Question

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())

配置和注册图像作品：

# 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，因此这似乎是我的代码/ Python SDK或Databricks与AMLS配合使用的方式的问题。

更新：我可以使用Azure门户将映像部署到AKS，并且Web服务可以按预期工作。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，将向您报告。

Answer 1

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，我在创建错误图像之前调用了CreateImage和DeployImage。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)

Answer 2

根据个人经验，我想说的是，您看到的错误消息可能表明图像中的脚本存在一些错误。此类错误不一定会阻止成功创建映像，但可能会阻止该映像在服务中使用。但是，如果您已经成功地将映像部署到其他服务中，那么应该可以排除此选项。

您可以关注this guide，以获取有关如何在本地调试Docker映像以及查找日志和其他有用信息的更多信息。

Answer 3

同意Arvid的回答。您能够成功运行它吗？您也可以尝试将其部署到ACI，但是如果问题出在score.py中，您将遇到相同的问题，但是尝试起来很快。另外，如果您要调试部署，则会有些痛苦，但是您可以在本地docker部署中公开端口tcp 5678，并使用VSCode和PTVSD进行连接并逐步调试。

无法将经过训练的模型部署到现有的AKS计算目标

3 个答案: