation state.
---------------------------------------------------------------------------
WebserviceException Traceback (most recent call last)
<command-201219424688503> in <module>()
7 image = image,
8 deployment_config = aks_config,
----> 9 deployment_target = aks_target)
10 # Wait for the deployment to complete
11 service.wait_for_deployment(show_output = True)
/databricks/python/lib/python3.5/site-packages/azureml/core/webservice/webservice.py in deploy_from_image(workspace, name, image, deployment_config, deployment_target)
284 return child._deploy(workspace, name, image, deployment_config, deployment_target)
285
--> 286 return deployment_config._webservice_type._deploy(workspace, name, image, deployment_config, deployment_target)
287
288 @staticmethod
/databricks/python/lib/python3.5/site-packages/azureml/core/webservice/aks.py in _deploy(workspace, name, image, deployment_config, deployment_target)
关于如何解决此问题的任何想法？我正在Databricks笔记本中编写代码。另外，我能够使用Azure Portal创建和部署群集没有问题，因此这似乎与我的代码/ Python SDK或Databricks与AMLS的工作方式有关。
更新：我能够使用Azure Portal将映像部署到AKS，并且Web服务按预期工作。这意味着问题出在Databricks，Azureml Python SDK和机器学习服务之间。
更新2：我正在与Microsoft一起解决此问题。一旦有解决方案，我们将向您报告。
在我的初始代码中，创建图像时，我没有使用：
image.wait_for_creation(show_output=True)
结果，我打电话来CreateImage，DeployImage在创建错误图像之前。简直不敢这么简单..
更新的图像创建片段：
# Register the image from the image configuration
# to Azure Container Registry
image = ContainerImage.create(name = Config.IMAGE_NAME,
models = [rf, le, ohc],
image_config = image_config,
workspace = ws)
image.wait_for_creation(show_output=True)
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